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 

机构地区：[1]Department of Urology,Nanjing Drum Tower Hospital,The Affiliated Hospital of Nanjing University Medical School,Nanjing,210008,China [2]School of Data Science,Perdana University,Serdang,43400,Malaysia [3]State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing,210008,China

出　　处：《Computer Modeling in Engineering & Sciences》2023年第7期347-362,共16页工程与科学中的计算机建模（英文）

基　　金：supported by Beijing Ronghe Medical Development Foundation。

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 plans.Data from45 patients with Xp11.2 tRCC fromJanuary 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 is proposed for the classification of renal masses.In this framework,ResNet-18 CNN is employed to classify renal cancers with CT images,while XGBoost is adopted with clinical data.Experiments 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 urologists.Especially,the possibility of misclassifying Xp11.2 tRCC,pRCC,and chRCC as ccRCC by the proposed framework is much lower than urologists.

关 键 词：ResNet-18 CNN XGBoost computed tomography TFE3 renal cell carcinoma 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]