Survival analysis of clear cell renal cell carcinoma based on radiomics and deep learning features from CT images.

ABSTRACT:

Purpose

To create a nomogram for accurate prognosis of patients with clear cell renal cell carcinoma (ccRCC) based on computed tomography images.

Methods

Eight hundred twenty-two ccRCC patients with contrast-enhanced computed tomography images involved in this study were collected. A rectangular region of interest surrounding the tumor was used to extract quantitative radiomics and deep-learning features, which were filtered by Cox proportional hazard regression model and least absolute shrinkage and selection operator. Then the selected features formed a fusion signature, which was assessed by Cox proportional hazard regression model method, Kaplan-Meier analysis, receiver operating characteristic curves, and concordance index (C-index) in different clinical subgroups. Fina

SUBMITTER: Lu Z 

PROVIDER: S-EPMC11666162 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature