Yanwen Lu1,#, Wenliang Ma1,#, Xiang Dong1,#, Mackenzie Brown2, Tong Lu3,*, Weidong Gan1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 347-362, 2023, DOI:10.32604/cmes.2023.024909
- 05 January 2023
Abstract This study aims to apply ResNet-18 convolutional neural network (CNN) and XGBoost to preoperative computed tomography (CT) images and clinical data for distinguishing Xp11.2 translocation renal cell carcinoma (Xp11.2 tRCC) from common subtypes of renal cell carcinoma (RCC) in order to provide patients with individualized treatment plans. Data from 45 patients with Xp11.2 tRCC from January 2007 to December 2021 are collected. Clear cell RCC (ccRCC), papillary RCC (pRCC), or chromophobe RCC (chRCC) can be detected from each patient. CT images are acquired in the following three phases: unenhanced, corticomedullary, and nephrographic. A unified framework More >
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