Multi-channel multiphase CT-based deep learning and radiomics fusion model for noninvasive pathological grading of clear cell renal cell carcinoma.

ABSTRACT:

Purposes

To develop a combined model integrating multi-channel deep learning features, radiomics features, and clinical variables for noninvasive pathological grading of clear cell renal cell carcinoma (ccRCC).

Material and methods

A retrospective study was conducted on 496 patients with pathologically confirmed ccRCC who underwent preoperative triple-phase contrast-enhanced CT. Multi-channel deep learning features were extracted from three ROI settings (conventional, tumor-only, and 5-mm expansion) by stacking arterial, medullary and excretory phases. These were fused with arterial-phase radiomics features and clinical data to construct and compare predictive models.

Results

In the ResNet50 model, the expanded 5mm ROI slice model had an AUC of 0.791 in the training c

SUBMITTER: Sun C 

PROVIDER: S-EPMC12851889 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature