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Application of Interpretable Machine Learning Algorithm to Predict Lymph Node Metastasis in Cutaneous Malignant

Xinyue Wang1, Wentao Liu2, Wei Wei3

  • 1School of Public Health, Chongqing Medical University, Chongqing, China, wangxinyue@stu.cqmu.edu.cn.

Dermatology (Basel, Switzerland)
|April 21, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts lymph node metastasis in cutaneous malignant melanoma (CMM). Random Forest models identified key factors like T stage and lactate dehydrogenase (LDH) for personalized CMM treatment.

Keywords:
Cutaneous malignant melanomaLymph node metastasisMachine learningSurveillance, Epidemiology, and End Results

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Area of Science:

  • Oncology
  • Bioinformatics
  • Machine Learning

Background:

  • Cutaneous malignant melanoma (CMM) is a lethal skin cancer.
  • Accurate prediction of lymph node metastasis is crucial for patient outcomes.
  • No prior study used interpretable machine learning for CMM metastasis prediction.

Purpose of the Study:

  • To develop interpretable machine learning models for predicting lymph node metastasis in CMM.
  • To integrate clinical, pathological, and biomarker data from the SEER database.
  • To identify key predictors of metastasis for personalized CMM treatment.

Main Methods:

  • Six machine learning models were constructed using data from 2,448 CMM patients.
  • Models included Random Forest (RF), XGBoost, and others.
  • SHAP analysis was used for interpretable insights and identification of influential factors like lactate dehydrogenase (LDH).

Main Results:

  • The RF model achieved the highest performance (AUC: 0.897, accuracy: 0.821).
  • Key predictors identified were T stage, chemotherapy, ulceration, pretreatment LDH, and radiation therapy.
  • SHAP analysis confirmed LDH's critical role as a predictive biomarker.

Conclusions:

  • An accurate machine learning model for CMM lymph node metastasis prediction was successfully established.
  • The findings provide a valuable reference for clinical decision-making in CMM treatment.
  • Interpretable machine learning offers a powerful tool for understanding and predicting cancer metastasis.