Journal Article

A CT-based deep learning radiomics nomogram outperforms the existing prognostic models for outcome prediction in clear cell renal cell carcinoma: a multicenter study

Zhang, Mingxin, Wang, Yanmei, Wang, Yicong, Yan, Lei, Wang, Ximing, Wang, Nan, Duan, Shaofeng, Cheng, Nan, Wang, Ning, Nie, Pei, Zhao, Xia, Wang, Zhenguang, Chen, Chengcheng, Zhao, Lianzi, Yang, Guangjie, Li, Xianjun, Xu, Yuchao

2023

Overview

Objectives To develop and validate a CT-based deep learning radiomics nomogram (DLRN) for outcome prediction in clear cell renal cell carcinoma (ccRCC), and its performance was compared with the Stage, Size, Grade, and Necrosis (SSIGN) score, the University of California, Los Angeles, Integrated Staging System (UISS), the Memorial Sloan-Kettering Cancer Center (MSKCC), and the International Metastatic Renal Cell Database Consortium (IMDC). Methods A multicenter of 799 localized (training/ test cohort, 558/241) and 45 metastatic ccRCC patients were studied. A DLRN was developed for predicting recurrence-free survival (RFS) in localized ccRCC patients, and another DLRN was developed for predicting overall survival (OS) in metastatic ccRCC patients. The performance of the two DLRNs was compared with that of the SSIGN, UISS, MSKCC, and IMDC. Model performance was assessed with Kaplan-Meier curves, time-dependent area under the curve (time-AUC), Harrell’s concordance index (C-index), and decision curve analysis (DCA). Results In the test cohort, the DLRN achieved higher time-AUCs (0.921, 0.911, and 0.900 for 1, 3, and 5 years, respectively), C-index (0.883), and net benefit than SSIGN and UISS in predicting RFS for localized ccRCC patients. The DLRN provided higher time-AUCs (0.594, 0.649, and 0.754 for 1, 3, and 5 years, respectively) than MSKCC and IMDC in predicting OS for metastatic ccRCC patients. Conclusions The DLRN can accurately predict outcomes and outperformed the existing prognostic models in ccRCC patients. Clinical relevance statement This deep learning radiomics nomogram may facilitate individualized treatment, surveillance, and adjuvant trial design for patients with clear cell renal cell carcinoma. Key Points • SSIGN, UISS, MSKCC, and IMDC may be insufficient for outcome prediction in ccRCC patients. • Radiomics and deep learning allow for the characterization of tumor heterogeneity. • The CT-based deep learning radiomics nomogram outperforms the existing prognostic models in ccRCC outcome prediction.

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V
• Subject: Cancer / Clear cell-type renal cell carcinoma / Decision analysis / Deep learning / Diagnostic Radiology / Heterogeneity / Imaging / Imaging Informatics and Artificial Intelligence / Internal Medicine / Interventional Radiology / Kidney cancer / Medical prognosis / Medicine / Medicine & Public Health / Metastases / Metastasis / Necrosis / Neuroradiology / Nomograms / Patients / Predictions / Quality / Radiology / Radiomics / Survival / Time dependence / Ultrasound