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Chong JJR, Kirpalani A, Moreland R, Colak E. Artificial Intelligence in Gastrointestinal Imaging: Advances and Applications. Radiol Clin North Am 2025; 63:477-490. [PMID: 40221188 DOI: 10.1016/j.rcl.2024.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/14/2025]
Abstract
While artificial intelligence (AI) has shown considerable progress in many areas of medical imaging, applications in abdominal imaging, particularly for the gastrointestinal (GI) system, have notably lagged behind advancements in other body regions. This article reviews foundational concepts in AI and highlights examples of AI applications in GI tract imaging. The discussion on AI applications includes acute & emergent GI imaging, inflammatory bowel disease, oncology, and other miscellaneous applications. It concludes with a discussion of important considerations for implementing AI tools in clinical practice, and steps we can take to accelerate future developments in the field.
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Affiliation(s)
- Jaron J R Chong
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, 800 Commissioners Road East, London, Ontario N6A 5W9, Canada
| | - Anish Kirpalani
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada; Department of Medical Imaging, St. Michael's Hospital, Unity Health Toronto, 30 Bond Street, Toronto, Ontario M5B 1C9, Canada; Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, Ontario, Canada
| | - Robert Moreland
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada; Department of Medical Imaging, St. Michael's Hospital, Unity Health Toronto, 30 Bond Street, Toronto, Ontario M5B 1C9, Canada
| | - Errol Colak
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada; Department of Medical Imaging, St. Michael's Hospital, Unity Health Toronto, 30 Bond Street, Toronto, Ontario M5B 1C9, Canada; Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, Ontario, Canada.
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Karahacioglu D, Atalay HO, Esmer R, Kabaoglu ZU, Senyurek S, Ozata IH, Taskin OÇ, Saka B, Selcukbiricik F, Selek U, Rencuzogullari A, Bugra D, Balik E, Gurses B. What is the predictive value of pretreatment MRI characteristics for achieving a complete response after total neoadjuvant treatment in locally advanced rectal cancer? Eur J Radiol 2025; 185:112005. [PMID: 39970545 DOI: 10.1016/j.ejrad.2025.112005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2024] [Revised: 02/04/2025] [Accepted: 02/13/2025] [Indexed: 02/21/2025]
Abstract
OBJECTIVES To investigate the value of pretreatment magnetic resonance imaging (MRI) features in predicting a complete response to total neoadjuvant treatment (TNT) in locally advanced rectal cancer (LARC). METHODS The data of patients who received TNT were analyzed retrospectively. MRI features, including T stage, morphology, length, and volume; the presence of MR-detected extramural venous invasion (mrEMVI), the number of mrEMVI, and the diameter of the largest invaded vein; main vein mrEMVI; presence of MR-detected tumor deposits (mrTDs), the number of mrTDs, and the size of the largest mrTD; MR-detected lymph node status (mrLN); tumor distance from the anal verge; mesorectal fascia involvement (mrMRF + ); and mean apparent diffusion coefficient (ADC) values were recorded. Patients were classified as complete (CRs) or noncomplete responders (non-CRs) according to the pathological/clinical outcomes. For patients managed nonoperatively, a sustained clinical complete response for > 2 years was deemed a surrogate endpoint for complete response. The MRI parameters were categorized into three distinct groups: baseline, advanced, and quantitative features, and were analyzed using multivariable stepwise logistic regression. The ability to predict complete response was evaluated by comparing different combinations of MRI parameters, and performance on an "independent" dataset was estimated using bootstrapped leave-one-out cross-validation (LOOCV). RESULTS The data of 84 patients were evaluated (CRs, n = 44; non-CRs, n = 40). The optimal model, which included baseline and quantitative MRI features, achieved an area under the curve of 0.837 for predicting complete response. Selected predictors were T stage and ADC mean value. Advanced MRI features did not improve the performance of the model. CONCLUSION A multivariable model combining T stage and the ADC mean value can help identify LARC patients who are likely to a achieve complete response before the initiation of TNT.
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Affiliation(s)
- Duygu Karahacioglu
- Department of Radiology, Koç University School of Medicine, Istanbul, Turkey.
| | - Hande Ozen Atalay
- Department of Radiology, Koç University School of Medicine, Istanbul, Turkey
| | - Rohat Esmer
- Koç University School of Medicine, Istanbul, Turkey
| | | | - Sukran Senyurek
- Department of Radiation Oncology, Koç University School of Medicine, Istanbul, Turkey
| | - Ibrahim Halil Ozata
- Department of General Surgery, Koç University School of Medicine, Istanbul, Turkey
| | - Orhun Çig Taskin
- Department of Pathology, Koç University School of Medicine, Istanbul, Turkey
| | - Burcu Saka
- Department of Pathology, Koç University School of Medicine, Istanbul, Turkey
| | - Fatih Selcukbiricik
- Department of Medical Oncology, Koç University School of Medicine, Istanbul, Turkey
| | - Ugur Selek
- Department of Radiation Oncology, Koç University School of Medicine, Istanbul, Turkey
| | - Ahmet Rencuzogullari
- Department of General Surgery, Koç University School of Medicine, Istanbul, Turkey
| | - Dursun Bugra
- Department of General Surgery, Koç University School of Medicine, Istanbul, Turkey; Department of General Surgery, VKV American Hospital, Istanbul, Turkey
| | - Emre Balik
- Department of General Surgery, Koç University School of Medicine, Istanbul, Turkey
| | - Bengi Gurses
- Department of Radiology, Koç University School of Medicine, Istanbul, Turkey
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Qu W, Wang J, Hu X, Shen Y, Peng Y, Hu D, Li Z. MRI radiomic study on prediction of nonenlarged lymph node metastasis of rectal cancer: reduced field-of-view versus conventional DWI. Eur Radiol Exp 2025; 9:34. [PMID: 40120024 PMCID: PMC11929653 DOI: 10.1186/s41747-025-00575-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2024] [Accepted: 03/03/2025] [Indexed: 03/25/2025] Open
Abstract
BACKGROUND Nonenlarged lymph node metastasis (NELNM) of rectal cancer is easily overlooked because these apparently normal lymph nodes are sometimes too small to measure directly using imaging techniques. Radiomic-based multiparametric imaging sequences could predict NELNM based on the primary lesion of rectal cancer. We aimed to study the performance of magnetic resonance imaging (MRI) radiomics derived from reduced field-of-view diffusion-weighted imaging (rDWI) and conventional DWI (cDWI) for the prediction of NELNM. METHODS A total of 86 rectal cancer patients (60 and 26 patients in training and test cohorts, respectively), underwent multiparametric MRI. Radiomic features were extracted from the whole primary lesion of rectal cancer segmented on T2-weighted imaging (T2WI), rDWI, and cDWI, both with b-value of 800 s/mm2 and apparent diffusion coefficient (ADC) maps from both DWI sequences (rADC and cADC). The radiomic models based on the above imaging methods were built for the assessment of NELNM status. Their diagnostic performances were evaluated in comparison with subjective evaluation by radiologists. RESULTS rADC demonstrated a significant advantage over subjective assessment in predicting NELNM in both training and test cohorts (p ≤ 0.002). In the test cohort, rADC exhibited a significantly higher area under the receiver operating characteristics curve than cADC, cDWIb800, and T2WI (p ≤ 0.020) in assessing NELNM for region-of-interest (ROI) delineation while excelling over rDWIb800 for prediction of NELNM (p = 0.0498). CONCLUSION Radiomic features based on rADC outperformed those derived from T2WI and fDWI in predicting the NELNM status of rectal cancer, rADC was more advantageous than rDWIb800 in assessing NELNM. RELEVANCE STATEMENT Advanced rDWI excelled over cDWI in radiomic assessment of NELNM of rectal cancer, with the best performance observed for rADC, in contrast to rDWIb800, cADC, cDWIb800, and T2WI. KEY POINTS rDWI, cDWI, and T2WI radiomics could help assess NELNM of rectal cancer. Radiomic features based on rADC outperformed those based on rDWIb800, cADC, cDWIb800, and T2WI in predicting NELNM. For rDWI radiomics, the ADC map was more accurate and reliable than DWI to assess NELNM for region of interest delineation.
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Affiliation(s)
- Weinuo Qu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Jing Wang
- Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Xuemei Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Yaqi Shen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Yang Peng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China.
| | - Daoyu Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
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Xu M, Wang Z, Qiao XF, Liao H, Su DK. A nomogram model for predicting lymph node metastasis of rectal cancer by combining preoperative magnetic resonance imaging signs and tumour markers. Pol J Radiol 2025; 90:e114-e123. [PMID: 40321712 PMCID: PMC12049155 DOI: 10.5114/pjr/200612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2025] [Accepted: 01/29/2025] [Indexed: 05/08/2025] Open
Abstract
Purpose This study aimed to explore the diagnostic value of high-resolution magnetic resonance images and tumour markers in predicting lymph node metastasis of rectal cancer. Material and methods The clinical, imaging, and pathological data of patients with suspected rectal cancer were collected. The baseline data, and surgical and pathological characteristics were compared between the lymph node metastasis group and no metastasis group. Univariate and multivariate logistic regression were used to analyse the clinical and pathological factors, and preoperative magnetic resonance imaging (MRI) signs of extramural vascular invasion and rectal cancer lymph node metastasis. A nomogram model was established with statistically significant factors. Results 150 patients were included. Among them, 50 (33.3%) presented with vascular tumour thrombus, and 72 (48.0%) had lymph node metastasis. The detection of regional lymph nodes (DWI-LN) was an independent risk factor for lymph node metastasis. The area under curve of the nomogram model was 0.804. Conclusion Preoperative serum CA19.9, and the relationship between tumour and peritoneal reflection in preoperative MRI and DWI-LN have clinical value in predicting lymph node metastasis in patients with rectal cancer.
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Affiliation(s)
- Meihai Xu
- Guangxi Medical University Cancer Hospital, China
| | - Zheng Wang
- Guangxi Medical University Cancer Hospital, China
| | - Xiu-Feng Qiao
- The Fifth Affiliated Hospital of Guangxi Medical University, China
| | - Hai Liao
- Guangxi Medical University Cancer Hospital, China
| | - Dan-Ke Su
- Guangxi Medical University Cancer Hospital, China
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Yazdi SNM, Moradi SA, Rasoulighasemlouei SS, Parouei F, Hashemi MG. Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging and Positron Emission Tomography (PET) for Distinguishing Metastatic Lymph Nodes from Nonmetastatic Among Patients with Rectal Cancer: A Systematic Review and Meta-Analysis. World J Nucl Med 2025; 24:3-12. [PMID: 39959143 PMCID: PMC11828646 DOI: 10.1055/s-0044-1788794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2025] Open
Abstract
Objective The objective of this research was to assess the proficiency of quantitative dynamic contrast-enhanced magnetic resonance imaging (QDCE-MRI) and positron emission tomography (PET) imaging in distinguishing between metastatic and nonmetastatic lymph nodes in cases of rectal carcinoma. Method This meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses standards. Two independent reviewers systematically searched databases including PubMed, Embase, Web of Science, and the Cochrane Library. The research took place in July 2022, with no restriction on the initial date of publication. For the analysis, we utilized Stata software (version 16.0), Review Manager (version 5.3), and the Open Meta-Analyst computational tool. Results A total of 19 studies consisting of 1,451 patients were included in the current meta-analysis. The differences between metastatic and nonmetastatic lymph node parameters were significant by using short axis and Ktrans (6.9 ± 4 vs. 5.4 ± 0.5, 0.22 ± 0.1 vs. 0.14 ± 0.1, respectively). Contrast-enhanced MRI (CE-MRI) showed 73% sensitivity, 71% specificity, and 79% accuracy in detecting metastatic lymph nodes among rectal cancer patients based on six included studies ( n = 530). The overall sensitivity, specificity, and accuracy of QDCE-MRI using Ktrans was calculated to be 80, 79, and 80%, respectively. Furthermore, PET-computed tomography (CT) showed a sensitivity of 80%, specificity of 91%, and accuracy of 86% in distinguishing metastatic lymph nodes. Quality utility analysis showed that using CE-MRI, QDCE-MRI, and PET-CT would increase the posttest probability to 69, 73, and 85%, respectively. Conclusion QDCE-MRI demonstrates a commendable sensitivity and specificity, but slightly overshadowed by the higher specificity of PET-CT at 91%, despite comparable sensitivities. However, the heterogeneity in PET-CT sensitivity across studies and its high specificity indicate variability that can influence clinical decision-making. Thus, combining these imaging techniques and perhaps newer methods like PET/MRI could enhance diagnostic accuracy, reduce variability, and improve patient management strategies in rectal cancer.
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Affiliation(s)
| | - Sahand Adib Moradi
- Iranian Center of Neurological Research, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran
| | | | - Fatemeh Parouei
- Department of Gastroenterology and Hepatology, Erasmus University Medical Center, Rotterdam, The Netherlands
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Jiang C, Fang W, Wei N, Ma W, Dai C, Liu R, Cai A, Feng Q. Node Reporting and Data System Combined With Computed Tomography Radiomics Can Improve the Prediction of Nonenlarged Lymph Node Metastasis in Gastric Cancer. J Comput Assist Tomogr 2025; 49:215-224. [PMID: 39438281 DOI: 10.1097/rct.0000000000001673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2024]
Abstract
OBJECTIVES To investigate the diagnostic performance of Node Reporting and Data System (Node-RADS) combined with computed tomography (CT) radiomics for assessing nonenlargement regional lymph nodes in gastric cancer (GC). METHODS Preoperative CT images were retrospectively collected from 376 pathologically confirmed of gastric adenocarcinoma from January 2019 to December 2023, with 605 lymph nodes included for analysis. They were divided into training (n = 362) and validation (n = 243) sets. Radiomics features were extracted from venous-phase, and the radiomics score was obtained. Clinical information, CT parameters, and Node-RADS classification were collected. A combined model was built using machine-learning approach and tested in validation set using receiver operating characteristic curve analysis. Further validation was conducted in different subgroups of lymph node short-axis diameter (SD) range. RESULTS Node-RADS score, SD, maximum diameter of thickness of tumor, and radiomics were identified as the most predictive factors. The results demonstrated that the integrated model combining SD, maximum diameter of thickness of tumor, Node-RADS, and radiomics outperformed the model excluding radiomics, yielding an area under the receiver operating characteristic curve of 0.82 compared with 0.79, with a statistically significant difference ( P < 0.001). Subgroup analysis based on different SDs of lymph nodes also revealed enhanced diagnostic accuracy when incorporating the radiomics score for the 4- to 7.9-mm subgroups, all P < 0.05. However, for the 8- to 9.9-mm subgroup, the combination of the radiomics did not significantly improve the prediction, with an area under the receiver operating characteristic curve of 0.85 versus 0.85, P = 0.877. CONCLUSION The integration of radiomics scores with Node-RADS assessments significantly enhances the accuracy of lymph node metastasis evaluation for GC. This combined model is particularly effective for lymph nodes with smaller standard deviations, yielding a marked improvement in diagnostic precision. CLINICAL RELEVANCE STATEMENT The findings of this study indicate that a composite model, which incorporates Node-RADS, radiomics features, and conventional parameters, may serve as an effective method for the assessment of nonenlarged lymph nodes in GC.
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Affiliation(s)
| | - Wei Fang
- Radiology Department, Yidu Central Hospital of Shandong Second Medical University, Qingzhou, Shandong
| | - Na Wei
- Yidu Central Hospital of Shandong Second Medical University, Qingzhou
| | - Wenwen Ma
- Radiology Department, Affiliated Hospital of Shandong Second Medical University, Weifang
| | - Cong Dai
- Radiology Department, Yidu Central Hospital of Shandong Second Medical University, Qingzhou, Shandong
| | - Ruixue Liu
- Pathology Department, Yidu Central Hospital of Shandong Second Medical University, Qingzhou, Shandong Province, China
| | - Anzhen Cai
- Radiology Department, Yidu Central Hospital of Shandong Second Medical University, Qingzhou, Shandong
| | - Qiang Feng
- Radiology Department, Yidu Central Hospital of Shandong Second Medical University, Qingzhou, Shandong
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Niu Y, Yu S, Chen P, Tang M, Wen L, Sun Y, Yang Y, Zhang Y, Fu Y, Lu Q, Luo T, Yu X. Diagnostic performance of Node-RADS score for mesorectal lymph node metastasis in rectal cancer. Abdom Radiol (NY) 2025; 50:38-48. [PMID: 39046482 DOI: 10.1007/s00261-024-04497-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 07/06/2024] [Accepted: 07/12/2024] [Indexed: 07/25/2024]
Abstract
PURPOSE To explore the diagnostic performance of the Node-RADS scoring system on preoperative assessment of mesorectal lymph node metastasis (LNM) status in rectal cancer, in comparison with the ESGAR category and size of lymph node (LN). METHODS Preoperative clinical and MRI data of 154 rectal adenocarcinoma patients treated with radical resection surgery were retrospectively analyzed. The differences in the clinical, pathological and imaging characteristics between the pN- and pN + groups were surveyed. The correlations of Node-RADS score and ESGAR category to pN stage, LNM number and lymph node ratio (LNR) were investigated. The performances on assessing pathological LNM were compared among individual approaches. A nomogram combined the imaging and clinical features was also established and evaluated. RESULTS Significant differences in CEA, tumor maximum diameter, tumor location, LN short-axis diameter, Node-RADS score and ESGAR category were found between the pN- and pN + groups. Node-RADS correlated significantly with pN stage, LNM number, and LNR (r = 0.665, 0.685, and 0.675, p < 0.001). Node-RADS had the highest AUC (0.862) for predicting pN + status, surpassing ESGAR (AUC = 0.797, p = 0.040) and LN size (AUC = 0.762, p = 0.015). The nomogram had the best diagnostic performance (AUC = 0.901), significantly outperforming Node-RADS alone (p = 0.037). CONCLUSIONS The Node-RADS scoring system is comparable to the ESGAR category and surpasses short-axis diameter in preoperatively predicting LNM in rectal cancer. Integrating imaging and clinical features will lead to an enhancement in diagnostic performance. Moreover, a clear relationship was demonstrated between the Node-RADS score and the quantity-dependent pathological characteristics of LNM.
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Affiliation(s)
- Yue Niu
- Department of Diagnostic Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Yuelu District, 283 Tongzipo Road, Changsha, 410013, Hunan, China
- Department of Radiology, Third Affiliated Hospital of Soochow University , Changzhou, 213000, Jiangsu, China
- Department of Diagnostic Radiology, Hengyang Medical School, Graduate Collaborative Training Base of Hunan Cancer Hospital, University of South China, Hengyang, 421001, Hunan, China
| | - Sanqiang Yu
- Norman Bethune Health Science Center of Jilin University , Changchun, 130021, Jilin, China
| | - Peng Chen
- Department of Diagnostic Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Yuelu District, 283 Tongzipo Road, Changsha, 410013, Hunan, China
| | - Mengjie Tang
- Department of Pathology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital , Changsha, 410013, Hunan, China
| | - Lu Wen
- Department of Diagnostic Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Yuelu District, 283 Tongzipo Road, Changsha, 410013, Hunan, China
| | - Yan Sun
- Department of Diagnostic Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Yuelu District, 283 Tongzipo Road, Changsha, 410013, Hunan, China
| | - Yanhui Yang
- Department of Diagnostic Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Yuelu District, 283 Tongzipo Road, Changsha, 410013, Hunan, China
- Department of Diagnostic Radiology, Hengyang Medical School, Graduate Collaborative Training Base of Hunan Cancer Hospital, University of South China, Hengyang, 421001, Hunan, China
| | - Yi Zhang
- Department of Diagnostic Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Yuelu District, 283 Tongzipo Road, Changsha, 410013, Hunan, China
- Department of Diagnostic Radiology, Hengyang Medical School, Graduate Collaborative Training Base of Hunan Cancer Hospital, University of South China, Hengyang, 421001, Hunan, China
| | - Yi Fu
- Medical Department, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital , Changsha, 410013, Hunan, China
| | - Qiang Lu
- Department of Diagnostic Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Yuelu District, 283 Tongzipo Road, Changsha, 410013, Hunan, China
| | - Tao Luo
- Department of Diagnostic Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Yuelu District, 283 Tongzipo Road, Changsha, 410013, Hunan, China
| | - Xiaoping Yu
- Department of Diagnostic Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Yuelu District, 283 Tongzipo Road, Changsha, 410013, Hunan, China.
- Department of Diagnostic Radiology, Hengyang Medical School, Graduate Collaborative Training Base of Hunan Cancer Hospital, University of South China, Hengyang, 421001, Hunan, China.
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Xu M, Xie X, Cai L, Liu D, Sun P. Preoperative scoring system for the prediction of risk of lymph node metastasis in cervical cancer. Sci Rep 2024; 14:23860. [PMID: 39394379 PMCID: PMC11470059 DOI: 10.1038/s41598-024-74871-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2024] [Accepted: 09/30/2024] [Indexed: 10/13/2024] Open
Abstract
The study aimed to develop and validate a preoperative scoring system to predict the risk of lymph node metastasis (LNM) in cervical cancer (CC). A total of 426 stage IB1-IIA1 CC patients were randomly divided into two sets. A logistic regression model was used to determine independent factors that contribute to LNM. A preoperative scoring system was developed based on beta (β) coefficients. An area under the receiver operating curve (AUC) was used to test for model discrimination. Five-year overall survival (OS) rate was 91.7%. Multivariable logistic regression analysis showed that FIGO stage, tumor size, depth of invasion on MRI, and squamous cell carcinoma antigen levels were independent risk factors in the development set (all P < 0.05). The AUCs of the scoring system for the development and validation sets were 0.833 (95% CI = 0.757-0.909) and 0.767 (95% CI = 0.634-0.891), respectively. Patients who scored 0-2, 3-5, and 6-8 were classified into low-risk, medium-risk, and high-risk groups. Predicted rates were in accord with observed rates in both sets. The 5-year OS rates of the new groups were also significantly different for the entire group, development set, and validation set (all P < 0.05). LNM affects the prognosis of CC patients. The scoring system can be used to assist in evaluating the risk of LNM in CC patients preoperatively. It is easy to obtain and can provide reference for clinical treatment decision-making.
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Affiliation(s)
- Mu Xu
- College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fujian, China
- Department of Gynecology, Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, No. 18 Daoshan Road, Fuzhou, 350001, Fujian, China
| | - Xiaoyan Xie
- Department of Gynecology, Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, No. 18 Daoshan Road, Fuzhou, 350001, Fujian, China
| | - Liangzhi Cai
- College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fujian, China
- Department of Gynecology, Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, No. 18 Daoshan Road, Fuzhou, 350001, Fujian, China
| | - DaBin Liu
- Department of Gynecology, Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, No. 18 Daoshan Road, Fuzhou, 350001, Fujian, China
| | - Pengming Sun
- College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fujian, China.
- Department of Gynecology, Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, No. 18 Daoshan Road, Fuzhou, 350001, Fujian, China.
- Laboratory of Gynecologic Oncology, Fujian Maternal and Child Health Hospital, Affiliated Hospital of Fujian Medical University, No. 18 Daoshan Road, Fuzhou, 350001, Fujian, China.
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Yang Y, Xu Z, Cai Z, Zhao H, Zhu C, Hong J, Lu R, Lai X, Guo L, Hu Q, Xu Z. Novel deep learning radiomics nomogram-based multiparametric MRI for predicting the lymph node metastasis in rectal cancer: A dual-center study. J Cancer Res Clin Oncol 2024; 150:450. [PMID: 39379733 PMCID: PMC11461781 DOI: 10.1007/s00432-024-05986-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2024] [Accepted: 10/03/2024] [Indexed: 10/10/2024]
Abstract
PURPOSE To develop and evaluate a nomogram that integrates clinical parameters with deep learning radiomics (DLR) extracted from Magnetic Resonance Imaging (MRI) data to enhance the predictive accuracy for preoperative lymph node (LN) metastasis in rectal cancer. METHODS A retrospective analysis was conducted on 356 patients diagnosed with rectal cancer. Of these, 286 patients were allocated to the training set, and 70 patients comprised the external validation cohort. Preprocessed T2-weighted and diffusion-weighted imaging performed preoperatively facilitated the extraction of DLR features. Five machine learning algorithms-k-nearest neighbor, light gradient boosting machine, logistic regression, random forest, and support vector machine-were utilized to develop DLR models. The most effective algorithm was identified and used to establish a clinical DLR (CDLR) nomogram specifically designed to predict LN metastasis in rectal cancer. The performance of the nomogram was evaluated using receiver operating characteristic curve analysis. RESULTS The logistic regression classifier demonstrated significant predictive accuracy using the DLR signature, achieving an Area Under the Curve (AUC) of 0.919 in the training cohort and 0.778 in the external validation cohort. The integrated CDLR nomogram exhibited robust predictive performance across both datasets, with AUC values of 0.921 in the training cohort and 0.818 in the external validation cohort. Notably, it outperformed both the clinical model, which had AUC values of 0.770 and 0.723 in the training and external validation cohorts, respectively, and the stand-alone DLR model. CONCLUSION The nomogram derived from multiparametric MRI data, referred to as the CDLR model, demonstrates strong predictive efficacy in forecasting LN metastasis in rectal cancer.
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Affiliation(s)
- Yunjun Yang
- Department of Radiology, The First People's Hospital of Foshan, No. 81 North Lingnan Avenue, Foshan, 528010, China
| | - Zhenyu Xu
- Department of Radiology, The First People's Hospital of Foshan, No. 81 North Lingnan Avenue, Foshan, 528010, China
| | - Zhiping Cai
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, China
| | - Hai Zhao
- Department of Radiology, The First People's Hospital of Foshan, No. 81 North Lingnan Avenue, Foshan, 528010, China
| | - Cuiling Zhu
- Department of Radiology, Foshan Hospital of Traditional Chinese Medicine, Guangzhou University of Traditional Chinese Medicine, Foshan, China
| | - Julu Hong
- Department of Radiology, The First People's Hospital of Foshan, No. 81 North Lingnan Avenue, Foshan, 528010, China
| | - Ruiliang Lu
- Department of Radiology, The First People's Hospital of Foshan, No. 81 North Lingnan Avenue, Foshan, 528010, China
| | - Xiaoyu Lai
- Department of Radiology, The First People's Hospital of Foshan, No. 81 North Lingnan Avenue, Foshan, 528010, China
| | - Li Guo
- Department of Institute of Translational Medicine, The First People's Hospital of Foshan, Foshan, China
| | - Qiugen Hu
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, China
| | - Zhifeng Xu
- Department of Radiology, The First People's Hospital of Foshan, No. 81 North Lingnan Avenue, Foshan, 528010, China.
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Niu Y, Wen L, Yang Y, Zhang Y, Fu Y, Lu Q, Wang Y, Yu X, Yu X. Diagnostic performance of Node Reporting and Data System (Node-RADS) for assessing mesorectal lymph node in rectal cancer by CT. BMC Cancer 2024; 24:716. [PMID: 38862951 PMCID: PMC11165899 DOI: 10.1186/s12885-024-12487-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Accepted: 06/07/2024] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND To compare the diagnostic performance of the Node-RADS scoring system and lymph node (LN) size in preoperative LN assessment for rectal cancer (RC), and to investigate whether the selection of size as the primary criterion whereas morphology as the secondary criterion for LNs can be considered the preferred method for clinical assessment. METHODS Preoperative CT data of 146 RC patients treated with radical resection surgery were retrospectively analyzed. The Node-RADS score and short-axis diameter of size-prioritized LNs and the morphology-prioritized LNs were obtained. The correlations of Node-RADS score to the pN stage, LNM number and lymph node ratio (LNR) were investigated. The performances on assessing pathological lymph node metastasis were compared between Node-RADS score and short-axis diameter. A nomogram combined the Node-RADS score and clinical features was also evaluated. RESULTS Node-RADS score showed significant correlation with pN stage, LNM number and LNR (Node-RADS of size-prioritized LN: r = 0.600, 0.592, and 0.606; Node-RADS of morphology-prioritized LN: r = 0.547, 0.538, and 0.527; Node-RADSmax: r = 0.612, 0.604, and 0.610; all p < 0.001). For size-prioritized LN, Node-RADS achieved an AUC of 0.826, significantly superior to short-axis diameter (0.826 vs. 0.743, p = 0.009). For morphology-prioritized LN, Node-RADS exhibited an AUC of 0.758, slightly better than short-axis diameter (0.758 vs. 0.718, p = 0.098). The Node-RADS score of size-prioritized LN was significantly better than that of morphology-prioritized LN (0.826 vs. 0.758, p = 0.038). The nomogram achieved the best diagnostic performance (AUC = 0.861) than all the other assessment methods (p < 0.05). CONCLUSIONS The Node-RADS scoring system outperforms the short-axis diameter in predicting lymph node metastasis in RC. Size-prioritized LN demonstrates superior predictive efficacy compared to morphology-prioritized LN. The nomogram combined the Node-RADS score of size-prioritized LN with clinical features exhibits the best diagnostic performance. Moreover, a clear relationship was demonstrated between the Node-RADS score and the quantity-dependent pathological characteristics of LNM.
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Affiliation(s)
- Yue Niu
- Department of Diagnostic Radiology, the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan, 410013, China
- Department of Diagnostic Radiology, Graduate Collaborative Training Base of Hunan Cancer Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Lu Wen
- Department of Diagnostic Radiology, the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan, 410013, China
| | - Yanhui Yang
- Department of Diagnostic Radiology, the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan, 410013, China
- Department of Diagnostic Radiology, Graduate Collaborative Training Base of Hunan Cancer Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Yi Zhang
- Department of Diagnostic Radiology, the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan, 410013, China
- Department of Diagnostic Radiology, Graduate Collaborative Training Base of Hunan Cancer Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Yi Fu
- Medical department, the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan, 410013, China
| | - Qiang Lu
- Department of Diagnostic Radiology, the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan, 410013, China
| | - Yu Wang
- Clinical and Technical Support, Philips Healthcare, Shanghai, 200072, China
| | - Xiao Yu
- Clinical and Technical Support, Philips Healthcare, Shanghai, 200072, China
| | - Xiaoping Yu
- Department of Diagnostic Radiology, the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan, 410013, China.
- Department of Diagnostic Radiology, Graduate Collaborative Training Base of Hunan Cancer Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
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Ye YX, Yang L, Kang Z, Wang MQ, Xie XD, Lou KX, Bao J, Du M, Li ZX. Magnetic resonance imaging-based lymph node radiomics for predicting the metastasis of evaluable lymph nodes in rectal cancer. World J Gastrointest Oncol 2024; 16:1849-1860. [PMID: 38764830 PMCID: PMC11099437 DOI: 10.4251/wjgo.v16.i5.1849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/23/2024] [Accepted: 03/04/2024] [Indexed: 05/09/2024] Open
Abstract
BACKGROUND Lymph node (LN) staging in rectal cancer (RC) affects treatment decisions and patient prognosis. For radiologists, the traditional preoperative assessment of LN metastasis (LNM) using magnetic resonance imaging (MRI) poses a challenge. AIM To explore the value of a nomogram model that combines Conventional MRI and radiomics features from the LNs of RC in assessing the preoperative metastasis of evaluable LNs. METHODS In this retrospective study, 270 LNs (158 nonmetastatic, 112 metastatic) were randomly split into training (n = 189) and validation sets (n = 81). LNs were classified based on pathology-MRI matching. Conventional MRI features [size, shape, margin, T2-weighted imaging (T2WI) appearance, and CE-T1-weighted imaging (T1WI) enhancement] were evaluated. Three radiomics models used 3D features from T1WI and T2WI images. Additionally, a nomogram model combining conventional MRI and radiomics features was developed. The model used univariate analysis and multivariable logistic regression. Evaluation employed the receiver operating characteristic curve, with DeLong test for comparing diagnostic performance. Nomogram performance was assessed using calibration and decision curve analysis. RESULTS The nomogram model outperformed conventional MRI and single radiomics models in evaluating LNM. In the training set, the nomogram model achieved an area under the curve (AUC) of 0.92, which was significantly higher than the AUCs of 0.82 (P < 0.001) and 0.89 (P < 0.001) of the conventional MRI and radiomics models, respectively. In the validation set, the nomogram model achieved an AUC of 0.91, significantly surpassing 0.80 (P < 0.001) and 0.86 (P < 0.001), respectively. CONCLUSION The nomogram model showed the best performance in predicting metastasis of evaluable LNs.
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Affiliation(s)
- Yong-Xia Ye
- Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
| | - Liu Yang
- Department of Colorectal Surgery, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research and The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing 210000, Jiangsu Province, China
| | - Zheng Kang
- Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
| | - Mei-Qin Wang
- Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
| | - Xiao-Dong Xie
- Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
| | - Ke-Xin Lou
- Department of Pathology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
| | - Jun Bao
- Colorectal Center, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
| | - Mei Du
- Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
| | - Zhe-Xuan Li
- Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
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Loch FN, Beyer K, Kreis ME, Kamphues C, Rayya W, Schineis C, Jahn J, Tronser M, Elsholtz FHJ, Hamm B, Reiter R. Diagnostic performance of Node Reporting and Data System (Node-RADS) for regional lymph node staging of gastric cancer by CT. Eur Radiol 2024; 34:3183-3193. [PMID: 37921924 PMCID: PMC11126430 DOI: 10.1007/s00330-023-10352-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 07/25/2023] [Accepted: 08/20/2023] [Indexed: 11/05/2023]
Abstract
OBJECTIVES Diagnostic performance of imaging for regional lymph node assessment in gastric cancer is still limited, and there is a lack of consensus on radiological evaluation. At the same time, there is an increasing demand for structured reporting using Reporting and Data Systems (RADS) to standardize oncological imaging. We aimed at investigating the diagnostic performance of Node-RADS compared to the use of various individual criteria for assessing regional lymph nodes in gastric cancer using histopathology as reference. METHODS In this retrospective single-center study, consecutive 91 patients (median age, 66 years, range 33-91 years, 54 men) with CT scans and histologically proven gastric adenocarcinoma were assessed using Node-RADS assigning scores from 1 to 5 for the likelihood of regional lymph node metastases. Additionally, different Node-RADS criteria as well as subcategories of altered border contour (lobulated, spiculated, indistinct) were assessed individually. Sensitivity, specificity, and Youden's index were calculated for Node-RADS scores, and all criteria investigated. Interreader agreement was calculated using Cohen's kappa. RESULTS Among all criteria, best performance was found for Node-RADS scores ≥ 3 and ≥ 4 with a sensitivity/specificity/Youden's index of 56.8%/90.7%/0.48 and 48.6%/98.1%/0.47, respectively, both with substantial interreader agreement (κ = 0.73 and 0.67, p < 0.01). Among individual criteria, the best performance was found for short-axis diameter of 10 mm with sensitivity/specificity/Youden's index of 56.8%/87.0%/0.44 (κ = 0.65, p < 0.01). CONCLUSION This study shows that structured reporting of combined size and configuration criteria of regional lymph nodes in gastric cancer slightly improves overall diagnostic performance compared to individual criteria including short-axis diameter alone. The results show an increase in specificity and unchanged sensitivity. CLINICAL RELEVANCE STATEMENT The results of this study suggest that Node-RADS may be a suitable tool for structured reporting of regional lymph nodes in gastric cancer. KEY POINTS • Assessment of lymph nodes in gastric cancer is still limited, and there is a lack of consensus on radiological evaluation. • Node-RADS in gastric cancer improves overall diagnostic performance compared to individual criteria including short-axis diameter. • Node-RADS may be a suitable tool for structured reporting of regional lymph nodes in gastric cancer.
