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Zhu W, Xu F, Lou K, Qiu X, Huang D, Huang S, Xie D, Hu H. The impact of Inter-observation variation on radiomic features of pulmonary nodules. Front Oncol 2025; 15:1567028. [PMID: 40342825 PMCID: PMC12058843 DOI: 10.3389/fonc.2025.1567028] [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: 01/26/2025] [Accepted: 04/03/2025] [Indexed: 05/11/2025] Open
Abstract
Objective In this study, we aimed to comprehensively and systematically analyze the radiomic features of pulmonary nodules and explore the influence of inter-observation variation (IOV) in segmentation regions of interest (ROI) on radiomic features, providing reference information for pulmonary nodule radiomics research. Method Six clinicians with varying experience and expertise manually outlined ROIs for 232 pulmonary nodules, while an artificial intelligence (AI) algorithm was trained for automated segmentation. The segmentation by the most experienced cardiothoracic diagnostician (Doctor A) served as the reference standard. Inter-observer variability was assessed through diameter measurements, segmentation ROI consistency analysis, and radiomic features stability analysis. Results Of all radiomics features analyzed, 1071 (85.96%) demonstrated good stability (overall concordance correlation coefficient [OCCC] ≥ 0.75), with 766 (61.48%) exhibiting very good stability (OCCC ≥ 0.90). Among the eight radiomic feature types, Original _first-order, Original_GLCM, Original_GLRLM, Original_GLSZM, LOG, and wavelet features all achieved stability rates exceeding 80.00%, with 91.59% of the LOG features having good stability. The Original features showed good stability (median OCCC: 0.92-0.95, IQR: 0.12-0.19), both in the overall distribution and in the different feature categories. The median OCCC value for the LOG features (median: 0.94, IQR: 0.08) was significantly higher than that for the Wavelet features (median: 0.91, IQR: 0.13). There was no statistically significant difference in stability between the Original and LOG feature subgroups (P > 0.05). In contrast, statistically significant differences were observed between the wavelet feature subgroups (P < 0.05), with Wavelet_LLL and Wavelet_LLH transformation yielding higher stability. Conclusion Segmentation results indicated that while IOV influenced radiomic features of pulmonary nodules, most (85.96%) of the features were well stabilized and relatively unaffected. Enhancing segmentation ROI consistency helps minimize the impact of IOV on the radiomic features of pulmonary nodule images. Original and LOG features demonstrated high stability, whereas Wavelet features were more susceptible to IOV.
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Affiliation(s)
- Wenchao Zhu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Medical Imaging International Scientific and Technological Cooperation Base of Zhejiang province, Hangzhou, China
| | - Fangyi Xu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Medical Imaging International Scientific and Technological Cooperation Base of Zhejiang province, Hangzhou, China
| | - Kaihua Lou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Medical Imaging International Scientific and Technological Cooperation Base of Zhejiang province, Hangzhou, China
| | - Xia Qiu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Medical Imaging International Scientific and Technological Cooperation Base of Zhejiang province, Hangzhou, China
| | - Dingping Huang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Medical Imaging International Scientific and Technological Cooperation Base of Zhejiang province, Hangzhou, China
| | - Shaoyu Huang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Medical Imaging International Scientific and Technological Cooperation Base of Zhejiang province, Hangzhou, China
| | - Dong Xie
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Medical Imaging International Scientific and Technological Cooperation Base of Zhejiang province, Hangzhou, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Medical Imaging International Scientific and Technological Cooperation Base of Zhejiang province, Hangzhou, China
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Okamoto H, Cap QH, Nomura T, Nabeshima K, Hashimoto J, Iyatomi H. Practical X-ray gastric cancer diagnostic support using refined stochastic data augmentation and hard boundary box training. Artif Intell Med 2025; 161:103075. [PMID: 39919469 DOI: 10.1016/j.artmed.2025.103075] [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: 10/02/2023] [Revised: 12/16/2024] [Accepted: 01/27/2025] [Indexed: 02/09/2025]
Abstract
Endoscopy is widely used to diagnose gastric cancer and has a high diagnostic performance, but it must be performed by a physician, which limits the number of people who can be diagnosed. In contrast, gastric X-rays can be taken by radiographers, thus allowing a much larger number of patients to undergo imaging. However, the diagnosis of X-ray images relies heavily on the expertise and experience of physicians, and few machine learning methods have been developed to assist in this process. We propose a novel and practical gastric cancer diagnostic support system for gastric X-ray images that will enable more people to be screened. The system is based on a general deep learning-based object detection model and incorporates two novel techniques: refined probabilistic stomach image augmentation (R-sGAIA) and hard boundary box training (HBBT). R-sGAIA enhances the probabilistic gastric fold region and provides more learning patterns for cancer detection models. HBBT is an efficient training method that improves model performance by allowing the use of unannotated negative (i.e., healthy control) samples, which are typically unusable in conventional detection models. The proposed system achieved a sensitivity (SE) for gastric cancer of 90.2%, higher than that of an expert (85.5%). Under these conditions, two out of five candidate boxes identified by the system were cancerous (precision = 42.5%), with an image processing speed of 0.51 s per image. The system also outperformed methods using the same object detection model and state-of-the-art data augmentation by showing a 5.9-point improvement in the F1 score. In summary, this system efficiently identifies areas for radiologists to examine within a practical time frame, thus significantly reducing their workload.
