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Estimating prognosis of gastric neuroendocrine neoplasms using machine learning: A step towards precision medicine

  • 0Graduate School of Medicine, Changzhi Medical College, Changzhi 046000, Shanxi Province, China.
World Journal of Gastrointestinal Oncology +

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December 16, 2024

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December 16, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Machine learning, specifically the random survival forest (RSF) model with lymph node ratio (LNR), shows improved prediction of disease-specific survival (DSS) in gastric neuroendocrine neoplasm (g-NEN) patients compared to traditional methods.

Area Of Science

  • Oncology
  • Medical Informatics
  • Biostatistics

Background

  • Gastric neuroendocrine neoplasms (g-NENs) have poor survival rates and high recurrence after surgery.
  • Existing prognostic models, like CoxPH, have limited predictive accuracy for g-NEN patient outcomes.
  • Machine learning (ML) offers advanced tools for analyzing complex biological data to improve outcome prediction.

Keywords: