Deep Learning–Based Classification of Early-Stage Mycosis Fungoides and Benign Inflammatory Dermatoses on H&E-Stained Whole-Slide Images: A Retrospective, Proof-of-Concept Study

Abstract

The diagnosis of early-stage mycosis fungoides (MF) is challenging due to shared clinical and histopathological features with benign inflammatory dermatoses (BIDs). Recent evidence has shown that deep learning (DL) can assist pathologists in cancer classification, but this field is largely unexplored for cutaneous lymphomas. This study evaluates DL in distinguishing early-stage MF from BIDs using a unique dataset of 924 hematoxylin and eosin-stained whole-slide images from skin biopsies, including 233 early-stage MF and 353 BID patients. All MF patients were diagnosed after clinicopathological correlation. The classification accuracy of weakly-supervised DL models was benchmarked against three expert pathologists. The highest performance on a temporal test set was at 200x magnification (0.25 μm per pixel resolution), with a mean area-under-the-curve of 0.827 ± 0.044 and a mean balanced accuracy of 76.2 ± 3.9%. This nearly matched the 77.7% mean balanced accuracy of the three expert-pathologists. Most (63.5%) attention heatmaps corresponded well with the pathologists’ region-of-interest. Considering the difficulty of the MF versus BID classification task, the results of this study show promise for future applications of weakly-supervised DL in diagnosing early-stage MF. Achieving clinical-grade performance will require larger multi-institutional datasets and improved methodologies, such as multimodal DL with incorporation of clinical data.