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Predicting sentinel lymph node metastasis in melanoma patients: A machine learning-based predictive model.

Hengxiang Zhang1, Hanbin Wang1, Shida Zhang1

  • 1Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.

JAAD International
|November 19, 2025
PubMed
Summary

A new machine learning model accurately predicts sentinel lymph node (SLN) metastasis in melanoma patients. This tool identifies key risk factors, improving diagnostic accuracy and patient care.

Keywords:
artificial intelligencemachine learningmelanomaneural networkpredictive modelsentinel lymph node biopsy

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Area of Science:

  • Oncology
  • Medical Informatics
  • Computational Biology

Background:

  • Sentinel lymph node (SLN) metastasis is a critical prognostic factor in melanoma.
  • Existing models for predicting SLN metastasis lack broad clinical applicability and predictive power.

Purpose of the Study:

  • To develop a high-performing, interpretable machine learning model for predicting SLN metastasis in melanoma.
  • To identify key clinical and pathological features associated with SLN metastasis.

Main Methods:

  • A cohort of 351 melanoma patients undergoing sentinel lymph node biopsy was analyzed.
  • Machine learning algorithms were evaluated, with the optimal model selected based on F1-score.
  • SHapley Additive exPlanations (SHAP) were used for model interpretability.

Main Results:

  • A neural network model achieved the highest F1-score of 0.73, demonstrating significant predictive accuracy.
  • Key predictors for SLN metastasis included Breslow thickness, microsatellites, Ki67 index, and melanoma subtype.
  • A web-based tool was developed for clinical implementation.

Conclusions:

  • The study presents a robust, interpretable machine learning model for melanoma SLN metastasis prediction.
  • The model's high sensitivity and accuracy can potentially reduce misdiagnosis rates and improve patient outcomes.