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Affiliation(s)
- Florian N Loch
- Department of Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Katharina Beyer
- Department of Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Martin E Kreis
- Department of Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Carsten Kamphues
- Department of Surgery, Parkklinik Weißensee, Schönstraße 80, 13086, Berlin, Germany
| | - Wael Rayya
- Department of Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Christian Schineis
- Department of Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Janosch Jahn
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Moritz Tronser
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Fabian H J Elsholtz
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Bernd Hamm
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Rolf Reiter
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany.
- BIH Charité Digital Clinician Scientist Program, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, Charitéplatz 1, 10117, Berlin, Germany.
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Safont MJ, García-Figueiras R, Hernando-Requejo O, Jimenez-Rodriguez R, Lopez-Vicente J, Machado I, Ayuso JR, Bustamante-Balén M, De Torres-Olombrada MV, Domínguez Tristancho JL, Fernández-Aceñero MJ, Suarez J, Vera R. Interdisciplinary Spanish consensus on a watch-and-wait approach for rectal cancer. Clin Transl Oncol 2024; 26:825-835. [PMID: 37787973 DOI: 10.1007/s12094-023-03322-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 09/07/2023] [Indexed: 10/04/2023]
Abstract
Watch-and-wait has emerged as a new strategy for the management of rectal cancer when a complete clinical response is achieved after neoadjuvant therapy. In an attempt to standardize this new clinical approach, initiated by the Spanish Cooperative Group for the Treatment of Digestive Tumors (TTD), and with the participation of the Spanish Association of Coloproctology (AECP), the Spanish Society of Pathology (SEAP), the Spanish Society of Gastrointestinal Endoscopy (SEED), the Spanish Society of Radiation Oncology (SEOR), and the Spanish Society of Medical Radiology (SERAM), we present herein a consensus on a watch-and-wait approach for the management of rectal cancer. We have focused on patient selection, the treatment schemes evaluated, the optimal timing for evaluating the clinical complete response, the oncologic outcomes after the implementation of this strategy, and a protocol for surveillance of these patients.
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Affiliation(s)
- Maria Jose Safont
- Oncology Department, Consorcio Hospital General Universitario de Valencia. Valencia University, Av. de les Tres Creus, 2, 46014, València, Spain.
| | - Roberto García-Figueiras
- Radiology Department, Hospital Clínico Universitario de Santiago de Compostela, Santiago de Compostela, Spain
| | | | | | - Jorge Lopez-Vicente
- Gastroenterology Department, Hospital Universitario de Mostoles, Mósteles, Spain
| | - Isidro Machado
- Instituto Valenciano de Oncología, Valencia, Spain
- Pathology Department, Patologika Laboratory QuironSalud, Valencia, Spain
- Pathology Department, University of Valencia, Valencia, Spain
| | | | - Marco Bustamante-Balén
- Gastrointestinal Endoscopy Unit, Hospital Universitari i Politècnic La Fe, Valencia, Spain
| | | | | | - Mª Jesús Fernández-Aceñero
- Surgical Pathology Department, Hospital Clínico San Carlos, IdiSSC, Universidad Complutense de Madrid, Madrid, Spain
| | - Javier Suarez
- General Surgery Department, Hospital Universitario de Navarra, Pamplona, Spain
| | - Ruth Vera
- Medical Oncology Department, Hospital Universitario de Navarra, Instituto de Investigación (Idisna), Pamplona, Spain
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Xia W, Li D, He W, Pickhardt PJ, Jian J, Zhang R, Zhang J, Song R, Tong T, Yang X, Gao X, Cui Y. Multicenter Evaluation of a Weakly Supervised Deep Learning Model for Lymph Node Diagnosis in Rectal Cancer at MRI. Radiol Artif Intell 2024; 6:e230152. [PMID: 38353633 PMCID: PMC10982819 DOI: 10.1148/ryai.230152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 12/13/2023] [Accepted: 01/24/2024] [Indexed: 03/07/2024]
Abstract
Purpose To develop a Weakly supervISed model DevelOpment fraMework (WISDOM) model to construct a lymph node (LN) diagnosis model for patients with rectal cancer (RC) that uses preoperative MRI data coupled with postoperative patient-level pathologic information. Materials and Methods In this retrospective study, the WISDOM model was built using MRI (T2-weighted and diffusion-weighted imaging) and patient-level pathologic information (the number of postoperatively confirmed metastatic LNs and resected LNs) based on the data of patients with RC between January 2016 and November 2017. The incremental value of the model in assisting radiologists was investigated. The performances in binary and ternary N staging were evaluated using area under the receiver operating characteristic curve (AUC) and the concordance index (C index), respectively. Results A total of 1014 patients (median age, 62 years; IQR, 54-68 years; 590 male) were analyzed, including the training cohort (n = 589) and internal test cohort (n = 146) from center 1 and two external test cohorts (cohort 1: 117; cohort 2: 162) from centers 2 and 3. The WISDOM model yielded an overall AUC of 0.81 and C index of 0.765, significantly outperforming junior radiologists (AUC = 0.69, P < .001; C index = 0.689, P < .001) and performing comparably with senior radiologists (AUC = 0.79, P = .21; C index = 0.788, P = .22). Moreover, the model significantly improved the performance of junior radiologists (AUC = 0.80, P < .001; C index = 0.798, P < .001) and senior radiologists (AUC = 0.88, P < .001; C index = 0.869, P < .001). Conclusion This study demonstrates the potential of WISDOM as a useful LN diagnosis method using routine rectal MRI data. The improved radiologist performance observed with model assistance highlights the potential clinical utility of WISDOM in practice. Keywords: MR Imaging, Abdomen/GI, Rectum, Computer Applications-Detection/Diagnosis Supplemental material is available for this article. Published under a CC BY 4.0 license.
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Affiliation(s)
| | | | - Wenguang He
- From the Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (W.X., J.J., R.Z., X.G.); Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China (D.L., J.Z., R.S., X.Y., X.G., Y.C.); Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (W.H.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, Madison, Wis (P.J.P.); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (T.T.); Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China (T.T.); and Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (Y.C.)
| | - Perry J. Pickhardt
- From the Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (W.X., J.J., R.Z., X.G.); Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China (D.L., J.Z., R.S., X.Y., X.G., Y.C.); Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (W.H.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, Madison, Wis (P.J.P.); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (T.T.); Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China (T.T.); and Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (Y.C.)
| | - Junming Jian
- From the Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (W.X., J.J., R.Z., X.G.); Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China (D.L., J.Z., R.S., X.Y., X.G., Y.C.); Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (W.H.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, Madison, Wis (P.J.P.); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (T.T.); Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China (T.T.); and Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (Y.C.)
| | - Rui Zhang
- From the Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (W.X., J.J., R.Z., X.G.); Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China (D.L., J.Z., R.S., X.Y., X.G., Y.C.); Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (W.H.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, Madison, Wis (P.J.P.); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (T.T.); Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China (T.T.); and Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (Y.C.)
| | - Junjie Zhang
- From the Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (W.X., J.J., R.Z., X.G.); Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China (D.L., J.Z., R.S., X.Y., X.G., Y.C.); Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (W.H.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, Madison, Wis (P.J.P.); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (T.T.); Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China (T.T.); and Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (Y.C.)
| | - Ruirui Song
- From the Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (W.X., J.J., R.Z., X.G.); Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China (D.L., J.Z., R.S., X.Y., X.G., Y.C.); Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (W.H.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, Madison, Wis (P.J.P.); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (T.T.); Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China (T.T.); and Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (Y.C.)
| | - Tong Tong
- From the Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (W.X., J.J., R.Z., X.G.); Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China (D.L., J.Z., R.S., X.Y., X.G., Y.C.); Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (W.H.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, Madison, Wis (P.J.P.); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (T.T.); Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China (T.T.); and Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (Y.C.)
| | - Xiaotang Yang
- From the Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (W.X., J.J., R.Z., X.G.); Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China (D.L., J.Z., R.S., X.Y., X.G., Y.C.); Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (W.H.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, Madison, Wis (P.J.P.); Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China (T.T.); Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China (T.T.); and Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China (Y.C.)
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Kikano EG, Matalon SA, Eskian M, Lee L, Melnitchouk N, Bleday R, Khorasani R. Concordance of MRI With Pathology for Primary Staging of Rectal Cancer in Routine Clinical Practice: A Single Institution Experience. Curr Probl Diagn Radiol 2024; 53:68-72. [PMID: 37704486 DOI: 10.1067/j.cpradiol.2023.08.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 07/01/2023] [Accepted: 08/23/2023] [Indexed: 09/15/2023]
Abstract
PURPOSE MRI is the preferred imaging modality for primary staging of rectal cancer, used to guide treatment. Patients identified with clinical stage I disease receive upfront surgical resection; those with clinical stage II or greater undergo upfront neoadjuvant therapy. Although clinical under-/over-staging may have consequences for patients and presents opportunities for organ preservation, the correlation between clinical and pathologic staging in routine clinical practice within a single institute has not been fully established. METHODS This retrospective, Institutional Review Board-approved study, conducted at a National Cancer Institute-Designated Comprehensive Cancer Center with a multi-disciplinary rectal cancer disease center, included patients undergoing rectal MRI for primary staging January 1, 2018-August 30, 2020. Data collection included patient demographics, initial clinical stage via MRI report, pathologic diagnosis, pathologic stage, and treatment. The primary outcome was concordance of overall clinical and pathologic staging. Secondary outcomes included reasons for mismatched staging. RESULTS A total 105 rectal adenocarcinoma patients (64 males, mean age 57 ± 12.7 years) had staging MRI followed by surgical resection. A total of 28 patients (27%) had mismatched under-/over- staging. Ten patients (10%) were understaged with mismatched T stage group (clinical stage I, pathologic stage II), five (5%) were understaged with mismatched N stage group (clinical stage I, pathologic stage III), and 13 (12%) were overstaged (clinical stage II-III, pathologic stage 0-I). Treatment matched concordance between clinical and pathologic stages was 86%. CONCLUSION MRI for primary rectal cancer staging has high concordance with pathology. Future studies to assess strategies for reducing clinically relevant understaging would be beneficial.
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Affiliation(s)
- Elias G Kikano
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA.
| | - Shanna A Matalon
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA
| | - Mahsa Eskian
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA
| | - Leslie Lee
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA
| | | | - Ron Bleday
- Department of Surgery, Brigham and Women's Hospital, Boston, MA
| | - Ramin Khorasani
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA
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Ma S, Lu H, Jing G, Li Z, Zhang Q, Ma X, Chen F, Shao C, Lu Y, Wang H, Shen F. Deep learning-based clinical-radiomics nomogram for preoperative prediction of lymph node metastasis in patients with rectal cancer: a two-center study. Front Med (Lausanne) 2023; 10:1276672. [PMID: 38105891 PMCID: PMC10722265 DOI: 10.3389/fmed.2023.1276672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 11/13/2023] [Indexed: 12/19/2023] Open
Abstract
Background Precise preoperative evaluation of lymph node metastasis (LNM) is crucial for ensuring effective treatment for rectal cancer (RC). This research aims to develop a clinical-radiomics nomogram based on deep learning techniques, preoperative magnetic resonance imaging (MRI) and clinical characteristics, enabling the accurate prediction of LNM in RC. Materials and methods Between January 2017 and May 2023, a total of 519 rectal cancer cases confirmed by pathological examination were retrospectively recruited from two tertiary hospitals. A total of 253 consecutive individuals were selected from Center I to create an automated MRI segmentation technique utilizing deep learning algorithms. The performance of the model was evaluated using the dice similarity coefficient (DSC), the 95th percentile Hausdorff distance (HD95), and the average surface distance (ASD). Subsequently, two external validation cohorts were established: one comprising 178 patients from center I (EVC1) and another consisting of 88 patients from center II (EVC2). The automatic segmentation provided radiomics features, which were then used to create a Radscore. A predictive nomogram integrating the Radscore and clinical parameters was constructed using multivariate logistic regression. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were employed to evaluate the discrimination capabilities of the Radscore, nomogram, and subjective evaluation model, respectively. Results The mean DSC, HD95 and ASD were 0.857 ± 0.041, 2.186 ± 0.956, and 0.562 ± 0.194 mm, respectively. The nomogram, which incorporates MR T-stage, CEA, CA19-9, and Radscore, exhibited a higher area under the ROC curve (AUC) compared to the Radscore and subjective evaluation in the training set (0.921 vs. 0.903 vs. 0.662). Similarly, in both external validation sets, the nomogram demonstrated a higher AUC than the Radscore and subjective evaluation (0.908 vs. 0.735 vs. 0.640, and 0.884 vs. 0.802 vs. 0.734). Conclusion The application of the deep learning method enables efficient automatic segmentation. The clinical-radiomics nomogram, utilizing preoperative MRI and automatic segmentation, proves to be an accurate method for assessing LNM in RC. This approach has the potential to enhance clinical decision-making and improve patient care. Research registration unique identifying number UIN Research registry, identifier 9158, https://www.researchregistry.com/browse-the-registry#home/registrationdetails/648e813efffa4e0028022796/.
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Affiliation(s)
- Shiyu Ma
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Haidi Lu
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Guodong Jing
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Zhihui Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Qianwen Zhang
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Xiaolu Ma
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Fangying Chen
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Yong Lu
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Hao Wang
- Department of Colorectal Surgery, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Fu Shen
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
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Niu Y, Yu X, Wen L, Bi F, Jian L, Liu S, Yang Y, Zhang Y, Lu Q. Comparison of preoperative CT- and MRI-based multiparametric radiomics in the prediction of lymph node metastasis in rectal cancer. Front Oncol 2023; 13:1230698. [PMID: 38074652 PMCID: PMC10708912 DOI: 10.3389/fonc.2023.1230698] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 11/08/2023] [Indexed: 01/22/2025] Open
Abstract
OBJECTIVE To compare computed tomography (CT)- and magnetic resonance imaging (MRI)-based multiparametric radiomics models and validate a multi-modality, multiparametric clinical-radiomics nomogram for individual preoperative prediction of lymph node metastasis (LNM) in rectal cancer (RC) patients. METHODS 234 rectal adenocarcinoma patients from our retrospective study cohort were randomly selected as the training (n = 164) and testing (n = 70) cohorts. The radiomics features of the primary tumor were extracted from the non-contrast enhanced computed tomography (NCE-CT), the enhanced computed tomography (CE-CT), the T2-weighted imaging (T2WI) and the gadolinium contrast-enhanced T1-weighted imaging (CE-TIWI) of each patient. Three kinds of models were constructed based on training cohort, including the Clinical model (based on the clinical features), the radiomics models (based on NCE-CT, CE-CT, T2WI, CE-T1WI, CT, MRI, CT combing with MRI) and the clinical-radiomics models (based on CT or MRI radiomics model combing with clinical data) and Clinical-IMG model (based on CT and MRI radiomics model combing with clinical data). The performances of the 11 models were evaluated via the area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, and specificity in the training and validation cohort. Differences in the AUCs among the 11 models were compared using DeLong's test. Finally, the optimal model (Clinical-IMG model) was selected to create a radiomics nomogram. The performance of the nomogram to evaluate clinical efficacy was verified by ROC curves and decision curve analysis (DCA). RESULTS The MRI radiomics model in the validation cohort significantly outperformed than CT radiomics model (AUC, 0.785 vs. 0.721, p<0.05). The Clinical-IMG nomogram had the highest prediction efficiency than all other predictive models (p<0.05), of which the AUC was 0.947, the sensitivity was 0.870 and the specificity was 0.884. CONCLUSION MRI radiomics model performed better than both CT radiomics model and Clinical model in predicting LNM of RC. The clinical-radiomics nomogram that combines the radiomics features obtained from both CT and MRI along with preoperative clinical characteristics exhibits the best diagnostic performance.
