Yanqing Ma1, Fang Cao2, Xiren Xu2, Weijun Ma3. 1. Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, 310000, Hangzhou, China. 704180026@qq.com. 2. Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, 310000, Hangzhou, China. 3. Department of Neurosurgery, Shaoxing City Keqiao District Hospital of Traditional Chinese Medicine, 312000, Shaoxing, China.
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.
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.
Authors: Karl-Friedrich Kowalewski; Luisa Egen; Chanel E Fischetti; Stefano Puliatti; Gomez Rivas Juan; Mark Taratkin; Rivero Belenchon Ines; Marie Angela Sidoti Abate; Julia Mühlbauer; Frederik Wessels; Enrico Checcucci; Giovanni Cacciamani Journal: Asian J Urol Date: 2022-06-18