Preoperative Prediction of Inferior Vena Cava Wall Invasion of Tumor Thrombus in Renal Cell Carcinoma: Radiomics Models Based on Magnetic Resonance Imaging

Frontiers in Oncology

Xiangpeng Wang¹  Changxin Li¹  Zhaonan Sun³  Zhiyong Lin³  Xiaodong Zhang³  Xiaoying Wang³  Xiang Liu³  Chao Han⁴  Yanfei Yu⁵  Chunru Xu⁵  Yingpu Cui⁶ 

[1] Beijing Smart Tree Medical Technology Co. Ltd, Research and Development Department, Beijing, China;Department of Nuclear Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China;Department of Radiology, Peking University First Hospital, Peking University, Beijing, China;Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China;Department of Urology, Peking University First Hospital, Institute of Urology, Peking University, National Urological Cancer Center, Beijing, China;State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China;

关键词: carcinoma;    renal cell;    thrombus;    vena cava;    inferior;    magnetic resonance imaging;   

DOI  :  10.3389/fonc.2022.863534

来源: DOAJ

【 摘 要 】

ObjectiveTo develop radiomics models to predict inferior vena cava (IVC) wall invasion by tumor thrombus (TT) in patients with renal cell carcinoma (RCC).MethodsPreoperative MR images were retrospectively collected from 91 patients with RCC who underwent radical nephrectomy (RN) and thrombectomy. The images were randomly allocated into a training (n = 64) and validation (n = 27) cohort. The inter-and intra-rater agreements were organized to compare masks delineated by two radiologists. The masks of TT and IVC were manually annotated on axial fat-suppression T2-weighted images (fsT2WI) by one radiologist. The following models were trained to predict the probability of IVC wall invasion: two radiomics models using radiomics features extracted from the two masks (model 1, radiomics model_IVC; model 2, radiomics model_TT), two combined models using radiomics features and radiological features (model 3, combined model_IVC; model 4, combined model_TT), and one radiological model (model 5) using radiological features. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were applied to validate the discriminatory effect and clinical benefit of the models.ResultsModel 1 to model 5 yielded area under the curves (AUCs) of 0.881, 0.857, 0.883, 0.889, and 0.769, respectively, in the validation cohort. No significant differences were found between these models (p = 0.108-0.951). The dicision curve analysis (DCA) showed that the model 3 had a higher overall net benefit than the model 1, model 2, model 4, and model 5.ConclusionsThe combined model_IVC (model 3) based on axial fsT2WI exhibited excellent predictive performance in predicting IVC wall invasion status.

【 授权许可】

Unknown