Year: 2025
Type: article
Abstract: Careful selection of skin lesions that require expert evaluation is important for early skin cancer detection. Yet challenges include lack of cost-effective asymptomatic screening, geographical inequality in access to specialty dermatology, and long wait times due to exam inefficiencies and staff shortages. Machine learning models trained on high-quality dermoscopy photos have been shown to aid clinicians in diagnosing individual, hand-selected skin lesions. In contrast, models designed for triage have been less explored due to limited datasets that represent a broader net of skin lesions. 3D total body photography is an emerging technology used in dermatology to document all apparent skin lesions on a patient for skin cancer monitoring. A multi-institutional and global project collected over 900,000 lesion crops off 3D total body photos for an online grand challenge in machine learning. Here we summarize the results of the competition, 'ISIC 2024 - Skin Cancer Detection with 3D-TBP', demonstrate superiority of a model that utilized intra-patient context against a prior published approach, and explore clinical plausibility of automated atypical skin lesion triage through an ablation study.
Source: npj Digital Medicine
Authors: Nicholas R. Kurtansky, Maura C. Gillis, Noel C. F. Codella, Brian M. D’Alessandro, Zongyuan Ge, Pascale Guitera, Allan C. Halpern, Harald Kittler, Josep Malvehy, Konstantinos Liopyris, Victoria J. Mar, Linda K. Martin, Lara Valeska Maul, Alexander Navarini, Tarlia Rajeswaran, Vin Rajeswaran, Nadia Reichman, H. Peter Soyer, Jochen Weber, Siyuan Yan, Veronica Rotemberg, Kivanc Kose
Institutions: Memorial Sloan Kettering Cancer Center, Microsoft (United States), Canfield Scientific (United States), Monash University, The University of Sydney, Royal Prince Alfred Hospital, Melanoma Institute Australia, Medical University of Vienna, Instituto de Salud Carlos III, Centre for Biomedical Network Research on Rare Diseases, Hospital Clínic de Barcelona, Consorci Institut D'Investigacions Biomediques August Pi I Sunyer, Universitat de Barcelona, National and Kapodistrian University of Athens, The Alfred Hospital, Alfred Health, UNSW Sydney, University of Zurich, University Hospital Zurich, University Hospital of Basel, The University of Queensland
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $4,090
Awards: G3994034673, G4102770861, GENERALIZABLE AND RELIABLE AI FOR EARLY DETECTION OF MELANOMA, G9000003249, Cancer Center Support Grant, ISIC-REPO; ISIC Skin Imaging Repository Enhancements for Promoting Interoperability and Utilization, M-ISIC: A Multimodal Open-Source International Skin Imaging Collaboration Informatics Platform for Automated Skin Cancer Detection
OpenAlex ID: W4416466474