Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: Achieving SOTA with Less Data using Swin Transformer

• URL: http://arxiv.org/abs/2208.10495v1
• Date: Mon, 22 Aug 2022 02:32:30 GMT
• Title: Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: Achieving SOTA with Less Data using Swin Transformer
• Authors: Bangwei Guo, Jitendra Jonnagaddala, Hong Zhang, Xu Steven Xu
• Abstract summary: We developed an efficient workflow for biomarkers in colorectal cancers using Shifted Windows (Swin-T) Swin-T was extremely efficient using small training datasets and exhibits robust predictive performance with only 200-500 training samples. These data indicate that Swin-T may be 5-10 times more efficient than the current state-of-the-art algorithms for MSI.
• Score: 3.6695403836792493
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial intelligence (AI) models have been developed for predicting clinically relevant biomarkers, including microsatellite instability (MSI), for colorectal cancers (CRC). However, the current deep-learning networks are data-hungry and require large training datasets, which are often lacking in the medical domain. In this study, based on the latest Hierarchical Vision Transformer using Shifted Windows (Swin-T), we developed an efficient workflow for biomarkers in CRC (MSI, hypermutation, chromosomal instability, CpG island methylator phenotype, BRAF, and TP53 mutation) that only required relatively small datasets, but achieved the state-of-the-art (SOTA) predictive performance. Our Swin-T workflow not only substantially outperformed published models in an intra-study cross-validation experiment using TCGA-CRC-DX dataset (N = 462), but also showed excellent generalizability in cross-study external validation and delivered a SOTA AUROC of 0.90 for MSI using the MCO dataset for training (N = 1065) and the same TCGA-CRC-DX for testing. Similar performance (AUROC=0.91) was achieved by Echle and colleagues using 8000 training samples (ResNet18) on the same testing dataset. Swin-T was extremely efficient using small training datasets and exhibits robust predictive performance with only 200-500 training samples. These data indicate that Swin-T may be 5-10 times more efficient than the current state-of-the-art algorithms for MSI based on ResNet18 and ShuffleNet. Furthermore, the Swin-T models showed promise as pre-screening tests for MSI status and BRAF mutation status, which could exclude and reduce the samples before the subsequent standard testing in a cascading diagnostic workflow to allow turnaround time reduction and cost saving.

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