Unveiling the Clinical Significance of Microsatellite Instability in Colorectal Cancer: Deep Learning and the Segment Anything Model for Accurate Segmentation and Classification

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Microsatellite instability (MSI) is crucial for colorectal cancer (CRC) diagnosis and prognosis. Accurate differentiation between MSI and microsatellite stability (MSS) tumors is essential for personalized treatment. This paper introduces a novel approach combining the segment anything model (SAM), Yolov8, and convolutional neural networks (CNNs) for precise segmentation and classification of histopathological images. SAM employs a prompt-based mechanism for segmenting tumor regions like invasive margins, tumor-infiltrating lymphocytes (TILs), and necrotic areas. Integrating SAM’s segmentation with CNN-based classification achieves high-accuracy MSI-H/MSS subtyping by focusing on key histopathological features. Tested on TCGA-CRC data, this approach outperformed traditional methods in segmentation and classification accuracy, enhancing MSI/MSS diagnostic potential and enabling efficient high-throughput analysis in clinical and research settings.

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