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Affiliation(s)
- Yue Niu
- Department of Diagnostic Radiology, Graduate Collaborative Training Base of Hunan Cancer Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
- Department of Diagnostic Radiology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Xiaoping Yu
- Department of Diagnostic Radiology, Graduate Collaborative Training Base of Hunan Cancer Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
- Department of Diagnostic Radiology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Lu Wen
- Department of Diagnostic Radiology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Feng Bi
- Department of Diagnostic Radiology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Lian Jian
- Department of Diagnostic Radiology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Siye Liu
- Department of Diagnostic Radiology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Yanhui Yang
- Department of Diagnostic Radiology, Graduate Collaborative Training Base of Hunan Cancer Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
- Department of Diagnostic Radiology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Yi Zhang
- Department of Diagnostic Radiology, Graduate Collaborative Training Base of Hunan Cancer Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
- Department of Diagnostic Radiology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Qiang Lu
- Department of Diagnostic Radiology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
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Lv B, Cheng X, Cheng Y, Kong X, Jin E. Predictive value of MRI-detected tumor deposits in locally advanced rectal cancer. Front Oncol 2023; 13:1153566. [PMID: 37671062 PMCID: PMC10476949 DOI: 10.3389/fonc.2023.1153566] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 08/03/2023] [Indexed: 09/07/2023] Open
Abstract
Background Although tumor deposits (TDs) are not the same as lymph nodes, the prognosis of patients with TDs is similar or worse than that of patients with metastatic lymph nodes. TDs are mostly assessed by the histology of samples after surgery, thus, not helpful for preoperative treatment strategies. The primary objective of this study was to detect TDs by MRI and evaluate its predictive value. Materials and methods A total of 114 patients with rectal cancer were retrospectively analyzed. Clinicopathological and MRI data mainly including MRI- detected TDs (mTDs), tumor border configuration (TBC) on MRI, MRI-detected extramural vascular invasion (mEMVI), MRI-detected lymph node metastasis (mLN), MRI T stage, MRI N stage, the range of rectal wall involved by the tumor, peritoneal reflection invasion, tumor length, tumor location, cord sign at the tumor edge, nodular protrusion at the tumor edge, maximal extramural depth and pathology-proven lymph node involvement (pLN) were evaluated. The correlation of MRI factors with postoperative distant metastasis (PDM) and pLN were analyzed by univariate analysis and multivariate logistic regression analysis, and nomograms were established based on the latter. The diagnostic efficiency was evaluated by the receiver operating characteristic curve (ROC) and area under the curve (AUC). Results A total of 38 cases of pLN, 13 of PDM and 17 of pathology-proven TDs (pTDs) were found. Ten cases of PDM and 22 cases of pLN in 30 mTDs cases were also found. Chi-square test showed that mTDs, mLN, TBC, mEMVI, MRI T stage, nodular protrusion, cord sign, maximal extramural depth and peritoneal reflection invasion were correlated with PDM and pLN (P<0.05). mTDs and peritoneal reflection invasion were independent risk factors for PDM (odds ratio: 10.15 and 8.77, P<0.05), mTDs and mLN were independent risk factors for pLN (odds ratio: 5.50 and 5.91, P<0.05), and Hosmer-Lemeshow test showed that the results of two models were not statistically significant, suggesting that the fit was good. On this basis, two nomograms for predicting PDM and pLN were confirmed by Bootstrap self-sampling, and the C-indices of the two nomograms were 0.837 and 0.817, respectively. The calibration curves and ROC curves of the two nomograms showed that the correlation between the predicted and the actual incidence of PDM and pLN was good. The DeLong test showed that the predictive efficiency of the nomogram in predicting pLN was better than that of mLN (P=0.0129). Conclusion mTDs are a risk factor for PDM and lymph node metastasis. The two nomograms based on mTDs showed a good performance in predicting PDM and lymph node metastasis, possessing a certain clinical value.
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Affiliation(s)
- Baohua Lv
- Department of Radiology, Taian City Central Hospital, Qingdao University, Tai’an, China
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Xiaojuan Cheng
- Clinical Skills Center, Taian Central Hospital, Tai’an, China
| | - Yanling Cheng
- Respiratory Department, Shandong Second Rehabilitation Hospital, Tai’an, China
| | - Xue Kong
- Department of Radiology, Taian City Central Hospital, Qingdao University, Tai’an, China
| | - Erhu Jin
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
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19
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Yang Y, Wei H, Fu F, Wei W, Wu Y, Bai Y, Li Q, Wang M. Preoperative prediction of lymphovascular invasion of colorectal cancer by radiomics based on 18F-FDG PET-CT and clinical factors. FRONTIERS IN RADIOLOGY 2023; 3:1212382. [PMID: 37614530 PMCID: PMC10442652 DOI: 10.3389/fradi.2023.1212382] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 07/28/2023] [Indexed: 08/25/2023]
Abstract
Purpose The purpose of this study was to investigate the value of a clinical radiomics model based on Positron emission tomography-computed tomography (PET-CT) radiomics features combined with clinical predictors of Lymphovascular invasion (LVI) in predicting preoperative LVI in patients with colorectal cancer (CRC). Methods A total of 95 CRC patients who underwent preoperative 18F-fluorodeoxyglucose (FDG) PET-CT examination were retrospectively enrolled. Univariate and multivariate logistic regression analyses were used to analyse clinical factors and PET metabolic data in the LVI-positive and LVI-negative groups to identify independent predictors of LVI. We constructed four prediction models based on radiomics features and clinical data to predict LVI status. The predictive efficacy of different models was evaluated according to the receiver operating characteristic curve. Then, the nomogram of the best model was constructed, and its performance was evaluated using calibration and clinical decision curves. Results Mean standardized uptake value (SUVmean), maximum tumour diameter and lymph node metastasis were independent predictors of LVI in CRC patients (P < 0.05). The clinical radiomics model obtained the best prediction performance, with an Area Under Curve (AUC) of 0.922 (95%CI 0.820-0.977) and 0.918 (95%CI 0.782-0.982) in the training and validation cohorts, respectively. A nomogram based on the clinical radiomics model was constructed, and the calibration curve fitted well (P > 0.05). Conclusion The clinical radiomics prediction model constructed in this study has high value in the preoperative individualized prediction of LVI in CRC patients.
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Affiliation(s)
- Yan Yang
- Department of Medical Imaging, People’s Hospital of Zhengzhou University, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Huanhuan Wei
- Department of Medical Imaging, People’s Hospital of Zhengzhou University, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Fangfang Fu
- Henan Key Laboratory of Neurological Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Wei Wei
- Henan Key Laboratory of Neurological Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Yaping Wu
- Henan Key Laboratory of Neurological Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Yan Bai
- Henan Key Laboratory of Neurological Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Qing Li
- Department of Medical Imaging, People’s Hospital of Zhengzhou University, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Meiyun Wang
- Henan Key Laboratory of Neurological Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
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Peng W, Qiao H, Mo L, Guo Y. Progress in the diagnosis of lymph node metastasis in rectal cancer: a review. Front Oncol 2023; 13:1167289. [PMID: 37519802 PMCID: PMC10374255 DOI: 10.3389/fonc.2023.1167289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 06/28/2023] [Indexed: 08/01/2023] Open
Abstract
Historically, the chief focus of lymph node metastasis research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen a rapid accumulation of massive omics and imaging data catalyzed by the rapid development of advanced technologies. This rapid increase in data has driven improvements in the accuracy of diagnosis of lymph node metastasis, and its analysis further demands new methods and the opportunity to provide novel insights for basic research. In fact, the combination of omics data, imaging data, clinical medicine, and diagnostic methods has led to notable advances in our basic understanding and transformation of lymph node metastases in rectal cancer. Higher levels of integration will require a concerted effort among data scientists and clinicians. Herein, we review the current state and future challenges to advance the diagnosis of lymph node metastases in rectal cancer.
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Affiliation(s)
- Wei Peng
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, Jiangxi, China
| | - Huimin Qiao
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, Jiangxi, China
| | - Linfeng Mo
- School of Health and Medicine, Guangzhou Huashang Vocational College, Guangzhou, Guangdong, China
| | - You Guo
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China
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Zhang Z, Chen Y, Wen Z, Wu X, Que Y, Ma Y, Wu Y, Liu Q, Fan W, Yu S. MRI for nodal restaging after neoadjuvant therapy in rectal cancer with histopathologic comparison. Cancer Imaging 2023; 23:67. [PMID: 37443085 DOI: 10.1186/s40644-023-00589-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 07/02/2023] [Indexed: 07/15/2023] Open
Abstract
BACKGROUND After neoadjuvant therapy, most of the lymph nodes (LNs) will shrink and disappear in patients with rectal cancer. However, LNs that are still detectable on MRI carry a risk of metastasis. This study aimed to evaluate the performance of the European Society of Gastrointestinal and Abdominal Radiology (ESGAR) criterion (short-axis diameter ≥ 5 mm) in diagnosing malignant LNs in patients with rectal cancer after neoadjuvant therapy, and whether nodal morphological characteristics (including shape, border, signal homogeneity, and enhancement homogeneity) could improve the diagnostic efficiency for LNs ≥ 5 mm. METHODS This retrospective study included 90 patients with locally advanced rectal cancer who underwent surgery after neoadjuvant therapy and performed preoperative MRI. Two radiologists independently measured the short-axis diameter of LNs and evaluated the morphological characteristics of LNs ≥ 5 mm in consensus. With a per node comparison with histopathology as the reference standard, a ROC curve was performed to evaluate the diagnostic performance of the size criterion. For categorical variables, either a χ2 test or Fisher's exact test was used. RESULTS A total of 298 LNs were evaluated. The AUC for nodal size in determining nodal status was 0.81. With a size cutoff value of 5 mm, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy were 65.9%, 87.0%, 46.8%, 93.6% and 83.9%, respectively. No significant differences were observed in any of the morphological characteristics between benign and malignant LNs ≥ 5 mm (all P > 0.05). CONCLUSIONS The ESGAR criterion demonstrated moderate diagnostic performance in identifying malignant LNs in patients with rectal cancer after neoadjuvant therapy. It was effective in determining the status of LNs < 5 mm but not for LNs ≥ 5 mm, and the diagnostic efficiency could not be improved by considering nodal morphological characteristics.
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Affiliation(s)
- Zhiwen Zhang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, 510080, Guangzhou, China
| | - Yan Chen
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, 510080, Guangzhou, China
| | - Ziqiang Wen
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, 510080, Guangzhou, China
| | - Xuehan Wu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, 510080, Guangzhou, China
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, 518036, Shenzhen, China
| | - Yutao Que
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, 510080, Guangzhou, China
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, 518036, Shenzhen, China
| | - Yuru Ma
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, 510080, Guangzhou, China
| | - Yunzhu Wu
- MR Scientific Marketing, SIEMENS Healthineers Ltd, 200124, Shanghai, China
| | - Quanmeng Liu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, 510080, Guangzhou, China
| | - Wenjie Fan
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, 510080, Guangzhou, China
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, 518036, Shenzhen, China
| | - Shenping Yu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, 510080, Guangzhou, China.
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Narihiro S, Kitaguchi D, Ikeda K, Hasegawa H, Teramura K, Tsukada Y, Nishizawa Y, Ito M. Two-team lateral lymph node dissection assisted by the transanal approach for locally advanced lower rectal cancer: comparison with the conventional transabdominal approach. Surg Endosc 2023:10.1007/s00464-023-10012-1. [PMID: 36973567 DOI: 10.1007/s00464-023-10012-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 03/09/2023] [Indexed: 03/29/2023]
Abstract
BACKGROUND An optimal surgical approach to lateral lymph node dissection (LLND) remains controversial. With the recent popularity of transanal total mesorectal excision, a two-team procedure combining the transabdominal and transanal approaches was established as a novel approach to LLND. This study aimed to clarify the safety and feasibility of two-team LLND (2team-LLND) and compare its short-term outcomes with those of conventional transabdominal LLND (Conv-LLND). METHODS Between April 2013 and March 2020, 463 patients diagnosed with primary locally advanced rectal cancer underwent a transanal total mesorectal excision; among them, 93 patients who underwent bilateral prophylactic LLND were included in this single-center, retrospective study. Among these patients, 50 and 43 patients underwent Conv-LLND (the Conv-LLND group) and 2team-LLND (the 2team-LLND group), respectively. The short-term outcomes, including the operation time, blood loss volume, number of complications, and number of harvested lymph nodes, were compared between the two groups. RESULTS The intraoperative and postoperative complications in the 2team-LLND group were equivalent to those in the Conv-LLND group; furthermore, the incidence of postoperative urinary retention in the 2team-LLND group was acceptably low (9%). Compared with the Conv-LLND group, the 2team-LLND group had a significantly shorter operation time (P = 0.003), lower median blood loss (P = 0.02), and higher number of harvested lateral lymph nodes (P = 0.0005). CONCLUSION The intraoperative and postoperative complications of 2team-LLND were comparable with those of Conv-LLND. Thus, 2team-LLND was safe and feasible for advanced lower rectal cancer. Moreover, it was superior to Conv-LLND in terms of the operation time, blood loss volume, and number of harvested lateral lymph nodes. Therefore, it can be a promising LLND approach.
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Affiliation(s)
- Satoshi Narihiro
- Department of Colorectal Surgery, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan
| | - Daichi Kitaguchi
- Department of Colorectal Surgery, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan
| | - Koji Ikeda
- Department of Colorectal Surgery, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan
| | - Hiro Hasegawa
- Department of Colorectal Surgery, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan
| | - Koichi Teramura
- Department of Colorectal Surgery, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan
| | - Yuichiro Tsukada
- Department of Colorectal Surgery, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan
| | - Yuji Nishizawa
- Department of Colorectal Surgery, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan
| | - Masaaki Ito
- Department of Colorectal Surgery, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
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Liu J, Pan H, Lin Q, Chen X, Huang Z, Huang X, Tang L. Added value of spectral parameters in diagnosing metastatic lymph nodes of pT1-2 rectal cancer. Abdom Radiol (NY) 2023; 48:1260-1267. [PMID: 36862166 DOI: 10.1007/s00261-023-03854-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 02/09/2023] [Accepted: 02/13/2023] [Indexed: 03/03/2023]
Abstract
PURPOSE To investigate the added value of spectral parameters derived from dual-layer spectral detector CT (SDCT) in diagnosing metastatic lymph nodes (LNs) of pT1-2 (stage 1-2 determined by pathology) rectal cancer. METHODS A total of 80 LNs (57 non-metastatic LNs and 23 metastatic LNs) from 42 patients with pT1-T2 rectal cancer were retrospectively analyzed. The short-axis diameter of LNs was measured, then its border and enhancement homogeneity were evaluated. All spectral parameters, including iodine concentration (IC), effective atomic number (Zeff), normalized IC (nIC), normalized Zeff (nZeff), and slope of the attenuation curve (λ), were measured or calculated. The chi-square test, Fisher's exact test, independent-samples t-test, or Mann-Whitney U test was used to compare the differences of each parameter between the non-metastatic group and the metastatic group. Multivariable logistic regression analyses were used to determine the independent factors for predicting LN metastasis. Diagnostic performances were assessed by ROC curve analysis and compared with the DeLong test. RESULTS The short-axis diameter, border, enhancement homogeneity, and each spectral parameter of LNs showed significant differences between the two groups (P < 0.05). The nZeff and short-axis diameter were independent predictors of metastatic LNs (P < 0.05), with areas under the curve (AUC) of 0.870 and 0.772, sensitivity of 82.5% and 73.9%, and specificity of 82.6% and 78.9%. After combining nZeff and the short-axis diameter, the AUC (0.966) was the highest with sensitivity of 100% and specificity of 87.7%. CONCLUSION The spectral parameters derived from SDCT might help us to improve the diagnostic accuracy of metastatic LNs in patients with pT1-2 rectal cancer, the highest diagnostic performance can be achieved after combining nZeff with the short-axis diameter of LNs.
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Affiliation(s)
- Jinkai Liu
- Department of Radiology, Longyan First Affiliated Hospital of Fujian Medical University, No. 105, North 91 Road, Xinluo District, Longyan, 364000, Fujian, People's Republic of China
| | - Hao Pan
- Department of Radiology, The Second Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang, People's Republic of China
| | - Qi Lin
- Department of Radiology, Longyan First Affiliated Hospital of Fujian Medical University, No. 105, North 91 Road, Xinluo District, Longyan, 364000, Fujian, People's Republic of China
| | - Xingbiao Chen
- Clinical Science, Philips Healthcare, Shanghai, People's Republic of China
| | - Zhenhuan Huang
- Department of Radiology, Longyan First Affiliated Hospital of Fujian Medical University, No. 105, North 91 Road, Xinluo District, Longyan, 364000, Fujian, People's Republic of China
| | - Xionghua Huang
- Department of Radiology, Longyan First Affiliated Hospital of Fujian Medical University, No. 105, North 91 Road, Xinluo District, Longyan, 364000, Fujian, People's Republic of China
| | - Langlang Tang
- Department of Radiology, Longyan First Affiliated Hospital of Fujian Medical University, No. 105, North 91 Road, Xinluo District, Longyan, 364000, Fujian, People's Republic of China.
