Self-supervised deep learning to predict molecular markers from routine histopathology slides for high-grade glioma tumors

Olivia Krebs; Shobhit Agarwal; Pallavi Tiwari

doi:10.1117/12.2653929

6 April 2023 Self-supervised deep learning to predict molecular markers from routine histopathology slides for high-grade glioma tumors

Olivia Krebs, Shobhit Agarwal, Pallavi Tiwari

Author Affiliations +

Proceedings Volume 12471, Medical Imaging 2023: Digital and Computational Pathology; 1247102 (2023) https://doi.org/10.1117/12.2653929
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

High-grade gliomas are aggressive forms of brain cancer associated with a poor prognosis of 12-15 months. The mutation of isocitrate dehydrogenase I (IDH) and O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation are biomarkers relevant to disease classification and prognosis. Current methods to identify patients’ molecular status are expensive and can be resource prohibitive. Morphological attributes as captured on histopathology contain rich phenotypic information indicative of the underlying molecular processes. Recently, deep learning methods have demonstrated diagnostic and prognostic value via computational analysis of histopathology to incorporate complex and subvisual morphometric features that may not be visually accessible to pathologists. We hypothesize that a computational deep learning approach applied to Hematoxylin and Eosin (H&E)-stained digitized tissue slides will be able to reliably predict the molecular (IDH, MGMT) status in high-grade glioma patients. Specifically, we present a deep learning approach that employs self-supervised and multiple instance learning, on a total of n=325 H&E stained high-grade glioma slides, for identifying (a) IDHmutant versus IDH-wild-type (WT) and (b) MGMT promoter methylated versus unmethylated tumors. The approach addressed challenges of patch selection and the unbalanced instances that arise from only subregions of whole-slide images providing diagnostic value. The deep learning approach achieved accuracy values of 91.17 (+/- 3.47) and 86.11 (+/- 4.45), for the prediction of IDH mutation and MGMT promoter methylation status respectively, demonstrating an improvement of over 5% compared to the reported accuracy values in previous studies.

Conference Presentation

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Olivia Krebs, Shobhit Agarwal, and Pallavi Tiwari "Self-supervised deep learning to predict molecular markers from routine histopathology slides for high-grade glioma tumors", Proc. SPIE 12471, Medical Imaging 2023: Digital and Computational Pathology, 1247102 (6 April 2023); https://doi.org/10.1117/12.2653929

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