AI Insight
Researchers developed machine learning models to predict the presence of distant melanoma metastasis at the time of initial diagnosis, using routinely collected clinical, pathological, and demographic data from 51,285 adult melanoma patients in the SEER database (2010-2022). The best-performing model, an Explainable Boosting Machine, achieved strong discriminative performance with an AUROC of 0.947 and a specificity of 0.843 at 90% sensitivity. Key predictive variables included T stage, Breslow thickness, ulceration status, and mitotic activity, with demographic factors contributing more modestly.
Why it matters
This model could help clinicians identify patients who may benefit from staging PET/CT scans at diagnosis, even when the clinical presentation appears localized, potentially improving treatment planning and resource allocation. An online calculator has been made publicly available to support individualized risk estimation in clinical settings.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Distant melanoma metastasis at the time of diagnosis is uncommon, but has major implications for patient prognosis and treatment selection. However, few tools can reliably predict the risk of distant metastasis at initial presentation. Here, we developed and evaluated machine learning models to predict distant melanoma metastasis using routinely captured clinicopathologic and demographic variables across all histologic subtypes. Using the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) program from 2010-2022, we identified adults aged 20 to 90 years with melanoma as the first and only primary malignancy (n=51,285). Explainable Boosting Machine achieved a strong balance of discrimination and precision (AUROC = 0.947, AUPRC = 0.610, Precision = 0.793, Brier = 0.015). At 90% sensitivity, specificity was 0.843 with consistent performance across cross-validation folds. Clinicopathologic variables, including T stage, Breslow thickness, ulceration, and mitotic activity, contributed the largest share of predictive signal across descriptive, regression-based, and SHAP analyses, with smaller contributions from demographic factors. Decision curve analysis supported clinical utility, showing a net reduction of 88.3 per 100 patients and a standardized net benefit of 0.541. This model could be used to identify patients at sufficiently elevated risk to justify staging PET/CT despite otherwise localized clinical presentation. Cost-consequence analysis further showed that imaging true- and false-positive patients at 85% to 95% sensitivity threshold nearly doubled downstream imaging cost. We deployed the final model as an online calculator to support exploration of individualized risk estimates (https://melanoma-calculator.streamlit.app/).
Source: Predicting Distant Melanoma Metastasis at Diagnosis Using Machine Learning