Deep Learning for Skin Melanocytic Tumors in Whole-Slide Images: A Systematic Review

Abstract

The rise of Artificial Intelligence (AI) has shown promising performance as a support tool in clinical pathology workflows. In addition to the well-known interobserver variability between dermatopathologists, melanomas present a significant challenge in their histological interpretation. This study aims to analyze all previously published studies on whole-slide images of melanocytic tumors that rely on deep learning techniques for automatic image analysis. Embase, Pubmed, Web of Science, and Virtual Health Library were used to search for relevant studies for the systematic review, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Articles from 2015 to July 2022 were included, with an emphasis placed on the used artificial intelligence methods. Twenty-eight studies that fulfilled the inclusion criteria were grouped into four groups based on their clinical objectives, including pathologists versus deep learning models (n = 10), diagnostic prediction (n = 7); prognosis (n = 5), and histological features (n = 6). These were then analyzed to draw conclusions on the general parameters and conditions of AI in pathology, as well as the necessary factors for better performance in real scenarios.

Keywords: cancer; classification; computational pathology; computer-aided diagnosis; deep learning; dermatopathology; melanocytic tumors; melanoma; segmentation; skin.

Figure 1

PRISMA flow diagram describing the search and selection process carried through for this systematic review [11].

Figure 2

Minimum, median, and maximum amount of patch size, number of WSIs, and magnification. The median amount of each parameter is represented by a red vertical bar.