ArticleJournal of the American Academy of Dermatology2024
Development and validation of time-to-event models to predict metastatic recurrence of localized cutaneous melanoma.
Article in Journal of the American Academy of Dermatology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
- Article
- Review
- Patterns in progression from early-stage melanoma to late-stage melanoma: implications for survivorship follow-up.Melanoma management · 2024Article
- SpatialCells: automated profiling of tumor microenvironments with spatially resolved multiplexed single-cell data.Briefings in bioinformatics · 2024Article
Corrections and comments
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Authors and funding
25 authors at 7 institutions in 3 countries.
Funding
Abstract
backgroundThe recent expansion of immunotherapy for stage IIB/IIC melanoma highlights a growing clinical need to identify patients at high risk of metastatic recurrence and, therefore, most likely to benefit from this therapeutic modality.
objectiveTo develop time-to-event risk prediction models for melanoma metastatic recurrence.
methodsPatients diagnosed with stage I/II primary cutaneous melanoma between 2000 and 2020 at Mass General Brigham and Dana-Farber Cancer Institute were included. Melanoma recurrence date and type were determined by chart review. Thirty clinicopathologic factors were extracted from electronic health records. Three types of time-to-event machine-learning models were evaluated internally and externally in the distant versus locoregional/nonrecurrence prediction.
resultsThis study included 954 melanomas (155 distant, 163 locoregional, and 636 1:2 matched nonrecurrences). Distant recurrences were associated with worse survival compared to locoregional/nonrecurrences (HR: 6.21, P < .001) and to locoregional recurrences only (HR: 5.79, P < .001). The Gradient Boosting Survival model achieved the best performance (concordance index: 0.816; time-dependent AUC: 0.842; Brier score: 0.103) in the external validation. LIMITATIONS: Retrospective nature and cohort from one geography.
conclusionsThese results suggest that time-to-event machine-learning models can reliably predict the metastatic recurrence from localized melanoma and help identify high-risk patients who are most likely to benefit from immunotherapy.
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