Department of Radiology, Mayo Clinic, Rochester, Minnesota 55905, USA.
Department of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, Minnesota 55905, USA.
J Acoust Soc Am. 2021 Feb;149(2):1318. doi: 10.1121/10.0003575.
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).
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).
The results showed that predictions using XGBoost regressor obtained an accuracy of 0.98 in the test dataset.
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.
本研究旨在使用不同梯度提升决策树（GBDT）算法，基于肺部超声表面波弹性成像（LUSWE）和肺功能测试（PFT）的测量结果，预测间质性肺疾病患者的体内肺质量密度。
研究对象的年龄和体重（57 名间质性肺疾病患者和 20 名健康受试者）、LUSWE 中三个振动频率（100、150 和 200Hz）的表面波速度以及 PFT 中预测的用力呼气量（FEV1%pre）和用力呼气量与用力肺活量的比值（FEV1%/FVC%）作为输入，而基于高分辨率计算机断层扫描（HRCT）的 Hounsfield 单位的肺质量密度则作为标签，用于在三个 GBDT 算法（XGBoost、CatBoost 和 LightGBM）中训练回归器。数据集的 80%（20%）用于训练（测试）。
结果表明，在测试数据集，XGBoost 回归器的预测精度为 0.98。
研究结果表明，基于 LUSWE 和 PFT 测量结果的 XGBoost 回归器可能能够非侵入性地评估肺部疾病患者的体内肺质量密度。
