Two-Stage Classification Method for MSI Status Prediction Based on Deep Learning Approach

Abstract

Colorectal cancer is one of the most common cancers with a high mortality rate. The determination of microsatellite instability (MSI) status in resected cancer tissue is vital because it helps diagnose the related disease and determine the relevant treatment. This paper presents a two-stage classification method for predicting the MSI status based on a deep learning approach. The proposed pipeline includes the serial connection of the segmentation network and the classification network. In the first stage, the tumor area is segmented from the given pathological image using the Feature Pyramid Network (FPN). In the second stage, the segmented tumor is classified as MSI-L or MSI-H using Inception-Resnet-V2. We examined the performance of the proposed method using pathological images with 10× and 20× magnifications, in comparison with that of the conventional multiclass classification method where the tissue type is identified in one stage. The F1-score of the proposed method was higher than that of the conventional method at both 10× and 20× magnifications. Furthermore, we verified that the F1-score for 20× magnification was better than that for 10× magnification.

Artificial intelligence for detection of microsatellite instability in colorectal cancer—a multicentric analysis of a pre-screening tool for clinical application

“…6,7 Since 2019, >10 studies have shown that MSI/dMMR status can be detected from digitized pathology slides stained with hematoxylin and eosin (H&E). [8][9][10][11][12][13][14][15][16][17] The key technology that enables this is deep learning (DL), an artificial intelligence (AI) method. Such AI-based systems for detection of MSI/ dMMR status from routine histopathology slides are also in the focus of commercial interest, as evident by a US patent application of this technology (#16/412362 filed on 2019-11-14 by Tempus Labs).…”

Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review

“…Another study conducted in 2020 by Lee et al [61] developed a two-stage DL-based classification pipeline for predicting MSI status in CRC patients. In the two-stage process, the first stage was responsible for segmenting the tumor area into two types of tissue (MSI-H and MSI-L).…”

Automated Recognition of Cancer Tissues through Deep Learning Framework from the Photoacoustic Specimen

“…To aggregate the patch-wise outcomes, other decision procedures were applied; nevertheless, they all showed similar results, and none of them outperformed the separate results. The researcher increased the performance of patch-based CNNs by combining the patch-wise results with a support vector machine (SVM) [ 9 ].…”