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Related Experiment Video

Updated: Jun 2, 2025

Surgical Procedures and Methodology for a Preclinical Murine Model of De Novo Mammary Cancer Metastasis
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Surgical Procedures and Methodology for a Preclinical Murine Model of De Novo Mammary Cancer Metastasis

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Machine learning-based prognostic modeling and surgical value analysis of de novo metastatic invasive ductal

Changlong Wei1, Honghui Li1, Jinsong Li1

  • 1Breast Disease Center, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People's Republic of China.

Updates in Surgery
|January 15, 2025
PubMed
Summary

This study developed an advanced machine learning model to predict survival for patients with de novo metastatic breast cancer (dnMBC). Findings suggest primary tumor surgery may improve survival outcomes for these patients.

Keywords:
Breast cancerMetastaticPrognosisSEERSurgeryXGBoost

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Area of Science:

  • Oncology
  • Medical Informatics
  • Biostatistics

Background:

  • Prognosis for de novo metastatic breast cancer (dnMBC) remains uncertain.
  • The role of primary lesion surgery in dnMBC survival is debated.
  • Accurate prognostic models are crucial for treatment decisions in dnMBC.

Purpose of the Study:

  • To develop a machine learning-based prognostic prediction model for de novo metastatic invasive ductal carcinoma (dnMBIDC).
  • To investigate the survival benefit of primary site surgery in dnMBIDC patients.
  • To provide a tool for clinicians to aid in dnMBIDC patient management.

Main Methods:

  • Utilized SEER database and hospital data (2010-2023) for 13,383 dnMBIDC patients.
  • Developed an Extreme Gradient Boosting (XGBoost) model for survival prediction, validated against Coxph.
  • Employed propensity score matching (PSM) and survival analyses to assess surgical impact.

Main Results:

  • The XGBoost model demonstrated strong predictive accuracy (e.g., C-index 0.726 training, 0.723 validation).
  • XGBoost outperformed the Coxph model in predictive power.
  • Primary lesion surgery was associated with improved prognosis in dnMBIDC patients.

Conclusions:

  • An XGBoost-based model accurately predicts dnMBIDC patient survival.
  • Primary tumor surgery may offer survival benefits for dnMBIDC patients.
  • The developed model and findings can guide clinical treatment strategies for dnMBC.