Prediction of BAP1 Expression in Uveal Melanoma Using Densely-Connected Deep Classification Networks

• Type of Material: Article
• Authors: Muyi Sun, Beijing University of Posts & Telecommunications; Wei Zhou, Emory University; Xingqun Qi, Beijing University of Posts & Telecommunications; Guanhong Zhang, Beijing University of Posts & Telecommunications; Leonard Girnita, St. Erik Eye Hospital; Stefan Seregard, St. Erik Eye Hospital; Hans Grossniklaus, Emory University; Zeyi Yao, Beijing University of Posts & Telecommunications; Gustav Stalhammar, St. Erik Eye Hospital
• Language: English
• Date: 2019-10-01
• Publisher: MDPI
• Publication Version: Final Published Version
• Copyright Statement: © 2019 by the authors. Licensee MDPI, Basel, Switzerland.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3390/cancers11101579
• Title of Journal or Parent Work: Cancers
• Volume: 11
• Issue: 10
• Grant/Funding Information: This work was supported in part by Cancerfonden, Karolinska Institutet (Karolinska Institutets stiftelsemedel för ögonforskning), Stockholm County Council (Stockholms läns landsting) and by the Open Foundation of State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications under grant SKLNST-2018-1-18.
• Keywords: Oncology; precision medicine; RISK; Life Sciences & Biomedicine; CANCER; densely-connected network; deep learning; immunohistochemistry; PATTERNS; ophthalmic histopathology images; RECEPTIVE-FIELDS; SEGMENTATION; Science & Technology; MUTATION; BAP1 expression prediction; artificial intelligence
• Research Categories: Health Sciences, Oncology; Biology, Genetics

Abstract

Uveal melanoma is the most common primary intraocular malignancy in adults, with nearly half of all patients eventually developing metastases, which are invariably fatal. Manual assessment of the level of expression of the tumor suppressor BRCA1-associated protein 1 (BAP1) in tumor cell nuclei can identify patients with a high risk of developing metastases, but may suffer from poor reproducibility. In this study, we verified whether artificial intelligence could predict manual assessments of BAP1 expression in 47 enucleated eyes with uveal melanoma, collected from one European and one American referral center. Digitally scanned pathology slides were divided into 8176 patches, each with a size of 256 × 256 pixels. These were in turn divided into a training cohort of 6800 patches and a validation cohort of 1376 patches. A densely-connected classification network based on deep learning was then applied to each patch. This achieved a sensitivity of 97.1%, a specificity of 98.1%, an overall diagnostic accuracy of 97.1%, and an F1-score of 97.8% for the prediction of BAP1 expression in individual high resolution patches, and slightly less with lower resolution. The area under the receiver operating characteristic (ROC) curves of the deep learning model achieved an average of 0.99. On a full tumor level, our network classified all 47 tumors identically with an ophthalmic pathologist. We conclude that this deep learning model provides an accurate and reproducible method for the prediction of BAP1 expression in uveal melanoma.