Deep learning-based approach to reveal tumor mutational burden status from whole slide images across multiple cancer types

• URL: http://arxiv.org/abs/2204.03257v2
• Date: Sat, 27 May 2023 09:36:11 GMT
• Title: Deep learning-based approach to reveal tumor mutational burden status from whole slide images across multiple cancer types
• Authors: Siteng Chen, Jinxi Xiang, Xiyue Wang, Jun Zhang, Sen Yang, Junzhou Huang, Wei Yang, Junhua Zheng, Xiao Han
• Abstract summary: Tumor mutational burden (TMB) is a potential genomic biomarker of immunotherapy. TMB detected through whole exome sequencing lacks clinical penetration in low-resource settings. In this study, we proposed a multi-scale deep learning framework to address the detection of TMB status from routinely used whole slide images.
• Score: 41.61294299606317
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tumor mutational burden (TMB) is a potential genomic biomarker of immunotherapy. However, TMB detected through whole exome sequencing lacks clinical penetration in low-resource settings. In this study, we proposed a multi-scale deep learning framework to address the detection of TMB status from routinely used whole slide images for a multiple cancer TMB prediction model (MC- TMB). The MC-TMB achieved a mean area under the curve (AUC) of 0.818 (0.804-0.831) in the cross-validation cohort, which showed superior performance to each single-scale model. The improvements of MC-TMB over the single-tumor models were also confirmed by the ablation tests on x10 magnification, and the highly concerned regions typically correspond to dense lymphocytic infiltration and heteromorphic tumor cells. MC-TMB algorithm also exhibited good generalization on the external validation cohort with an AUC of 0.732 (0.683-0.761), and better performance when compared to other methods. In conclusion, we proposed a deep learning-based approach to reveal tumor mutational burden status from routinely used pathological slides across multiple cancer types.

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