Boran Zhou1, Brian J Bartholmai1, Sanjay Kalra2, Thomas Osborn3, Xiaoming Zhang1. 1. Department of Radiology, Mayo Clinic, Rochester, Minnesota 55905, USA. 2. Department of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, Minnesota 55905, USA. 3. Department of Rheumatology, Mayo Clinic, Rochester, Minnesota 55905, USA.
Abstract
OBJECTIVE: The objective of this study is to predict in vivo lung mass density for patients with interstitial lung disease using different gradient boosting decision tree (GBDT) algorithms based on measurements from lung ultrasound surface wave elastography (LUSWE) and pulmonary function testing (PFT). METHODS: Age and weight of study subjects (57 patients with interstitial lung disease and 20 healthy subjects), surface wave speeds at three vibration frequencies (100, 150, and 200 Hz) from LUSWE, and predicted forced expiratory volume (FEV1% pre) and ratio of forced expiratory volume to forced vital capacity (FEV1%/FVC%) from PFT were used as inputs while lung mass densities based on the Hounsfield Unit from high resolution computed tomography (HRCT) were used as labels to train the regressor in three GBDT algorithms, XGBoost, CatBoost, and LightGBM. 80% (20%) of the dataset was used for training (testing). RESULTS: The results showed that predictions using XGBoost regressor obtained an accuracy of 0.98 in the test dataset. CONCLUSION: The obtained results suggest that XGBoost regressor based on the measurements from LUSWE and PFT may be able to noninvasively assess lung mass density in vivo for patients with pulmonary disease.
OBJECTIVE: The objective of this study is to predict in vivo lung mass density for patients with interstitial lung disease using different gradient boosting decision tree (GBDT) algorithms based on measurements from lung ultrasound surface wave elastography (LUSWE) and pulmonary function testing (PFT). METHODS: Age and weight of study subjects (57 patients with interstitial lung disease and 20 healthy subjects), surface wave speeds at three vibration frequencies (100, 150, and 200 Hz) from LUSWE, and predicted forced expiratory volume (FEV1% pre) and ratio of forced expiratory volume to forced vital capacity (FEV1%/FVC%) from PFT were used as inputs while lung mass densities based on the Hounsfield Unit from high resolution computed tomography (HRCT) were used as labels to train the regressor in three GBDT algorithms, XGBoost, CatBoost, and LightGBM. 80% (20%) of the dataset was used for training (testing). RESULTS: The results showed that predictions using XGBoost regressor obtained an accuracy of 0.98 in the test dataset. CONCLUSION: The obtained results suggest that XGBoost regressor based on the measurements from LUSWE and PFT may be able to noninvasively assess lung mass density in vivo for patients with pulmonary disease.
Authors: Scott M Lundberg; Gabriel Erion; Hugh Chen; Alex DeGrave; Jordan M Prutkin; Bala Nair; Ronit Katz; Jonathan Himmelfarb; Nisha Bansal; Su-In Lee Journal: Nat Mach Intell Date: 2020-01-17
Authors: Xiaoming Zhang; Thomas Osborn; Boran Zhou; Duane Meixner; Randall R Kinnick; Brian Bartholmai; James F Greenleaf; Sanjay Kalra Journal: IEEE Trans Ultrason Ferroelectr Freq Control Date: 2017-09 Impact factor: 2.725