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.,One hundred forty H&E-stained UMs were scanned at 40 × magnification, using commercially available WSI image scanners.,The training cohort comprised 66 BAP1+ and 74 BAP1− UM, with known chromosome 3 status and clinical outcomes.,Nonoverlapping areas of three different dimensions (512 × 512, 1024 × 1024, and 2048 × 2048 pixels) for comparison were extracted from tumor regions in each WSI, and were resized to 256 × 256 pixels.,Deep convolutional neural networks (Resnet18 pre-trained on Imagenet) and auto-encoder-decoders (U-Net) were trained to predict nBAP1 expression of these patches.,Trained models were tested on the patches cropped from a test cohort of WSIs of 16 BAP1+ and 28 BAP1− UM cases.,The trained model with best performance achieved area under the curve values of 0.90 for patches and 0.93 for slides on the test set.,Our results show the effectiveness of DL for predicting nBAP1 expression in UM on the basis of H&E sections only.,Our pilot demonstrates a high capacity of artificial intelligence-related techniques for automated prediction on the basis of histomorphology, and may be translatable into routine histology laboratories.
Uveal melanoma represents ∼85% of all ocular melanomas and up to 50% of patients develop metastatic disease.,Metastases are most frequently localised to the liver and, as few patients are candidates for potentially curative surgery, this is associated with a poor prognosis.,There is currently little published evidence for the optimal management and treatment of metastatic uveal melanoma and the lack of effective therapies in this setting has led to the widespread use of systemic treatments for patients with cutaneous melanoma.,Uveal and cutaneous melanomas are intrinsically different diseases and so dedicated management strategies and therapies for uveal melanoma are much needed.,This review explores the biology of uveal melanoma and how this relates to ongoing trials of targeted therapies in the metastatic disease setting.,In addition, we consider the options to optimise patient management and care.