A multi-data fusion deep learning model for prognostic prediction in upper tract urothelial carcinoma.

ABSTRACT: Upper tract urothelial carcinoma (UTUC) is a rare but highly invasive urinary malignancy with a high postoperative recurrence rate. We retrospectively collected data from 133 UTUC patients who underwent radical nephroureterectomy between 2005 and 2017. Patients were divided into a training set (n=103) and a testing set (n=30). A multi-modal deep learning model named Multi-modal Image-Clinical Combination Classifier (MICC) was developed by integrating multi-phase contrast-enhanced CT imaging and clinical data. The model's prognostic performance was compared with two unimodal models-ImageNet (CT-based) and ClinicalNet (clinical data-based)-and traditional clinical parameters including pathological T stage. Feature importance was evaluated using SHapley Additive exPlanations (SHAP). The MICC

SUBMITTER: Sun H 

PROVIDER: S-EPMC12364636 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature