Use of Artificial Intelligence Model Associated with Masson’s Trichrome Staining as a Predictor of Muscle Invasion in Bladder Cancer

TL;DR

This study uses AI with Masson’s trichrome staining to accurately predict muscle invasion in bladder cancer, potentially improving diagnosis.

Contribution

The novel use of a convolutional neural network with Masson’s trichrome staining to predict muscle invasion in bladder cancer is introduced.

Findings

A CNN achieved 95.2% accuracy in training and 90.1% in validation for predicting muscle invasion.

Model outputs were significantly associated with tumor grade, lesion size, and muscle invasion.

Logistic regression showed a strong association between model predictions and invasive disease (OR = 0.07, p = 0.017).

Abstract

Bladder cancer (BC) is the most common malignancy of the urinary tract. Approximately 75% of cases are non-muscle-invasive BC (NMIBC), while muscle-invasive BC (MIBC) and advanced tumors account for most cancer-specific mortality. Accurate assessment of tumor invasion is essential, as staging variability may lead to inappropriate treatments. Tumor invasion involves several mechanisms including extracellular matrix (ECM) remodeling mediated by metalloproteinases, angiogenesis, and cell adhesions. Masson’s trichrome staining (MTS) provides relevant information on ECM composition. This study evaluated the application of machine learning to MTS-stained bladder biopsies to predict muscle invasion. A retrospective analysis of bladder biopsy images obtained from transurethral resections and cystectomies (2022–2024). A total of 702 histological images were analyzed. A convolutional neural network (CNN) was trained to classify tumors as MIBC or NMIBC and model outputs were correlated with clinical variables. The CNN achieved an accuracy of 95.2% in the training set and 90.1% in validation. Model-derived probabilities were significantly associated with tumor grade, lesion size, and muscle invasion. Logistic regression demonstrated a strong association with invasive disease (OR = 0.07, p = 0.017). CNN-based analysis of MTS-stained bladder biopsy images enable accurate prediction of muscle invasion, with potential to improve diagnostic precision.

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