# A Deep Learning-Generated Mixed Tumor–Stroma Ratio for Prognostic Stratification and Multi-omics Profiling in Bladder Cancer

## TL;DR

A deep learning model segments bladder cancer tissue to predict patient outcomes and identify molecular patterns linked to tumor-stroma ratios.

## Contribution

A novel deep learning-generated tumor–stroma ratio (MTSR) is introduced for bladder cancer prognosis and multi-omics analysis.

## Key findings

- The MTSR model achieved >90% classification accuracy with high reproducibility across multiple centers.
- High-MTSR tumors showed increased macrophage infiltration and enrichment in extracellular matrix and WNT signaling pathways.
- An mpMRI radiomics model predicted MTSR status with 70% accuracy, enabling noninvasive risk stratification.

## Abstract

Background: Quantifying tumor–stroma architecture on routine hematoxylin and eosin slides may refine risk stratification in bladder cancer (BCa). We developed a convolutional neural network to segment whole-slide images, compute the mixed tumor–stroma ratio (MTSR), evaluate its prognostic value across multicenter cohorts, explore underlying molecular programs through multi-omics analysis, and construct a preoperative multiparametric MRI (mpMRI) radiomics model to estimate MTSR noninvasively. Methods: The ResNet50 convolutional network was customized using The Cancer Genome Atlas BCa slides labeled into 9 histological classes and background, followed by internal validation and multicenter external testing. Whole-slide-image-level segmentation yielded quantitative tissue ratios. The prognostic value was evaluated using Cox regression, Kaplan–Meier analysis, and meta-analysis, with a nomogram constructed by incorporating independent predictors. Prognostic significance was assessed by Cox regression, Kaplan–Meier analysis, and meta-analysis, and a nomogram was developed by integrating independent predictors. Bulk RNA sequencing underwent gene set variation analysis/gene set enrichment analysis, immune deconvolution, and ESTIMATE analyses, while single-cell RNA sequencing of high- vs. low-MTSR tumors profiled cellular heterogeneity, pseudotime trajectories, and regulon activity using SCENIC. An mpMRI-based random forest radiomics model was trained to predict high vs. low MTSR. Results: The convolutional neural network achieved >90% classification accuracy with Cohen’s kappa >0.95 in all cohorts. A nomogram combining MTSR and N stage outperformed clinicopathological predictors. Molecular analyses revealed that high-MTSR tumors displayed increased macrophage infiltration and enrichment of pathways related to extracellular matrix remodeling, cell adhesion, and transforming growth factor-β/WNT signaling. Single-cell analysis identified an integrin subunit beta 8 (ITGB8)-high urothelial subtype (cluster 8) with terminal differentiation, enhanced WNT activity, and sender-dominant communication networks. The mpMRI radiomics model achieved accuracies of 0.701 and 0.710 for predicting MTSR status in the training and validation sets, respectively. Conclusions: The deep learning-generated MTSR showed consistent reproducibility and prognostic independence across cohorts, mechanistically connected with an ITGB8-enriched stromal–oncogenic pathway. Its estimation via mpMRI radiomics enables integrative, noninvasive risk stratification for precision management of BCa.