Deep learning for prognostic stratification and biomarker exploration in upper tract urothelial carcinoma: a multicenter retrospective cohort study.

backgroundUpper tract urothelial carcinoma (UTUC) necessitates precise prognostic assessment for personalized therapy due to its aggressive nature and the limitations of traditional methods. An objective, interpretable pathological evaluation system is urgently needed to improve UTUC risk stratification and patient outcomes.

methodsIn this multicenter retrospective study, we developed a prior knowledge-guided deep learning system using whole-slide images from 805 UTUC patients who underwent radical nephroureterectomy. The UCSegNet tile classifier was employed for eight-category tissue segmentation, and the CONCH vision-language model was used for tumor patch categorization, creating integrated probability heatmaps. Two prognostic networks, MacroContextNet and PGCA-Net, were then trained to predict the overall survival (OS) by integrating these multiscale pathological features. AI-derived quantitative pathological biomarkers were explored, including tissue fraction scores and co-localization scores. Model performance was evaluated using the concordance index (C-index), time-dependent area under the receiver operating characteristic curve (AUC), and multivariable Cox regression.

resultsThe UCSegNet tile classifier achieved excellent multiclass tissue classification accuracy across all cohorts (AUC range: 0.9916-0.9948). The primary prognostic model, PGCA-Net, demonstrated superior prognostic performance (C-index range: 0.672-0.795 across validation cohorts) compared to MacroContextNet (C-index range: 0.643-0.730) and outperformed current state-of-the-art models. After adjustment for clinical covariates, PGCA-Net effectively stratified patients into high-risk and low-risk groups for OS, with hazard ratios (HRs) for the high-risk group ranging from 4.93 to 9.38 across different cohorts. Seven AI-derived quantitative pathological biomarkers were identified and validated; notably, high tumor-muscle co-localization (Coloc_M) scores (HR up to 5.06) and high tumor-renal parenchyma co-localization (Coloc_R) scores (HR up to 4.52) consistently predicted increased mortality risk.

conclusionsThis prior knowledge-guided deep learning system significantly improves OS prediction and risk stratification in UTUC by integrating multiscale pathological features. The AI-driven, interpretable tool offers an objective approach for prognostic assessment and biomarker discovery, with strong potential to refine personalized UTUC management and enhance prognostic accuracy.