Predict Microsatellite Instability (MSI) and Microsatellite Stability (MSS) status in gastrointestinal cancers

Abstract

In colorectal cancer (CRC), MSI status is the crucial biomarker that influences treatment approaches, especially immunotherapy. However, the current diagnostic techniques, such as polymerase chain reaction (PCR) and immunohistochemistry (IHC), are time-consuming and variable.This study uses histopathological images to automatically predict the Microsatellite Instability (MSI) and Microsatellite Stability (MSS) status in gastrointestinal cancers using a novel deep learning framework. We suggest an AI-powered approach, Deep learning models to address these issues by combining cutting-edge computer vision methods for nuclear feature segmentation and classification in histopathology slides.The results we achieve shows how deep learning can revolutionise the digital pathology by providing a reliable, accurate, and scalable substitutes for conventional MSI and MSS diagnostics. With implications for enhancing patient outcomes by quicker and more accurate diagnosis, this work is an important breakthrough towards achieving precision oncology.