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Integrating Machine Learning for Early Mortality Prediction in Lung Adenosquamous Carcinoma: A Web-Based Prognostic Model

  • 0Department of Respiratory and Critical Care Medicine, Maoming People's Hospital, Maoming, China.
Cancer Control : Journal of the Moffitt Cancer Center +

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July 1, 2025

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July 1, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study developed a machine learning model to predict 90-day mortality in lung adenosquamous carcinoma (ASC) patients. The novel XGBoost model, integrated into a web platform, aids personalized treatment decisions for this aggressive cancer.

Area Of Science

  • Oncology
  • Machine Learning in Medicine
  • Cancer Research

Background

  • Lung adenosquamous carcinoma (ASC) is a rare but aggressive lung cancer subtype.
  • Understanding its prognostic factors and mortality is crucial for effective treatment.
  • Existing predictive models may not fully capture ASC's complex behavior.

Purpose Of The Study

  • To quantify 90-day mortality in ASC patients.
  • To identify significant clinical features associated with ASC outcomes.
  • To develop and validate a machine learning model for predicting ASC mortality.

Main Methods

  • Retrospective analysis of 2820 ASC patients from the SEER database (2000-2018).
  • Utilized logistic regression, Lasso, and XGBoost for feature selection and model development.
  • Assessed model performance using AUC, KS statistic, DCA, and calibration plots; employed RCS for non-linear relationships.

Main Results

  • Identified 6 significant clinical features impacting ASC patient outcomes.
  • The XGBoost model demonstrated superior predictive performance (AUC 0.97 training, 0.84 validation) over other models.
  • Discovered a non-linear association between tumor size (cutoff 44 mm) and prognosis.

Conclusions

  • A novel, high-performing machine learning model for predicting 90-day mortality in ASC has been developed.
  • The model, accessible via a web platform, supports personalized clinical decision-making.
  • This tool can help optimize treatment strategies for lung adenosquamous carcinoma.

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