Piloting a Deep Learning Model for Predicting Nuclear BAP1 Immunohistochemical Expression of Uveal Melanoma from Hematoxylin-and-Eosin Sections.

ABSTRACT:

Background

Uveal melanoma (UM) is the most common primary intraocular malignancy in adults. Monosomy 3 and BAP1 mutation are strong prognostic factors predicting metastatic risk in UM. Nuclear BAP1 (nBAP1) expression is a close immunohistochemical surrogate for both genetic alterations. Not all laboratories perform routine BAP1 immunohistochemistry or genetic testing, and rely mainly on clinical information and anatomic/morphologic analyses for UM prognostication. The purpose of our study was to pilot deep learning (DL) techniques to predict nBAP1 expression on whole slide images (WSIs) of hematoxylin and eosin (H&E) stained UM sections.

Methods

One hundred forty H&E-stained UMs were scanned at 40 × magnification, using commercially available WSI image scanners. The t

SUBMITTER: Zhang H 

PROVIDER: S-EPMC7476670 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature