Incremental value of radiomics with machine learning to the existing prognostic models for predicting outcome in renal cell carcinoma.

ABSTRACT:

Purpose

To systematically evaluate the potential of radiomics coupled with machine-learning algorithms to improve the predictive power for overall survival (OS) of renal cell carcinoma (RCC).

Methods

A total of 689 RCC patients (281 in the training cohort, 225 in the validation cohort 1 and 183 in the validation cohort 2) who underwent preoperative contrast-enhanced CT and surgical treatment were recruited from three independent databases and one institution. 851 radiomics features were screened using machine-learning algorithm, including Random Forest and Lasso-COX Regression, to establish radiomics signature. The clinical and radiomics nomogram were built by multivariate COX regression. The models were further assessed by Time-dependent receiver operator characteristic, co

SUBMITTER: Xing J 

PROVIDER: S-EPMC10175776 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature