JoVE - Journal of Visualized Experiments
JoVE Visualize
Contact Us

Development and validation of machine learning model to predict early death of melanoma brain metastasis patients

  • 0Department of Bone and Soft Tissue, Affiliated Tumor Hospital of Xinjiang Medical University, Xinjiang, China.
Frontiers in Oncology +

|

July 23, 2025

|

July 23, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Machine learning models can predict early death in melanoma brain metastasis (MBM) patients. These tools aid clinicians in risk stratification and treatment decisions for improved patient outcomes.

Area Of Science

  • Oncology
  • Medical Informatics
  • Biostatistics

Background

  • Melanoma brain metastases (MBM) are common and associated with poor survival.
  • Accurate prediction of early mortality in MBM patients is crucial for clinical decision-making.

Purpose Of The Study

  • To develop and validate machine learning (ML) models for predicting early death in MBM patients.
  • To identify key factors associated with early mortality in MBM.
  • To provide tools for clinical decision support in MBM patient management.

Main Methods

  • Analysis of 1,547 MBM patients from SEER and Xinjiang Medical University databases.
  • Development and validation of seven ML models using training/testing cohorts (7:3 ratio).
  • Models were evaluated using cross-validation, ROC analysis, decision curve analysis, and calibration curves to predict cancer-specific early death (CSED) and all-cause early death (ACED) within 3 months.

Main Results

  • Over 34% of MBM patients experienced CSED and 35.8% experienced ACED.
  • Predictive factors included age, treatment (radiation, chemotherapy, surgery), tumor ulceration, and extracranial metastases (bone, liver).
  • XGBoost model showed best performance for ACED (AUC=0.776), and logistic regression for CSED (AUC=0.694). External validation confirmed reliability.

Conclusions

  • Developed ML models show strong predictive performance for early death in MBM patients.
  • These models can assist clinicians in early risk stratification and treatment planning.
  • Objective risk assessment tools can improve patient counseling and guide care decisions (aggressive vs. palliative).

Keywords: