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Ultrasonographic Evaluation of Breast Cancer-related Lymphedema

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Lung mass density prediction using machine learning based on ultrasound surface wave elastography and pulmonary

Boran Zhou¹, Brian J Bartholmai¹, Sanjay Kalra²

¹Department of Radiology, Mayo Clinic, Rochester, Minnesota 55905, USA.

The Journal of the Acoustical Society of America

|February 28, 2021

Summary

This summary is machine-generated.

This study shows XGBoost can accurately predict lung mass density in interstitial lung disease patients using noninvasive lung ultrasound and pulmonary function tests. This offers a new way to assess lung density without CT scans.

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Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models

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Published on: June 20, 2025

Area of Science:

Medical Imaging
Pulmonary Medicine
Machine Learning

Background:

Interstitial lung disease (ILD) diagnosis and monitoring often rely on invasive or radiation-based imaging.
Accurate assessment of lung mass density is crucial for understanding disease progression and treatment efficacy.
Noninvasive methods for evaluating lung density are highly desirable.

Purpose of the Study:

To predict in vivo lung mass density in patients with ILD.
To utilize measurements from lung ultrasound surface wave elastography (LUSWE) and pulmonary function testing (PFT).
To compare the performance of different gradient boosting decision tree (GBDT) algorithms, including XGBoost, CatBoost, and LightGBM.

Main Methods:

Input variables included patient demographics, LUSWE surface wave speeds at various frequencies, and PFT parameters (FEV1% pre, FEV1%/FVC%).
Lung mass densities derived from high-resolution computed tomography (HRCT) Hounsfield Units served as labels.
Three GBDT algorithms (XGBoost, CatBoost, LightGBM) were trained on 80% of the data and tested on 20%.

Main Results:

The XGBoost regressor achieved a high accuracy of 0.98 in the test dataset.
This indicates a strong predictive performance for lung mass density.

Conclusions:

XGBoost, utilizing LUSWE and PFT data, shows potential for noninvasive assessment of lung mass density in patients with pulmonary diseases.
This approach may offer a valuable alternative to traditional imaging methods.