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Machine-Learning-Based Computed Tomography Radiomics Regression Model for Predicting Pulmonary Function
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DOI：10.1016/j.acra.2025.03.038.png)
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Abstract
En 中文
Chest computed tomography (CT) radiomics can be utilized for categorical predictions; however, models predicting pulmonary function indices directly are lacking. This study aimed to develop machine-learning-based regression models to predict pulmonary function using chest CT radiomics.
Keywords:
AUC
area under the curve
CCC
concordance correlation coefficient
FEV1
forced expiratory volume in 1 s
FEV1_PRED_percent
percentage-predicted forced expiratory volume in 1 s
FVC
forced vital capacity
FVC_PRED_percent
percentage-predicted forced vital capacity
MAE
mean absolute error
MSE
mean squared error
PFT
pulmonary function test
R2
R-squared
RMSE
root mean squared error
ROC
receiver operating characteristic
SHAP
SHapley Additive exPlanations
Pulmonary function tests
Radiomics
Regression diagnostics
X-ray computed tomography
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