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Wei J, Ma Y, Liu J, Zhao J, Zhou J. A noninvasive comprehensive model based on medium sample size had good diagnostic performance in distinguishing renal fat-poor angiomyolipoma from homogeneous clear cell renal cell carcinoma. Urol Oncol 2025; 43:332.e1-332.e10. [PMID: 39648090 DOI: 10.1016/j.urolonc.2024.11.013] [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: 08/07/2024] [Revised: 11/01/2024] [Accepted: 11/08/2024] [Indexed: 12/10/2024]
Abstract
PURPOSE To determine the diagnostic value of a comprehensive model based on unenhanced computed tomography (CT) images for distinguishing fat-poor angiomyolipoma (fp-AML) from homogeneous clear cell renal cell carcinoma (hm-ccRCC). METHODS We retrospectively reviewed 27 patients with fp-AML and 63 with hm-ccRCC. Demographic data and conventional CT features of the lesions were recorded (including sex, age, symptoms, lesion location, shape, boundary, unenhanced CT attenuation and so on). Whole tumor regions of interest were drawn on all slices to obtain histogram parameters (including minimum, maximum, mean, percentile, standard deviation, variance, coefficient of variation, skewness, kurtosis, and entropy) by two radiologists. Chi-square test, Mann-Whitney U test, or independent samples t-test were used to compare demographic data, CT features, and histogram parameters. Multivariate logistic regression analyses were used to screen for independent predictors distinguishing fp-AML from hm-ccRCC. Receiver operating characteristic curves were constructed to evaluate the diagnostic performances of the models. RESULTS Age, sex, tumor boundary, unenhanced CT attenuation, maximum tumor diameter, and tumor volume significantly differed between patients with fp-AML and those with hm-ccRCC (P < 0.05). The minimum, mean, first percentile (Perc.01), Perc.05, Perc.10, Perc.25, Perc.50, Perc.75, Perc.90, Perc.95, and Perc.99 of the Fp-AML group were higher than those of the hm-ccRCC group (P < 0.05). Coefficient of variance, skewness, and kurtosis were lower than those in the hm-ccRCC group (all P < 0.05). Age, maximum tumor diameter, unenhanced CT attenuation, and Perc.25 were independent predictors for distinguishing fp-AML from hm-ccRCC (all P < 0.05). The comprehensive model, incorporating age, maximum tumor diameter, unenhanced CT attenuation, and Perc.25, showed the best diagnostic performance (AUC = 0.979). CONCLUSION The comprehensive model based on unenhanced CT imaging can accurately distinguish fp-AML from hm-ccRCC and may assist clinicians in tailoring precise therapy, while also helping to improve the diagnosis and management of renal tumors, leading to the selection of effective treatment options.
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Affiliation(s)
- Jinyan Wei
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou, China; Second Clinical School, Lanzhou University, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Yurong Ma
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou, China; Second Clinical School, Lanzhou University, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Jianqiang Liu
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou, China; Second Clinical School, Lanzhou University, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Jianhong Zhao
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou, China; Second Clinical School, Lanzhou University, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou, China; Second Clinical School, Lanzhou University, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
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Labra A, Schiappacasse G, Constenla D, Cristi J. Renal angiomyolipomas: Typical and atypical features on computed tomography and magnetic resonance imaging. World J Radiol 2025; 17:104282. [DOI: 10.4329/wjr.v17.i2.104282] [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: 12/16/2024] [Revised: 01/24/2025] [Accepted: 02/18/2025] [Indexed: 02/26/2025] Open
Abstract
Angiomyolipomas (AMLs) represent the most common benign solid renal tumors. The frequency of their detection in the general population is increasing owing to advances in imaging technology. The objective of this review is to discuss computed tomography (CT) and magnetic resonance imaging findings for both typical and atypical renal AMLs, along with their associated complications. AMLs are typically defined as solid triphasic tumors composed of varying amounts of dysmorphic and tortuous blood vessels, smooth muscle components and adipose tissue. In an adult, a classical renal AML appears as a solid, heterogeneous renal cortical mass with macroscopic fat. However, up to 5% of AMLs contain minimal fat and cannot be reliably diagnosed by imaging. Fat-poor AMLs can appear as hyperattenuating masses on unenhanced CT and as hypointense masses on T2WI; other AMLs may be isodense or exhibit cystic components. Hemorrhage is the most common complication, and AMLs with hemorrhage can mimic other tumors, making their diagnosis challenging. Understanding the variable and heterogeneous nature of this neoplasm to correctly classify renal AMLs and to avoid misdiagnosis of other renal lesions is crucial.
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Affiliation(s)
- Andres Labra
- Departamento de Imágenes, Facultad de Medicina Clínica Alemana-Universidad del Desarrollo, Santiago 7650568, Región Metropolitana, Chile
| | - Giancarlo Schiappacasse
- Departamento de Imágenes, Facultad de Medicina Clínica Alemana-Universidad del Desarrollo, Santiago 7650568, Región Metropolitana, Chile
| | - Diego Constenla
- Departamento de Imágenes, Facultad de Medicina Clínica Alemana-Universidad del Desarrollo, Santiago 7650568, Región Metropolitana, Chile
| | - Joaquin Cristi
- Departamento de Imágenes, Facultad de Medicina Clínica Alemana-Universidad del Desarrollo, Santiago 7650568, Región Metropolitana, Chile
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Jin P, Zhang L, Yang H, Jiang T, Xu C, Huang J, Zhang Z, Shi L, Wang X. Development of modified multi-parametric CT algorithms for diagnosing clear-cell renal cell carcinoma in small solid renal masses. Cancer Imaging 2025; 25:22. [PMID: 40022184 PMCID: PMC11869432 DOI: 10.1186/s40644-025-00847-3] [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: 02/24/2025] [Indexed: 03/03/2025] Open
Abstract
OBJECTIVE To refine the existing CT algorithm to enhance inter-reader agreement and improve the diagnostic performance for clear-cell renal cell carcinoma (ccRCC) in solid renal masses less than 4 cm. METHODS A retrospective collection of 331 patients with pathologically confirmed renal masses were enrolled in this study. Two radiologists independently assessed the CT images: in addition to heterogeneity score (HS) and mass-to-cortex corticomedullary attenuation ratio (MCAR), measured parameters included ratio of major diameter to minor diameter at the maximum axial section (Major axis / Minor axis), tumor-renal interface, standardized heterogeneity ratio (SHR), and standardized nephrographic reduction rate (SNRR). Spearman's correlation analysis was performed to evaluate the relationship between SHR and HS. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors and then CT-score was adjusted by those indicators. The diagnostic efficacy of the modified CT-scores was evaluated using ROC curve analysis. RESULTS The SHR and heterogeneity grade (HG) of mass were correlated positively with the HS (R = 0.749, 0.730, all P < 0.001). Logistic regression analysis determined that the Major axis / Minor axis (> 1.16), the tumor-renal interface (> 22.3 mm), and the SNRR (> 0.16) as additional independent risk factors to combine with HS and MCAR. Compared to the original CT-score, the two CT algorithms combined tumor-renal interface and SNRR showed significantly improved diagnostic efficacy for ccRCC (AUC: 0.770 vs. 0.861 and 0.862, all P < 0.001). The inter-observer agreement for HG was higher than that for HS (weighted Kappa coefficient: 0.797 vs. 0.722). The consistency of modified CT-score was also superior to original CT-score (weighted Kappa coefficient: 0.935 vs. 0.878). CONCLUSION The modified CT algorithms not only enhanced inter-reader consistency but also improved the diagnostic capability for ccRCC in small renal masses.
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Affiliation(s)
- Pengfei Jin
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
| | - Linghui Zhang
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
| | - Hong Yang
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
| | - Tingting Jiang
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
| | - Chenyang Xu
- Department of Pathology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
| | - Jiehui Huang
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
| | - Zhongyu Zhang
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
| | - Lei Shi
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
| | - Xu Wang
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
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Wang R, Zhong L, Zhu P, Pan X, Chen L, Zhou J, Ding Y. MRI-based radiomics machine learning model to differentiate non-clear cell renal cell carcinoma from benign renal tumors. Eur J Radiol Open 2024; 13:100608. [PMID: 39525508 PMCID: PMC11550165 DOI: 10.1016/j.ejro.2024.100608] [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: 08/04/2024] [Revised: 10/09/2024] [Accepted: 10/20/2024] [Indexed: 11/16/2024] Open
Abstract
Purpose We aim to develop an MRI-based radiomics model to improve the accuracy of differentiating non-ccRCC from benign renal tumors preoperatively. Methods The retrospective study included 195 patients with pathologically confirmed renal tumors (134 non-ccRCCs and 61 benign renal tumors) who underwent preoperative renal mass protocol MRI examinations. The patients were divided into a training set (n = 136) and test set (n = 59). Simple t-test and the Least Absolute Shrink and Selection Operator (LASSO) were used to select the most valuable features and the rad-scores of them were calculated. The clinicoradiologic models, single-sequence radiomics models, multi-sequence radiomics models and combined models for differentiation were constructed with 2 classifiers (support vector machine (SVM), logistic regression (LR)) in the training set and used for differentiation in the test set. Ten-fold cross validation was applied to obtain the optimal hyperparameters of the models. The performances of the models were evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). Delong's test was performed to compare the performances of models. Results After univariate and multivariate logistic regression analysis, the independent risk factors to differentiate non-ccRCC from benign renal tumors were selected as follows: age, tumor region, hemorrhage, pseudocapsule and enhancement degree. Among the 14 machine learning classification models constructed, the combined model with LR has the highest efficiency in differentiating non-ccRCC from benign renal tumors. The AUC in the training set is 0.964, and the accuracy is 0.919. The AUC in the test set is 0.936, and the accuracy is 0.864. Conclusion The MRI-based radiomics machine learning is feasible to differentiate non-ccRCC from benign renal tumors, which could improve the accuracy of clinical diagnosis.
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Affiliation(s)
- Ruiting Wang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Institute of Medical Imaging, Shanghai, China
| | - Lianting Zhong
- Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian, China
| | - Pingyi Zhu
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Institute of Medical Imaging, Shanghai, China
| | - Xianpan Pan
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Lei Chen
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Jianjun Zhou
- Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian, China
- Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, Fujian, China
- Fujian Province Key Clinical Specialty for Medical Imaging, Xiamen, Fujian, China
| | - Yuqin Ding
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Institute of Medical Imaging, Shanghai, China
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Dmitry F, Evgeniy S, Vasiliy K, Alexandra P, Khalil I, Evgeny S, Mikhail C, Kirill P, Alexander T, Dmitry K, Camilla A, Andrey V, Denis B, Petr G, Leonid R. Tumor morphology evaluation using 3D-morphometric features of renal masses. Urologia 2024; 91:665-673. [PMID: 39058231 DOI: 10.1177/03915603241261499] [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: 07/28/2024]
Abstract
OBJECTIVE To assess the correlation between the general (gender, age, and maximum tumor size) and 3D morphotopometric features of the renal tumor node, following the MSCT data post-processing, and the tumor histological structure; to propose an equation allowing for kidney malignancy assessment based on general and morphometric features. MATERIALS AND METHODS In total, 304 patients with unilateral solitary renal neoplasms underwent laparoscopic (retroperitoneoscopic) or robotic partial or radical nephrectomy. Before the procedure, kidney contrast-enhanced MSCT followed by the tumor 3D-modeling was performed. 3D model of the kidney tumor, and its morphotopometric features, and histological structure were analyzed. The morphotopometric ones include the side of the lesion, location by segments, the surface where the tumor, the depth of the tumor invasion into the kidney, and the shape of tumor. RESULTS Out of 304 patients, 254 (83.6%) had malignant kidney tumors and 50 (16.4%) benign kidney tumors. In total, 231 patients, out of 254 (90.9%) were assessed for the degree of malignant tumor differentiation. Malignant tumors were more frequent in men than in women (p < 0.001). Mushroom-shaped tumors were the most common shapes among benign renal masses (35.2%). The most common malignant kidney tumors had spherical with a partially uneven surface (27.6%), multinodular (tuberous (27.2%)), and spherical with a conical base (24.8%) shapes. Logistic regression model enabled the development of prognostic equation for tumor malignancy prediction ("low" or "high"). The univariate analysis revealed the correlation only between high differentiation (G1) and a spherical tumor with a conical base (p = 0.029). CONCLUSION The resulting logistic model, based on the analysis of such predictors as gender and form of kidney lesions, demonstrated a large share (87.6%) of correct predictions of the kidney tumor malignancy.
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Affiliation(s)
- Fiev Dmitry
- Institute for Urology and Human Reproductive Health, Sechenov University, Moscow, Russia
| | - Sirota Evgeniy
- Institute for Urology and Human Reproductive Health, Sechenov University, Moscow, Russia
| | - Kozlov Vasiliy
- Semashko Department of Public Health and Healthcare, Sechenov University, Moscow, Russia
| | - Proskura Alexandra
- Institute for Urology and Human Reproductive Health, Sechenov University, Moscow, Russia
| | - Ismailov Khalil
- Institute for Urology and Human Reproductive Health, Sechenov University, Moscow, Russia
| | - Shpot Evgeny
- Institute for Urology and Human Reproductive Health, Sechenov University, Moscow, Russia
| | - Chernenkiy Mikhail
- Institute for Urology and Human Reproductive Health, Sechenov University, Moscow, Russia
| | - Puzakov Kirill
- Department of Radiology, The Second University Clinic, Sechenov University, Moscow, Russia
| | - Tarasov Alexander
- Institute of Linguistics and Intercultural Communication, Sechenov University, Moscow, Russia
| | - Korolev Dmitry
- Institute for Urology and Human Reproductive Health, Sechenov University, Moscow, Russia
| | - Azilgareeva Camilla
- Institute for Urology and Human Reproductive Health, Sechenov University, Moscow, Russia
| | - Vinarov Andrey
- Institute for Urology and Human Reproductive Health, Sechenov University, Moscow, Russia
| | - Butnaru Denis
- Institute for Urology and Human Reproductive Health, Sechenov University, Moscow, Russia
| | - Glybochko Petr
- Institute for Urology and Human Reproductive Health, Sechenov University, Moscow, Russia
| | - Rapoport Leonid
- Institute for Urology and Human Reproductive Health, Sechenov University, Moscow, Russia
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Han J, Chen B, Cheng C, Liu T, Tao Y, Lin J, Yin S, He Y, Chen H, Lu Y, Zhang Y. Development and Validation of a Diagnostic Model for Identifying Clear Cell Renal Cell Carcinoma in Small Renal Masses Based on CT Radiological Features: A Multicenter Study. Acad Radiol 2024; 31:4085-4095. [PMID: 38749869 DOI: 10.1016/j.acra.2024.03.022] [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: 02/10/2024] [Revised: 03/10/2024] [Accepted: 03/19/2024] [Indexed: 10/21/2024]
Abstract
RATIONALE AND OBJECTIVES This study aimed to develop a diagnostic model based on clinical and CT features for identifying clear cell renal cell carcinoma (ccRCC) in small renal masses (SRMs). MATERIAL AND METHODS This retrospective multi-centre study enroled patients with pathologically confirmed SRMs. Data from three centres were used as training set (n = 229), with data from one centre serving as an independent test set (n = 81). Univariate and multivariate logistic regression analyses were utilised to screen independent risk factors for ccRCC and build the classification and regression tree (CART) diagnostic model. The area under the curve (AUC) was used to evaluate the performance of the model. To demonstrate the clinical utility of the model, three radiologists were asked to diagnose the SRMs in the test set based on professional experience and re-evaluated with the aid of the CART model. RESULTS There were 310 SRMs in 309 patients and 71% (220/310) were ccRCC. In the testing cohort, the AUC of the CART model was 0.90 (95% CI: 0.81, 0.97). For the radiologists' assessment, the AUC of the three radiologists based on the clinical experience were 0.78 (95% CI:0.66,0.89), 0.65 (95% CI:0.53,0.76), and 0.68 (95% CI:0.57,0.79). With the CART model support, the AUC of the three radiologists were 0.93 (95% CI:0.86,0.97), 0.87 (95% CI:0.78,0.95) and 0.87 (95% CI:0.78,0.95). Interobserver agreement was improved with the CART model aids (0.323 vs 0.654, P < 0.001). CONCLUSION The CART model can identify ccRCC with better diagnostic efficacy than that of experienced radiologists and improve diagnostic performance, potentially reducing the number of unnecessary biopsies.
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Affiliation(s)
- Jiayue Han
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-Sen University, No. 52 Meihua East Road, Zhuhai 519000, Guangdong, China; Department of Radiology, Inner Mongolia Autonomous Region People's Hospital, No. 20 Zhaowuda Road, Hohhot 010017, Inner Mongolia, China
| | - Binghui Chen
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-Sen University, No. 52 Meihua East Road, Zhuhai 519000, Guangdong, China
| | - Ci Cheng
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-Sen University, No. 52 Meihua East Road, Zhuhai 519000, Guangdong, China
| | - Tao Liu
- Perception Vision Medical Technologies Co Ltd, No. 12 Yuyan Road, Guangzhou 510000, Guangdong, China
| | - Yuxi Tao
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-Sen University, No. 52 Meihua East Road, Zhuhai 519000, Guangdong, China
| | - Junyu Lin
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-Sen University, No. 52 Meihua East Road, Zhuhai 519000, Guangdong, China
| | - Songtao Yin
- Department of Radiology, Inner Mongolia Autonomous Region People's Hospital, No. 20 Zhaowuda Road, Hohhot 010017, Inner Mongolia, China
| | - Yanlin He
- Department of Radiology, Inner Mongolia Autonomous Region People's Hospital, No. 20 Zhaowuda Road, Hohhot 010017, Inner Mongolia, China
| | - Hao Chen
- Department of Radiology, Anhui Provincial Hospital, No. 17 Lujiang Road, Hefei 230061, Anhui, China
| | - Yao Lu
- School of Computer Science and Engineering, Sun Yat-sen University, No. 135 Xin Gang Road West, Guangzhou 510006, Guangdong, China
| | - Yaqin Zhang
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-Sen University, No. 52 Meihua East Road, Zhuhai 519000, Guangdong, China.
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Toide M, Tanaka H, Kobayashi M, Fujiwara M, Nakamura Y, Fukuda S, Kimura K, Waseda Y, Yoshida S, Tateishi U, Fujii Y. Stepwise algorithm using computed tomography and magnetic resonance imaging for differential diagnosis of fat-poor angiomyolipoma in small renal masses: A prospective validation study. Int J Urol 2024; 31:778-784. [PMID: 38632863 DOI: 10.1111/iju.15464] [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: 09/19/2023] [Accepted: 03/28/2024] [Indexed: 04/19/2024]
Abstract
OBJECTIVES To validate the diagnostic accuracy of a stepwise algorithm to differentiate fat-poor angiomyolipoma (fp-AML) from renal cancer in small renal masses (SRMs). METHODS We prospectively enrolled 223 patients with solid renal masses <4 cm and no visible fat on unenhanced computed tomography (CT). Patients were assessed using an algorithm that utilized the dynamic CT and MRI findings in a stepwise manner. The diagnostic accuracy of the algorithm was evaluated in patients whose histology was confirmed through surgery or biopsy. The clinical course of the patients was further analyzed. RESULTS The algorithm classified 151 (68%)/42 (19%)/30 (13%) patients into low/intermediate/high AML probability groups, respectively. Pathological diagnosis was made for 183 patients, including 10 (5.5%) with fp-AML. Of these, 135 (74%)/36 (20%)/12 (6.6%) were classified into the low/intermediate/high AML probability groups, and each group included 1 (0.7%)/3 (8.3%)/6 (50%) fp-AMLs, respectively, leading to the area under the curve for predicting AML of 0.889. Surgery was commonly opted in the low and intermediate AML probability groups (84% and 64%, respectively) for initial management, while surveillance was selected in the high AML probability group (63%). During the 56-month follow-up, 36 (82%) of 44 patients initially surveyed, including 13 of 18 (72%), 6 of 7 (86%), and 17 of 19 (89%) in the low/intermediate/high AML probability groups, respectively, continued surveillance without any progression. CONCLUSIONS This study confirmed the high diagnostic accuracy for differentiating fp-AMLs. These findings may help in the management of patients with SRMs.
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Affiliation(s)
- Masahiro Toide
- Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Hajime Tanaka
- Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Masaki Kobayashi
- Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Motohiro Fujiwara
- Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yuki Nakamura
- Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Shohei Fukuda
- Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Koichiro Kimura
- Department of Radiology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yuma Waseda
- Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Soichiro Yoshida
- Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Ukihide Tateishi
- Department of Radiology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yasuhisa Fujii
- Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan
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Alhussaini AJ, Steele JD, Jawli A, Nabi G. Radiomics Machine Learning Analysis of Clear Cell Renal Cell Carcinoma for Tumour Grade Prediction Based on Intra-Tumoural Sub-Region Heterogeneity. Cancers (Basel) 2024; 16:1454. [PMID: 38672536 PMCID: PMC11048006 DOI: 10.3390/cancers16081454] [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: 02/06/2024] [Revised: 03/22/2024] [Accepted: 04/03/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Renal cancers are among the top ten causes of cancer-specific mortality, of which the ccRCC subtype is responsible for most cases. The grading of ccRCC is important in determining tumour aggressiveness and clinical management. OBJECTIVES The objectives of this research were to predict the WHO/ISUP grade of ccRCC pre-operatively and characterise the heterogeneity of tumour sub-regions using radiomics and ML models, including comparison with pre-operative biopsy-determined grading in a sub-group. METHODS Data were obtained from multiple institutions across two countries, including 391 patients with pathologically proven ccRCC. For analysis, the data were separated into four cohorts. Cohorts 1 and 2 included data from the respective institutions from the two countries, cohort 3 was the combined data from both cohort 1 and 2, and cohort 4 was a subset of cohort 1, for which both the biopsy and subsequent histology from resection (partial or total nephrectomy) were available. 3D image segmentation was carried out to derive a voxel of interest (VOI) mask. Radiomics features were then extracted from the contrast-enhanced images, and the data were normalised. The Pearson correlation coefficient and the XGBoost model were used to reduce the dimensionality of the features. Thereafter, 11 ML algorithms were implemented for the purpose of predicting the ccRCC grade and characterising the heterogeneity of sub-regions in the tumours. RESULTS For cohort 1, the 50% tumour core and 25% tumour periphery exhibited the best performance, with an average AUC of 77.9% and 78.6%, respectively. The 50% tumour core presented the highest performance in cohorts 2 and 3, with average AUC values of 87.6% and 76.9%, respectively. With the 25% periphery, cohort 4 showed AUC values of 95.0% and 80.0% for grade prediction when using internal and external validation, respectively, while biopsy histology had an AUC of 31.0% for the classification with the final grade of resection histology as a reference standard. The CatBoost classifier was the best for each of the four cohorts with an average AUC of 80.0%, 86.5%, 77.0% and 90.3% for cohorts 1, 2, 3 and 4 respectively. CONCLUSIONS Radiomics signatures combined with ML have the potential to predict the WHO/ISUP grade of ccRCC with superior performance, when compared to pre-operative biopsy. Moreover, tumour sub-regions contain useful information that should be analysed independently when determining the tumour grade. Therefore, it is possible to distinguish the grade of ccRCC pre-operatively to improve patient care and management.
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Affiliation(s)
- Abeer J. Alhussaini
- Division of Imaging Sciences and Technology, School of Medicine, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK
- Department of Clinical Radiology, Al-Amiri Hospital, Ministry of Health, Sulaibikhat 1300, Kuwait
| | - J. Douglas Steele
- Division of Imaging Sciences and Technology, School of Medicine, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK
| | - Adel Jawli
- Division of Imaging Sciences and Technology, School of Medicine, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK
- Department of Clinical Radiology, Sheikh Jaber Al-Ahmad Al-Sabah Hospital, Ministry of Health, Sulaibikhat 1300, Kuwait
| | - Ghulam Nabi
- Division of Imaging Sciences and Technology, School of Medicine, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK
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Gao Y, Wang X, Zhao X, Zhu C, Li C, Li J, Wu X. Multiphase CT radiomics nomogram for preoperatively predicting the WHO/ISUP nuclear grade of small (< 4 cm) clear cell renal cell carcinoma. BMC Cancer 2023; 23:953. [PMID: 37814228 PMCID: PMC10561466 DOI: 10.1186/s12885-023-11454-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 09/27/2023] [Indexed: 10/11/2023] Open
Abstract
BACKGROUND Small (< 4 cm) clear cell renal cell carcinoma (ccRCC) is the most common type of small renal cancer and its prognosis is poor. However, conventional radiological characteristics obtained by computed tomography (CT) are not sufficient to predict the nuclear grade of small ccRCC before surgery. METHODS A total of 113 patients with histologically confirmed ccRCC were randomly assigned to the training set (n = 67) and the testing set (n = 46). The baseline and CT imaging data of the patients were evaluated statistically to develop a clinical model. A radiomics model was created, and the radiomics score (Rad-score) was calculated by extracting radiomics features from the CT images. Then, a clinical radiomics nomogram was developed using multivariate logistic regression analysis by combining the Rad-score and critical clinical characteristics. The receiver operating characteristic (ROC) curve was used to evaluate the discrimination of small ccRCC in both the training and testing sets. RESULTS The radiomics model was constructed using six features obtained from the CT images. The shape and relative enhancement value of the nephrographic phase (REV of the NP) were found to be independent risk factors in the clinical model. The area under the curve (AUC) values for the training and testing sets for the clinical radiomics nomogram were 0.940 and 0.902, respectively. Decision curve analysis (DCA) revealed that the radiomics nomogram model was a better predictor, with the highest degree of coincidence. CONCLUSION The CT-based radiomics nomogram has the potential to be a noninvasive and preoperative method for predicting the WHO/ISUP grade of small ccRCC.
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Affiliation(s)
- Yankun Gao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Xia Wang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Xiaoying Zhao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Chao Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Cuiping Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Jianying Li
- CT Research Center, GE Healthcare China, Shanghai, 210000, China
| | - Xingwang Wu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China.
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10
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Dehghani Firouzabadi F, Gopal N, Hasani A, Homayounieh F, Li X, Jones EC, Yazdian Anari P, Turkbey E, Malayeri AA. CT radiomics for differentiating fat poor angiomyolipoma from clear cell renal cell carcinoma: Systematic review and meta-analysis. PLoS One 2023; 18:e0287299. [PMID: 37498830 PMCID: PMC10374097 DOI: 10.1371/journal.pone.0287299] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 06/03/2023] [Indexed: 07/29/2023] Open
Abstract
PURPOSE Differentiation of fat-poor angiomyolipoma (fp-AMLs) from renal cell carcinoma (RCC) is often not possible from just visual interpretation of conventional cross-sectional imaging, typically requiring biopsy or surgery for diagnostic confirmation. However, radiomics has the potential to characterize renal masses without the need for invasive procedures. Here, we conducted a systematic review on the accuracy of CT radiomics in distinguishing fp-AMLs from RCCs. METHODS We conducted a search using PubMed/MEDLINE, Google Scholar, Cochrane Library, Embase, and Web of Science for studies published from January 2011-2022 that utilized CT radiomics to discriminate between fp-AMLs and RCCs. A random-effects model was applied for the meta-analysis according to the heterogeneity level. Furthermore, subgroup analyses (group 1: RCCs vs. fp-AML, and group 2: ccRCC vs. fp-AML), and quality assessment were also conducted to explore the possible effect of interstudy differences. To evaluate CT radiomics performance, the pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were assessed. This study is registered with PROSPERO (CRD42022311034). RESULTS Our literature search identified 10 studies with 1456 lesions in 1437 patients. Pooled sensitivity was 0.779 [95% CI: 0.562-0.907] and 0.817 [95% CI: 0.663-0.910] for groups 1 and 2, respectively. Pooled specificity was 0.933 [95% CI: 0.814-0.978]and 0.926 [95% CI: 0.854-0.964] for groups 1 and 2, respectively. Also, our findings showed higher sensitivity and specificity of 0.858 [95% CI: 0.742-0.927] and 0.886 [95% CI: 0.819-0.930] for detecting ccRCC from fp-AML in the unenhanced phase of CT scan as compared to the corticomedullary and nephrogenic phases of CT scan. CONCLUSION This study suggested that radiomic features derived from CT has high sensitivity and specificity in differentiating RCCs vs. fp-AML, particularly in detecting ccRCCs vs. fp-AML. Also, an unenhanced CT scan showed the highest specificity and sensitivity as compared to contrast CT scan phases. Differentiating between fp-AML and RCC often is not possible without biopsy or surgery; radiomics has the potential to obviate these invasive procedures due to its high diagnostic accuracy.
