The predictive value of radiomics and deep learning for synchronous distant metastasis in clear cell renal cell carcinoma.

ABSTRACT:

Objective

The objective of this research was to devise and authenticate a predictive model that employs CT radiomics and deep learning methodologies for the accurate prediction of synchronous distant metastasis (SDM) in clear cell renal cell carcinoma (ccRCC).

Methods

A total of 143 ccRCC patients were included in the training cohort, and 62 ccRCC patients were included in the validation cohort. The CT images from all patients were normalized, and the tumor regions were manually segmented via ITK-SNAP software. Radiomic features were extracted via the FAE toolkit. The least absolute shrinkage and selection operator (LASSO) algorithm was employed to select features and build various machine learning models. Additionally, the largest cross-section of the tumor was cropped to t

SUBMITTER: He WB 

PROVIDER: S-EPMC11762027 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature