Machine-Learning-Based Computed Tomography Radiomics Regression Model for Predicting Pulmonary Function.
Authors
Affiliations (5)
Affiliations (5)
- Department 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.).
- Department of Respiratory Medicine, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200062, PR China (Z.S.).
- The Second People's Hospital of Kashi District, Kashi 844000, PR China (B.T.).
- Department of Gerontology, Huadong Hospital, Fudan University, Shanghai Key Laboratory of Clinical Geriatric Medicine, Research Center on Aging and Medicine, Fudan University, Shanghai 200040, PR China (S.Y.).
- Department 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.); Diagnosis and Treatment Center of Small Lung Nodules of Huadong Hospital, Shanghai 200040, PR China (M.L.). Electronic address: [email protected].
Abstract
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. This retrospective study enrolled patients who underwent chest CT and pulmonary function tests between January 2018 and April 2024. Machine-learning regression models were constructed and validated to predict pulmonary function indices, including forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV<sub>1</sub>). The models incorporated radiomics of the whole lung and clinical features. Model performance was evaluated using mean absolute error, mean squared error, root mean squared error, concordance correlation coefficient (CCC), and R-squared (R<sup>2</sup>) value and compared to spirometry results. Individual explanations of the models' decisions were analyzed using an explainable approach based on SHapley Additive exPlanations. In total, 1585 cases were included in the analysis, with 102 of them being external cases. Across the training, validation, test, and external test sets, the combined model consistently achieved the best performance in the regression task for predicting FVC (e.g. external test set: CCC, 0.745 [95% confidence interval 0.642-0.818]; R<sup>2</sup>, 0.601 [0.453-0.707]) and FEV<sub>1</sub> (e.g. external test set: CCC, 0.744 [0.633-0.824]; R<sup>2</sup>, 0.527 [0.298-0.675]). Age, sex, and emphysema were important factors for both FVC and FEV<sub>1</sub>, while distinct radiomics features contributed to each. Whole-lung-based radiomics features can be used to construct regression models to improve pulmonary function prediction.