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bioRxiv · 10.1101/2025.03.30.646246

Predicting microsatellite instability from whole slide images using texture features

Swanson, N.·Castro, M. A. A.·Robertson, A. G.·Shmulevich, I.·Tercan, B.
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Abstract

Identifying MSI in whole slide images (WSIs), one of the most widely used diagnostic imaging formats, is of great importance and in demand. In this study we employed color-based texture features to predict MSI on both a tile and sample based level. We found that within cohorts of hematoxylin and eosin (H&E) stained WSIs, texture morphology is able to predict MSI on a tile level with an AUC of up to 0.95 and on a sample level with an AUC of up to 0.98. This runs in contrast to other methods for predicting MSI in H&E WSIs which either utilized artificial intelligence based models, or achieved lower accuracy scores. Our results demonstrate that texture morphology is a significantly notable factor when it comes to identifying MSI in H&E WSIs, and should be used when constructing future models for MSI identification in a clinical setting.

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Swanson, N., Castro, M. A. A., Robertson, A. G., Shmulevich, I., Tercan, B.. 2025-04-04. Predicting microsatellite instability from whole slide images using texture features. https://doi.org/10.1101/2025.03.30.646246

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