Differentiate Xp11.2 Translocation Renal Cell Carcinoma from Computed Tomography Images and Clinical Data with ResNet-18 CNN and XGBoost

作     者：Yanwen Lu Wenliang Ma Xiang Dong Mackenzie Brown Tong Lu Weidong Gan 

作者机构：Department of UrologyNanjing Drum Tower HospitalThe Affiliated Hospital of Nanjing University Medical SchoolNanjing210008China School of Data SciencePerdana UniversitySerdang43400Malaysia State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjing210008China 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2023年第136卷第7期

页      面：347-362页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported by Beijing Ronghe Medical Development Foundation 

主　　题：ResNet-18 CNN XGBoost computed tomography TFE3 renal cell carcinoma 

摘      要：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 *** from45 patients with Xp11.2 tRCC fromJanuary 2007 to December 2021 are *** cell RCC(ccRCC),papillary RCC(pRCC),or chromophobe RCC(chRCC)can be detected from each *** images are acquired in the following three phases:unenhanced,corticomedullary,and nephrographic.A unified framework is proposed for the classification of renal *** this framework,ResNet-18 CNN is employed to classify renal cancers with CT images,while XGBoost is adopted with clinical *** demonstrate that,if applying ResNet-18 CNN or XGBoost singly,the latter outperforms the former,while the framework integrating both technologies performs similarly or better than ***,the possibility of misclassifying Xp11.2 tRCC,pRCC,and chRCC as ccRCC by the proposed framework is much lower than urologists.