ArticleNPJ precision oncology2022
Prediction of early-stage melanoma recurrence using clinical and histopathologic features.
Article in NPJ precision oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Clinical, humanistic and economic burden associated with recurrence among patients with early-stage cancers: a systematic literature review.Frontiers in oncology · 2025Pooled it
- Characteristics and Treatment Patterns of Early Stage Melanoma: Results from a Multisite Chart Review.Drugs - real world outcomes · 2026Article
- Article
- Simulating Cancer Recurrence Patterns From Post-Treatment Viable Tumor Burden Distributions.JCO clinical cancer informatics · 2026Article
- Epigenetics of Malignant Melanoma: Mechanisms, Diagnostic Approaches and Therapeutic Applications.Oncology research · 2026Review
- Recurrence risk prediction in resected stage I-III melanoma utilizing circulating tumor DNA.BMC cancer · 2025Article
- The Current State and Future Prospects for Artificial Intelligence in Dermatology.Dermatologic clinics · 2025Review
- Article
- Improved prediction of MAPKi response duration in melanoma patients using genomic data and machine learning.NPJ precision oncology · 2025Article
- Mitochondrial proteome landscape unveils key insights into melanoma severity and treatment strategies.Cancer · 2025Article
- Spatial determinants of tumor cell dedifferentiation and plasticity in primary cutaneous melanoma.bioRxiv : the preprint server for biology · 2025Article
- Random forest-based model for the recurrence prediction of borderline ovarian tumor: clinical development and validation.Journal of cancer research and clinical oncology · 2025Article
- Defining the high-risk category of patients with cutaneous melanoma: a practical tool based on prognostic modeling.Frontiers in molecular biosciences · 2025Article
- Mini review on skin biopsy: traditional and modern techniques.Frontiers in medicine · 2025Review
- Association between immune-related adverse events and recurrence dynamics under adjuvant anti-PD-1 therapy in resected melanoma.Frontiers in oncology · 2025Article
- Patterns in progression from early-stage melanoma to late-stage melanoma: implications for survivorship follow-up.Melanoma management · 2024Article
- Genomic heterogeneity and ploidy identify patients with intrinsic resistance to PD-1 blockade in metastatic melanoma.Science advances · 2024Article
- Article
- The Prognostic Value of the 31-Gene Expression Profile Test in Cutaneous Melanoma: A Systematic Review and Meta-Analysis.Cancers · 2024Review
- A Novel Rat Model to Simulate Positive Margins in the Wound Bed of a Resected Sarcoma.Plastic and reconstructive surgery. Global open · 2024Article
Corrections and comments
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Authors and funding
28 authors.
Funding
Abstract
Prognostic analysis for early-stage (stage I/II) melanomas is of paramount importance for customized surveillance and treatment plans. Since immune checkpoint inhibitors have recently been approved for stage IIB and IIC melanomas, prognostic tools to identify patients at high risk of recurrence have become even more critical. This study aims to assess the effectiveness of machine-learning algorithms in predicting melanoma recurrence using clinical and histopathologic features from Electronic Health Records (EHRs). We collected 1720 early-stage melanomas: 1172 from the Mass General Brigham healthcare system (MGB) and 548 from the Dana-Farber Cancer Institute (DFCI). We extracted 36 clinicopathologic features and used them to predict the recurrence risk with supervised machine-learning algorithms. Models were evaluated internally and externally: (1) five-fold cross-validation of the MGB cohort; (2) the MGB cohort for training and the DFCI cohort for testing independently. In the internal and external validations, respectively, we achieved a recurrence classification performance of AUC: 0.845 and 0.812, and a time-to-event prediction performance of time-dependent AUC: 0.853 and 0.820. Breslow tumor thickness and mitotic rate were identified as the most predictive features. Our results suggest that machine-learning algorithms can extract predictive signals from clinicopathologic features for early-stage melanoma recurrence prediction, which will enable the identification of patients that may benefit from adjuvant immunotherapy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.