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Affiliation(s)
- Fatemeh Dehghani Firouzabadi
- Radiology Department, National Institutes of Health, Clinical Center (CC), Bethesda, Maryland, United States of America
| | - Nikhil Gopal
- Urology Department, National Cancer Institutes (NCI), Clinical Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Amir Hasani
- Radiology Department, National Institutes of Health, Clinical Center (CC), Bethesda, Maryland, United States of America
| | - Fatemeh Homayounieh
- Radiology Department, National Institutes of Health, Clinical Center (CC), Bethesda, Maryland, United States of America
| | - Xiaobai Li
- Biostatistics and Clinical Epidemiology Service, NIH Clinical Center, Bethesda, MD, United States of America
| | - Elizabeth C Jones
- Radiology Department, National Institutes of Health, Clinical Center (CC), Bethesda, Maryland, United States of America
| | - Pouria Yazdian Anari
- Radiology Department, National Institutes of Health, Clinical Center (CC), Bethesda, Maryland, United States of America
| | - Evrim Turkbey
- Radiology Department, National Institutes of Health, Clinical Center (CC), Bethesda, Maryland, United States of America
| | - Ashkan A Malayeri
- Radiology Department, National Institutes of Health, Clinical Center (CC), Bethesda, Maryland, United States of America
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11
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Lu SQ, Lv W, Liu YJ, Deng H. Fat-poor renal angiomyolipoma with prominent cystic degeneration: A case report and review of the literature. World J Clin Cases 2023; 11:417-425. [PMID: 36686346 PMCID: PMC9850960 DOI: 10.12998/wjcc.v11.i2.417] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/16/2022] [Accepted: 12/21/2022] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Angiomyolipoma (AML), the most common benign tumor of the kidney, is usually composed of dysmorphic blood vessels, smooth muscle, and mature adipose tissue. To our knowledge, AML with cystic degeneration has rarely been documented. Cystic degeneration, hemorrhage, and a lack of fat bring great challenges to the diagnosis.
CASE SUMMARY A 60-year-old man with hypertension presented with a 5-year history of cystic mass in his left kidney. He fell 2 mo ago. A preoperative computed tomography (CT) scan showed a mixed-density cystic lesion without macroscopic fat density, the size of which had increased compared with before, probably due to hemorrhage caused by a trauma. Radical nephrectomy was performed. Histopathological studies revealed that the lesion mainly consisted of tortuous, ectatic, and thick-walled blood vessels, mature adipose tissue, and smooth muscle-like spindle cells arranged around the abnormal blood vessels. The tumor cells exhibited positivity for human melanoma black-45, Melan-A, smooth muscle actin, calponin, S-100, and neuron-specific enolase, rather than estrogen receptor, progesterone receptor, CD68, and cytokeratin. The Ki-67 labeling index was less than 5%. The final diagnosis was a fat-poor renal AML (RAML) with prominent cystic degeneration.
CONCLUSION When confronting a large renal cystic mass, RAML should be included in the differential diagnosis.
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Affiliation(s)
- Shi-Qi Lu
- Medical College, Nanchang University, Nanchang 330006, Jiangxi Province, China
| | - Wei Lv
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu 610044, Sichuan Province, China
| | - You-Jun Liu
- Department of Radiology, The Fourth Affiliated Hospital of Nanchang University, Nanchang 330003, Jiangxi Province, China
| | - Huan Deng
- Department of Pathology, The Fourth Affiliated Hospital of Nanchang University, Nanchang 330003, Jiangxi Province, China
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12
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Anai K, Hayashida Y, Ueda I, Hozuki E, Yoshimatsu Y, Tsukamoto J, Hamamura T, Onari N, Aoki T, Korogi Y. The effect of CT texture-based analysis using machine learning approaches on radiologists' performance in differentiating focal-type autoimmune pancreatitis and pancreatic duct carcinoma. Jpn J Radiol 2022; 40:1156-1165. [PMID: 35727458 PMCID: PMC9616757 DOI: 10.1007/s11604-022-01298-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 05/28/2022] [Indexed: 02/07/2023]
Abstract
PURPOSE To develop a support vector machine (SVM) classifier using CT texture-based analysis in differentiating focal-type autoimmune pancreatitis (AIP) and pancreatic duct carcinoma (PD), and to assess the radiologists' diagnostic performance with or without SVM. MATERIALS AND METHODS This retrospective study included 50 patients (20 patients with focal-type AIP and 30 patients with PD) who underwent dynamic contrast-enhanced CT. Sixty-two CT texture-based features were extracted from 2D images of the arterial and portal phase CTs. We conducted data compression and feature selections using principal component analysis (PCA) and produced the SVM classifier. Four readers participated in this observer performance study and the statistical significance of differences with and without the SVM was assessed by receiver operating characteristic (ROC) analysis. RESULTS The SVM performance indicated a high performance in differentiating focal-type AIP and PD (AUC = 0.920). The AUC for all 4 readers increased significantly from 0.827 to 0.911 when using the SVM outputs (p = 0.010). The AUC for inexperienced readers increased significantly from 0.781 to 0.905 when using the SVM outputs (p = 0.310). The AUC for experienced readers increased from 0.875 to 0.912 when using the SVM outputs, however, there was no significant difference (p = 0.018). CONCLUSION The use of SVM classifier using CT texture-based features improved the diagnostic performance for differentiating focal-type AIP and PD on CT.
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Affiliation(s)
- Kenta Anai
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Yoshiko Hayashida
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Issei Ueda
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Eri Hozuki
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Yuuta Yoshimatsu
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Jun Tsukamoto
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Toshihiko Hamamura
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Norihiro Onari
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Takatoshi Aoki
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Yukunori Korogi
- Department of Radiology, Kyushu Rosai Hospital, Moji Medical Center, 3-1, Higashiminatomachi, Moji-ku, Kitakyushu, Fukuoka 801-8502 Japan
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13
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Gao J, Han F, Wang X, Duan S, Zhang J. Multi-Phase CT-Based Radiomics Nomogram for Discrimination Between Pancreatic Serous Cystic Neoplasm From Mucinous Cystic Neoplasm. Front Oncol 2021; 11:699812. [PMID: 34926238 PMCID: PMC8672034 DOI: 10.3389/fonc.2021.699812] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 11/15/2021] [Indexed: 12/25/2022] Open
Abstract
Purpose This study aimed to develop and verify a multi-phase (MP) computed tomography (CT)-based radiomics nomogram to differentiate pancreatic serous cystic neoplasms (SCNs) from mucinous cystic neoplasms (MCNs), and to compare the diagnostic efficacy of radiomics models for different phases of CT scans. Materials and Methods A total of 170 patients who underwent surgical resection between January 2011 and December 2018, with pathologically confirmed pancreatic cystic neoplasms (SCN=115, MCN=55) were included in this single-center retrospective study. Radiomics features were extracted from plain scan (PS), arterial phase (AP), and venous phase (VP) CT scans. Algorithms were performed to identify the optimal features to build a radiomics signature (Radscore) for each phase. All features from these three phases were analyzed to develop the MP-Radscore. A combined model comprised the MP-Radscore and imaging features from which a nomogram was developed. The accuracy of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration tests, and decision curve analysis. Results For each scan phase, 1218 features were extracted, and the optimal ones were selected to construct the PS-Radscore (11 features), AP-Radscore (11 features), and VP-Radscore (12 features). The MP-Radscore (14 features) achieved better performance based on ROC curve analysis than any single phase did [area under the curve (AUC), training cohort: MP-Radscore 0.89, PS-Radscore 0.78, AP-Radscore 0.83, VP-Radscore 0.85; validation cohort: MP-Radscore 0.88, PS-Radscore 0.77, AP-Radscore 0.83, VP-Radscore 0.84]. The combination nomogram performance was excellent, surpassing those of all other nomograms in both the training cohort (AUC, 0.91) and validation cohort (AUC, 0.90). The nomogram also performed well in the calibration and decision curve analyses. Conclusions Radiomics for arterial and venous single-phase models outperformed the plain scan model. The combination nomogram that incorporated the MP-Radscore, tumor location, and cystic number had the best discriminatory performance and showed excellent accuracy for differentiating SCN from MCN.
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Affiliation(s)
- Jiahao Gao
- Department of Radiology, Huashan Hospital North, Fudan University, Shanghai, China.,Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Fang Han
- Department of Radiology, Huashan Hospital North, Fudan University, Shanghai, China.,Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaoshuang Wang
- Department of Radiology, Huashan Hospital North, Fudan University, Shanghai, China
| | - Shaofeng Duan
- Department of Life Sciences, GE Healthcare, Shanghai, China
| | - Jiawen Zhang
- Department of Radiology, Huashan Hospital North, Fudan University, Shanghai, China.,Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
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14
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Abdelmegeed SA, Farok HM, Refaat MM, Eldiasty TAE. Role of multidetector ct in quantitative enhancement- washout analysis of solid renal masses. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2021. [DOI: 10.1186/s43055-021-00650-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Enhancement washout technique in solid renal masses using multidetector computed tomography (MDCT) can differentiate different type of lesions. 99 Patients who are presenting with suspected renal masses or renal tumour for staging are included in this study. CT examination are carried out at urology and nephrology centre using MDCT. The attenuation values (Hounsfield Unit) will be assesed for each lesion on the pre enhanced, corticomedullary, nephrographic and delayed phases. Washout ratio will be calculated for each phase of enhancement in comparison to the unenhanced attenuation value. The characteristics of enhancement-washout will be correlated with the final histopathological diagnosis.
Results
Early enhancement and washout pattern was noted in 54 renal lesions (54.5%) representing 4 types of renal lesions; Oncocytoma (n = 13), clear cell renal cell carcinoma (n = 16), Chromophobe renal cell carcinoma (n = 15) and unclassified renal cell carcinoma (n = 10).Prolonged enhancement pattern was noted 45 lesions (45.4%); PRCC (n = 14), 10 case of lipid poor AML (n = 10), metanephric adenoma (n = 10) and Xp11 RCC (n = 11). High pre-contrast attenuation was noted in Xp 11RCC showing attenuation value 41.7 ± 6.823HU. The highest CMP values were noted in CCRCC (151.9 ± 20.4) followed by oncocytomas (137.6 ± 19.15HU) and then CHRCC (123.6 ± 16.6 HU)while the lowest values were noted in Metanephric adenoma)57.1 ± 17.4HU)and followed by PRCC (59.9 ± 4.8)and followed by lipid poor AML (79.17 ± 13.666) and RCC unclassified (89.06 ± 18.1).
Conclusions
Four-phase MDCT (the unenhanced, corticomedullary, nephrographic, and excretory phases) evaluate role of MDCT in differentiation of solid renal masses.
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15
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Wang X, Song G, Jiang H. Differentiation of renal angiomyolipoma without visible fat from small clear cell renal cell carcinoma by using specific region of interest on contrast-enhanced CT: a new combination of quantitative tools. Cancer Imaging 2021; 21:47. [PMID: 34225784 PMCID: PMC8259143 DOI: 10.1186/s40644-021-00417-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 06/28/2021] [Indexed: 11/26/2022] Open
Abstract
Background To investigate the value of using specific region of interest (ROI) on contrast-enhanced CT for differentiating renal angiomyolipoma without visible fat (AML.wovf) from small clear cell renal cell carcinoma (ccRCC). Methods Four-phase (pre-contrast phase [PCP], corticomedullary phase [CMP], nephrographic phase [NP], and excretory phase [EP]) contrast-enhanced CT images of AML.wovf (n = 31) and ccRCC (n = 74) confirmed by histopathology were retrospectively analyzed. The CT attenuation value of tumor (AVT), net enhancement value (NEV), relative enhancement ratio (RER), heterogeneous degree of tumor (HDT) and standardized heterogeneous ratio (SHR) were obtained by using different ROIs [small: ROI (1), smaller: ROI (2), large: ROI (3)], and the differences of these quantitative data between AML.wovf and ccRCC were statistically analyzed. Multivariate regression was used to screen the main factors for differentiation in each scanning phase, and the prediction models were established and evaluated. Results Among the quantitative parameters determined by different ROIs, the degree of enhancement measured by ROI (2) and the enhanced heterogeneity measured by ROI (3) performed better than ROI (1) in distinguishing AML.wovf from ccRCC. The receiver operating characteristic (ROC) curves showed that the area under the curve (AUC) of RER_CMP (2), RER_NP (2) measured by ROI (2) and HDT_CMP and SHR_CMP measured by ROI (3) were higher (AUC = 0.876, 0.849, 0.837 and 0.800). Prediction models that incorporated demographic data, morphological features and quantitative data derived from the enhanced phase were superior to quantitative data derived from the pre-contrast phase in differentiating between AML.wovf and ccRCC. Among them, the model in CMP was the best prediction model with the highest AUC (AUC = 0.986). Conclusion The combination of quantitative data obtained by specific ROI in CMP can be used as a simple quantitative tool to distinguish AML.wovf from ccRCC, which has a high diagnostic value after combining demographic data and morphological features.
