DP03 Melanoma detection in whole-slide images using a convolutional neural network for objective prognostic biomarker generation

Abstract The current melanoma staging system is predictive of 74% of the variance in survival, with prognostic biomarkers subject to high levels of interobserver and intraobserver variation. Melanoma morphology appears to be of greater significance than in other solid tumours, with Breslow thickness remaining the strongest prognostic indicator. The application of convolutional neural networks (CNNs) to whole-slide images (WSIs) may reveal new insights into tumour morphology and therefore patient prognosis. This work outlines the development and evaluation of a CNN for invasive cutaneous melanoma detection in WSIs, to enable the creation of objective prognostic biomarkers based on the tissue morphology. In total, 1157 WSIs containing cutaneous melanoma from five sources have been used in the initial development and evaluation of a custom-designed two-class tumour segmentation network with a fully convolutional architecture. 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 five test sets (three external). There were no statistical differences between tumour dimensions generated by the CNN compared with manual annotation. Similarly, there were no statistically significant differences between CNN-generated tumour dimensions across three scanning platforms. Furthermore, we have identified that this CNN can be used to calculate the ‘digital Breslow thickness’, which is a strong independent prognostic predictor of overall survival and melanoma-specific survival, across three test sets with follow-up data (hazard ratio 1.26, 95% confidence interval 1.19–1.34, P < 0.001). We have also shown that the ‘nodularity index’ determined by the tumour shape, independently of size, is predictive of survival; the rounder the tumour the worse the outcome (hazard ratio 0.71, 95% confidence interval 0.60–0.83, P < 0.001). We have developed and performed initial evaluation of a CNN that accurately detects invasive cutaneous melanoma in WSIs, enabling an objective evaluation of tumour morphology. This CNN has afforded the development of the first objective biomarkers based on the tumour’s architectural morphology. The utility of these biomarkers will be further evaluated on WSIs from additional institutions – this work is currently underway.