Optimize Deep Learning Models for Prediction of Gene Mutations Using Unsupervised Clustering

• URL: http://arxiv.org/abs/2204.01593v1
• Date: Thu, 31 Mar 2022 11:48:21 GMT
• Title: Optimize Deep Learning Models for Prediction of Gene Mutations Using Unsupervised Clustering
• Authors: Zihan Chen, Xingyu Li, Miaomiao Yang, Hong Zhang, Xu Steven Xu
• Abstract summary: Deep learning has become the mainstream methodological choice for analyzing and interpreting whole-slide digital pathology images. In this paper, we proposed an unsupervised clustering-based multiple-instance learning, and apply our method to develop deep-learning models for prediction of gene mutations using WSIs from three cancer types. We showed that unsupervised clustering of image patches could help identify predictive patches, exclude patches lack of predictive information, and therefore improve prediction on gene mutations in all three different cancer types.
• Score: 6.494144125433731
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning has become the mainstream methodological choice for analyzing and interpreting whole-slide digital pathology images (WSIs). It is commonly assumed that tumor regions carry most predictive information. In this paper, we proposed an unsupervised clustering-based multiple-instance learning, and apply our method to develop deep-learning models for prediction of gene mutations using WSIs from three cancer types in The Cancer Genome Atlas (TCGA) studies (CRC, LUAD, and HNSCC). We showed that unsupervised clustering of image patches could help identify predictive patches, exclude patches lack of predictive information, and therefore improve prediction on gene mutations in all three different cancer types, compared with the WSI based method without selection of image patches and models based on only tumor regions. Additionally, our proposed algorithm outperformed two recently published baseline algorithms leveraging unsupervised clustering to assist model prediction. The unsupervised-clustering-based approach for mutation prediction allows identification of the spatial regions related to mutation of a specific gene via the resolved probability scores, highlighting the heterogeneity of a predicted genotype in the tumor microenvironment. Finally, our study also demonstrated that selection of tumor regions of WSIs is not always the best way to identify patches for prediction of gene mutations, and other tissue types in the tumor micro-environment may provide better prediction ability for gene mutations than tumor tissues.

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