Deep Learning Classification of Microsatellite Status in Colorectal Cancer Whole Slide Images

This paper aims to analyse the effectiveness of deep learning models for the classification of microsatellite instability (MSI) in colorectal cancer (CRC) using whole slide images (WSIs). MSI tumours have complex and heterogeneous morphological characteristics, such as Tumor Infiltrating Lym-phocytes (TILs), mucinous differentiation, and medullary growth patterns. The complexity of these morphological characteristics makes automated classification challenging but crucial for aiding pathologists in identifying the presence of MSI in CRC. In the proposed method we develop and train multiple deep learning models based on the various ResNet architectures, to classify MSI and microsatellite stable (MSS) cases. We leverage image pre-processing techniques, including greyscale conversion, Contrast Limited Adaptive Histogram Equalization (CLAHE), and stain normalization, to enhance key MSI-related features in WSIs. The models are trained and evaluated on a New Zealand cohort of CRC cases, and the results demonstrate the potential of deep learning in improving diagnostic accuracy, while also highlighting the challenges posed by preprocessing and model selection. This work provides insights into the optimisation of deep learning approaches for MSI classification and suggests directions for future research.