Jiule Ding1, Zhaoyu Xing2, Zhenxing Jiang1, Jie Chen1, Liang Pan1, Jianguo Qiu1, Wei Xing3. 1. Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213003, China. 2. Department of Urology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213003, China. 3. Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213003, China. Electronic address: suzhxingwei@126.com.
Abstract
PURPOSE: To compare the predictive models that can incorporate a set of CT image features for preoperatively differentiating the high grade (Fuhrman III-IV) from low grade (Fuhrman I-II) clear cell renal cell carcinoma (ccRCC). MATERIAL AND METHODS: One hundred and fourteen patients with ccRCC treated with a partial or radical nephrectomy were enrolled in the training cohort. The six non-texture features, including Pseudocapsule, Round mass, maximal tumor diameter (Diametermax), intratumoral artery (Arterytumor), enhancement value of the tumor (TEV) and relative TEV (rTEV), were assessed for each tumor. The texture features were extracted from the CT images of the section with the largest area of renal mass at both corticomedullary and nephrographic phases. The least absolute shrinkage and selection operator (LASSO) was used to screen the most valuable texture features to calculate a texture score (Texture-score) for each patient. A logistic regression model was used in the training cohort to discriminate the high from low grade ccRCC at nephrectomy. The predictors would include all non-texture features in Model 1, all non-texture features and Texture-score in Model 2, and Texture-score in Model 3. The performance of the predictive models were tested and compared in an independent validation cohort composed of 92 cases with ccRCC. RESULTS: Inter-rater agreement was good for each non-texture feature and Texture-score (the concordance correlation coefficient or Kappa coefficient > 0.70). The Texture-score was calculated via a linear combination of the 4 selected texture features. The three models shown good discrimination of the high from low grade ccRCC in the training cohort and the area under receiver operating characteristic curve (AUC) was 0.826 in Mode 1, 0.878 in Model 2 and 0.843 in Model 3, and a significant different AUC was found between Model 1 and Model 2. Application of the predictive models in the validation cohort still gave a discrimination (AUC > 0.670), and the Texture-score based models with or without the non-texture features (Model 2 and 3) showed a better discrimination of the high from low grade ccRCC (P < 0.05). CONCLUSION: This study presented the Texture-score based models can facilitate the preoperative discrimination of the high from low grade ccRCC.
PURPOSE: To compare the predictive models that can incorporate a set of CT image features for preoperatively differentiating the high grade (Fuhrman III-IV) from low grade (Fuhrman I-II) clear cell renal cell carcinoma (ccRCC). MATERIAL AND METHODS: One hundred and fourteen patients with ccRCC treated with a partial or radical nephrectomy were enrolled in the training cohort. The six non-texture features, including Pseudocapsule, Round mass, maximal tumor diameter (Diametermax), intratumoral artery (Arterytumor), enhancement value of the tumor (TEV) and relative TEV (rTEV), were assessed for each tumor. The texture features were extracted from the CT images of the section with the largest area of renal mass at both corticomedullary and nephrographic phases. The least absolute shrinkage and selection operator (LASSO) was used to screen the most valuable texture features to calculate a texture score (Texture-score) for each patient. A logistic regression model was used in the training cohort to discriminate the high from low grade ccRCC at nephrectomy. The predictors would include all non-texture features in Model 1, all non-texture features and Texture-score in Model 2, and Texture-score in Model 3. The performance of the predictive models were tested and compared in an independent validation cohort composed of 92 cases with ccRCC. RESULTS: Inter-rater agreement was good for each non-texture feature and Texture-score (the concordance correlation coefficient or Kappa coefficient > 0.70). The Texture-score was calculated via a linear combination of the 4 selected texture features. The three models shown good discrimination of the high from low grade ccRCC in the training cohort and the area under receiver operating characteristic curve (AUC) was 0.826 in Mode 1, 0.878 in Model 2 and 0.843 in Model 3, and a significant different AUC was found between Model 1 and Model 2. Application of the predictive models in the validation cohort still gave a discrimination (AUC > 0.670), and the Texture-score based models with or without the non-texture features (Model 2 and 3) showed a better discrimination of the high from low grade ccRCC (P < 0.05). CONCLUSION: This study presented the Texture-score based models can facilitate the preoperative discrimination of the high from low grade ccRCC.
Authors: Felix Y Yap; Bino A Varghese; Steven Y Cen; Darryl H Hwang; Xiaomeng Lei; Bhushan Desai; Christopher Lau; Lindsay L Yang; Austin J Fullenkamp; Simin Hajian; Marielena Rivas; Megha Nayyar Gupta; Brian D Quinn; Manju Aron; Mihir M Desai; Monish Aron; Assad A Oberai; Inderbir S Gill; Vinay A Duddalwar Journal: Eur Radiol Date: 2020-08-15 Impact factor: 5.315