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Affiliation(s)
- Xu Wang
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), No 1, Banshan East Road, Hangzhou, Zhejiang Province, 310022, People's Republic of China. .,Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, No 1, Banshan East Road, Hangzhou, Zhejiang Province, 310022, People's Republic of China.
| | - Ge Song
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), No 1, Banshan East Road, Hangzhou, Zhejiang Province, 310022, People's Republic of China.,Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, No 1, Banshan East Road, Hangzhou, Zhejiang Province, 310022, People's Republic of China
| | - Haitao Jiang
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), No 1, Banshan East Road, Hangzhou, Zhejiang Province, 310022, People's Republic of China.,Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, No 1, Banshan East Road, Hangzhou, Zhejiang Province, 310022, People's Republic of China
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16
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Salvador R, Sebastià M, Cárdenas G, Páez-Carpio A, Paño B, Solé M, Nicolau C. CT differentiation of fat-poor angiomyolipomas from papillary renal cell carcinomas: development of a predictive model. Abdom Radiol (NY) 2021; 46:3280-3287. [PMID: 33674961 DOI: 10.1007/s00261-021-02988-y] [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: 11/06/2020] [Revised: 01/19/2021] [Accepted: 02/09/2021] [Indexed: 11/29/2022]
Abstract
PURPOSE To identify specific contrast-enhanced CT (CECT) findings and develop a predictive model with logistic regression to differentiate fat-poor angiomyolipomas (fpAML) from papillary renal cell carcinomas (pRCC). METHODS This is a single-institution retrospective study that assess CT features of histologically proven 67 pRCC and 13 fpAML. CECT variables were studied by means of univariate logistic regression. Variables included patients' demographics, tumor attenuation (unenhanced and at arterial, venous and excretory post-contrast phases), type of enhancement, morphological features (axial long and short diameters, long-short axis ratio (LSR) and tumor to kidney angle interface) and presence of visible calcifications or vessels. Those variables with a p ≤ 0.05 underwent standard stepwise logistic regression to find predictive combinations of clinical variables. Best models were evaluated by AUROC curves and were subjected to Leave-one-out cross validation to assess their robustness. RESULTS Odds ratio (OR) between pRCC and fpAML was statistically significant for patient's gender, tumor attenuation in arterial, venous and excretory phases, tumor's long diameter, short diameter, LSR, type of enhancement, presence of intratumoral vessels and tumor-kidney angle interface. The best predictive model resulted in an area under the curve (AUC) of 0.971 and included gender, tumor-kidney angle interface and venous attenuation with the following equation: Log(p/1 - p) = - 2.834 + 4.052 * gender + - 0.066 * AngleInterface + 0.074 * VenousphaseHU. CONCLUSIONS The combination of patients' gender, tumor to kidney angle interface and venous enhancement helps to distinguish fpAML from pRCC.
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Affiliation(s)
- R Salvador
- Department of Radiology, Hospital Clínic, Villarroel 170, 08036, Barcelona, Spain.
- Faculty of Medicine and Health Sciences, Universitat de Barcelona, Casanova 143, 08036, Barcelona, Spain.
| | - M Sebastià
- Department of Radiology, Hospital Clínic, Villarroel 170, 08036, Barcelona, Spain
| | - G Cárdenas
- Department of Radiology, Hospital Clínico de la Universidad de Chile, Dr. Carlos Lorca Tobar 999, Independencia, Región Metropolitana, Chile
| | - A Páez-Carpio
- Department of Radiology, Hospital Clínic, Villarroel 170, 08036, Barcelona, Spain
| | - B Paño
- Department of Radiology, Hospital Clínic, Villarroel 170, 08036, Barcelona, Spain
| | - M Solé
- Department of Pathology, Hospital Clínic, Villarroel 170, 08036, Barcelona, Spain
| | - C Nicolau
- Department of Radiology, Hospital Clínic, Villarroel 170, 08036, Barcelona, Spain
- Faculty of Medicine and Health Sciences, Universitat de Barcelona, Casanova 143, 08036, Barcelona, Spain
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17
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Value of Quantitative CTTA in Differentiating Malignant From Benign Bosniak III Renal Lesions on CT Images. J Comput Assist Tomogr 2021; 45:528-536. [PMID: 34176873 DOI: 10.1097/rct.0000000000001181] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
OBJECTIVE The aim of this study was to investigate whether computed tomography texture analysis can differentiate malignant from benign Bosniak III renal lesions on computed tomography (CT) images. METHODS This retrospective case-control study included 45 patients/lesions (22 benign and 23 malignant lesions) with Bosniak III renal lesions who underwent CT examination. Axial image slices in the unenhanced phase, corticomedullary phase, and nephrographic phase were selected and delineated manually. Computed tomography texture analysis was performed on each lesion during these 3 phases. Histogram-based, gray-level co-occurrence matrix, and gray-level run-length matrix features were extracted using open-source software and analyzed. In addition, receiver operating characteristic curve was constructed, and the area under the receiver operating characteristic curve (AUC) of each feature was constructed. RESULTS Of the 33 extracted features, 16 features showed significant differences (P < 0.05). Eight features were significantly different between the 2 groups after Holm-Bonferroni correction, including 3 histogram-based, 4 gray-level co-occurrence matrix, and 1 gray-level run-length matrix features (P < 0.01). The texture features resulted in the highest AUC of 0.769 ± 0.074. Renal cell carcinomas were labeled with a higher degree of lesion gray-level disorder and lower lesion homogeneity, and a model incorporating the 3 most discriminative features resulted in an AUC of 0.846 ± 0.058. CONCLUSIONS The results of this study showed that CT texture features were related to malignancy in Bosniak III renal lesions. Computed tomography texture analysis might help in differentiating malignant from benign Bosniak III renal lesions on CT images.
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18
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Tsili AC, Andriotis E, Gkeli MG, Krokidis M, Stasinopoulou M, Varkarakis IM, Moulopoulos LA. The role of imaging in the management of renal masses. Eur J Radiol 2021; 141:109777. [PMID: 34020173 DOI: 10.1016/j.ejrad.2021.109777] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/09/2021] [Accepted: 05/14/2021] [Indexed: 12/26/2022]
Abstract
The wide availability of cross-sectional imaging is responsible for the increased detection of small, usually asymptomatic renal masses. More than 50 % of renal cell carcinomas (RCCs) represent incidental findings on noninvasive imaging. Multimodality imaging, including conventional US, contrast-enhanced US (CEUS), CT and multiparametric MRI (mpMRI) is pivotal in diagnosing and characterizing a renal mass, but also provides information regarding its prognosis, therapeutic management, and follow-up. In this review, imaging data for renal masses that urologists need for accurate treatment planning will be discussed. The role of US, CEUS, CT and mpMRI in the detection and characterization of renal masses, RCC staging and follow-up of surgically treated or untreated localized RCC will be presented. The role of percutaneous image-guided ablation in the management of RCC will be also reviewed.
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Affiliation(s)
- Athina C Tsili
- Department of Clinical Radiology, School of Health Sciences, Faculty of Medicine, University of Ioannina, 45110, Ioannina, Greece.
| | - Efthimios Andriotis
- Department of Newer Imaging Methods of Tomography, General Anti-Cancer Hospital Agios Savvas, 11522, Athens, Greece.
| | - Myrsini G Gkeli
- 1st Department of Radiology, General Anti-Cancer Hospital Agios Savvas, 11522, Athens, Greece.
| | - Miltiadis Krokidis
- 1st Department of Radiology, School of Medicine, National and Kapodistrian University of Athens, Areteion Hospital, 11528, Athens, Greece; Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital Bern University Hospital, University of Bern, 3010, Bern, Switzerland.
| | - Myrsini Stasinopoulou
- Department of Newer Imaging Methods of Tomography, General Anti-Cancer Hospital Agios Savvas, 11522, Athens, Greece.
| | - Ioannis M Varkarakis
- 2nd Department of Urology, National and Kapodistrian University of Athens, Sismanoglio Hospital, 15126, Athens, Greece.
| | - Lia-Angela Moulopoulos
- 1st Department of Radiology, School of Medicine, National and Kapodistrian University of Athens, Areteion Hospital, 11528, Athens, Greece.
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Kang HS, Park JJ. Circularity Index on Contrast-Enhanced Computed Tomography Helps Distinguish Fat-Poor Angiomyolipoma from Renal Cell Carcinoma: Retrospective Analyses of Histologically Proven 257 Small Renal Tumors Less Than 4 cm. Korean J Radiol 2021; 22:735-741. [PMID: 33660463 PMCID: PMC8076823 DOI: 10.3348/kjr.2020.0865] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 09/05/2020] [Accepted: 10/08/2020] [Indexed: 01/12/2023] Open
Abstract
OBJECTIVE To evaluate circularity as a quantitative shape factor of small renal tumor on computed tomography (CT) in differentiating fat-poor angiomyolipoma (AML) from renal cell carcinoma (RCC). MATERIALS AND METHODS In 257 consecutive patients, 257 pathologically confirmed renal tumors (either AML or RCC less than 4 cm), which did not include visible fat on unenhanced CT, were retrospectively evaluated. A radiologist drew the tumor margin to measure the perimeter and area in all the contrast-enhanced axial CT images. In each image, a quantitative shape factor, circularity, was calculated using the following equation: 4 × π × (area ÷ perimeter²). The median circularity (circularity index) was adopted as a representative value in each tumor. The circularity index was compared between fat-poor AML and RCC, and the receiver operating characteristic (ROC) curve analysis was performed. Univariable and multivariable binary logistic regression analysis was performed to determine the independent predictor of fat-poor AML. RESULTS Of the 257 tumors, 26 were AMLs and 231 were RCCs (184 clear cell RCCs, 25 papillary RCCs, and 22 chromophobe RCCs). The mean circularity index of AML was significantly lower than that of RCC (0.86 ± 0.04 vs. 0.93 ± 0.02, p < 0.001). The mean circularity index was not different between the subtypes of RCCs (0.93 ± 0.02, 0.92 ± 0.02, and 0.92 ± 0.02 for clear cell, papillary, and chromophobe RCCs, respectively, p = 0.210). The area under the ROC curve of circularity index was 0.924 for differentiating fat-poor AML from RCC. The sensitivity and specificity were 88.5% and 90.9%, respectively (cut-off, 0.90). Lower circularity index (≤ 0.9) was an independent predictor (odds ratio, 41.0; p < 0.001) for predicting fat-poor AML on multivariable logistic regression analysis. CONCLUSION Circularity is a useful quantitative shape factor of small renal tumor for differentiating fat-poor AML from RCC.
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Affiliation(s)
- Hye Seon Kang
- Department of Radiology, Chungnam National University Hospital, Daejeon, Korea
| | - Jung Jae Park
- Department of Radiology, Chungnam National University Hospital, Daejeon, Korea.,Department of Radiology, Chungnam National University College of Medicine, Daejeon, Korea.
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20
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Ambrosi F, Ricci C, Malvi D, Cillia CD, Ravaioli M, Fiorentino M, Cardillo M, Vasuri F, D'Errico A. Pathological features and outcomes of incidental renal cell carcinoma in candidate solid organ donors. Kidney Res Clin Pract 2020; 39:487-494. [PMID: 32855366 PMCID: PMC7770991 DOI: 10.23876/j.krcp.20.050] [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: 04/06/2020] [Revised: 07/09/2020] [Accepted: 07/13/2020] [Indexed: 11/10/2022] Open
Abstract
Background We report the findings of a single Italian center in the evaluation of renal lesions in deceased donors from 2001 to 2017. In risk evaluation, we applied the current Italian guidelines, which include donors with small (< 4 cm, stage pT1a) renal carcinomas in the category of non-standard donors with a negligible risk of cancer transmission. Methods From the revision of our registries, 2,406 donors were considered in the Emilia Romagna region of Italy; organs were accepted from 1,321 individuals for a total of 3,406 organs. Results The evaluation of donor safety required frozen section analysis for 51 donors, in which a renal suspicious lesion was detected by ultrasound. Thirty-two primary renal tumors were finally diagnosed 26 identified by frozen sections and 6 in discarded kidneys. The 32 tumors included 13 clear cell renal cell carcinomas (RCCs), 6 papillary RCCs, 6 angiomyolipomas, 5 oncocytomas, 1 chromophobe RCC, and 1 papillary adenoma. No cases of tumor transmission were recorded in follow-up of the recipients. Conclusion Donors with small RCCs can be accepted to increase the donor pool. Collaboration in a multidisciplinary setting is fundamental to accurately evaluate donor candidate risk assessment and to improve standardized protocols for surgeons and pathologists.
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Affiliation(s)
- Francesca Ambrosi
- Pathology Unit, S. Orsola-Malpighi Hospital, University of Bologna, Bologna, Italy
| | - Costantino Ricci
- Pathology Unit, S. Orsola-Malpighi Hospital, University of Bologna, Bologna, Italy
| | - Deborah Malvi
- Pathology Unit, S. Orsola-Malpighi Hospital, University of Bologna, Bologna, Italy
| | - Carlo De Cillia
- Emilia-Romagna Transplant Reference Centre, S. Orsola-Malpighi Hospital, University of Bologna, Bologna, Italy
| | - Matteo Ravaioli
- Transplant Surgery Unit, S. Orsola-Malpighi Hospital, University of Bologna, Bologna, Italy
| | | | | | - Francesco Vasuri
- Pathology Unit, S. Orsola-Malpighi Hospital, University of Bologna, Bologna, Italy
| | - Antonia D'Errico
- Pathology Unit, S. Orsola-Malpighi Hospital, University of Bologna, Bologna, Italy
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Zhang Y, Li X, Lv Y, Gu X. Review of Value of CT Texture Analysis and Machine Learning in Differentiating Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma. Tomography 2020; 6:325-332. [PMID: 33364422 PMCID: PMC7744193 DOI: 10.18383/j.tom.2020.00039] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The diagnosis of patients with suspected angiomyolipoma relies on the detection of abundant macroscopic intralesional fat, which is always of no use to differentiate fat-poor angiomyolipoma (fp-AML) from renal cell carcinoma and diagnosis of fp-AML excessively depends on individual experience. Texture analysis was proven to be a potentially useful biomarker for distinguishing between benign and malignant tumors because of its capability of providing objective and quantitative assessment of lesions by analyzing features that are not visible to the human eye. This review aimed to summarize the literature on the use of texture analysis to diagnose patients with fat-poor angiomyolipoma vs those with renal cell carcinoma and to evaluate its current application, limitations, and future challenges in order to avoid unnecessary surgical resection.
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Affiliation(s)
- Yuhan Zhang
- Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, China; and
| | - Xu Li
- Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, China; and
| | - Yang Lv
- Department of Anesthesia, The Second Hospital of Jilin University, Changchun, China
| | - Xinquan Gu
- Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, China; and
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22
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Wang ZJ, Nikolaidis P, Khatri G, Dogra VS, Ganeshan D, Goldfarb S, Gore JL, Gupta RT, Hartman RP, Heilbrun ME, Lyshchik A, Purysko AS, Savage SJ, Smith AD, Wolfman DJ, Wong-You-Cheong JJ, Lockhart ME. ACR Appropriateness Criteria® Indeterminate Renal Mass. J Am Coll Radiol 2020; 17:S415-S428. [PMID: 33153554 DOI: 10.1016/j.jacr.2020.09.010] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 09/01/2020] [Indexed: 12/15/2022]
Abstract
Renal masses are increasingly detected in asymptomatic individuals as incidental findings. CT and MRI with intravenous contrast and a dedicated multiphase protocol are the mainstays of evaluation for indeterminate renal masses. A single-phase postcontrast dual-energy CT can be useful when a dedicated multiphase renal protocol CT is not available. Contrast-enhanced ultrasound with microbubble agents is a useful alternative for characterizing renal masses, especially for patients in whom iodinated CT contrast or gadolinium-based MRI contrast is contraindicated. 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)
- Zhen J Wang
- University of California San Francisco School of Medicine, San Francisco, California.
| | | | - Gaurav Khatri
- Panel Vice-Chair, UT Southwestern Medical Center, Dallas, Texas
| | - Vikram S Dogra
- University of Rochester Medical Center, Rochester, New York
| | | | - Stanley Goldfarb
- University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania; American Society of Nephrology
| | - John L Gore
- University of Washington, Seattle, Washington; American Urological Association
| | - Rajan T Gupta
- Duke University Medical Center, Durham, North Carolina
| | | | | | - Andrej Lyshchik
- Thomas Jefferson University Hospital, Philadelphia, Pennsylvania
| | | | - Stephen J Savage
- Medical University of South Carolina, Charleston, South Carolina; American Urological Association
| | - Andrew D Smith
- University of Alabama at Birmingham, Birmingham, Alabama
| | - Darcy J Wolfman
- Johns Hopkins University School of Medicine, Washington, District of Columbia
| | | | - Mark E Lockhart
- Specialty Chair, University of Alabama at Birmingham, Birmingham, Alabama
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23
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Erdim C, Yardimci AH, Bektas CT, Kocak B, Koca SB, Demir H, Kilickesmez O. Prediction of Benign and Malignant Solid Renal Masses: Machine Learning-Based CT Texture Analysis. Acad Radiol 2020; 27:1422-1429. [PMID: 32014404 DOI: 10.1016/j.acra.2019.12.015] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Revised: 12/09/2019] [Accepted: 12/16/2019] [Indexed: 02/06/2023]
Abstract
RATIONALE AND OBJECTIVES This study aimed to investigate whether benign and malignant renal solid masses could be distinguished through machine learning (ML)-based computed tomography (CT) texture analysis. MATERIALS AND METHODS Seventy-nine patients with 84 solid renal masses (21 benign; 63 malignant) from a single center were included in this retrospective study. Malignant masses included common renal cell carcinoma (RCC) subtypes: clear cell RCC, papillary cell RCC, and chromophobe RCC. Benign masses are represented by oncocytomas and fat-poor angiomyolipomas. Following preprocessing steps, a total of 271 texture features were extracted from unenhanced and contrast-enhanced CT images. Dimension reduction was done with a reliability analysis and then with a feature selection algorithm. A nested-approach was used for feature selection, model optimization, and validation. Eight ML algorithms were used for the classifications: decision tree, locally weighted learning, k-nearest neighbors, naive Bayes, logistic regression, support vector machine, neural network, and random forest. RESULTS The number of features with good reproducibility was 198 for unenhanced CT and 244 for contrast-enhanced CT. Random forest algorithm demonstrated the best predictive performance using five selected contrast-enhanced CT texture features. The accuracy and area under the curve metrics were 90.5% and 0.915, respectively. Having eliminated the highly collinear features from the analysis, the accuracy and area under the curve values slightly increased to 91.7% and 0.916, respectively. CONCLUSION ML-based contrast-enhanced CT texture analysis might be a potential method for distinguishing benign and malignant solid renal masses with satisfactory performance.
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Affiliation(s)
- Cagri Erdim
- Department of Radiology, Sultangazi Haseki Training and Research Hospital, Sultangazi, Istanbul, Turkey
| | - Aytul Hande Yardimci
- Department of Radiology, Istanbul Training and Research Hospital, Samatya, Istanbul 34098, Turkey
| | - Ceyda Turan Bektas
- Department of Radiology, Istanbul Training and Research Hospital, Samatya, Istanbul 34098, Turkey
| | - Burak Kocak
- Department of Radiology, Istanbul Training and Research Hospital, Samatya, Istanbul 34098, Turkey.
| | - Sevim Baykal Koca
- Department of Pathology, Istanbul Training and Research Hospital, Samatya, Istanbul, Turkey
| | - Hale Demir
- Department of Pathology, Amasya University School of Medicine, Amasya, Turkey
| | - Ozgur Kilickesmez
- Department of Radiology, Istanbul Training and Research Hospital, Samatya, Istanbul 34098, Turkey
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24
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Can whole-tumor radiomics-based CT analysis better differentiate fat-poor angiomyolipoma from clear cell renal cell caricinoma: compared with conventional CT analysis? Abdom Radiol (NY) 2020; 45:2500-2507. [PMID: 31980867 DOI: 10.1007/s00261-020-02414-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
PURPOSE This study aimed to discriminate fat-poor angiomyolipoma (fp-AML) from clear cell renal cell carcinoma (ccRCC) by constructing radiomics-based logistic classifiers in comparison with conventional computed tomography (CT) analysis at three CT phases. MATERIALS AND METHODS Twenty-two fp-AML patients and 62 ccRCC patients who were pathologically identified were enrolled in this study, and underwent three-phase (unenhanced phase, UP; corticomedullary phase, CMP; nephrographic phase, NP) CT examinations. Whole-tumor regions of interest (ROIs) were contoured in ITK software by two radiologists. Radiomic features were dimensionally reduced by means of ANOVA + MW, correlation analysis, and LASSO. Four radiomics logistic classifiers including the UP group, CMP group, NP group, and sum group were built, and the radiomic scores (rad-scores) were calculated. After collecting the qualitative and quantitative conventional CT characteristics, the conventional CT analysis logistic classifier and the radiomics-based logistic classifier were constructed. The receiver operating characteristic curve (ROC) was constructed to evaluate the validity of each classifier. RESULTS The area under curve (AUC) of the conventional CT analysis logistic classifier including angular interface, cyst degeneration, and pseudocapsule was 0.935 (95% CI 0.860-0.977). Regarding logistic classifiers for radiomics analysis, the AUCs of the UP group were 0.950 (95% CI 0.895-1.000) and 0.917 (95% CI 0.801-1.000) in the training set and testing set, respectively, which were higher than those of the CMP and NP groups. The AUCs of the sum group were observed to be the highest. The top three selected features for the UP and sum groups belonged to GLCM parameters and histogram parameters. The radiomics-based logistic classifier encompassed cyst degeneration, pseudocapsule, and sum rad-score, and the AUC of the model was 0.988 (95% CI 0.935-1.000). CONCLUSION Whole-tumor radiomics-based CT analysis is superior to conventional CT analysis in the differentiation of fp-AML from ccRCC. Cyst degeneration, pseudocapsule, and sum rad-score are the most significant factors. The radiomics analysis of the UP group shows a higher AUC than that of the CMP and NP groups.
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25
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Abstract
Radiomics allows for high throughput extraction of quantitative data from images. This is an area of active research as groups try to capture and quantify imaging parameters and convert these into descriptive phenotypes of organs or tumors. Texture analysis is one radiomics tool that extracts information about heterogeneity within a given region of interest. This is used with or without associated machine learning classifiers or a deep learning approach is applied to similar types of data. These tools have shown utility in characterizing renal masses, renal cell carcinoma, and assessing response to targeted therapeutic agents in metastatic renal cell carcinoma.
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Affiliation(s)
- Meghan G Lubner
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Sciences Center, 600 Highland Avenue, Madison, WI 53792, USA.
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26
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Yang G, Gong A, Nie P, Yan L, Miao W, Zhao Y, Wu J, Cui J, Jia Y, Wang Z. Contrast-Enhanced CT Texture Analysis for Distinguishing Fat-Poor Renal Angiomyolipoma From Chromophobe Renal Cell Carcinoma. Mol Imaging 2020; 18:1536012119883161. [PMID: 31625454 PMCID: PMC6801892 DOI: 10.1177/1536012119883161] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Objective: To evaluate the value of 2-dimensional (2D) and 3-dimensional (3D) computed tomography
texture analysis (CTTA) models in distinguishing fat-poor angiomyolipoma (fpAML) from
chromophobe renal cell carcinoma (chRCC). Methods: We retrospectively enrolled 32 fpAMLs and 24 chRCCs. Texture features were extracted
from 2D and 3D regions of interest in triphasic CT images. The 2D and 3D CTTA models
were constructed with the least absolute shrinkage and selection operator algorithm and
texture scores were calculated. The diagnostic performance of the 2D and 3D CTTA models
was evaluated with respect to calibration, discrimination, and clinical usefulness. Results: Of the 177 and 183 texture features extracted from 2D and 3D regions of interest,
respectively, 5 2D features and 8 3D features were selected to build 2D and 3D CTTA
models. The 2D CTTA model (area under the curve [AUC], 0.811; 95% confidence interval
[CI], 0.695-0.927) and the 3D CTTA model (AUC, 0.915; 95% CI, 0.838-0.993) showed good
discrimination and calibration (P > .05). There was no significant
difference in AUC between the 2 models (P = .093). Decision curve
analysis showed the 3D model outperformed the 2D model in terms of clinical
usefulness. Conclusions: The CTTA models based on contrast-enhanced CT images had a high value in
differentiating fpAML from chRCC.
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Affiliation(s)
- Guangjie Yang
- PET-CT Center, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Aidi Gong
- PET-CT Center, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Pei Nie
- Radiology Department, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Lei Yan
- PET-CT Center, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Wenjie Miao
- PET-CT Center, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Yujun Zhao
- PET-CT Center, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Jie Wu
- Pathology Department, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Jingjing Cui
- Huiying Medical Technology Co, Ltd, Beijing, China
| | - Yan Jia
- Huiying Medical Technology Co, Ltd, Beijing, China
| | - Zhenguang Wang
- PET-CT Center, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
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Hu J, Liu W, Xie S, Li M, Wang K, Li W. Abdominal perivascular epithelioid cell tumor (PEComa) without visible fat: a clinicopathologic and radiological analysis of 16 cases. Radiol Med 2020; 126:189-199. [PMID: 32562157 DOI: 10.1007/s11547-020-01241-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 06/07/2020] [Indexed: 11/25/2022]
Abstract
OBJECTIVE To retrospectively review the clinicopathological features and computed tomography (CT) and magnetic resonance imaging (MRI) findings of abdominal perivascular epithelioid cell tumor without visible fat (PEComawvf). MATERIALS AND METHODS Sixteen patients with surgically and pathologically confirmed perivascular epithelioid cell tumor without visible fat were enrolled. Their clinicopathological data and imaging findings were retrospectively reviewed. The CT and MRI features, including location, size, shape, margin, density, calcification, cystic necrosis and enhancement pattern, were analyzed. RESULTS There were 4 males and 12 females (median age, 46 years; range, 21-65 years) in this study. All 16 patients were diagnostic asymptomatic unenhanced CT or MRI and revealed a well-defined (n = 13), oval (n = 10), mass with heterogeneous (n = 6) or homogeneous density/signal intensity (n = 7), calcification and hemorrhage was no found in any cases. On enhanced CT/MRI, markedly enhancement patterns (n = 14) were observed. The "peripheral enhancement" sign was observed in 13 cases. One in 16 cases recurrence was discovered during the follow-up period. CONCLUSIONS Dynamic CT, MRI and pathology of PEComawvf had some characteristics of non-aggressive pattern of performance, and MRI would provide beneficial detection of microscopic fat. Enhanced imaging showed PEComawvf is characterized by a "peripheral enhancement" with a marked enhancement pattern. Knowing these characteristics could contribute to improving the understanding abdominal PEComawvf and related palliative care.
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Affiliation(s)
- Jiaxi Hu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China
| | - Wenguang Liu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China
| | - Simin Xie
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China
| | - Mengsi Li
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China
| | - Kangtao Wang
- Department of Gastrointestinal Surgery, Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China
| | - Wenzheng Li
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China.
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Ren S, Zhao R, Zhang J, Guo K, Gu X, Duan S, Wang Z, Chen R. Diagnostic accuracy of unenhanced CT texture analysis to differentiate mass-forming pancreatitis from pancreatic ductal adenocarcinoma. Abdom Radiol (NY) 2020; 45:1524-1533. [PMID: 32279101 DOI: 10.1007/s00261-020-02506-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
PURPOSE To investigate the value of texture analysis on unenhanced computed tomography (CT) to potentially differentiate mass-forming pancreatitis (MFP) from pancreatic ductal adenocarcinoma (PDAC). METHODS A retrospective study consisting of 109 patients (30 MFP patients vs 79 PDAC patients) who underwent preoperative unenhanced CT between January 2012 and December 2017 was performed. Synthetic minority oversampling technique (SMOTE) algorithm was adopted to reconstruct and balance MFP and PDAC samples. A total of 396 radiomic features were extracted from unenhanced CT images. Mann-Whitney U test and minimum redundancy maximum relevance (MRMR) methods were used for the purpose of dimension reduction. Predictive models were constructed using random forest (RF) method, and were validated using leave group out cross-validation (LGOCV) method. Diagnostic performance of the predictive model, including sensitivity, specificity, accuracy, positive predicting value (PPV), and negative predicting value (NPV), was recorded. RESULTS We applied 200% of SMOTE to MFP and PDAC patients, resulting in 90 MFP patients compared with 120 PDAC patients. Dimension reduction steps yielded 30 radiomic features using Mann-Whitney U test and MRMR methods. Ten radiomic features were retained using RF method. Four most predictive parameters, including GreyLevelNonuniformity_angle90_offset1, VoxelValueSum, HaraVariance, and ClusterProminence_AllDirection_offset1_SD, were used to generate the predictive model with preferable 92.2% sensitivity, 94.2% specificity, 93.3% accuracy, 92.2% PPV, and 94.2% NPV. Finally, in LGOCV analysis, a high pooled mean sensitivity, specificity, and accuracy (82.6%, 80.8%, and 82.1%, respectively) indicate a relatively reliable and stable predictive model. CONCLUSIONS Unenhanced CT texture analysis can be a promising noninvasive method in discriminating MFP from PDAC.
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Affiliation(s)
- Shuai Ren
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Nanjing, 210029, Jiangsu Province, China
- The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu Province, China
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Rui Zhao
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Nanjing, 210029, Jiangsu Province, China
| | - Jingjing Zhang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Nanjing, 210029, Jiangsu Province, China
| | - Kai Guo
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Nanjing, 210029, Jiangsu Province, China
| | - Xiaoyu Gu
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Nanjing, 210029, Jiangsu Province, China
| | | | - Zhongqiu Wang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Nanjing, 210029, Jiangsu Province, China.
| | - Rong Chen
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
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Rundo L, Beer L, Ursprung S, Martin-Gonzalez P, Markowetz F, Brenton JD, Crispin-Ortuzar M, Sala E, Woitek R. Tissue-specific and interpretable sub-segmentation of whole tumour burden on CT images by unsupervised fuzzy clustering. Comput Biol Med 2020; 120:103751. [PMID: 32421652 PMCID: PMC7248575 DOI: 10.1016/j.compbiomed.2020.103751] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 04/03/2020] [Accepted: 04/05/2020] [Indexed: 12/18/2022]
Abstract
BACKGROUND Cancer typically exhibits genotypic and phenotypic heterogeneity, which can have prognostic significance and influence therapy response. Computed Tomography (CT)-based radiomic approaches calculate quantitative features of tumour heterogeneity at a mesoscopic level, regardless of macroscopic areas of hypo-dense (i.e., cystic/necrotic), hyper-dense (i.e., calcified), or intermediately dense (i.e., soft tissue) portions. METHOD With the goal of achieving the automated sub-segmentation of these three tissue types, we present here a two-stage computational framework based on unsupervised Fuzzy C-Means Clustering (FCM) techniques. No existing approach has specifically addressed this task so far. Our tissue-specific image sub-segmentation was tested on ovarian cancer (pelvic/ovarian and omental disease) and renal cell carcinoma CT datasets using both overlap-based and distance-based metrics for evaluation. RESULTS On all tested sub-segmentation tasks, our two-stage segmentation approach outperformed conventional segmentation techniques: fixed multi-thresholding, the Otsu method, and automatic cluster number selection heuristics for the K-means clustering algorithm. In addition, experiments showed that the integration of the spatial information into the FCM algorithm generally achieves more accurate segmentation results, whilst the kernelised FCM versions are not beneficial. The best spatial FCM configuration achieved average Dice similarity coefficient values starting from 81.94±4.76 and 83.43±3.81 for hyper-dense and hypo-dense components, respectively, for the investigated sub-segmentation tasks. CONCLUSIONS The proposed intelligent framework could be readily integrated into clinical research environments and provides robust tools for future radiomic biomarker validation.
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Affiliation(s)
- Leonardo Rundo
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK.
| | - Lucian Beer
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna 1090, Austria.
| | - Stephan Ursprung
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK.
| | - Paula Martin-Gonzalez
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK.
| | - Florian Markowetz
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK.
| | - James D Brenton
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK.
| | - Mireia Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK.
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK.
| | - Ramona Woitek
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna 1090, Austria.
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Differentiation of Small (≤ 4 cm) Renal Masses on Multiphase Contrast-Enhanced CT by Deep Learning. AJR Am J Roentgenol 2020; 214:605-612. [PMID: 31913072 DOI: 10.2214/ajr.19.22074] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
OBJECTIVE. This study evaluated the utility of a deep learning method for determining whether a small (≤ 4 cm) solid renal mass was benign or malignant on multiphase contrast-enhanced CT. MATERIALS AND METHODS. This retrospective study included 1807 image sets from 168 pathologically diagnosed small (≤ 4 cm) solid renal masses with four CT phases (unenhanced, corticomedullary, nephrogenic, and excretory) in 159 patients between 2012 and 2016. Masses were classified as malignant (n = 136) or benign (n = 32). The dataset was randomly divided into five subsets: four were used for augmentation and supervised training (48,832 images), and one was used for testing (281 images). The Inception-v3 architecture convolutional neural network (CNN) model was used. The AUC for malignancy and accuracy at optimal cutoff values of output data were evaluated in six different CNN models. Multivariate logistic regression analysis was also performed. RESULTS. Malignant and benign lesions showed no significant difference of size. The AUC value of corticomedullary phase was higher than that of other phases (corticomedullary vs excretory, p = 0.022). The highest accuracy (88%) was achieved in corticomedullary phase images. Multivariate analysis revealed that the CNN model of corticomedullary phase was a significant predictor for malignancy compared with other CNN models, age, sex, and lesion size. CONCLUSION. A deep learning method with a CNN allowed acceptable differentiation of small (≤ 4 cm) solid renal masses in dynamic CT images, especially in the corticomedullary image model.
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Mohammad Ahmad MI, Sabr M, Roshy E. Assessment of apparent diffusion coefficient value as prognostic factor for renal cell carcinoma aggressiveness. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2019. [DOI: 10.1186/s43055-019-0038-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Assurance of prognostic elements is important for the management of renal cell carcinoma (RCC). Our goal was to check the relation between apparent diffusion coefficient (ADC) values and parameters predicting prognosis of RCC. Fifty pathologically confirmed RCC underwent diffusion-weighted (DW) MRI. ADC values were calculated using b factor (800 s/mm2). The correlation between ADC values and tumor size, cystic/necrotic feature, growth pattern, unenhanced T1, histological grade, clinical stage, and distant metastasis were analyzed.
Results
The optimal ADC threshold for prognosis of RCC appeared to be 1.4 × 10−3 mm2/s. There was a significant inverse correlation between ADC values and growth pattern (R = − 0, P = 0.05), unenhanced T1(R = − 0.41, P = 0.01), cystic/necrotic feature (R = − 0.4, P = 0.01), histological grade (R = − 0.37, P = 0.02), clinical stage (r = − 0.4, P = 0.01), and distant metastasis (R = − 0.33, P = 0.04), and significant linear correlation with tumor size (R = 0.39, P < 0.02).
Conclusion
The performance of ADC value as a newly proposed prognostic parameter follows with the degree of tumor differentiation and that may recognize extremely aggressive RCC. RCC with low ADC values should be inspected extensively for the risk of high pathological grade, high clinical stage, and distant metastasis.
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Cui EM, Lin F, Li Q, Li RG, Chen XM, Liu ZS, Long WS. Differentiation of renal angiomyolipoma without visible fat from renal cell carcinoma by machine learning based on whole-tumor computed tomography texture features. Acta Radiol 2019; 60:1543-1552. [PMID: 30799634 DOI: 10.1177/0284185119830282] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Affiliation(s)
- En-Ming Cui
- Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun Yat-Sen University, Jiangmen, PR China
| | - Fan Lin
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen, PR China
| | - Qing Li
- Department of Pathology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun Yat-Sen University, Jiangmen, PR China
| | - Rong-Gang Li
- Department of Pathology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun Yat-Sen University, Jiangmen, PR China
| | - Xiang-Meng Chen
- Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun Yat-Sen University, Jiangmen, PR China
| | - Zhuang-Sheng Liu
- Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun Yat-Sen University, Jiangmen, PR China
| | - Wan-Sheng Long
- Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun Yat-Sen University, Jiangmen, PR China
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Deng Y, Soule E, Cui E, Samuel A, Shah S, Lall C, Sundaram C, Sandrasegaran K. Usefulness of CT texture analysis in differentiating benign and malignant renal tumours. Clin Radiol 2019; 75:108-115. [PMID: 31668402 DOI: 10.1016/j.crad.2019.09.131] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 09/12/2019] [Indexed: 12/22/2022]
Abstract
AIM To elucidate visually imperceptible differences between benign and malignant renal tumours using computed tomography texture analysis (CTTA) using filtration histogram based parameters. MATERIALS AND METHODS A retrospective study was performed by texture analysis of pretreatment contrast-enhanced CT examinations in 354 histopathologically confirmed renal cell carcinomas (RCCs) and 147 benign renal tumours. A region-of-interest was drawn encompassing the largest cross-section of the tumour on venous phase axial CT. CTTA features of entropy, kurtosis, mean positive pixel density, and skewness at different spatial filters were calculated and compared in an attempt to differentiate benign lesions from malignancy. RESULTS Entropy with fine spatial filter was significantly higher in RCC than benign renal tumours (p=0.022). Entropy with fine and medium filters was higher in RCC than lipid-poor angiomyolipoma (p=0.050 and 0.052, respectively). Entropy >5.62 had high specificity of 85.7%, but low sensitivity of 31.3%, respectively, for predicting RCC. CONCLUSIONS Differences in entropy were helpful in differentiating RCC from lipid-poor angiomyolipoma, and chromophobe RCC from oncocytoma. This technique may be useful to differentiate lesions that appear equivocal on visual assessment or alter management in poor surgical candidates.
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Affiliation(s)
- Y Deng
- Department of Radiology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - E Soule
- Department of Radiology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - E Cui
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun YAT-SEN University, Jiangmen, China
| | - A Samuel
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - S Shah
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - C Lall
- Department of Radiology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - C Sundaram
- Department of Urology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - K Sandrasegaran
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Radiology, Mayo Clinic, Phoenix, AZ, USA.
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Radiomics of Renal Masses: Systematic Review of Reproducibility and Validation Strategies. AJR Am J Roentgenol 2019; 214:129-136. [PMID: 31613661 DOI: 10.2214/ajr.19.21709] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
OBJECTIVE. The purpose of this study was to systematically review the radiomics literature on renal mass characterization in terms of reproducibility and validation strategies. MATERIALS AND METHODS. With use of PubMed and Google Scholar, a systematic literature search was performed to identify original research papers assessing the value of radiomics in characterization of renal masses. The data items were extracted on the basis of three main categories: baseline study characteristics, radiomic feature reproducibility strategies, and statistical model validation strategies. RESULTS. After screening and application of the eligibility criteria, a total of 41 papers were included in the study. Almost one-half of the papers (19 [46%]) presented at least one reproducibility analysis. Segmentation variability (18 [44%]) was the main theme of the analyses, outnumbering image acquisition or processing (3 [7%]). No single paper considered slice selection bias. The most commonly used statistical tool for analysis was intraclass correlation coefficient (14 of 19 [74%]), with no consensus on the threshold or cutoff values. Approximately one-half of the papers (22 [54%]) used at least one validation method, with a predominance of internal validation techniques (20 [49%]). The most frequently used internal validation technique was k-fold cross-validation (12 [29%]). Independent or external validation was used in only three papers (7%). CONCLUSION. Workflow characteristics described in the radiomics literature about renal mass characterization are heterogeneous. To bring radiomics from a mere research area to clinical use, the field needs many more papers that consider the reproducibility of radiomic features and include independent or external validation in their workflow.
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Nie P, Yang G, Wang Z, Yan L, Miao W, Hao D, Wu J, Zhao Y, Gong A, Cui J, Jia Y, Niu H. A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma. Eur Radiol 2019; 30:1274-1284. [PMID: 31506816 DOI: 10.1007/s00330-019-06427-x] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 08/05/2019] [Accepted: 08/14/2019] [Indexed: 02/07/2023]
Abstract
OBJECTIVES To develop and validate a radiomics nomogram for preoperative differentiating renal angiomyolipoma without visible fat (AML.wovf) from homogeneous clear cell renal cell carcinoma (hm-ccRCC). METHODS Ninety-nine patients with AML.wovf (n = 36) and hm-ccRCC (n = 63) were divided into a training set (n = 80) and a validation set (n = 19). Radiomics features were extracted from corticomedullary phase and nephrographic phase CT images. A radiomics signature was constructed and a radiomics score (Rad-score) was calculated. Demographics and CT findings were assessed to build a clinical factors model. Combined with the Rad-score and independent clinical factors, a radiomics nomogram was constructed. Nomogram performance was assessed with respect to calibration, discrimination, and clinical usefulness. RESULTS Fourteen features were used to build the radiomics signature. The radiomics signature showed good discrimination in the training set (AUC [area under the curve], 0.879; 95%; confidence interval [CI], 0.793-0.966) and the validation set (AUC, 0.846; 95% CI, 0.643-1.000). The radiomics nomogram showed good calibration and discrimination in the training set (AUC, 0.896; 95% CI, 0.810-0.983) and the validation set (AUC, 0.949; 95% CI, 0.856-1.000) and showed better discrimination capability (p < 0.05) compared with the clinical factor model (AUC, 0.788; 95% CI, 0.683-0.893) in the training set. Decision curve analysis demonstrated the nomogram outperformed the clinical factors model and radiomics signature in terms of clinical usefulness. CONCLUSIONS The CT-based radiomics nomogram, a noninvasive preoperative prediction tool that incorporates the Rad-score and clinical factors, shows favorable predictive efficacy for differentiating AML.wovf from hm-ccRCC, which might assist clinicians in tailoring precise therapy. KEY POINTS • Differential diagnosis between AML.wovf and hm-ccRCC is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of AML.wovf from hm-ccRCC with improved diagnostic efficacy. • The CT-based radiomics nomogram might spare unnecessary surgery for AML.wovf.
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Affiliation(s)
- Pei Nie
- Radiology Department, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Guangjie Yang
- PET-CT Center, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China
| | - Zhenguang Wang
- PET-CT Center, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China.
| | - Lei Yan
- PET-CT Center, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China
| | - Wenjie Miao
- PET-CT Center, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China
| | - Dapeng Hao
- Radiology Department, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Jie Wu
- Pathology Department, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Yujun Zhao
- PET-CT Center, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China
| | - Aidi Gong
- PET-CT Center, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China
| | - Jingjing Cui
- Huiying Medical Technology Co., Ltd, Beijing, China
| | - Yan Jia
- Huiying Medical Technology Co., Ltd, Beijing, China
| | - Haitao Niu
- Urology Department, The Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Qingdao, 266005, Shandong, China.
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Update on Indications for Percutaneous Renal Mass Biopsy in the Era of Advanced CT and MRI. AJR Am J Roentgenol 2019; 212:1187-1196. [PMID: 30917018 DOI: 10.2214/ajr.19.21093] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
OBJECTIVE. The objective of this article is to review the burgeoning role of percutaneous renal mass biopsy (RMB). CONCLUSION. Percutaneous RMB is safe, accurate, and indicated for an expanded list of clinical scenarios. The chief scenarios among them are to prevent treatment of benign masses and help select patients for active surveillance (AS). Imaging characterization of renal masses has improved; however, management decisions often depend on a histologic diagnosis and an assessment of biologic behavior of renal cancers, both of which are currently best achieved with RMB.
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Deng Y, Soule E, Samuel A, Shah S, Cui E, Asare-Sawiri M, Sundaram C, Lall C, Sandrasegaran K. CT texture analysis in the differentiation of major renal cell carcinoma subtypes and correlation with Fuhrman grade. Eur Radiol 2019; 29:6922-6929. [PMID: 31127316 DOI: 10.1007/s00330-019-06260-2] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 04/01/2019] [Accepted: 04/30/2019] [Indexed: 12/20/2022]
Abstract
OBJECTIVE CT texture analysis (CTTA) using filtration-histogram-based parameters has been associated with tumor biologic correlates such as glucose metabolism, hypoxia, and tumor angiogenesis. We investigated the utility of these parameters for differentiation of clear cell from papillary renal cancers and prediction of Fuhrman grade. METHODS A retrospective study was performed by applying CTTA to pretreatment contrast-enhanced CT scans in 290 patients with 298 histopathologically confirmed renal cell cancers of clear cell and papillary types. The largest cross section of the tumor on portal venous phase axial CT was chosen to draw a region of interest. CTTA comprised of an initial filtration step to extract features of different sizes (fine, medium, coarse spatial scales) followed by texture quantification using histogram analysis. RESULTS A significant increase in entropy with fine and medium spatial filters was demonstrated in clear cell RCC (p = 0.047 and 0.033, respectively). Area under the ROC curve of entropy at fine and medium spatial filters was 0.804 and 0.841, respectively. An increased entropy value at coarse filter correlated with high Fuhrman grade tumors (p = 0.01). The other texture parameters were not found to be useful. CONCLUSION Entropy, which is a quantitative measure of heterogeneity, is increased in clear cell renal cancers. High entropy is also associated with high-grade renal cancers. This parameter may be considered as a supplementary marker when determining aggressiveness of therapy. KEY POINTS • CT texture analysis is easy to perform on contrast-enhanced CT. • CT texture analysis may help to separate different types of renal cancers. • CT texture analysis may enhance individualized treatment of renal cancers.
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Affiliation(s)
- Yu Deng
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Erik Soule
- Department of Radiology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - Aster Samuel
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sakhi Shah
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Enming Cui
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun YAT-SEN University, Jiangmen, China
| | - Michael Asare-Sawiri
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Oncology, Hope Regional Cancer Center, Panama, FL, USA
| | - Chandru Sundaram
- Department of Urology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Chandana Lall
- Department of Radiology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - Kumaresan Sandrasegaran
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA.
- Department of Radiology, Mayo Clinic, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA.
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Reliability of Single-Slice-Based 2D CT Texture Analysis of Renal Masses: Influence of Intra- and Interobserver Manual Segmentation Variability on Radiomic Feature Reproducibility. AJR Am J Roentgenol 2019; 213:377-383. [PMID: 31063427 DOI: 10.2214/ajr.19.21212] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
OBJECTIVE. The objective of our study was to investigate the potential influence of intra- and interobserver manual segmentation variability on the reliability of single-slice-based 2D CT texture analysis of renal masses. MATERIALS AND METHODS. For this retrospective study, 30 patients with clear cell renal cell carcinoma were included from a public database. For intra- and interobserver analyses, three radiologists with varying degrees of experience segmented the tumors from unenhanced CT and corticomedullary phase contrast-enhanced CT (CECT) in different sessions. Each radiologist was blind to the image slices selected by other radiologists and him- or herself in the previous session. A total of 744 texture features were extracted from original, filtered, and transformed images. The intraclass correlation coefficient was used for reliability analysis. RESULTS. In the intraobserver analysis, the rates of features with good to excellent reliability were 84.4-92.2% for unenhanced CT and 85.5-93.1% for CECT. Considering the mean rates of unenhanced CT and CECT, having high experience resulted in better reliability rates in terms of the intraobserver analysis. In the interobserver analysis, the rates were 76.7% for unenhanced CT and 84.9% for CECT. The gray-level cooccurrence matrix and first-order feature groups yielded higher good to excellent reliability rates on both unenhanced CT and CECT. Filtered and transformed images resulted in more features with good to excellent reliability than the original images did on both unenhanced CT and CECT. CONCLUSION. Single-slice-based 2D CT texture analysis of renal masses is sensitive to intra- and interobserver manual segmentation variability. Therefore, it may lead to nonreproducible results in radiomic analysis unless a reliability analysis is considered in the workflow.
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Whittle R, Peat G, Belcher J, Collins GS, Riley RD. Measurement error and timing of predictor values for multivariable risk prediction models are poorly reported. J Clin Epidemiol 2018; 102:38-49. [PMID: 29782997 DOI: 10.1016/j.jclinepi.2018.05.008] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Revised: 04/26/2018] [Accepted: 05/14/2018] [Indexed: 10/16/2022]
Abstract
OBJECTIVE Measurement error in predictor variables may threaten the validity of clinical prediction models. We sought to evaluate the possible extent of the problem. A secondary objective was to examine whether predictors are measured at the intended moment of model use. METHODS A systematic search of Medline was used to identify a sample of articles reporting the development of a clinical prediction model published in 2015. After screening according to a predefined inclusion criteria, information on predictors, strategies to control for measurement error, and intended moment of model use were extracted. Susceptibility to measurement error for each predictor was classified into low and high risks. RESULTS Thirty-three studies were reviewed, including 151 different predictors in the final prediction models. Fifty-one (33.7%) predictors were categorized as high risk of error; however, this was not accounted for in the model development. Only 8 (24.2%) studies explicitly stated the intended moment of model use and when the predictors were measured. CONCLUSION Reporting of measurement error and intended moment of model use is poor in prediction model studies. There is a need to identify circumstances where ignoring measurement error in prediction models is consequential and whether accounting for the error will improve the predictions.
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Affiliation(s)
- Rebecca Whittle
- Centre for Prognosis Research, Arthritis Research UK Primary Care Centre, Research Institute for Primary Care & Health Sciences, Keele University, Keele, Staffordshire, UK.
| | - George Peat
- Centre for Prognosis Research, Arthritis Research UK Primary Care Centre, Research Institute for Primary Care & Health Sciences, Keele University, Keele, Staffordshire, UK
| | - John Belcher
- Centre for Prognosis Research, Arthritis Research UK Primary Care Centre, Research Institute for Primary Care & Health Sciences, Keele University, Keele, Staffordshire, UK
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK
| | - Richard D Riley
- Centre for Prognosis Research, Arthritis Research UK Primary Care Centre, Research Institute for Primary Care & Health Sciences, Keele University, Keele, Staffordshire, UK
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Sandrasegaran K, Lin Y, Asare-Sawiri M, Taiyini T, Tann M. CT texture analysis of pancreatic cancer. Eur Radiol 2018; 29:1067-1073. [PMID: 30116961 DOI: 10.1007/s00330-018-5662-1] [Citation(s) in RCA: 106] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 06/15/2018] [Accepted: 07/13/2018] [Indexed: 02/07/2023]
Abstract
OBJECTIVES We investigated the value of CT texture analysis (CTTA) in predicting prognosis of unresectable pancreatic cancer. METHODS Sixty patients with unresectable pancreatic cancers at presentation were enrolled for post-processing with CTTA using commercially available software (TexRAD Ltd, Cambridge, UK). The largest cross-section of the tumour on axial CT was chosen to draw a region-of-interest. CTTA parameters (mean value of positive pixels (MPP), kurtosis, entropy, skewness), arterial and venous invasion, metastatic disease and tumour size were correlated with overall and progression-free survivals. RESULTS The median overall and progression-free survivals of cohort were 13.3 and 7.8 months, respectively. On multivariate Cox proportional hazard regression analysis, presence of metastatic disease at presentation had the highest association with overall survival (p = 0.003-0.05) and progression-free survival (p < 0.001 to p = 0.004). MPP at medium spatial filter was significantly associated with poor overall survival (p = 0.04). On Kaplan-Meier survival analysis of CTTA parameters at medium spatial filter, MPP of more than 31.625 and kurtosis of more than 0.565 had significantly worse overall survival (p = 0.036 and 0.028, respectively). CONCLUSIONS CTTA features were significantly associated with overall survival in pancreas cancer, particularly in patients with non-metastatic, locally advanced disease. KEY POINTS • CT texture analysis is easy to perform on contrast-enhanced CT. • CT texture analysis can determine prognosis in patients with unresectable pancreas cancer. • The best predictors of poor prognosis were high kurtosis and MPP.
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Affiliation(s)
- Kumar Sandrasegaran
- Department of Radiology, Indiana University School of Medicine, 550 N. University Blvd., UH 0279, Indianapolis, IN, 46202, USA.
| | - Yuning Lin
- Department of Radiology, Indiana University School of Medicine, 550 N. University Blvd., UH 0279, Indianapolis, IN, 46202, USA.,Department of Medical Imaging, Fuzhou General Hospital, Fuzhou, China
| | - Michael Asare-Sawiri
- Department of Radiology, Indiana University School of Medicine, 550 N. University Blvd., UH 0279, Indianapolis, IN, 46202, USA.,Hope Radiation Cancer, Panama City, FL, USA
| | - Tai Taiyini
- Department of Radiology, Indiana University School of Medicine, 550 N. University Blvd., UH 0279, Indianapolis, IN, 46202, USA
| | - Mark Tann
- Department of Radiology, Indiana University School of Medicine, 550 N. University Blvd., UH 0279, Indianapolis, IN, 46202, USA
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Abstract
Small renal masses are increasingly detected incidentally at imaging. They vary widely in histology and aggressiveness, and include benign renal tumors and renal cell carcinomas that can be either indolent or aggressive. Imaging plays a key role in the characterization of these small renal masses. While a confident diagnosis can be made in many cases, some renal masses are indeterminate at imaging and can present as diagnostic dilemmas for both the radiologists and the referring clinicians. This article will summarize the current evidence of imaging features that correlate with the biology of small solid renal masses, and discuss key approaches in imaging characterization of these masses using CT and MRI.
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Affiliation(s)
- Zhen J Wang
- 1 Department of Radiology and Biomedical Imaging, University of California San Francisco , San Francisco, CA , USA
| | - Antonio C Westphalen
- 1 Department of Radiology and Biomedical Imaging, University of California San Francisco , San Francisco, CA , USA
| | - Ronald J Zagoria
- 1 Department of Radiology and Biomedical Imaging, University of California San Francisco , San Francisco, CA , USA
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Yap FY, Hwang DH, Cen SY, Varghese BA, Desai B, Quinn BD, Gupta MN, Rajarubendra N, Desai MM, Aron M, Liang G, Aron M, Gill IS, Duddalwar VA. Quantitative Contour Analysis as an Image-based Discriminator Between Benign and Malignant Renal Tumors. Urology 2018; 114:121-127. [DOI: 10.1016/j.urology.2017.12.018] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Revised: 12/04/2017] [Accepted: 12/12/2017] [Indexed: 12/12/2022]
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Schieda N, Lim RS, McInnes MDF, Thomassin I, Renard-Penna R, Tavolaro S, Cornelis FH. Characterization of small (<4cm) solid renal masses by computed tomography and magnetic resonance imaging: Current evidence and further development. Diagn Interv Imaging 2018; 99:443-455. [PMID: 29606371 DOI: 10.1016/j.diii.2018.03.004] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Accepted: 03/07/2018] [Indexed: 12/15/2022]
Abstract
Diagnosis of renal cell carcinomas (RCC) subtypes on computed tomography (CT) and magnetic resonance imaging (MRI) is clinically important. There is increased evidence that confident imaging diagnosis is now possible while standardization of the protocols is still required. Fat-poor angiomyolipoma show homogeneously increased unenhanced attenuation, homogeneously low signal on T2-weighted MRI and apparent diffusion coefficient (ADC) map, may contain microscopic fat and are classically avidly enhancing. Papillary RCC are also typically hyperattenuating and of low signal on T2-weighted MRI and ADC map; however, their gradual progressive enhancement after intravenous administration of contrast material is a differentiating feature. Clear cell RCC are avidly enhancing and may show intracellular lipid; however, these tumors are heterogeneous and are of characteristically increased signal on T2-weighted MRI. Oncocytomas and chromophobe tumors (collectively oncocytic neoplasms) show intermediate imaging findings on CT and MRI and are the most difficult subtype to characterize accurately; however, both show intermediately increased signal on T2-weighted with more gradual enhancement compared to clear cell RCC. Chromophobe tumors tend to be more homogeneous compared to oncocytomas, which can be heterogeneous, but other described features (e.g. scar, segmental enhancement inversion) overlap considerably between tumors. Tumor grade is another important consideration in small solid renal masses with emerging studies on both CT and MRI suggesting that high grade tumors may be separated from lower grade disease based upon imaging features.
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Affiliation(s)
- N Schieda
- Department of Medical Imaging, The Ottawa Hospital, The University of Ottawa, Ottawa, ON, Canada
| | - R S Lim
- Department of Medical Imaging, The Ottawa Hospital, The University of Ottawa, Ottawa, ON, Canada
| | - M D F McInnes
- Department of Medical Imaging, The Ottawa Hospital, The University of Ottawa, Ottawa, ON, Canada
| | - I Thomassin
- Sorbonne Université, Institut des Sciences du Calcul et des Données, Department of Radiology, Tenon Hospital - HUEP - APHP, 4 rue de la Chine, 75020 Paris, France
| | - R Renard-Penna
- Sorbonne Université, Institut des Sciences du Calcul et des Données, Department of Radiology, Tenon Hospital - HUEP - APHP, 4 rue de la Chine, 75020 Paris, France
| | - S Tavolaro
- Sorbonne Université, Institut des Sciences du Calcul et des Données, Department of Radiology, Tenon Hospital - HUEP - APHP, 4 rue de la Chine, 75020 Paris, France
| | - F H Cornelis
- Sorbonne Université, Institut des Sciences du Calcul et des Données, Department of Radiology, Tenon Hospital - HUEP - APHP, 4 rue de la Chine, 75020 Paris, France.
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Abstract
PURPOSE OF REVIEW Renal cell carcinoma is a heterogeneous disease with a spectrum of subtypes and clinical behavior. Quantitative and qualitative imaging biomarkers are sought to correlate with genetic and histologic features and complement pathologic analysis. RECENT FINDINGS Texture analysis, radiogenomics, and modality-specific advancements have yielded an array of renal cell carcinoma imaging biomarkers in the research domain. Although many techniques are promising, standardization and validation of these procedures are needed prior to implementation into clinical practice. SUMMARY We introduce novel imaging techniques and analytic methods which have been shown to contribute to characterization of renal cell carcinoma and its subtypes, aggressiveness, and responsiveness to therapy, including associated advantages and limitations.
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Lee H, Hong H, Kim J, Jung DC. Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation. Med Phys 2018; 45:1550-1561. [DOI: 10.1002/mp.12828] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 12/20/2017] [Accepted: 02/07/2018] [Indexed: 01/05/2023] Open
Affiliation(s)
- Hansang Lee
- School of Electrical Engineering; Korea Advanced Institute of Science and Technology; 291 Daehak-ro, Yuseong-gu Daejeon 34141 Korea
| | - Helen Hong
- Department of Software Convergence; College of Interdisciplinary Studies for Emerging Industries; Seoul Women's University; 621 Hwarang-ro Nowon-gu, Seoul 01797 Korea
| | - Junmo Kim
- School of Electrical Engineering; Korea Advanced Institute of Science and Technology; 291 Daehak-ro, Yuseong-gu Daejeon 34141 Korea
| | - Dae Chul Jung
- Department of Radiology; Severance Hospital; Research Institute of Radiological Science; Yonsei University College of Medicine; 50-1 Yonsei-ro, Seodaemun-gu Seoul 03722 Korea
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46
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Razik A, Das CJ, Sharma S. Angiomyolipoma of the Kidneys: Current Perspectives and Challenges in Diagnostic Imaging and Image-Guided Therapy. Curr Probl Diagn Radiol 2018; 48:251-261. [PMID: 29685402 DOI: 10.1067/j.cpradiol.2018.03.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2018] [Revised: 03/14/2018] [Accepted: 03/16/2018] [Indexed: 12/22/2022]
Abstract
Angiomyolipomas (AML) are benign tumors of the kidneys frequently encountered in radiologic practice in large tertiary centers. In comparison to renal cell carcinomas (RCC), AML are seldom treated unless they are large, undergo malignant transformation or develop complications like acute hemorrhage. The common garden triphasic (classic) AML is an easy diagnosis, however, some variants lack macroscopic fat in which case the radiologic differentiation from RCC becomes challenging. Several imaging features, both qualitative and quantitative, have been described in differentiating the 2 entities. Although minimal fat AML is not entirely a radiologic diagnosis, the suspicion raised on imaging necessitates sampling and potentially avoids an unwanted surgery. Recently a new variant, epitheloid AML has been described which often has atypical imaging features and is at a higher risk for malignant transformation. Apart from the diagnosis, the radiologist also needs to convey information regarding nephrometric scores which help in surgical decision-making. Recently, more and more AMLs are managed with selective arterial embolization and percutaneous ablation, both of which are associated with less morbidity when compared to surgery. The purpose of this article is to review the imaging and pathologic features of classic AML as well as the differentiation of minimal fat AML from RCC. In addition, an overview of nephrometric scoring and image-guided interventions is also provided.
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Affiliation(s)
- Abdul Razik
- Department of Radiology, All India Institute of Medical Sciences (A.I.I.M.S), New Delhi, India
| | - Chandan J Das
- Department of Radiology, All India Institute of Medical Sciences (A.I.I.M.S), New Delhi, India.
| | - Sanjay Sharma
- Department of Radiology, All India Institute of Medical Sciences (A.I.I.M.S), New Delhi, India
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Diagnostic Accuracy of Unenhanced CT Analysis to Differentiate Low-Grade From High-Grade Chromophobe Renal Cell Carcinoma. AJR Am J Roentgenol 2018; 210:1079-1087. [PMID: 29547054 DOI: 10.2214/ajr.17.18874] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
OBJECTIVE The objective of our study was to evaluate tumor attenuation and texture on unenhanced CT for potential differentiation of low-grade from high-grade chromophobe renal cell carcinoma (RCC). MATERIALS AND METHODS A retrospective study of 37 consecutive patients with chromophobe RCC (high-grade, n = 13; low-grade, n = 24) who underwent preoperative unenhanced CT between 2011 and 2016 was performed. Two radiologists (readers 1 and 2) blinded to the histologic grade of the tumor and outcome of the patients subjectively evaluated tumor homogeneity (3-point scale: completely homogeneous, mildly heterogeneous, or mostly heterogeneous). A third radiologist, also blinded to tumor grade and patient outcome, measured attenuation and contoured tumors for quantitative texture analysis. Comparisons were performed between high-grade and low-grade tumors using the chi-square test for subjective variables and sex, independent t tests for patient age and tumor attenuation, and Mann-Whitney U tests for texture analysis. Logistic regression models and ROC curves were computed. RESULTS There were no differences in age or sex between the groups (p = 0.652 and 0.076). High-grade tumors were larger (mean ± SD, 62.6 ± 34.9 mm [range, 17.0-141.0 mm] vs 39.0 ± 17.9 mm [16.0-72.3 mm]; p = 0.009) and had higher attenuation (mean ± SD, 45.5 ± 8.2 HU [range, 29.0-55.0 HU] vs 35.3 ± 8.5 HU [14.0-51.0 HU]; p = 0.001) than low-grade tumors. CT size and attenuation achieved good accuracy to diagnose high-grade chromophobe RCC: The AUC ± standard error was 0.85 ± 0.08 (p < 0.0001) with a sensitivity of 69.0% and a specificity of 100%. Subjectively, high-grade tumors were more heterogeneous (mildly or markedly heterogeneous: 69.2% [9/13] for reader 1 and 76.9% [10/13] for reader 2; reader 1, p = 0.024; reader 2, p = 0.001) with moderate agreement (κ = 0.57). Combined texture features diagnosed high-grade tumors with a maximal AUC of 0.84 ± 0.06 (p < 0.0001). CONCLUSION Tumor attenuation and heterogeneity assessed on unenhanced CT are associated with high-grade chromophobe RCC and correlate well with the histopathologic chromophobe tumor grading system.
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Are growth patterns on MRI in small (< 4 cm) solid renal masses useful for predicting benign histology? Eur Radiol 2018; 28:3115-3124. [PMID: 29492598 DOI: 10.1007/s00330-018-5324-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 01/02/2018] [Accepted: 01/10/2018] [Indexed: 12/21/2022]
Abstract
PURPOSE To evaluate previously described growth patterns in < 4 cm solid renal masses. MATERIALS AND METHODS With IRB approval, 63 renal cell carcinomas (RCC; clear cell n = 22, papillary n = 28, chromophobe n = 13) and 36 benign masses [minimal-fat (mf) angiomyolipoma (AML) n = 13, oncocytoma n = 23) from a single institution were independently evaluated by two blinded radiologists (R1/R2) using T2-weighted MRI for (1) the angular interface sign (AIS), (2) bubble-over sign (BOS), (3) percentage (%) exophytic growth and (4) long-to-short axis ratio. Comparisons were performed using ANOVA, chi-square and multi-variate regression. RESULTS AIS was present in 11.1% (7/63) -9.5% (6/63) R1/R2 RCC compared to 13.9% (5/36) -19.4% (7/36) R1/R2 benign masses (p = 0.68 and 0.16). BOS was present in 11.1% (7/63) -3.2% (2/63) R1/R2 RCC compared to 16.7% (6/36) -8.3% (3/36) R1/R2 benign masses (p = 0.432 and 0.261). Agreement was moderate (K = 0.50 and 0.55). mf-AML [66 ± 32% (range 0-100%)] and oncocytoma [53 ± 26% (0-90%)] had larger % exophytic growth compared to RCC [32 ± 23% (0-80%)] (p < 0.001). No RCC had 90-100% exophytic growth, present in 38.5% (5/13) mf-AMLs and 17.4% (4/23) oncocytomas. The long-to-short axis did not differ between groups (p = 0.053). CONCLUSIONS Benign masses show greater % exophytic growth whereas other growth patterns are not useful. Future studies evaluating % exophytic growth using multi-variate MR analysis in renal masses are required. KEY POINTS • Greater exophytic growth is associated with benignity among solid renal masses. • Only minimal fat AMLs and oncocytomas had 90-100% exophytic growth. • The angular interface sign was not useful to differentiate benign masses from RCC. • The bubble-over sign was not useful to differentiate benign masses from RCC. • Subjective analysis of growth patterns had fair-to-moderate agreement.
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Sasaguri K, Takahashi N. CT and MR imaging for solid renal mass characterization. Eur J Radiol 2017; 99:40-54. [PMID: 29362150 DOI: 10.1016/j.ejrad.2017.12.008] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Revised: 12/04/2017] [Accepted: 12/09/2017] [Indexed: 12/15/2022]
Abstract
As our understanding has expanded that relatively large fraction of incidentally discovered renal masses, especially in small size, are benign or indolent even if malignant, there is growing acceptance of more conservative management including active surveillance for small renal masses. As for advanced renal cell carcinomas (RCCs), nonsurgical and subtype specific treatment options such as immunotherapy and targeted therapy is developing. On these backgrounds, renal mass characterization including differentiation of benign from malignant tumors, RCC subtyping and prediction of RCC aggressiveness is receiving much attention and a variety of imaging techniques and analytic methods are being investigated. In addition to conventional imaging techniques, integration of texture analysis, functional imaging (i.e. diffusion weighted and perfusion imaging) and multivariate diagnostic methods including machine learning have provided promising results for these purposes in research fields, although standardization and external, multi-institutional validations are needed.
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Affiliation(s)
- Kohei Sasaguri
- Department of Radiology, Faculty of Medicine, Saga University, 5-1-1 Nabeshima, Saga, 849-8501, Japan.
| | - Naoki Takahashi
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States.
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50
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Feng Z, Rong P, Cao P, Zhou Q, Zhu W, Yan Z, Liu Q, Wang W. Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma. Eur Radiol 2017; 28:1625-1633. [PMID: 29134348 DOI: 10.1007/s00330-017-5118-z] [Citation(s) in RCA: 160] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Revised: 09/23/2017] [Accepted: 10/03/2017] [Indexed: 12/25/2022]
Abstract
OBJECTIVE To evaluate the diagnostic performance of machine-learning based quantitative texture analysis of CT images to differentiate small (≤ 4 cm) angiomyolipoma without visible fat (AMLwvf) from renal cell carcinoma (RCC). METHODS This single-institutional retrospective study included 58 patients with pathologically proven small renal mass (17 in AMLwvf and 41 in RCC groups). Texture features were extracted from the largest possible tumorous regions of interest (ROIs) by manual segmentation in preoperative three-phase CT images. Interobserver reliability and the Mann-Whitney U test were applied to select features preliminarily. Then support vector machine with recursive feature elimination (SVM-RFE) and synthetic minority oversampling technique (SMOTE) were adopted to establish discriminative classifiers, and the performance of classifiers was assessed. RESULTS Of the 42 extracted features, 16 candidate features showed significant intergroup differences (P < 0.05) and had good interobserver agreement. An optimal feature subset including 11 features was further selected by the SVM-RFE method. The SVM-RFE+SMOTE classifier achieved the best performance in discriminating between small AMLwvf and RCC, with the highest accuracy, sensitivity, specificity and AUC of 93.9 %, 87.8 %, 100 % and 0.955, respectively. CONCLUSION Machine learning analysis of CT texture features can facilitate the accurate differentiation of small AMLwvf from RCC. KEY POINTS • Although conventional CT is useful for diagnosis of SRMs, it has limitations. • Machine-learning based CT texture analysis facilitate differentiation of small AMLwvf from RCC. • The highest accuracy of SVM-RFE+SMOTE classifier reached 93.9 %. • Texture analysis combined with machine-learning methods might spare unnecessary surgery for AMLwvf.
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Affiliation(s)
- Zhichao Feng
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, China
| | - Pengfei Rong
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, China
| | - Peng Cao
- GE Healthcare, Shanghai, 210000, China
| | - Qingyu Zhou
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, China
| | - Wenwei Zhu
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, China
| | - Zhimin Yan
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, China
| | - Qianyun Liu
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, China
| | - Wei Wang
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, China.
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