Deep learning-based spatial analysis on tumor and immune cells of pathology images predicts MIBC prognosis.

ABSTRACT: Muscle-invasive bladder cancer (MIBC) is a highly aggressive disease with a poor prognosis. This study aims to explore the correlation between the spatial distribution of lymphocyte aggregates and the prognosis of MIBC using deep learning. Whole-slide images (WSIs) and clinical information of MIBC patients were collected from The Cancer Genome Atlas (TCGA) and the HBlaU079Su01 microarray. After manually annotating tumor and lymphocyte aggregation regions, we developed an automated analysis pipeline, including segmentation and quality control of patches. A convolutional neural network (CNN) classification model was constructed. Based on the definition of the border region of tumor cell nests, we assessed 12 spatial indicators for different patch types within, around and outside the tumor cl

SUBMITTER: Hu C 

PROVIDER: S-EPMC12367112 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature