Abstract 6173: The development and evaluation of a convolutional neural network for melanoma detection in whole slide images
Authors: Emily L. Clarke, Derek Magee, Julia Newton-Bishop, William Merchant, Marlous Hall, Robert Insall, Nigel Maher, Richard Scolyer, Grace Farnworth, Anisah Ali, Sally O'Shea, Darren Treanor
Journal: Cancer Research (2024)

Abstract The current melanoma staging system is predictive of 74% of the variance in survival, with prognostic biomarkers subject to high levels of inter-observer variation. The application of convolutional neural networks (CNNs) to whole slide images (WSIs), may reveal new insights into tumor morphology and therefore patient prognosis. Melanoma morphology appears to be of greater significance than in other solid tumors, with Breslow thickness remaining the strongest prognostic indicator. Other biomarkers based on tumor morphology have been generated and although some have been found to be prognostically superior to Breslow thickness, none have been integrated into clinical workflows. This may in part be explained by their demands on pathologist time. Therefore, this work outlines the development and evaluation of a CNN for invasive cutaneous melanoma detection in WSIs, which may be used for prognostic biomarker generation. 1,157 WSIs containing cutaneous melanoma from five datasets (three from the University of Leeds, one from the Melanoma Institute Australia, as well as The Cancer Genome Atlas) have been used in the initial development and evaluation of the CNN. A custom-designed 2-class tumor segmentation network with a fully convolutional architecture was trained using annotations. The CNN was evaluated using various methodologies, including comparison at per-pixel and per-tumor levels as compared to manual annotation, as well as variation across 3 scanning platforms (Leica Aperio AT2 (Milton Keynes, UK), Roche Ventana DP600 (Arizona, US) and Hamamatsu NanoZoomer S360 (Hamamatsu City, Japan). The CNN detected and located invasive melanoma tissue of no specific type with an average per-pixel sensitivity and specificity of 97.6% and 99.9% respectively across the 5 test sets. There were no statistical differences between tumor dimensions generated by the CNN as compared to manual annotation. Similarly, there were no statistically significant differences between CNN generated tumor dimensions across three scanning platforms. We have developed and performed initial evaluation of a CNN which appears to accurately detect invasive cutaneous melanoma of no specific type in WSIs for objective evaluation of tumor morphology. Future work should interrogate these data further for its propensity to predict survival outcomes. Citation Format: Emily L. Clarke, Derek Magee, Julia Newton-Bishop, William Merchant, Marlous Hall, Robert Insall, Nigel Maher, Richard Scolyer, Grace Farnworth, Anisah Ali, Sally O'Shea, Darren Treanor. The development and evaluation of a convolutional neural network for melanoma detection in whole slide images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6173.