Predicting left/right lung volumes, thoracic cavity volume, and heart volume from subject demographics to improve lung transplant

Abstract

Purpose: Lung transplantation is the standard treatment for end-stage lung diseases. A crucial factor affecting its success is size matching between the donor's lungs and the recipient's thorax. Computed tomography (CT) scans can accurately determine recipient's lung size, but donor's lung size is often unknown due to the absence of medical images. We aim to predict donor's right/left/total lung volume, thoracic cavity, and heart volume from only subject demographics to improve the accuracy of size matching.

Approach: A cohort of 4610 subjects with chest CT scans and basic demographics (i.e., age, gender, race, smoking status, smoking history, weight, and height) was used in this study. The right and left lungs, thoracic cavity, and heart depicted on chest CT scans were automatically segmented using U-Net, and their volumes were computed. Eight machine learning models [i.e., random forest, multivariate linear regression, support vector machine, extreme gradient boosting (XGBoost), multilayer perceptron (MLP), decision tree, $k$ -nearest neighbors, and Bayesian regression) were developed and used to predict the volume measures from subject demographics. The 10-fold cross-validation method was used to evaluate the performances of the prediction models. $R$ -squared ( $R^2$ ), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used as performance metrics.

Results: The MLP model demonstrated the best performance for predicting the thoracic cavity volume ( $R^2$ : 0.628, MAE: 0.736 L, MAPE: 10.9%), right lung volume ( $R^2$ : 0.501, MAE: 0.383 L, MAPE: 13.9%), and left lung volume ( $R^2$ : 0.507, MAE: 0.365 L, MAPE: 15.2%), and the XGBoost model demonstrated the best performance for predicting the total lung volume ( $R^2$ : 0.514, MAE: 0.728 L, MAPE: 14.0%) and heart volume ( $R^2$ : 0.430, MAE: 0.075 L, MAPE: 13.9%).

Conclusions: Our results demonstrate the feasibility of predicting lung, heart, and thoracic cavity volumes from subject demographics with superior performance compared with available studies in predicting lung volumes.

Keywords: lung transplant; machine learning; prediction modeling; subject demographics.