A 3D-Optimized AI Imaging Model for the Skin Tumor Burden Assessment of Mycosis Fungoides

Huizhong Wang; Zihao Liu; Haihao Pan; Kecen Liu; Yujie Wen; Yao Qin; Jingyang Dang; Mingjia Li; Ziyang Cui; Tingting Jiang; Yang Wang

doi:10.1016/j.jid.2025.06.1567

A 3D-Optimized AI Imaging Model for the Skin Tumor Burden Assessment of Mycosis Fungoides

J Invest Dermatol. 2025 Jun 24:S0022-202X(25)02144-X. doi: 10.1016/j.jid.2025.06.1567. Online ahead of print.

Authors

Huizhong Wang¹, Zihao Liu², Haihao Pan¹, Kecen Liu¹, Yujie Wen¹, Yao Qin³, Jingyang Dang¹, Mingjia Li¹, Ziyang Cui¹, Tingting Jiang⁴, Yang Wang⁵

Affiliations

¹ Department of Dermatology and Venereology, Peking University First Hospital, Beijing 100034, China; National Clinical Research Center for Skin and Immune Diseases, Beijing 100034, China; NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing 100034, China.
² National Engineering Research Center of Visual Technology, National Key Laboratory for Multimedia Information Processing, School of Computer Science, National Biomedical Imaging Center, Peking University, Beijing 100871, China; Intel Labs China.
³ Department of Dermatology and Venereology, Peking University First Hospital, Beijing 100034, China; National Clinical Research Center for Skin and Immune Diseases, Beijing 100034, China; NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing 100034, China; Department of Dermatology, Nanfang Hospital, Southern Medical University, Guangzhou 510515.
⁴ National Engineering Research Center of Visual Technology, National Key Laboratory for Multimedia Information Processing, School of Computer Science, National Biomedical Imaging Center, Peking University, Beijing 100871, China. Electronic address: ttjiang@pku.edu.cn.
⁵ Department of Dermatology and Venereology, Peking University First Hospital, Beijing 100034, China; National Clinical Research Center for Skin and Immune Diseases, Beijing 100034, China; NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing 100034, China; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China. Electronic address: yangwang_dr@bjmu.edu.cn.

PMID: 40571158
DOI: 10.1016/j.jid.2025.06.1567

Abstract

Mycosis fungoides (MF) is characterized by widespread skin patches that may progress to plaques and tumors, necessitating precise tumor burden assessment for staging and treatment guidance. However, existing methods, including the widely accepted modified Severity Weighted Assessment Tool (mSWAT), present significant challenges in routine practice due to their time-consuming nature and inter-observer variability. This study developed an AI model, mSWAT-Net, to estimate mSWAT scores using clinical images of MF patients. Notably, the overlap area segmentation sub-module of mSWAT-Net addressed double-counting errors in multi-angle photos through training on 3,904 annotated images generated from 61 three-dimensional (3D) human images. Across 2,463 standardized full-body photographs from 134 imaging series, mSWAT-Net demonstrated comparable performance to experienced cutaneous lymphoma specialists, achieving intraclass correlation coefficients (ICCs) of 0.917 (internal validation) and 0.846 (temporal validation) for mSWAT score. Moreover, mSWAT-Net outperformed three junior dermatologists in image-based scoring (ICC: 0.917 vs. 0.777), and demonstrated robust performance when compared to ground truth derived from 3D patient imaging (ICC: 0.812). Finally, mSWAT-Net was deployed as a free web application to support MF management in clinical settings. These findings highlight the potential of mSWAT-Net as an accurate, automated clinical tool for facilitating patient follow-up, treatment monitoring, and remote consultations.

Keywords: Mycosis fungoides; artificial intelligence; deep learning; modified Severity Weighted Assessment Tool (mSWAT); three-dimensional.