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Related Experiment Video

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Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty
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Development and Validation of a Frailty Risk Prediction Model for Preoperative Non-Small-Cell Lung Cancer Patients: A

Hang Yi1, Miao Liu1, Yihao Chen2

  • 1Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Annals of Surgical Oncology
|February 19, 2026
PubMed
Summary

A new machine learning model accurately predicts frailty risk in non-small-cell lung cancer patients before surgery. This tool improves preoperative assessment using clinical data and physiological markers.

Keywords:
FrailtyNon-small cell lung cancerPrediction modelPreoperative assessment

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Area of Science:

  • Oncology
  • Geriatrics
  • Medical Informatics

Background:

  • Frailty is a significant predictor of adverse surgical outcomes in non-small-cell lung cancer (NSCLC) patients.
  • Accurate preoperative frailty assessment in NSCLC is crucial but remains challenging.
  • Traditional assessment methods may not fully capture the complexity of frailty in this population.

Purpose of the Study:

  • To develop and validate a high-performance predictive model for frailty risk in NSCLC patients.
  • To utilize routinely available clinical parameters and machine learning (ML) techniques for enhanced prediction.
  • To provide a reliable tool for preoperative risk stratification.

Main Methods:

  • A single-center, cross-sectional study included 489 preoperative NSCLC patients.
  • Patients were divided into training (n=342) and validation (n=147) sets.
  • Frailty was assessed using the FRAIL scale; logistic regression and six ML models were developed and compared using AUC, calibration curves, and decision curve analysis.

Main Results:

  • The prevalence of frailty or pre-frailty was 36.1%.
  • Key predictors identified included age, BMI, comorbidity grade, fatigue, walking difficulty, DLCO, and the triglyceride-glucose (TyG) index.
  • The Light Gradient Boosting Machine (LGBM) model showed superior performance (AUC=0.807 in validation) compared to the logistic regression nomogram (AUC=0.77).

Conclusions:

  • A robust frailty risk prediction framework was developed using ML.
  • Integrating ML with objective markers like the TyG index and respiratory reserve significantly improved predictive accuracy.
  • This framework offers a reliable tool for preoperative risk stratification in NSCLC patients.