ArXiv Preprint
Northern Europe has the second highest mortality rate of melanoma globally.
In 2020, the mortality rate of melanoma rose to 1.9 per 100 000 habitants.
Melanoma prognosis is based on a pathologist's subjective visual analysis of
the patient's tumor. This methodology is heavily time-consuming, and the
prognosis variability among experts is notable, drastically jeopardizing its
reproducibility. Thus, the need for faster and more reproducible methods
arises. Machine learning has paved its way into digital pathology, but so far,
most contributions are on localization, segmentation, and diagnostics, with
little emphasis on prognostics. This paper presents a convolutional neural
network (CNN) method based on VGG16 to predict melanoma prognosis as the
presence of metastasis within five years. Patches are extracted from regions of
interest from Whole Slide Images (WSIs) at different magnification levels used
in model training and validation. Results infer that utilizing WSI patches at
20x magnification level has the best performance, with an F1 score of 0.7667
and an AUC of 0.81.
Christopher Andreassen, Saul Fuster, Helga Hardardottir, Emiel A. M. Janssen, Kjersti Engan
2023-03-10
