G-M-Y Zhang1, B Shi2, H-D Xue3, B Ganeshan4, H Sun5, Z-Y Jin6. 1. Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China. Electronic address: zhang_stacey@outlook.com. 2. Department of Radiology, Shenzhen Sun Yat-Sen Cardiovascular Hospital, No. 1021 Dongmen Road North, Luohu District, Shenzhen 518001, China. Electronic address: dearicy@yahoo.com. 3. Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China. Electronic address: bjdanna@163.com. 4. Feedback Plc, Cambridge, England, UK. Electronic address: balaji@texrad.co.uk. 5. Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China. Electronic address: sunhao_robert@126.com. 6. Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China. Electronic address: zhengyu_jin@126.com.
Abstract
AIM: To investigate whether computed tomography (CT) texture analysis (TA) can be used to differentiate non-clear-cell renal cell carcinoma (non-ccRCC) from clear-cell RCC (ccRCC) and classify non-ccRCC subtypes. MATERIALS AND METHODS: One hundred ccRCC and 27 non-ccRCC (12 papillary and 15 chromophobe) were analysed. Texture parameters quantified from multiphasic CT images were compared for the objectives. Receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was calculated. The optimal discriminative texture parameters were used to produce support vector machine (SVM) classifiers. Diagnostic accuracy and 10-fold cross-validation was performed. RESULTS: Compared to ccRCC, non-ccRCC had significantly lower mean grey-level intensity (mean), standard deviation (SD), entropy, mean of positive pixels (MPP), and higher kurtosis (p<0.001). A model incorporating SD, entropy, MPP, and kurtosis produced an AUC of 0.94±0.03 with an accuracy of 87% (sensitivity=89%, specificity=92%) to identify non-ccRCC from ccRCC. Compared to chromophobe RCC, papillary RCC had significantly lower mean and MPP (p=0.002). A model incorporating SD, MPP, and skewness resulted in an AUC of 0.96±0.04 with an accuracy of 78% (sensitivity=87%, specificity=92%) to differentiate between papillary and chromophobe RCC. CONCLUSION: CT TA could potentially be used as a less invasive tool to classify histological subtypes of RCC.
AIM: To investigate whether computed tomography (CT) texture analysis (TA) can be used to differentiate non-clear-cell renal cell carcinoma (non-ccRCC) from clear-cell RCC (ccRCC) and classify non-ccRCC subtypes. MATERIALS AND METHODS: One hundred ccRCC and 27 non-ccRCC (12 papillary and 15 chromophobe) were analysed. Texture parameters quantified from multiphasic CT images were compared for the objectives. Receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was calculated. The optimal discriminative texture parameters were used to produce support vector machine (SVM) classifiers. Diagnostic accuracy and 10-fold cross-validation was performed. RESULTS: Compared to ccRCC, non-ccRCC had significantly lower mean grey-level intensity (mean), standard deviation (SD), entropy, mean of positive pixels (MPP), and higher kurtosis (p<0.001). A model incorporating SD, entropy, MPP, and kurtosis produced an AUC of 0.94±0.03 with an accuracy of 87% (sensitivity=89%, specificity=92%) to identify non-ccRCC from ccRCC. Compared to chromophobe RCC, papillary RCC had significantly lower mean and MPP (p=0.002). A model incorporating SD, MPP, and skewness resulted in an AUC of 0.96±0.04 with an accuracy of 78% (sensitivity=87%, specificity=92%) to differentiate between papillary and chromophobe RCC. CONCLUSION: CT TA could potentially be used as a less invasive tool to classify histological subtypes of RCC.
Authors: Nityanand Miskin; Lei Qin; Shanna A Matalon; Sree H Tirumani; Francesco Alessandrino; Stuart G Silverman; Atul B Shinagare Journal: Abdom Radiol (NY) Date: 2020-07-01
Authors: Blanca Paño; Alexandre Soler; Debra A Goldman; Rafael Salvador; Laura Buñesch; Carmen Sebastià; Carlos Nicolau Journal: Br J Radiol Date: 2020-08-26 Impact factor: 3.039