Abstract 2100: Deep learning can predict microsatellite instability from histology in colorectal cancer across different ethnic groups

Abstract

Abstract Microsatellite instability (MSI) is known to be an important genomic marker for prediction of a patient's response to immune checkpoint inhibitors across various types of cancer. Conventional methods to measure DNA mismatch repair (MMR) status are pCR-based panel sequencing or immunohistochemical staining based methods. Recently, deep learning has been proposed as an efficient diagnostic tool in this regard, as it can learn to predict MSI directly from H&E stained slides. However, it is thought that deep learning may not generalize effectively beyond the ethnicities present in the training dataset. Therefore, we investigated the impact of ethnic differences on deep learning performance at MSI classification across a range of model architectures and hyperparameters. A total of 360 WSIs from a subset of the TCGA colorectal cancer (TCGA set) and another 170 WSIs from Gangnam Severance Hospital (Yonsei University College of Medicine, Seoul, Republic of Korea, Gangnam set) were included in this study. 360 H&E stained WSIs from TCGA set were divided into a set of square tiles of size 256x256um (0.5um/pixel) and then color normalized using Macenko normalization. Those tiles were divided into two classes - MSI-H and MSS (Microsatellite stable) based on the patient's MSI status. We trained various models on those tiles using a range of hyperparameters including model architecture, number of trainable layers, learning rate, and learning rate scheduling. The performance of this model was measured using the AUROC in the TCGA set (internal validation) and Gangnam set (external validation). Our results for ResNet18 - a popular convolutional network architecture - on TCGA and Gangnam test datasets are around 0.79 and 0.76 respectively, with a standard deviation on Gangnam of 0.03. Given good hyperparameters and model architecture, the model's performance on the Gangnam test data was within one standard deviation of its performance on the TCGA test data, despite the fact that said model was trained using only the TCGA data containing few asian patients. The lack of a significant performance difference challenges the notion that there are morphological differences in colorectal cancers between patients of Caucasian and Asian ethnicities that would prevent a deep learning system from generalizing effectively. Citation Format: Isaiah S. Pressman, Hongming Xu, Jeonghyun Kang, Yoon Jin Cha, Sung Hak Lee, Tae Hyun Hwang. Deep learning can predict microsatellite instability from histology in colorectal cancer across different ethnic groups [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2100.