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Affiliation(s)
- Hideaki Okamoto
- Department of Applied Informatics, Graduate School of Science and Engineering, Hosei University, 3-7-2 Kajino, Koganei, 184-8584, Tokyo, Japan
| | - Quan Huu Cap
- Department of Applied Informatics, Graduate School of Science and Engineering, Hosei University, 3-7-2 Kajino, Koganei, 184-8584, Tokyo, Japan; AI Development Department, Aillis Inc., 2-2-1 Yaesu, Chuo, 104-0028, Tokyo, Japan
| | - Takakiyo Nomura
- Department of Radiology, Tokai University School of Medicine, 143 Shimokasuya, Isehara, 259-1193, Kanagawa, Japan
| | - Kazuhito Nabeshima
- Department of Radiology, Tokai University School of Medicine, 143 Shimokasuya, Isehara, 259-1193, Kanagawa, Japan
| | - Jun Hashimoto
- Department of Radiology, Tokai University School of Medicine, 143 Shimokasuya, Isehara, 259-1193, Kanagawa, Japan
| | - Hitoshi Iyatomi
- Department of Applied Informatics, Graduate School of Science and Engineering, Hosei University, 3-7-2 Kajino, Koganei, 184-8584, Tokyo, Japan.
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He X, Yang S, Ren J, Wang N, Li M, You Y, Li Y, Li Y, Shi G, Yang L. Synergizing traditional CT imaging with radiomics: a novel model for preoperative diagnosis of gastric neuroendocrine and mixed adenoneuroendocrine carcinoma. Front Oncol 2024; 14:1480466. [PMID: 39507752 PMCID: PMC11538776 DOI: 10.3389/fonc.2024.1480466] [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: 08/14/2024] [Accepted: 10/07/2024] [Indexed: 11/08/2024] Open
Abstract
Objective To develop diagnostic models for differentiating gastric neuroendocrine carcinoma (g-NEC) and gastric mixed adeno-neuroendocrine carcinoma (g-MANEC) from gastric adenocarcinoma (g-ADC) based on traditional contrast enhanced CT imaging features and radiomics features. Methods We retrospectively analyzed 90 g-(MA)NEC (g-MANEC and g-NEC) patients matched 1:1 by T-stage with 90 g-ADC patients. Traditional CT features were analyzed using univariable and multivariable logistic regression. Tumor segmentation and radiomics features extraction were performed with Slicer and PyRadiomics. Feature selection was conducted through univariable analysis, correlation analysis, LASSO, and multivariable stepwise logistic. The combined model incorporated clinical and radiomics predictors. Diagnostic performance was assessed with ROC curves and DeLong's test. The models' diagnostic efficacy was further validated in subgroup of g-NEC vs. g-ADC and g-MANEC vs. g-ADC cases. Results Tumor necrosis and lymph node metastasis were independent predictors for differentiating g-(MA)NEC from g-ADC (P < 0.05). The clinical model's AUC was 0.700 (training) and 0.667(validation). Five radiomics features were retained, with the radiomics model showing AUC of 0.809 (training) and 0.802 (validation). The combined model's AUCs were 0.853 (training) and 0.812 (validation), significantly outperforming the clinical model (P < 0.05). Subgroup analysis revealed that the combined model exhibited acceptable performance in differentiating g-NEC from g-ADC and g-MANEC from g-ADC, with AUC of 0.887 and 0.823 in the training cohort and 0.852 and 0.762 in the validation cohort. Conclusion A combined model based on traditional CT imaging and radiomic features provides a non-invasive and effective preoperative diagnostic method for differentiating g-(MA)NEC from g-ADC.
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Affiliation(s)
- Xiaoxiao He
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Sujun Yang
- Department of Computed Tomography and Magnetic Resonance, Handan Central Hospital, Handan, Hebei, China
| | - Jialiang Ren
- Department of Pharmaceuticals Diagnostics, GE HealthCare, Beijing, China
| | - Ning Wang
- Department of Computed Tomography, Zhengding Country People’s Hospital, Shijiazhuang, Hebei, China
| | - Min Li
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Yang You
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Yang Li
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Yu Li
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Gaofeng Shi
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Li Yang
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
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Xu M, Chen Y, Liu D, Wang L, Wu M. Clinical utility of multi-row spiral CT in diagnosing hepatic nodular lesions, gastric cancer, and Crohn's disease: a comprehensive meta-analysis. AMERICAN JOURNAL OF CLINICAL AND EXPERIMENTAL IMMUNOLOGY 2024; 13:165-176. [PMID: 39310125 PMCID: PMC11411159 DOI: 10.62347/srej4505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Accepted: 08/17/2024] [Indexed: 09/25/2024]
Abstract
A retrieval of relevant literature on hepatic nodular lesions, gastric cancer (GC), and Crohn's disease (CD) was conducted from Chinese and English databases. Meta-analysis was performed using Review Manager 5.4 software and the MIDAS package in Stata 18.0. Results from 11 studies comprising 1847 patients were synthesized. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio with 95% confidence intervals were: 0.91 (0.84-0.95), 0.73 (0.65-0.79), 3.30 (2.60-4.30), 0.13 (0.07-0.23), and 26.00 (12.00-53.00), respectively. Significant statistical heterogeneity was found in sensitivity and specificity (P<0.05), with specificity heterogeneity originating from n, type, and mode (P<0.05). Sensitivity and specificity for n, type, object, and mode were non-heterogeneous (P>0.05). The combined AUC from SROC curve analysis of the 11 studies was 0.85. Deeks' funnel plot asymmetry test yielded a p-value of 0.01, indicating potential bias across studies in the diagnostic odds ratio funnel plot. Fagan's nomogram demonstrated that using CT for diagnostic modeling increased the post-test probability of correctly diagnosing hepatic nodular lesions, GC, and CD from 50.00% to 77.00%. Overall, multi-detector CT shows good diagnostic value for hepatic nodular lesions, GC, and CD, supporting its clinical flexibility based on patient-specific considerations.
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Affiliation(s)
- Ming Xu
- Department of Gastroenterology Medicine, Hunan Provincial People’s Hospital/The First Affiliated Hospital of Hunan Normal UniversityChangsha 410016, Hunan, China
| | - Yinyun Chen
- Department of Gastroenterology Medicine, Hunan Provincial People’s Hospital/The First Affiliated Hospital of Hunan Normal UniversityChangsha 410016, Hunan, China
| | - Dan Liu
- Department of Gastroenterology Medicine, Hunan Provincial People’s Hospital/The First Affiliated Hospital of Hunan Normal UniversityChangsha 410016, Hunan, China
| | - Lile Wang
- Department of Respiratory Medicine, Hunan Provincial People’s Hospital/The First Affiliated Hospital of Hunan Normal UniversityChangsha 410016, Hunan, China
| | - Minghao Wu
- Department of Gastroenterology Medicine, Hunan Provincial People’s Hospital/The First Affiliated Hospital of Hunan Normal UniversityChangsha 410016, Hunan, China
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Sun S, Li L, Xu M, Wei Y, Shi F, Liu S. Epstein-Barr virus positive gastric cancer: the pathological basis of CT findings and radiomics models prediction. Abdom Radiol (NY) 2024; 49:1779-1791. [PMID: 38656367 DOI: 10.1007/s00261-024-04306-8] [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: 09/23/2023] [Revised: 03/17/2024] [Accepted: 03/20/2024] [Indexed: 04/26/2024]
Abstract
PURPOSE To analyze the clinicopathologic information and CT imaging features of Epstein-Barr virus (EBV)-positive gastric cancer (GC) and establish CT-based radiomics models to predict the EBV status of GC. METHODS This retrospective study included 144 GC cases, including 48 EBV-positive cases. Pathological and immunohistochemical information was collected. CT enlarged LN and morphological characteristics were also assessed. Radiomics models were constructed to predict the EBV status, including decision tree (DT), logistic regression (LR), random forest (RF), and support vector machine (SVM). RESULTS T stage, Lauren classification, histological differentiation, nerve invasion, VEGFR2, E-cadherin, PD-L1, and Ki67 differed significantly between the EBV-positive and -negative groups (p = 0.015, 0.030, 0.006, 0.022, 0.028, 0.030, < 0.001, and < 0.001, respectively). CT enlarged LN and large ulceration differed significantly between the two groups (p = 0.019 and 0.043, respectively). The number of patients in the training and validation cohorts was 100 (with 33 EBV-positive cases) and 44 (with 15 EBV-positive cases). In the training cohort, the radiomics models using DT, LR, RF, and SVM yielded areas under the curve (AUCs) of 0.905, 0.771, 0.836, and 0.886, respectively. In the validation cohort, the diagnostic efficacy of radiomics models using the four classifiers were 0.737, 0.722, 0.751, and 0.713, respectively. CONCLUSION A significantly higher proportion of CT enlarged LN and a significantly lower proportion of large ulceration were found in EBV-positive GC. The prediction efficiency of radiomics models with different classifiers to predict EBV status in GC was good.
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Affiliation(s)
- Shuangshuang Sun
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Lin Li
- Department of Pathology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210008, China
| | - Mengying Xu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Ying Wei
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, 200000, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, 200000, China
| | - Song Liu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
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Pan HY, Wang XW, He QX, Lu YD, Zhang WY, Jin JW, Lin B. Efficacy and safety of gastroscopic hemostasis in the treatment of acute gastric hemorrhage: A meta-analysis. World J Gastrointest Oncol 2023; 15:1988-1997. [DOI: 10.4251/wjgo.v15.i11.1988] [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: 08/24/2023] [Revised: 09/19/2023] [Accepted: 09/27/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Gastric cancer (GC) is a malignant tumor with a high incidence and mortality rate worldwide for which acute bleeding is a common clinical complication. Gastroscopic hemostasis is an important method for treating acute bleeding in GC; however, its efficacy and safety remain controversial.
AIM To systematically analyze the efficacy and safety of gastroscopic hemostasis for the treatment of acute gastric hemorrhage.
METHODS The PUBMED, Web of Science, Wiley Library, EMBASE, Wanfang, CNKI, and VIP databases were searched for studies related to gastroscopic hemostatic treatment for acute GC published through February 20, 2023. The literature was screened according to the inclusion and exclusion criteria, data were extracted, and literature quality was evaluated. The meta-analysis was performed using RevMan software (version 5.3), while Begg’s test for publication bias was performed using Stata 13.0 software.
RESULTS Six randomized controlled trials and two retrospective analyses were retrieved. Five studies had a low, two had an uncertain, and one had a high risk of bias. Compared with the control group, the hemostatic rate of gastroscopic hemostasis was increased [relative risk (RR) = 1.24; 95% confidence interval (CI): 1.08 to 1.43; P = 0.003]; the rate of rebleeding (RR = 0.27; 95%CI: 0.09 to 0.80; P = 0.02), rate of surgery transfer (RR = 0.16; 95%CI: 0.06 to 0.43; P = 0.0003), serum C-reactive protein level [mean difference (MD) = -5.16; 95%CI: -6.11 to 4.21; P < 0.00001], interleukin-6 level (MD = -6.37; 95%CI: -10.33 to -2.42; P = 0.002), and tumor necrosis factor-α level (MD = -2.29; 95%CI: -4.06 to -0.52; P = 0.01) were decreased; and the quality of life improvement rate was increased (RR = 1.95; 95%C I= 1.41-2.71; P < 0.0001). Begg’s test revealed no significant publication bias.
CONCLUSION The efficacy and safety of endoscopic hemostasis were higher than those of the control group, suggesting that it is an effective treatment for acute GC hemorrhage.
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Affiliation(s)
- Hai-Yan Pan
- Department of Emergency Medicine, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 310005, Zhejiang Province, China
| | - Xiao-Wei Wang
- Department of Cardiopulmonary Rehabilitation, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 310005, Zhejiang Province, China
| | - Qiong-Xiao He
- Department of Emergency Medicine, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 310005, Zhejiang Province, China
| | - Yi-Dan Lu
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310005, Zhejiang Province, China
| | - Wan-Yi Zhang
- Department of Emergency Medicine, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 310005, Zhejiang Province, China
| | - Jian-Wei Jin
- Department of Oncology, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 310005, Zhejiang Province, China
| | - Bin Lin
- Department of Emergency Medicine, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 310005, Zhejiang Province, China
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Xue Y, Zhang H, Zheng Z, Liu X, Yin J, Zhang J. Predictive performance of radiomics for peritoneal metastasis in patients with gastric cancer: a meta-analysis and radiomics quality assessment. J Cancer Res Clin Oncol 2023; 149:12103-12113. [PMID: 37422882 DOI: 10.1007/s00432-023-05096-0] [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: 05/23/2023] [Accepted: 06/30/2023] [Indexed: 07/11/2023]
Abstract
PURPOSE The purpose of this meta-analysis is to systematically review the diagnostic performance of radiomic techniques in predicting peritoneal metastasis in patients with gastric cancer, and to evaluate the quality of current research. METHODS We searched PubMed, Web of Science, EBSCO, Embase, and Cochrane databases for relevant studies up to April 3, 2023. Data extraction and quality evaluation were performed by two independent reviewers. Then we performed statistical analysis, including plotting the forest plot and summary receiver operating characteristic (SROC) curve, and source of heterogeneity analysis, through the MIDAS module in Stata 15. We performed meta-regression and subgroup analyses to analyze the sources of heterogeneity. Using the QUADAS-2 scale and the RQS scale to assess the quality of retrieved studies. RESULTS Ten studies with 6199 patients were finally included in our meta-analysis. Pooled sensitivity and specificity were 0.77 (95% confidence interval [CI]: 0.66, 0.86), and 0.88 (95% CI 0.80, 0.93), respectively. The overall AUC was 0.89 (95% CI 0.86, 0.92). The heterogeneity of this meta-analysis was high, with I2 = 88% (95% CI 75,100). The result of meta-regression showed that QUADAS-2 results, RQS results and machine learning method led to heterogeneity in sensitivity and specificity (P < 0.05). Furthermore, the image segmentation area and the presence or absence of combined clinical factors were associated with sensitivity heterogeneity and specificity heterogeneity, respectively. CONCLUSION Undoubtedly, radiomics has potential value in diagnosing peritoneal metastasis of gastric cancer, but the quality of current research is inconsistent, and more standardized and high-quality research is still needed in the future to achieve the transformation of radiomics results into clinical applications.
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Affiliation(s)
- Yasheng Xue
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing, 100050, China
| | - Haiqiao Zhang
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing, 100050, China
| | - Zhi Zheng
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing, 100050, China
| | - Xiaoye Liu
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing, 100050, China
| | - Jie Yin
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing, 100050, China
| | - Jun Zhang
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing, 100050, China.
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Giandola T, Maino C, Marrapodi G, Ratti M, Ragusi M, Bigiogera V, Talei Franzesi C, Corso R, Ippolito D. Imaging in Gastric Cancer: Current Practice and Future Perspectives. Diagnostics (Basel) 2023; 13:diagnostics13071276. [PMID: 37046494 PMCID: PMC10093088 DOI: 10.3390/diagnostics13071276] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/19/2023] [Accepted: 03/27/2023] [Indexed: 03/31/2023] Open
Abstract
Gastric cancer represents one of the most common oncological causes of death worldwide. In order to treat patients in the best possible way, the staging of gastric cancer should be accurate. In this regard, endoscopy ultrasound (EUS) has been considered the reference standard for tumor (T) and nodal (N) statuses in recent decades. However, thanks to technological improvements, computed tomography (CT) has gained an important role, not only in the assessment of distant metastases (M status) but also in T and N staging. In addition, magnetic resonance imaging (MRI) can contribute to the detection and staging of primary gastric tumors thanks to its excellent soft tissue contrast and multiple imaging sequences without radiation-related risks. In addition, MRI can help with the detection of liver metastases, especially small lesions. Finally, positron emission tomography (PET) is still considered a useful diagnostic tool for the staging of gastric cancer patients, with a focus on nodal metastases and peritoneal carcinomatosis. In addition, it may play a role in the treatment of gastric cancer in the coming years thanks to the introduction of new labeling peptides. This review aims to summarize the most common advantages and pitfalls of EUS, CT, MRI and PET in the TNM staging of gastric cancer patients.
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