Prediction of tumor mutation burden from H&E whole-slide images: a comparison of training strategies with convolutional neural networks

Antong Chen; Tosha Shah; Andrew Brown; Haleh Akrami; Albert Swiston; Amir Vajdi; Radha Krishnan; Razvan Cristescu

doi:10.1117/12.2613196

4 April 2022 Prediction of tumor mutation burden from H&E whole-slide images: a comparison of training strategies with convolutional neural networks

Antong Chen, Tosha Shah, Andrew Brown, Haleh Akrami, Albert Swiston, Amir Vajdi, Radha Krishnan, Razvan Cristescu

Author Affiliations +

Proceedings Volume 12039, Medical Imaging 2022: Digital and Computational Pathology; 120391D (2022) https://doi.org/10.1117/12.2613196
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Tumor mutation burden (TMB) is an important biomarker for the prediction of response to anti-PD-1 immunotherapies. Studies have shown that higher level of TMB (TMB-H) is associated with higher response rate to immunotherapies in patients with various types of advanced solid tumors. However, the measurement of TMB depends on whole exome sequencing (WES) which is an expensive assay and not always available in standard clinical oncology settings. In this work, we assess the feasibility of predicting TMB-H based upon hematoxylin and eosin (H&E)-stained histopathology images, which is a routinely conducted assay in clinical oncology. Using an Inception-V3 convolutional neural network (CNN) as a baseline feature extractor, we compare adding a multi-layer perceptron (MLP) and a squeeze-and-excitation (SE) network on top of the baseline CNN. Training from random initialization and tuning with pretrained weights are also compared. Experiments are conducted on the H&E whole-slide images (WSI) of the melanoma dataset of The Cancer Genome Atlas (TCGA). Results from a 4-fold cross-validation show that the highest average area under the receiver operating characteristic curve (AUC) is 0.589, which implies that the prediction of TMB based on H&E WSI for melanoma remains a challenging problem that will warrant further investigations.

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Antong Chen, Tosha Shah, Andrew Brown, Haleh Akrami, Albert Swiston, Amir Vajdi, Radha Krishnan, and Razvan Cristescu "Prediction of tumor mutation burden from H&E whole-slide images: a comparison of training strategies with convolutional neural networks", Proc. SPIE 12039, Medical Imaging 2022: Digital and Computational Pathology, 120391D (4 April 2022); https://doi.org/10.1117/12.2613196

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