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24
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Bedrikovetski S, Dudi-Venkata NN, Kroon HM, Traeger LH, Seow W, Vather R, Wilks M, Moore JW, Sammour T. A prospective study of diagnostic accuracy of multidisciplinary team and radiology reporting of preoperative colorectal cancer local staging. Asia Pac J Clin Oncol 2023; 19:206-213. [PMID: 35712999 PMCID: PMC10084150 DOI: 10.1111/ajco.13795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 04/22/2022] [Accepted: 05/07/2022] [Indexed: 01/20/2023]
Abstract
INTRODUCTION The aim of this study was to correlate and assess diagnostic accuracy of preoperative staging at multidisciplinary team meeting (MDT) against the original radiology reports and pathological staging in colorectal cancer patients. METHODS A prospective observational study was conducted at two institutions. Patients with histologically proven colorectal cancer and available preoperative imaging were included. Preoperative tumor and nodal staging (cT and cN) as determined by the MDT and the radiology report (computed tomography [CT] and/or magnetic resonance imaging [MRI]) were recorded. Kappa statistics were used to assess agreement between MDT and the radiology report for cN staging in colon cancer, cT and cN in rectal cancer, and tumor regression grade (TRG) in patients with rectal cancer who received neoadjuvant therapy. Pathological report after surgery served as the reference standard for local staging, and AUROC curves were constructed to compare diagnostic accuracy of the MDT and radiology report. RESULTS A total of 481 patients were included. Agreement between MDT and radiology report for cN stage was good in colon cancer (k = .756, Confidence Interval (CI) 95% .686-.826). Agreement for cT and cN and in rectal cancer was very good (kw = .825, CI 95% .758-.892) and good (kw = .792, CI 95% .709-.875), respectively. In the rectal cancer group that received neoadjuvant therapy, agreement on TRG was very good (kw = .919, CI 95% .846-.993). AUROC curves using pathological staging indicated no difference in diagnostic accuracy between MDT and radiology reports for either colon or rectal cancer. CONCLUSION Preoperative colorectal cancer local staging was consistent between specialist MDT review and original radiology reports, with no significant differences in diagnostic accuracy identified.
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Affiliation(s)
- Sergei Bedrikovetski
- Discipline of Surgery, Faculty of Health and Medical Sciences, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.,Colorectal Unit, Department of Surgery, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Nagendra N Dudi-Venkata
- Discipline of Surgery, Faculty of Health and Medical Sciences, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.,Colorectal Unit, Department of Surgery, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Hidde M Kroon
- Discipline of Surgery, Faculty of Health and Medical Sciences, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.,Colorectal Unit, Department of Surgery, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Luke H Traeger
- Discipline of Surgery, Faculty of Health and Medical Sciences, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.,Colorectal Unit, Department of Surgery, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Warren Seow
- Discipline of Surgery, Faculty of Health and Medical Sciences, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
| | - Ryash Vather
- Colorectal Unit, Department of Surgery, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Michael Wilks
- Department of Interventional Radiology, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - James W Moore
- Discipline of Surgery, Faculty of Health and Medical Sciences, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.,Colorectal Unit, Department of Surgery, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Tarik Sammour
- Discipline of Surgery, Faculty of Health and Medical Sciences, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.,Colorectal Unit, Department of Surgery, Royal Adelaide Hospital, Adelaide, South Australia, Australia
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25
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Fu C, Shao T, Hou M, Qu J, Li P, Yang Z, Shan K, Wu M, Li W, Wang X, Zhang J, Luo F, Zhou L, Sun J, Zhao F. Preoperative prediction of tumor deposits in rectal cancer with clinical-magnetic resonance deep learning-based radiomic models. Front Oncol 2023; 13:1078863. [PMID: 36890815 PMCID: PMC9986582 DOI: 10.3389/fonc.2023.1078863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 02/06/2023] [Indexed: 02/22/2023] Open
Abstract
Background This study aimed to establish an effective model for preoperative prediction of tumor deposits (TDs) in patients with rectal cancer (RC). Methods In 500 patients, radiomic features were extracted from magnetic resonance imaging (MRI) using modalities such as high-resolution T2-weighted (HRT2) imaging and diffusion-weighted imaging (DWI). Machine learning (ML)-based and deep learning (DL)-based radiomic models were developed and integrated with clinical characteristics for TD prediction. The performance of the models was assessed using the area under the curve (AUC) over five-fold cross-validation. Results A total of 564 radiomic features that quantified the intensity, shape, orientation, and texture of the tumor were extracted for each patient. The HRT2-ML, DWI-ML, Merged-ML, HRT2-DL, DWI-DL, and Merged-DL models demonstrated AUCs of 0.62 ± 0.02, 0.64 ± 0.08, 0.69 ± 0.04, 0.57 ± 0.06, 0.68 ± 0.03, and 0.59 ± 0.04, respectively. The clinical-ML, clinical-HRT2-ML, clinical-DWI-ML, clinical-Merged-ML, clinical-DL, clinical-HRT2-DL, clinical-DWI-DL, and clinical-Merged-DL models demonstrated AUCs of 0.81 ± 0.06, 0.79 ± 0.02, 0.81 ± 0.02, 0.83 ± 0.01, 0.81 ± 0.04, 0.83 ± 0.04, 0.90 ± 0.04, and 0.83 ± 0.05, respectively. The clinical-DWI-DL model achieved the best predictive performance (accuracy 0.84 ± 0.05, sensitivity 0.94 ± 0. 13, specificity 0.79 ± 0.04). Conclusions A comprehensive model combining MRI radiomic features and clinical characteristics achieved promising performance in TD prediction for RC patients. This approach has the potential to assist clinicians in preoperative stage evaluation and personalized treatment of RC patients.
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Affiliation(s)
- Chunlong Fu
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Tingting Shao
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Min Hou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiali Qu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ping Li
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Department of Radiology, Jiaxing Hospital of Traditional Chinese Medicine, Jiaxing, China
| | - Zebin Yang
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Kangfei Shan
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Meikang Wu
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Weida Li
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Xuan Wang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingfeng Zhang
- Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo, China
| | - Fanghong Luo
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Long Zhou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jihong Sun
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo, China.,Cancer Center, Zhejiang University, Hangzhou, China
| | - Fenhua Zhao
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
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26
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Kawai K, Shiomi A, Miura T, Uehara K, Watanabe J, Kazama S, Ueno H, Sakamoto K, Kinugasa Y, Takahashi K, Hida K, Hamada M, Ishihara S, Sugihara K. Optimal diagnostic criteria for lateral lymph node dissection using magnetic resonance imaging: a multicenter prospective study. ANZ J Surg 2023; 93:206-213. [PMID: 36069323 DOI: 10.1111/ans.18029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 08/23/2022] [Accepted: 08/24/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND LLND in cases with suspected lateral lymph node (LLN) metastasis has been focused on as a novel treatment strategy in recent years. However, the optimal indication for LLND in rectal cancer patients has not been determined. This study aimed to establish the optimal indication for lateral lymph node dissection (LLND) in patients with rectal cancer using magnetic resonance imaging (MRI). METHODS A total of 209 patients with rectal adenocarcinoma who underwent total mesorectal excision and LLND in 13 hospitals were prospectively registered. By matching the sizes of the harvested LNs and those in magnetic resonance imaging (MRI), the pathological outcome of each LN was confirmed one-by-one. Using parameters of the LLNs in MRI, the optimal diagnostic criteria for LLND were established. RESULTS Of 3241 harvested LLNs, including 83 metastatic nodes, 1010 (31.1%) were visualized on MRI. Although all parameters assessed showed strong correlations with the presence of metastasis, none of these parameters could discriminate metastatic LLNs from non-metastatic nodes with sufficient sensitivity. However, by using the combination of long axis and short/long ratio in pretreatment MRI, we could establish optimal criteria for LLND. The sensitivity and specificity of the criteria for LLN metastasis were 94.3% and 40.2%, respectively. CONCLUSIONS In conclusion, we established novel criteria for LLND in rectal cancer patients using MRI. Our criteria will be of great clinical use in determining indications for LLND.
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Affiliation(s)
- Kazushige Kawai
- Department of Colorectal Surgery, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Tokyo, Japan
| | - Akio Shiomi
- Division of Colon and Rectal Surgery, Shizuoka Cancer Center Hospital, Nagaizumi, Japan
| | - Takuya Miura
- Department of Gastroenterological Surgery, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Kay Uehara
- Division of Surgical Oncology, Department of Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Jun Watanabe
- Department of Surgery, Gastroenterological Center, Yokohama City University Medical Center, Yokohama, Japan
| | - Shinsuke Kazama
- Division of Gastroenterological Surgery, Saitama Cancer Center, Ina, Japan
| | - Hideki Ueno
- Department of Surgery, National Defense Medical College, Tokorozawa, Japan
| | - Kazuhiro Sakamoto
- Department of Coloproctological Surgery, Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Yusuke Kinugasa
- Department of Gastrointestinal Surgery, Tokyo Medical and Dental University, Tokyo, Japan
| | - Keiichi Takahashi
- Department of Colorectal Surgery, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Tokyo, Japan
| | - Koya Hida
- Department of Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Madoka Hamada
- Division of Gastrointestinal Surgery, Kansai Medical University Hospital, Hirakata, Japan
| | - Soichiro Ishihara
- Department of Surgical Oncology, The University of Tokyo, Tokyo, Japan
| | - Kenichi Sugihara
- Department of Gastrointestinal Surgery, Tokyo Medical and Dental University, Tokyo, Japan
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27
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Herold A, Wassipaul C, Weber M, Lindenlaub F, Rasul S, Stift A, Stift J, Mayerhoefer ME, Hacker M, Ba-Ssalamah A, Haug AR, Tamandl D. Added value of quantitative, multiparametric 18F-FDG PET/MRI in the locoregional staging of rectal cancer. Eur J Nucl Med Mol Imaging 2022; 50:205-217. [PMID: 36063201 PMCID: PMC9668962 DOI: 10.1007/s00259-022-05936-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 08/05/2022] [Indexed: 02/07/2023]
Abstract
PURPOSE The purpose of this study was to determine whether multiparametric positron emission tomography/magnetic resonance imaging (mpPET/MRI) can improve locoregional staging of rectal cancer (RC) and to assess its prognostic value after resection. METHODS In this retrospective study, 46 patients with primary RC, who underwent multiparametric 18F-fluorodeoxyglucose (FDG) PET/MRI, followed by surgical resection without chemoradiotherapy, were included. Two readers reviewed T- and N- stage, mesorectal involvement, sphincter infiltration, tumor length, and distance from anal verge. In addition, diffusion-weighted imaging (DWI) and PET parameters were extracted from the multiparametric protocol and were compared to radiological staging as well as to the histopathological reference standard. Clinical and imaging follow-up was systematically assessed for tumor recurrence and death. RESULTS Locally advanced rectal cancers (LARC) exhibited significantly higher metabolic tumor volume (MTV, AUC 0.74 [95% CI 0.59-0.89], p = 0.004) and total lesion glycolysis (TLG, AUC 0.70 [95% CI 0.53-0.87], p = 0.022) compared to early tumors. T-stage was associated with MTV (AUC 0.70 [95% CI 0.54-0.85], p = 0.021), while N-stage was better assessed using anatomical MRI sequences (AUC 0.72 [95% CI 0.539-0.894], p = 0.032). In the multivariate regression analysis, depending on the model, both anatomical MRI sequences and MTV/TLG were capable of detecting LARC. Combining anatomical MRI stage and MTV/TLG led to a superior diagnostic performance for detecting LARC (AUC 0.81, [95% CI 0.68-0.94], p < 0.001). In the survival analysis, MTV was independently associated with overall survival (HR 1.05 [95% CI 1.01-1.10], p = 0.044). CONCLUSION Multiparametric PET-MRI can improve identification of locally advanced tumors and, hence, help in treatment stratification. It provides additional information on RC tumor biology and may have prognostic value.
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Affiliation(s)
- Alexander Herold
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Christian Wassipaul
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Michael Weber
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Florian Lindenlaub
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Sazan Rasul
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Anton Stift
- Department of General Surgery, Medical University of Vienna, Vienna, Austria
| | - Judith Stift
- Department of Pathology, Medical University of Vienna, Vienna, Austria
- INNPATH GmbH, Tirolkliniken, Innsbruck, Austria
| | - Marius E Mayerhoefer
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Ahmed Ba-Ssalamah
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Alexander R Haug
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, Vienna, Austria
| | - Dietmar Tamandl
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
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28
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Song G, Li P, Wu R, Jia Y, Hong Y, He R, Li J, Zhang R, Li A. Development and validation of a high-resolution T2WI-based radiomic signature for the diagnosis of lymph node status within the mesorectum in rectal cancer. Front Oncol 2022; 12:945559. [PMID: 36185279 PMCID: PMC9523667 DOI: 10.3389/fonc.2022.945559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 08/23/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose The aim of this study was to explore the feasibility of a high-resolution T2-weighted imaging (HR-T2WI)-based radiomics prediction model for diagnosing metastatic lymph nodes (LNs) within the mesorectum in rectal cancer. Method A total of 604 LNs (306 metastatic and 298 non-metastatic) from 166 patients were obtained. All patients underwent HR-T2WI examination and total mesorectal excision (TME) surgery. Four kinds of segmentation methods were used to select region of interest (ROI), including method 1 along the border of LNs; method 2 along the expanded border of LNs with an additional 2–3 mm; method 3 covering the border of LNs only; and method 4, a circle region only within LNs. A total of 1,409 features were extracted for each method. Variance threshold method, Select K Best, and Lasso algorithm were used to reduce the dimension. All LNs were divided into training and test sets. Fivefold cross-validation was used to build the logistic model, which was evaluated by the receiver operating characteristic (ROC) with four indicators, including area under the curve (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). Three radiologists with different working experience in diagnosing rectal diseases assessed LN metastasis respectively. The diagnostic efficiencies with each of four segmentation methods and three radiologists were compared to each other. Results For the test set, the AUCs of four segmentation methods were 0.820, 0.799, 0.764, and 0.741; the ACCs were 0.725, 0.704, 0.709, and 0.670; the SEs were 0.756, 0.634, 0.700, and 0.589; and the SPs were 0.696, 0.772, 0.717, and 0.750, respectively. There was no statistically significant difference in AUC between the four methods (p > 0.05). Method 1 had the highest values of AUC, ACC, and SE. For three radiologists, the overall diagnostic efficiency was moderate. The corresponding AUCs were 0.604, 0.634, and 0.671; the ACCs were 0.601, 0.632, and 0.667; the SEs were 0.366, 0.552, and 0.392; and the SPs were 0.842, 0.715, and 0.950, respectively. Conclusions The proposed HR-T2WI-based radiomic signature exhibited a robust performance on predicting mesorectal LN status and could potentially be used for clinicians in order to determine the status of metastatic LNs in rectal cancer patients.
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Affiliation(s)
- Gesheng Song
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Panpan Li
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Rui Wu
- Department of Radiology, Shandong University, Jinan, China
| | - Yuping Jia
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Yu Hong
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Rong He
- Department of Radiology, The Shandong First Medical University, Jinan, China
| | - Jinye Li
- Department of Radiology, Shandong Provincial ENT Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Ran Zhang
- Marketing, Medical Technology Co., Ltd., Beijing, China
| | - Aiyin Li
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
- *Correspondence: Aiyin Li,
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Crafa F, Vanella S, Catalano OA, Pomykala KL, Baiamonte M. Role of one-step nucleic acid amplification in colorectal cancer lymph node metastases detection. World J Gastroenterol 2022; 28:4019-4043. [PMID: 36157105 PMCID: PMC9403438 DOI: 10.3748/wjg.v28.i30.4019] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 06/03/2022] [Accepted: 07/22/2022] [Indexed: 02/06/2023] Open
Abstract
Current histopathological staging procedures in colorectal cancer (CRC) depend on midline division of the lymph nodes (LNs) with one section of hematoxylin and eosin staining. Cancer cells outside this transection line may be missed, which could lead to understaging of Union for International Cancer Control Stage II high-risk patients. The one-step nucleic acid amplification (OSNA) assay has emerged as a rapid molecular diagnostic tool for LN metastases detection. It is a molecular technique that can analyze the entire LN tissue using a reverse-transcriptase loop-mediated isothermal amplification reaction to detect tumor-specific cytokeratin 19 mRNA. Our findings suggest that the OSNA assay has a high diagnostic accuracy in detecting metastatic LNs in CRC and a high negative predictive value. OSNA is a standardized, observer-independent technique, which may lead to more accurate staging. It has been suggested that in stage II CRC, the upstaging can reach 25% and these patients can access postoperative adjuvant chemotherapy. Moreover, intraoperative OSNA sentinel node evaluation may allow early CRC to be treated with organ-preserving surgery, while in more advanced-stage disease, a tailored lymphadenectomy can be performed considering the presence of aberrant lymphatic drainage and skip metastases.
