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Related Experiment Video

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Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
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Machine-Learning-Based Computed Tomography Radiomics Regression Model for Predicting Pulmonary Function.

Wenfang Wang1, Yingli Sun1, Ruoyu Wu1

  • 1Department of Radiology, Huadong Hospital, Fudan University, 221, Yanan West Road, Jingan District, Shanghai 200040, PR China (W.W., Y.S., R.W., L.J., M.L.).

Academic Radiology
|April 11, 2025
PubMed
Summary

Machine learning models using chest CT radiomics can predict pulmonary function, including forced vital capacity (FVC) and forced expiratory volume in 1 second (FEV1). This approach enhances pulmonary function prediction beyond traditional methods.

Keywords:
Pulmonary function testsRadiomicsRegression diagnosticsX-ray computed tomography

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Chest computed tomography (CT) radiomics offers potential for predicting categorical outcomes.
  • Direct prediction of pulmonary function indices using CT radiomics models is currently limited.

Purpose of the Study:

  • To develop and validate machine-learning-based regression models for predicting pulmonary function indices.
  • To utilize whole-lung CT radiomics and clinical features for improved pulmonary function prediction.

Main Methods:

  • Retrospective study of patients undergoing chest CT and pulmonary function tests.
  • Construction and validation of machine-learning regression models for forced vital capacity (FVC) and forced expiratory volume in 1 second (FEV1).
  • Evaluation of model performance using metrics like CCC and R-squared, with analysis of feature importance via SHapley Additive exPlanations.

Main Results:

  • A combined model incorporating radiomics and clinical features demonstrated strong performance in predicting FVC and FEV1 on external test sets (e.g., FVC CCC: 0.745, R-squared: 0.601; FEV1 CCC: 0.744, R-squared: 0.527).
  • Key predictors included age, sex, and emphysema, alongside distinct radiomics features.
  • Model performance was rigorously evaluated against spirometry results.

Conclusions:

  • Whole-lung radiomics features are valuable for constructing regression models.
  • These models show promise for improving the prediction of pulmonary function.