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Affiliation(s)
- Francesco Crafa
- Division of General and Surgical Oncology, St. Giuseppe Moscati Hospital, Center of National Excellence and High Specialty, Avellino 83100, Italy
| | - Serafino Vanella
- Division of General and Surgical Oncology, St. Giuseppe Moscati Hospital, Center of National Excellence and High Specialty, Avellino 83100, Italy
| | - Onofrio A Catalano
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States
| | - Kelsey L Pomykala
- Department of Nuclear Medicine, Department of Radiological Sciences, David Geffen School of Medicine at University of California, Los Angeles, University Hospital Essen, University of Duisburg-Essen, Essen 45141, Germany
| | - Mario Baiamonte
- Division of General and Surgical Oncology, St. Giuseppe Moscati Hospital, Center of National Excellence and High Specialty, Avellino 83100, Italy
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30
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Li J, Gao X, Dominik Nickel M, Cheng J, Zhu J. Native T1 mapping for differentiating the histopathologic type, grade, and stage of rectal adenocarcinoma: a pilot study. Cancer Imaging 2022; 22:30. [PMID: 35715848 PMCID: PMC9204907 DOI: 10.1186/s40644-022-00461-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 05/12/2022] [Indexed: 11/10/2022] Open
Abstract
Background Previous studies have indicated that T1 relaxation time could be utilized for the analysis of tissue characteristics. T1 mapping technology has been gradually used on research of body tumor. In this study, the application of native T1 relaxation time for differentiating the histopathologic type, grade, and stage of rectal adenocarcinoma was investigated. Methods One hundred and twenty patients with pathologically confirmed rectal adenocarcinoma were retrospectively evaluated. All patients underwent high-resolution anatomical magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), and T1 mapping sequences. Parameters of T1 relaxation time and apparent diffusion coefficient (ADC) were measured between the different groups. The diagnostic power was evaluated though the receiver operating characteristic (ROC) curve. Results The T1 and ADC values varied significantly between rectal mucinous adenocarcinoma (MC) and non-mucinous rectal adenocarcinoma (AC) ([1986.1 ± 163.3 ms] vs. [1562.3 ± 244.2 ms] and [1.38 ± 0.23 × 10−3mm2/s] vs. [1.03 ± 0.15 × 10−3mm2/s], respectively; P < 0.001). In the AC group, T1 relaxation time were significantly different between the low- and high-grade adenocarcinoma cases ([1508.7 ± 188.6 ms] vs. [1806.5 ± 317.5 ms], P < 0.001), while no differences were apparent in the ADC values ([1.03 ± 0.14 × 10−3mm2/s] vs. [1.04 ± 0.18 × 10−3mm2/s], P > 0.05). No significant differences in T1 and ADC values were identified between the different T and N stage groups for both MC and AC (all P > 0.05). Conclusions Native T1 relaxation time can be used to discriminate MC from AC. The T1 relaxation time was helpful for differentiating the low- and high-grade of AC.
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Affiliation(s)
- Juan Li
- Department of MRI, the First Affiliated Hospital of Zhengzhou University, No.1, Jianshe Dong Road, Zhengzhou, 450052, China
| | - Xuemei Gao
- Department of MRI, the First Affiliated Hospital of Zhengzhou University, No.1, Jianshe Dong Road, Zhengzhou, 450052, China
| | | | - Jingliang Cheng
- Department of MRI, the First Affiliated Hospital of Zhengzhou University, No.1, Jianshe Dong Road, Zhengzhou, 450052, China.
| | - Jinxia Zhu
- MR Collaboration, Siemens Healthcare Ltd, Beijing, 100000, China
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Wang D, Zhuang Z, Wu S, Chen J, Fan X, Liu M, Zhu H, Wang M, Zou J, Zhou Q, Zhou P, Xue J, Meng X, Ju S, Zhang L. A Dual-Energy CT Radiomics of the Regional Largest Short-Axis Lymph Node Can Improve the Prediction of Lymph Node Metastasis in Patients With Rectal Cancer. Front Oncol 2022; 12:846840. [PMID: 35747803 PMCID: PMC9209707 DOI: 10.3389/fonc.2022.846840] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 05/19/2022] [Indexed: 12/24/2022] Open
Abstract
ObjectiveTo explore the value of dual-energy computed tomography (DECT) radiomics of the regional largest short-axis lymph nodes for evaluating lymph node metastasis in patients with rectal cancer.Materials and MethodsOne hundred forty-one patients with rectal cancer (58 in LNM+ group, 83 in LNM- group) who underwent preoperative total abdominal DECT were divided into a training group and testing group (7:3 ratio). After post-processing DECT venous phase images, 120kVp-like images and iodine (water) images were obtained. The highest-risk lymph nodes were identified, and their long-axis and short-axis diameter and DECT quantitative parameters were measured manually by two experienced radiologists who were blind to the postoperative pathological results. Four DECT parameters were analyzed: arterial phase (AP) normalized iodine concentration, AP normalized effective atomic number, the venous phase (VP) normalized iodine concentration, and the venous phase normalized effective atomic number. The carcinoembryonic antigen (CEA) levels were recorded one week before surgery. Radiomics features of the largest lymph nodes were extracted, standardized, and reduced before modeling. Radomics signatures of 120kVp-like images (Rad-signature120kVp) and iodine map (Rad-signatureImap) were built based on Logistic Regression via Least Absolute Shrinkage and Selection Operator (LASSO).ResultsEight hundred thirty-three features were extracted from 120kVp-like and iodine images, respectively. In testing group, the radiomics features based on 120kVp-like images showed the best diagnostic performance (AUC=0.922) compared to other predictors [CT morphological indicators (short-axis diameter (AUC=0.779, IDI=0.262) and long-axis diameter alone (AUC=0.714, IDI=0.329)), CEA alone (AUC=0.540, IDI=0.414), and normalized DECT parameters alone (AUC=0.504-0.718, IDI=0.290-0.476)](P<0.05 in Delong test). Contrary, DECT iodine map-based radiomic signatures showed similar performance in predicting lymph node metastasis (AUC=0.866). The decision curve showed that the 120kVp-like-based radiomics signature has the highest net income.ConclusionPredictive model based on DECT and the largest short-axis diameter lymph nodes has the highest diagnostic value in predicting lymph node metastasis in patients with rectal cancer.
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Affiliation(s)
- Dongqing Wang
- Department of Medical Imaging, The Affiliated Hospital of Jiangsu University, Zhenjiang, China
- School of Medicine, Jiangsu University, Zhenjiang, China
| | - Zijian Zhuang
- Department of Medical Imaging, The Affiliated Hospital of Jiangsu University, Zhenjiang, China
- School of Medicine, Jiangsu University, Zhenjiang, China
| | - Shuting Wu
- School of Medicine, Jiangsu University, Zhenjiang, China
| | - Jixiang Chen
- Department of General Surgery, The Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Xin Fan
- Department of General Surgery, The Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Mengsi Liu
- School of Medicine, Jiangsu University, Zhenjiang, China
| | - Haitao Zhu
- Department of Medical Imaging, The Affiliated Hospital of Jiangsu University, Zhenjiang, China
- School of Medicine, Jiangsu University, Zhenjiang, China
| | - Ming Wang
- Department of Medical Imaging, The Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Jinmei Zou
- Department of Medical Imaging, The Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Qun Zhou
- Department of Medical Imaging, The Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Peng Zhou
- School of Medicine, Jiangsu University, Zhenjiang, China
| | - Jing Xue
- School of Medicine, Jiangsu University, Zhenjiang, China
| | - Xiangpan Meng
- School of Medicine, Southeast University, Nanjing, China
- Department of Radiology, Zhongda Hospital, Southeast University, Nanjing, China
| | - Shenghong Ju
- School of Medicine, Southeast University, Nanjing, China
- Department of Radiology, Zhongda Hospital, Southeast University, Nanjing, China
| | - Lirong Zhang
- Department of Medical Imaging, The Affiliated Hospital of Jiangsu University, Zhenjiang, China
- School of Medicine, Southeast University, Nanjing, China
- *Correspondence: Lirong Zhang,
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Korngold EK, Moreno C, Kim DH, Fowler KJ, Cash BD, Chang KJ, Gage KL, Gajjar AH, Garcia EM, Kambadakone AR, Liu PS, Macomber M, Marin D, Pietryga JA, Santillan CS, Weinstein S, Zreloff J, Carucci LR. ACR Appropriateness Criteria® Staging of Colorectal Cancer: 2021 Update. J Am Coll Radiol 2022; 19:S208-S222. [PMID: 35550803 DOI: 10.1016/j.jacr.2022.02.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 02/22/2022] [Indexed: 12/19/2022]
Abstract
Preoperative imaging of rectal carcinoma involves accurate assessment of the primary tumor as well as distant metastatic disease. Preoperative imaging of nonrectal colon cancer is most beneficial in identifying distant metastases, regardless of primary T or N stage. Surgical treatment remains the definitive treatment for colon cancer, while organ-sparing approach may be considered in some rectal cancer patients based on imaging obtained before and after neoadjuvant treatment. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.
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Affiliation(s)
- Elena K Korngold
- Oregon Health and Science University, Portland, Oregon; Section Chief, Body Imaging; Chair, P&T Committee; Modality Chief, CT.
| | - Courtney Moreno
- Emory University, Atlanta, Georgia; Chair America College of Radiology CT Colonography Registry Committee
| | - David H Kim
- Panel Chair, University of Wisconsin Hospital & Clinics, Madison, Wisconsin; Vice Chair of Education (University of Wisconsin Dept of Radiology)
| | - Kathryn J Fowler
- Panel Vice-Chair, University of California San Diego, San Diego, California; ACR LI-RADS Working Group Chair
| | - Brooks D Cash
- University of Texas Health Science Center at Houston and McGovern Medical School, Houston, Texas; American Gastroenterological Association; Chief of GI, UTHealth
| | - Kevin J Chang
- Boston University Medical Center, Boston, Massachusetts; Director of MRI, Associate Chief of Abdominal Imaging; ACR Chair of Committee on C-RADS
| | - Kenneth L Gage
- H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Aakash H Gajjar
- PRiSMA Proctology Surgical Medicine & Associates, Houston, Texas; American College of Surgeons
| | - Evelyn M Garcia
- Virginia Tech Carilion School of Medicine, Roanoke, Virginia
| | - Avinash R Kambadakone
- Massachusetts General Hospital, Boston, Massachusetts; Division Chief, Abdominal Imaging, Massachusetts General Hospital; Medical Director, Martha's Vineyard Hospital Imaging
| | - Peter S Liu
- Cleveland Clinic, Cleveland, Ohio; Section Head, Abdominal Imaging, Cleveland Clinic, Cleveland OH
| | | | - Daniele Marin
- Duke University Medical Center, Durham, North Carolina
| | | | - Cynthia S Santillan
- University of California San Diego, San Diego, California; Vice Chair of Clinical Operations for Department of Radiology
| | - Stefanie Weinstein
- University of California San Francisco, San Francisco, California; Associate Chief of Radiology, San Francisco VA Health Systems
| | | | - Laura R Carucci
- Specialty Chair, Virginia Commonwealth University Medical Center, Richmond, Virginia; Director MR and CT at VCUHS; Section Chief Abdominal Imaging VCUHS
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Nougaret S, Rousset P, Gormly K, Lucidarme O, Brunelle S, Milot L, Salut C, Pilleul F, Arrivé L, Hordonneau C, Baudin G, Soyer P, Brun V, Laurent V, Savoye-Collet C, Petkovska I, Gerard JP, Rullier E, Cotte E, Rouanet P, Beets-Tan RGH, Frulio N, Hoeffel C. Structured and shared MRI staging lexicon and report of rectal cancer: A consensus proposal by the French Radiology Group (GRERCAR) and Surgical Group (GRECCAR) for rectal cancer. Diagn Interv Imaging 2022; 103:127-141. [PMID: 34794932 DOI: 10.1016/j.diii.2021.08.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 08/13/2021] [Indexed: 12/18/2022]
Abstract
PURPOSE To develop French guidelines by experts to standardize data acquisition, image interpretation, and reporting in rectal cancer staging with magnetic resonance imaging (MRI). MATERIALS AND METHODS Evidence-based data and opinions of experts of GRERCAR (Groupe de REcherche en Radiologie sur le CAncer du Rectum [i.e., Rectal Cancer Imaging Research Group]) and GRECCAR (Groupe de REcherche en Chirurgie sur le CAncer du Rectum [i.e., Rectal Cancer Surgery Research Group]) were combined using the RAND-UCLA Appropriateness Method to attain consensus guidelines. Experts scoring of reporting template and protocol for data acquisition were collected; responses were analyzed and classified as "Recommended" versus "Not recommended" (when ≥ 80% consensus among experts) or uncertain (when < 80% consensus among experts). RESULTS Consensus regarding patient preparation, MRI sequences, staging and reporting was attained using the RAND-UCLA Appropriateness Method. A consensus was reached for each reporting template item among the experts. Tailored MRI protocol and standardized report were proposed. CONCLUSION These consensus recommendations should be used as a guide for rectal cancer staging with MRI.
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Affiliation(s)
- Stephanie Nougaret
- Department of Radiology, Institut Régional du Cancer de Montpellier, Montpellier Cancer Research Institute, INSERM U1194, University of Montpellier, 34295, Montpellier, France.
| | - Pascal Rousset
- Department of Radiology, Lyon 1 Claude-Bernard University, 69495 Pierre-Benite, France
| | - Kirsten Gormly
- Dr Jones & Partners Medical Imaging, Kurralta Park, 5037, Australia; University of Adelaide, North Terrace, Adelaide, South Australia 5000, Australia
| | - Oliver Lucidarme
- Department of Radiology, Pitié-Salpêtrière Hospital, Sorbonne Université, 75013 Paris, France; LIB, INSERM, CNRS, UMR7371-U1146, 75013 Paris, France
| | - Serge Brunelle
- Department of Radiology, Institut Paoli-Calmettes, 13009 Marseille, France
| | - Laurent Milot
- Radiology Department, Hospices Civils de Lyon, Lyon Sud University Hospital, 69495 Pierre Bénite, France; Lyon 1 Claude Bernard University, 69100 Villeurbanne, France
| | - Cécile Salut
- Department of Radiology, CHU de Bordeaux, Université de Bordeaux, 33000 Bordeaux, France
| | - Franck Pilleul
- Department of Radiology, Centre Léon Bérard, Lyon, France Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, 69621, Lyon, France
| | - Lionel Arrivé
- Department of Radiology, Hopital St Antoine, Paris, France
| | - Constance Hordonneau
- Department of Radiology, CHU Estaing, Université Clermont-Auvergne, 63000 Clermont-Ferrand, France
| | - Guillaume Baudin
- Department of Radiology, Centre Antoine Lacassagne, 06100 Nice, France
| | - Philippe Soyer
- Department of Radiology, Hôpital Cochin, AP-HP, 75014 Paris, France; Université de Paris, 75006 Paris, France
| | - Vanessa Brun
- Department of Radiology, CHU Hôpital Pontchaillou, 35000 Rennes Cedex, France
| | - Valérie Laurent
- Department of Radiology, Brabois-Nancy University Hospital, Université de Lorraine, 54500 Vandoeuvre-lès-Nancy, France
| | | | - Iva Petkovska
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Jean Pierre Gerard
- Department of Radiotherapy, Centre Antoine Lacassagne, 06100 Nice, France
| | - Eric Rullier
- Department of Digestive Surgery, Hôpital Haut-Lévèque, Université de Bordeaux, 33600 Pessac, France
| | - Eddy Cotte
- Department of Digestive Surgery, Hospices Civils de Lyon, Lyon Sud University Hospital, 69310 Pierre Bénite, France; Lyon 1 Claude Bernard University, 69100 Villeurbanne, France
| | - Philippe Rouanet
- Department of surgery, Institut Régional du Cancer de Montpellier, Montpellier Cancer Research Institute, INSERM U1194, University of Montpellier, 34295, Montpellier, France
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, 1066 CX, Amsterdam, the Netherlands
| | - Nora Frulio
- Department of Radiology, CHU de Bordeaux, Université de Bordeaux, 33000 Bordeaux, France
| | - Christine Hoeffel
- Department of Radiology, Hôpital Robert Debré & CRESTIC, URCA, 51092 Reims, France
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Jia H, Jiang X, Zhang K, Shang J, Zhang Y, Fang X, Gao F, Li N, Dong J. A Nomogram of Combining IVIM-DWI and MRI Radiomics From the Primary Lesion of Rectal Adenocarcinoma to Assess Nonenlarged Lymph Node Metastasis Preoperatively. J Magn Reson Imaging 2022; 56:658-667. [PMID: 35090079 DOI: 10.1002/jmri.28068] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 12/29/2021] [Accepted: 12/29/2021] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Lymph node (LN) staging plays an important role in treatment decision-making. Current problem is that preoperative detection of LN involvement is always highly challenging for radiologists. PURPOSE To explore the value of the nomogram model combining intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and radiomics features from the primary lesion of rectal adenocarcinoma in assessing the non-enlarged lymph node metastasis (N-LNM) preoperatively. STUDY TYPE Retrospective. POPULATION A total of 126 patients (43% female) comprising a training group (n = 87) and a validation group (n = 39) with pathologically confirmed rectal adenocarcinoma. FIELD STRENGTH/SEQUENCE A 3.0 Tesla (T); T2 -weighted imaging (T2 WI) with fast spin-echo (FSE) sequence; IVIM-DWI spin-echo echo-planar imaging sequence. ASSESSMENT Based on pathological analysis of the surgical specimen, patients were classified into negative LN (LN-) and positive LN (LN+) groups. Apparent diffusion coefficient (ADC), diffusion coefficient (D), pseudo diffusion coefficient (D*) and microvascular volume fraction (f) values of primary lesion of rectal adenocarcinoma were measured. Three-dimensional (3D) radiomics features were measured on T2 WI and IVIM-DWI. A nomogram model including IVIM-DWI and radiomics features was developed. STATISTICAL TESTS General_univariate_analysis and multivariate logistic regression were used for radiomics features selection. The performance of the nomogram was assessed by the receiver operating characteristic (ROC) curve, calibration, and decision curve analysis (DCA). RESULTS The LN+ group had a significantly lower D* value ([13.20 ± 13.66 vs. 23.25 ± 18.71] × 10-3 mm2 /sec) and a higher f value (0.43 ± 0.12 vs. 0.34 ± 0.10) than the LN- group in the training cohort. The nomogram model combined D*, f, and radiomics features had a better evaluated performance (AUC = 0.864) than any other model in the training cohort. DATE CONCLUSION The nomogram model including IVIM-DWI and MRI radiomics features in the primary lesion of rectal adenocarcinoma was associated with the N-LNM. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Haodong Jia
- Department of Radiology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, 230001, China
| | - Xueyan Jiang
- Graduate school, Bengbu Medical College, Anhui Province, 233030, China
| | - Kaiyue Zhang
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, 230001, China
| | - Jin Shang
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, 230031, China
| | - Yu Zhang
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, 230001, China
| | - Xin Fang
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, 230031, China
| | - Fei Gao
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, 230031, China
| | - Naiyu Li
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, 230031, China
| | - Jiangning Dong
- Department of Radiology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, 230001, China.,Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, 230031, China
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Li J, Wang P, Zhou Y, Liang H, Lu Y, Luan K. A novel classification method of lymph node metastasis in colorectal cancer. Bioengineered 2021; 12:2007-2021. [PMID: 34024255 PMCID: PMC8806456 DOI: 10.1080/21655979.2021.1930333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 05/07/2021] [Accepted: 05/08/2021] [Indexed: 11/21/2022] Open
Abstract
Colorectal cancer lymph node metastasis, which is highly associated with the patient's cancer recurrence and survival rate, has been the focus of many therapeutic strategies that are highly associated with the patient's cancer recurrence and survival rate. The popular methods for classification of lymph node metastasis by neural networks, however, show limitations as the available low-level features are inadequate for classification, and the radiologists are unable to quickly review the images. Identifying lymph node metastasis in colorectal cancer is a key factor in the treatment of patients with colorectal cancer. In the present work, an automatic classification method based on deep transfer learning was proposed. Specifically, the method resolved the problem of repetition of low-level features and combined these features with high-level features into a new feature map for classification; and a merged layer which merges all transmitted features from previous layers into a map of the first full connection layer. With a dataset collected from Harbin Medical University Cancer Hospital, the experiment involved a sample of 3,364 patients. Among these samples, 1,646 were positive, and 1,718 were negative. The experiment results showed the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 0.8732, 0.8746, 0.8746 and 0.8728, respectively, and the accuracy and AUC were 0.8358 and 0.8569, respectively. These demonstrated that our method significantly outperformed the previous classification methods for colorectal cancer lymph node metastasis without increasing the depth and width of the model.
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Affiliation(s)
- Jin Li
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, Heilongjiang Province, China
| | - Peng Wang
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, Heilongjiang Province, China
| | - Yang Zhou
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, Heilongjiang Province, China
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang Province, China
| | - Hong Liang
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, Heilongjiang Province, China
| | - Yang Lu
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing, Heilongjiang Province, China
| | - Kuan Luan
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, Heilongjiang Province, China
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Differential detection of metastatic and inflammatory lymph nodes using inflow-based vascular-space-occupancy (iVASO) MR imaging. Magn Reson Imaging 2021; 85:128-132. [PMID: 34687849 DOI: 10.1016/j.mri.2021.10.035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 08/31/2021] [Accepted: 10/17/2021] [Indexed: 12/19/2022]
Abstract
PURPOSE To investigate the potential value of inflow-based vascular-space-occupancy (iVASO) MR imaging in differentiating metastatic from inflammatory lymph nodes (LNs). METHODS Ten female New Zealand rabbits with 2.5-3.0 kg body weight were studied. VX2 cells and egg yolk emulsion were inoculated into left and right thighs, respectively, to induce ten metastatic and ten inflammatory popliteal LNs. Conventional MRI and iVASO were performed 2 h prior to, and 10, 20 days after inoculation (D0, D10, D20). The short-axis diameter (S), short- to long-axis diameter ratio (SLR), and arteriolar blood volume (BVa) at each time point and their longitudinal changes of each model were recorded and compared. At D20, all rabbits were sacrificed to perform histological evaluation after the MR scan. RESULTS The mean values of S, SLR and BVa showed no significant difference between the two groups at D0 (P = 0.987, P = 0.778, P = 0.975). The BVa of the metastatic group was greater than that of the inflammatory at both D10 and D20 (P < 0.05; P < 0.001), whereas the S and SLR of the metastatic group were greater only at D20 (P < 0.001; P = 0.001). Longitudinal analyses showed that the BVa of the metastatic group increased at both D10 and D20 (P = 0.004; P = 0.001), while that of the inflammatory group only increased at D10 (P = 0.024). CONCLUSION The BVa measured with iVASO has the potential to detect early metastatic LNs.
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Grimm P, Loft MK, Dam C, Pedersen MRV, Timm S, Rafaelsen SR. Intra- and Interobserver Variability in Magnetic Resonance Imaging Measurements in Rectal Cancer Patients. Cancers (Basel) 2021; 13:cancers13205120. [PMID: 34680269 PMCID: PMC8534180 DOI: 10.3390/cancers13205120] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 10/04/2021] [Accepted: 10/07/2021] [Indexed: 12/14/2022] Open
Abstract
Colorectal cancer is the second most common cancer in Europe, and accurate lymph node staging in rectal cancer patients is essential for the selection of their treatment. MRI lymph node staging is complex, and few studies have been published regarding its reproducibility. This study assesses the inter- and intraobserver variability in lymph node size, apparent diffusion coefficient (ADC) measurements, and morphological characterization among inexperienced and experienced radiologists. Four radiologists with different levels of experience in MRI rectal cancer staging analyzed 36 MRI scans of 36 patients with rectal adenocarcinoma. Inter- and intraobserver variation was calculated using interclass correlation coefficients and Cohens-kappa statistics, respectively. Inter- and intraobserver agreement for the length and width measurements was good to excellent, and for that of ADC it was fair to good. Interobserver agreement for the assessment of irregular border was moderate, heterogeneous signal was fair, round shape was fair to moderate, and extramesorectal lymph node location was moderate to almost perfect. Intraobserver agreement for the assessment of irregular border was fair to substantial, heterogeneous signal was fair to moderate, round shape was fair to moderate, and extramesorectal lymph node location was substantial to almost perfect. Our data indicate that subjective variables such as morphological characteristics are less reproducible than numerical variables, regardless of the level of experience of the observers.
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Affiliation(s)
- Peter Grimm
- Department of Radiology, Vejle Hospital, University Hospital of Southern Denmark, 7100 Vejle, Denmark; (M.K.L.); (C.D.); (M.R.V.P.); (S.R.R.)
- Correspondence:
| | - Martina Kastrup Loft
- Department of Radiology, Vejle Hospital, University Hospital of Southern Denmark, 7100 Vejle, Denmark; (M.K.L.); (C.D.); (M.R.V.P.); (S.R.R.)
- Department of Regional Health Research, University of Southern Denmark, 5230 Odense, Denmark;
| | - Claus Dam
- Department of Radiology, Vejle Hospital, University Hospital of Southern Denmark, 7100 Vejle, Denmark; (M.K.L.); (C.D.); (M.R.V.P.); (S.R.R.)
| | - Malene Roland Vils Pedersen
- Department of Radiology, Vejle Hospital, University Hospital of Southern Denmark, 7100 Vejle, Denmark; (M.K.L.); (C.D.); (M.R.V.P.); (S.R.R.)
- Department of Regional Health Research, University of Southern Denmark, 5230 Odense, Denmark;
| | - Signe Timm
- Department of Regional Health Research, University of Southern Denmark, 5230 Odense, Denmark;
- Research Unit, Kolding Hospital, University Hospital of Southern Denmark, 6000 Kolding, Denmark
| | - Søren Rafael Rafaelsen
- Department of Radiology, Vejle Hospital, University Hospital of Southern Denmark, 7100 Vejle, Denmark; (M.K.L.); (C.D.); (M.R.V.P.); (S.R.R.)
- Department of Regional Health Research, University of Southern Denmark, 5230 Odense, Denmark;
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Surov A, Meyer HJ, Pech M, Powerski M, Omari J, Wienke A. Apparent diffusion coefficient cannot discriminate metastatic and non-metastatic lymph nodes in rectal cancer: a meta-analysis. Int J Colorectal Dis 2021; 36:2189-2197. [PMID: 34184127 PMCID: PMC8426255 DOI: 10.1007/s00384-021-03986-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/16/2021] [Indexed: 02/04/2023]
Abstract
BACKGROUND Our aim was to provide data regarding use of diffusion-weighted imaging (DWI) for distinguishing metastatic and non-metastatic lymph nodes (LN) in rectal cancer. METHODS MEDLINE library, EMBASE, and SCOPUS database were screened for associations between DWI and metastatic and non-metastatic LN in rectal cancer up to February 2021. Overall, 9 studies were included into the analysis. Number, mean value, and standard deviation of DWI parameters including apparent diffusion coefficient (ADC) values of metastatic and non-metastatic LN were extracted from the literature. The methodological quality of the studies was investigated according to the QUADAS-2 assessment. The meta-analysis was undertaken by using RevMan 5.3 software. DerSimonian, and Laird random-effects models with inverse-variance weights were used to account the heterogeneity between the studies. Mean DWI values including 95% confidence intervals were calculated for metastatic and non-metastatic LN. RESULTS ADC values were reported for 1376 LN, 623 (45.3%) metastatic LN, and 754 (54.7%) non-metastatic LN. The calculated mean ADC value (× 10-3 mm2/s) of metastatic LN was 1.05, 95%CI (0.94, 1.15). The calculated mean ADC value of the non-metastatic LN was 1.17, 95%CI (1.01, 1.33). The calculated sensitivity and specificity were 0.81, 95%CI (0.74, 0.89) and 0.67, 95%CI (0.54, 0.79). CONCLUSION No reliable ADC threshold can be recommended for distinguishing of metastatic and non-metastatic LN in rectal cancer.
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Affiliation(s)
- Alexey Surov
- Department of Radiology and Nuclear Medicine, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.
| | - Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Maciej Pech
- Department of Radiology and Nuclear Medicine, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Maciej Powerski
- Department of Radiology and Nuclear Medicine, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Jasan Omari
- Department of Radiology and Nuclear Medicine, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Andreas Wienke
- Institute of Medical Epidemiology, Martin-Luther-University Halle-Wittenberg, Biostatistics, and Informatics, Halle (Saale), Germany
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Hu J, Guo J, Pei Y, Hu P, Li M, Sack I, Li W. Rectal Tumor Stiffness Quantified by In Vivo Tomoelastography and Collagen Content Estimated by Histopathology Predict Tumor Aggressiveness. Front Oncol 2021; 11:701336. [PMID: 34485136 PMCID: PMC8415020 DOI: 10.3389/fonc.2021.701336] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 07/29/2021] [Indexed: 01/23/2023] Open
Abstract
PURPOSE To investigate the significance of collagen in predicting the aggressiveness of rectal tumors in patients, examined in vivo based on tomoelastography quantified stiffness and ex vivo by histologically measured collagen volume fraction (CVF). EXPERIMENTAL DESIGN 170 patients with suspected rectal cancer were prospectively enrolled and underwent preoperative magnetic resonance imaging (MRI) and rectal tomoelastography, a technique based on multifrequency magnetic resonance elastography. Histopathologic analysis identified eighty patients with rectal cancer who were divided into subgroups by tumor-node (TN) stage, prognostic stage, and risk level. Rectal tumor stiffness was correlated with histopathologic CVF. Area-under-the-curve (AUC) and contingency analysis were used to evaluate the performance of rectal stiffness in distinguishing tumor stages which was compared to standard clinical MRI. RESULTS In vivo tomoelastography revealed that rectal tumor stiffened significantly with increased TN stage (p<0.05). Tumors with poorly differentiated status, perineural and lymphovascular invasion also displayed higher stiffness than well-to-moderately differentiated, noninvasive tumors (all p<0.05). Similar to in vivo stiffness, CVF indicated an abnormally high collagen content in tumors with perineural invasion and poor differentiation status. CVF was also positively correlated with stiffness (p<0.05). Most importantly, both stiffness (AUROC: 0.82) and CVF (AUROC: 0.89) demonstrated very good diagnostic accuracy in detecting rectal tumors that have high risk for progressing to an aggressive state with poorer prognosis. CONCLUSION In human rectal carcinomas, overexpression of collagen is correlated with increased tissue stiffness and high risk for tumor advancing more aggressively. In vivo tomoelastography quantifies rectal tumor stiffness which improves the diagnostic performance of standard MRI in the assessment of lymph nodes metastasis. Therefore, in vivo stiffness mapping by tomoelastography can predict rectal tumor aggressiveness and add diagnostic value to MRI.
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Affiliation(s)
- Jiaxi Hu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Jing Guo
- Department of Radiology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Yigang Pei
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Ping Hu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Mengsi Li
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Ingolf Sack
- Department of Radiology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Wenzheng Li
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
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Zhuang Z, Zhang Y, Wei M, Yang X, Wang Z. Magnetic Resonance Imaging Evaluation of the Accuracy of Various Lymph Node Staging Criteria in Rectal Cancer: A Systematic Review and Meta-Analysis. Front Oncol 2021; 11:709070. [PMID: 34327144 PMCID: PMC8315047 DOI: 10.3389/fonc.2021.709070] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 06/22/2021] [Indexed: 02/05/2023] Open
Abstract
Background Magnetic resonance imaging (MRI)-based lymph node staging remains a significant challenge in the treatment of rectal cancer. Pretreatment evaluation of lymph node metastasis guides the formulation of treatment plans. This systematic review aimed to evaluate the diagnostic performance of MRI in lymph node staging using various morphological criteria. Methods A systematic search of the EMBASE, Medline, and Cochrane databases was performed. Original articles published between 2000 and January 2021 that used MRI for lymph node staging in rectal cancer were eligible. The included studies were assessed using the QUADAS-2 tool. A bivariate random-effects model was used to conduct a meta-analysis of diagnostic test accuracy. Results Thirty-seven studies were eligible for this meta-analysis. The pooled sensitivity, specificity, and diagnostic odds ratio of preoperative MRI for the lymph node stage were 0.73 (95% confidence interval [CI], 0.68–0.77), 0.74 (95% CI, 0.68–0.80), and 7.85 (95% CI, 5.78–10.66), respectively. Criteria for positive mesorectal lymph node metastasis included (A) a short-axis diameter of 5 mm, (B) morphological standard, including an irregular border and mixed-signal intensity within the lymph node, (C) a short-axis diameter of 5 mm with the morphological standard, (D) a short-axis diameter of 8 mm with the morphological standard, and (E) a short-axis diameter of 10 mm with the morphological standard. The pooled sensitivity/specificity for these criteria were 75%/64%, 81%/67%, 74%/79%, 72%/66%, and 62%/91%, respectively. There was no significant difference among the criteria in sensitivity/specificity. The area under the receiver operating characteristic (ROC) curve values of the fitted summary ROC indicated a diagnostic accuracy rate of 0.75–0.81. Conclusion MRI scans have minimal accuracy as a reference index for pretreatment staging of various lymph node staging criteria in rectal cancer. Multiple types of evidence should be used in clinical decision-making.
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Affiliation(s)
- Zixuan Zhuang
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yang Zhang
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Mingtian Wei
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Xuyang Yang
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Ziqiang Wang
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China
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Liu Y, Wan L, Peng W, Zou S, Zheng Z, Ye F, Jiang J, Ouyang H, Zhao X, Zhang H. A magnetic resonance imaging (MRI)-based nomogram for predicting lymph node metastasis in rectal cancer: a node-for-node comparative study of MRI and histopathology. Quant Imaging Med Surg 2021; 11:2586-2597. [PMID: 34079725 PMCID: PMC8107309 DOI: 10.21037/qims-20-1049] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 02/05/2021] [Indexed: 01/13/2023]
Abstract
BACKGROUND The aim of the present study was to investigate the potential risk factors for lymph node metastasis (LNM) in rectal cancer using magnetic resonance imaging (MRI), and to construct and validate a nomogram to predict its occurrence with node-for-node histopathological validation. METHODS Our prediction model was developed between March 2015 and August 2016 using a prospective primary cohort (32 patients, mean age: 57.3 years) that included 324 lymph nodes (LNs) from MR images with node-for-node histopathological validation. We evaluated multiple MRI variables, and a multivariable logistic regression analysis was used to develop the predictive nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. The performance of the nomogram in predicting LNM was validated in an independent clinical validation cohort comprising 182 consecutive patients. RESULTS The predictors included in the individualized prediction nomogram were chemical shift effect (CSE), nodal border, short-axis diameter of nodes, and minimum distance to rectal cancer or rectal wall. The nomogram showed good discrimination (C-index: 0.947; 95% confidence interval: 0.920-0.974) and good calibration in the primary cohort. Decision curve analysis confirmed the clinical usefulness of the nomogram in predicting the status of each LN. For the prediction of LN status in the clinical validation cohort by readers 1 and 2, the areas under the curves using the nomogram were 0.890 and 0.841, and the areas under the curves of readers using their experience were 0.754 and 0.704, respectively. Diagnostic efficiency was significantly improved by using the nomogram (P<0.001). CONCLUSIONS The nomogram, which incorporates CSE, nodal location, short-axis diameter, and minimum distance to rectal cancer or rectal wall, can be conveniently applied in clinical practice to facilitate the prediction of LNM in patients with rectal cancer.
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Affiliation(s)
- Yuan Liu
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lijuan Wan
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenjing Peng
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shuangmei Zou
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhaoxu Zheng
- Department of Colorectal Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Feng Ye
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jun Jiang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Han Ouyang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xinming Zhao
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongmei Zhang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Li J, Zhou Y, Wang P, Zhao H, Wang X, Tang N, Luan K. Deep transfer learning based on magnetic resonance imaging can improve the diagnosis of lymph node metastasis in patients with rectal cancer. Quant Imaging Med Surg 2021; 11:2477-2485. [PMID: 34079717 DOI: 10.21037/qims-20-525] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background Lymph node (LN) metastasis is the main prognostic factor for local recurrence and overall survival of patients with rectal cancer. The accurate evaluation of LN status in rectal cancer patients is associated with improved treatment and prognosis. This study aimed to apply deep transfer learning to classify LN status in patients with rectal cancer to improve N staging accuracy. Methods The study included 129 patients with 325 rectal cancer screenshots of LN T2-weighted (T2W) images from April 2018 to March 2019. Deep learning was applied through a pre-trained model, Inception-v3, for recognition and detection of LN status. The results were compared to manual identification by experienced radiologists. Two radiologists reviewed images and independently identified their status using various criteria with or without short axial (SA) diameter measurements. The accuracy, positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC) were calculated. Results When the same radiologist performed the analysis, the AUC was not significantly different in the presence or absence of LN diameter measurements (P>0.05). In the deep transfer learning method, the PPV, NPV, sensitivity, and specificity were 95.2%, 95.3%, 95.3%, and 95.2%, respectively, and the AUC and accuracy were 0.994 and 95.7%, respectively. These results were all higher than that achieved with manual diagnosis by the radiologists. Conclusions The internal details of LNs should be used as the main criteria for positive diagnosis when using MRI. Deep transfer learning can improve the MRI diagnosis of positive LN metastasis in patients with rectal cancer.
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Affiliation(s)
- Jin Li
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
| | - Yang Zhou
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.,Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Peng Wang
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
| | - Henan Zhao
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xinxin Wang
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Na Tang
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Kuan Luan
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
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Editorial Comment: Shift in Rectal Cancer Treatment Strategies. AJR Am J Roentgenol 2021; 217:1294. [PMID: 34037414 DOI: 10.2214/ajr.21.26213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Li C, Yin J. Radiomics Based on T2-Weighted Imaging and Apparent Diffusion Coefficient Images for Preoperative Evaluation of Lymph Node Metastasis in Rectal Cancer Patients. Front Oncol 2021; 11:671354. [PMID: 34041033 PMCID: PMC8141802 DOI: 10.3389/fonc.2021.671354] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 04/12/2021] [Indexed: 12/24/2022] Open
Abstract
Purpose To develop and validate a radiomics nomogram based on T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) features for the preoperative prediction of lymph node (LN) metastasis in rectal cancer patients. Materials and Methods One hundred and sixty-two patients with rectal cancer confirmed by pathology were retrospectively analyzed, who underwent T2WI and DWI sequences. The data sets were divided into training (n = 97) and validation (n = 65) cohorts. For each case, a total of 2,752 radiomic features were extracted from T2WI, and ADC images derived from diffusion-weighted imaging. A two-sample t-test was used for prefiltering. The least absolute shrinkage selection operator method was used for feature selection. Three radiomics scores (rad-scores) (rad-score 1 for T2WI, rad-score 2 for ADC, and rad-score 3 for the combination of both) were calculated using the support vector machine classifier. Multivariable logistic regression analysis was then used to construct a radiomics nomogram combining rad-score 3 and independent risk factors. The performances of three rad-scores and the nomogram were evaluated using the area under the receiver operating characteristic curve (AUC). Decision curve analysis (DCA) was used to assess the clinical usefulness of the radiomics nomogram. Results The AUCs of the rad-score 1 and rad-score 2 were 0.805, 0.749 and 0.828, 0.770 in the training and validation cohorts, respectively. The rad-score 3 achieved an AUC of 0.879 in the training cohort and an AUC of 0.822 in the validation cohort. The radiomics nomogram, incorporating the rad-score 3, age, and LN size, showed good discrimination with the AUC of 0.937 for the training cohort and 0.884 for the validation cohort. DCA confirmed that the radiomics nomogram had clinical utility. Conclusions The radiomics nomogram, incorporating rad-score based on features from the T2WI and ADC images, and clinical factors, has favorable predictive performance for preoperative prediction of LN metastasis in patients with rectal cancer.
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Affiliation(s)
- Chunli Li
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.,Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, China
| | - Jiandong Yin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
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Li J, Zhou Y, Wang X, Zhou M, Chen X, Luan K. An MRI-based multi-objective radiomics model predicts lymph node status in patients with rectal cancer. Abdom Radiol (NY) 2021; 46:1816-1824. [PMID: 33241428 DOI: 10.1007/s00261-020-02863-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 11/05/2020] [Accepted: 11/11/2020] [Indexed: 02/07/2023]
Abstract
PURPOSE To apply a multi-objective radiomics model based on pre-operative magnetic resonance imaging (MRI) for improving diagnostic accuracy of LN metastasis in rectal cancer patients. METHODS This study consisted of 91 patients diagnosed with rectal cancer from April 2018 to March 2019. All patients underwent rectal MRI before surgery without any other treatment. Clinical data, subjective radiologist assessments, and radiomic features of LNs were obtained. A total of 1409 radiomic features were extracted from T2WI LN images. Multi-objective optimization with the iterative multi-objective immune algorithm (IMIA) was used to select radiomic features to build prediction models. Predictive performances of radiomic, radiologist, and combined radiomic and radiologist models were assessed for accuracy by receiver operating characteristics (ROC) curves. RESULTS For the radiologist analysis, heterogeneity was the only significant independent predictor of LN status. The sensitivity, specificity, and accuracy of the subjective radiologist analysis were 72.09%, 73.81%, and 78.12%, respectively. The sensitivity, specificity, and accuracy of the solitary radiomic model consisting of 10 features were 89.81%, 82.57%, and 87.77%, respectively. The sensitivity, specificity, and accuracy of the combined model, which consisted of 12 radiomic and radiologist features, were 92.23%, 84.69%, and 89.88%, respectively. The combined model had the best prediction performance with an AUC of 0.94. CONCLUSIONS The multi-objective radiomics model based on T2WI images was very useful in predicting pre-operative LN status in rectal cancer patients.
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Yakar M, Etiz D. Artificial intelligence in rectal cancer. Artif Intell Gastroenterol 2021; 2:10-26. [DOI: 10.35712/aig.v2.i2.10] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 03/03/2021] [Accepted: 03/16/2021] [Indexed: 02/06/2023] Open
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[The importance of radiotherapy in rectal cancer-an update from a surgeon's perspective]. Chirurg 2021; 92:591-598. [PMID: 33893541 DOI: 10.1007/s00104-021-01414-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/26/2021] [Indexed: 11/11/2022]
Abstract
BACKGROUND Neoadjuvant chemoradiotherapy was implemented in the treatment of rectal cancer for UICC stages II and III in 2004. Recent studies have provided new insights with respect to the indications and sequence of radiotherapy in the concept of multimodal treatment. OBJECTIVE The aim of the study was to review the status of radiotherapy in the context of current developments in the treatment of rectal cancer, such as magnetic resonance imaging (MRI)-based surgery, total neoadjuvant therapy and the watch and wait strategy for complete clinical remission. RESULTS The indications for neoadjuvant radiotherapy based on the clinical T and N stages are not exact and can lead to overtreatment in 18-27% of cases. Radiotherapy is associated with a worsening of anorectal and urogenital functions. Local recurrence rates of 3% with surgery alone can be achieved in patients with negative circumferential resection margins (low risk cancer) in MRI. For rectal cancer with high-risk features, such as cT4 tumor, positive circumferential resection margins and extramural vascular invasion, an improved disease-free survival and a lower rate of distant metastases could be achieved with total neoadjuvant therapy compared to standard neoadjuvant chemoradiotherapy in recent phase III randomized trials. Pathological complete remission is achieved in 28% of patients after total neoadjuvant therapy. CONCLUSION The high rate of complete remission has fired the debate regarding watch and wait after total neoadjuvant therapy; however, no prospective randomized phase III trials comparing total mesorectal resection vs. watch and wait in complete clinical remission have been published. Hence, resection remains the gold standard in this scenario given the excellent long-term oncological results.
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Li J, Zhou Y, Wang X, Yu Y, Zhou X, Luan K. Histogram Analysis of Diffusion-Weighted Magnetic Resonance Imaging as a Biomarker to Predict Lymph Node Metastasis in T3 Stage Rectal Carcinoma. Cancer Manag Res 2021; 13:2983-2993. [PMID: 33833581 PMCID: PMC8021267 DOI: 10.2147/cmar.s298907] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Accepted: 03/03/2021] [Indexed: 01/08/2023] Open
Abstract
Purpose This study investigated the predictive value of apparent diffusion coefficient (ADC) histogram parameters of the primary tumor for regional lymph node metastasis (LNM) in pathological T3 stage rectal cancer. Patients and Methods We retrospectively studied 175 patients with T3 stage rectal cancer who underwent preoperative MRI, including diffusion-weighted imaging, between January 2015 and October 2017. Based on pathological analysis of surgical specimens, 113 patients were classified into the LN− group and 62 in the LN+ group. We analyzed clinical data, radiological characteristics and histogram parameters derived from ADC maps. Then, receiver operating characteristic curve (ROC) analyses were generated to determine the best diagnostic performance. Results The mean (p=0.002, cutoff=1.08×10–3 s/mm2), coefficient of variation (CV) (p=0.040, cutoff=0.249) of the ADC map, carbohydrate antigen 199, and N stage with magnetic resonance (mrN stage) were independent factors for LNM. Combining these factors yielded the best diagnostic performance, with the area under the ROC curve of 0.838, 72.9% sensitivity, 79.1% specificity, 65.2% positive predictive value, and 84.5% negative predictive value. Conclusion With the mean >1.08×10–3 s/mm2 and CV <0.249, the ADC improved the diagnostic performance of LNM in T3 stage rectal cancer, which could assist surgeons with neoadjuvant chemoradiotherapy.
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Affiliation(s)
- Jin Li
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150001, Heilongjiang Province, People's Republic of China
| | - Yang Zhou
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150001, Heilongjiang Province, People's Republic of China.,Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, 150001, Heilongjiang Province, People's Republic of China
| | - Xinxin Wang
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, 150001, Heilongjiang Province, People's Republic of China
| | - Yanyan Yu
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, 150001, Heilongjiang Province, People's Republic of China
| | - Xueyan Zhou
- School of Technology, Harbin University, Harbin, 150001, Heilongjiang Province, People's Republic of China
| | - Kuan Luan
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150001, Heilongjiang Province, People's Republic of China
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Fields AC, Lu P, Hu F, Hirji S, Irani J, Bleday R, Melnitchouk N, Goldberg JE. Lymph Node Positivity in T1/T2 Rectal Cancer: a Word of Caution in an Era of Increased Incidence and Changing Biology for Rectal Cancer. J Gastrointest Surg 2021; 25:1029-1035. [PMID: 32246393 DOI: 10.1007/s11605-020-04580-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 03/23/2020] [Indexed: 01/31/2023]
Abstract
BACKGROUND The evaluation of lymph nodes in rectal cancer dictates treatment. The goals of this study are to characterize the contemporary rate of lymph node metastasis in early stage rectal cancer and to re-investigate histologic factors that predict positive lymph nodes. MATERIALS AND METHODS Using the National Cancer Database, we identified patients with clinical stage I rectal adenocarcinoma. Multivariable logistic regression was used to determine risk factors for lymph node positivity. RESULTS 12.2% of patients with T1 tumors and 18.0% of patients with T2 tumors had positive lymph nodes. For T1 tumors, positive lymph nodes were present in 9.3% with neither poor differentiation nor lymphovascular invasion (LVI), 17.3% with poor differentiation alone, 34.7% with LVI alone, and 45.0% with both poor differentiation and LVI. For T2 tumors, positive lymph nodes were present in 11.7% with neither poor differentiation nor LVI, 25.3% with poor differentiation alone, 47.3% with LVI alone, and 41.5% with both poor differentiation and LVI. LVI was an independent predictor of positive lymph nodes (OR;4.75,95%CI;3.17-7.11,p < 0.001) for T1 and (OR;6.20,95%CI;4.53-8.51,p < 0.001) T2 tumors. CONCLUSIONS T1/T2 tumors have higher rates of positive lymph nodes when poor differentiation and LVI are present. These results should be taken into consideration prior to surgical treatment.
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Affiliation(s)
- Adam C Fields
- Department of Surgery, Division of Colorectal Surgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA.
| | - Pamela Lu
- Department of Surgery, Division of Colorectal Surgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - Frances Hu
- Department of Surgery, Division of Colorectal Surgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - Sameer Hirji
- Department of Surgery, Division of Colorectal Surgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - Jennifer Irani
- Department of Surgery, Division of Colorectal Surgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - Ronald Bleday
- Department of Surgery, Division of Colorectal Surgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - Nelya Melnitchouk
- Department of Surgery, Division of Colorectal Surgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - Joel E Goldberg
- Department of Surgery, Division of Colorectal Surgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA.
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Rutegård MK, Båtsman M, Blomqvist L, Rutegård M, Axelsson J, Ljuslinder I, Rutegård J, Palmqvist R, Brännström F, Brynolfsson P, Riklund K. Rectal cancer: a methodological approach to matching PET/MRI to histopathology. Cancer Imaging 2020; 20:80. [PMID: 33129352 PMCID: PMC7603757 DOI: 10.1186/s40644-020-00347-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Accepted: 09/17/2020] [Indexed: 12/18/2022] Open
Abstract
Purpose To enable the evaluation of locoregional disease in the on-going RECTOPET (REctal Cancer Trial on PET/MRI/CT) study; a methodology to match mesorectal imaging findings to histopathology is presented, along with initial observations. Methods FDG-PET/MRI examinations were performed in twenty-four consecutively included patients with rectal adenocarcinoma. In nine patients, of whom five received neoadjuvant treatment, a postoperative MRI of the surgical specimen was performed. The pathological cut-out was performed according to clinical routine with the addition of photo documentation of each slice of the surgical specimen, meticulously marking the location, size, and type of pathology of each mesorectal finding. This allowed matching individual nodal structures from preoperative MRI, via the specimen MRI, to histopathology. Results Preoperative MRI identified 197 mesorectal nodal structures, of which 92 (47%) could be anatomically matched to histopathology. Of the matched nodal structures identified in both MRI and histopathology, 25% were found to be malignant. These malignant structures consisted of lymph nodes (43%), tumour deposits (48%), and extramural venous invasion (9%). One hundred eleven nodal structures (55%) could not be matched anatomically. Of these, 97 (87%) were benign lymph nodes, and 14 (13%) were malignant nodal structures. Five were malignant lymph nodes, and nine were tumour deposits, all of which had a short axis diameter < 5 mm. Conclusions We designed a method able to anatomically match and study the characteristics of individual mesorectal nodal structures, enabling further research on the impact of each imaging modality. Initial observations suggest that small malignant nodal structures assessed as lymph nodes in MRI often comprise other forms of mesorectal tumour spread. Trial registration Clinical Trials Identifier:NCT03846882.
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Affiliation(s)
- Miriam K Rutegård
- Department of Radiation Sciences, Diagnostic Radiology, Umeå University, Umeå, Sweden.
| | - Malin Båtsman
- Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden
| | - Lennart Blomqvist
- Department of Radiation Sciences, Diagnostic Radiology, Umeå University, Umeå, Sweden.,Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Martin Rutegård
- Department of Surgical and Perioperative Sciences, Surgery, Umeå University, Umeå, Sweden.,Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Jan Axelsson
- Department of Radiation Sciences, Diagnostic Radiology, Umeå University, Umeå, Sweden
| | - Ingrid Ljuslinder
- Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
| | - Jörgen Rutegård
- Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
| | - Richard Palmqvist
- Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden
| | - Fredrik Brännström
- Department of Surgical and Perioperative Sciences, Surgery, Umeå University, Umeå, Sweden
| | - Patrik Brynolfsson
- Department of Radiation Sciences, Diagnostic Radiology, Umeå University, Umeå, Sweden
| | - Katrine Riklund
- Department of Radiation Sciences, Diagnostic Radiology, Umeå University, Umeå, Sweden
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