Predicting Thoracolumbar Vertebral Osteoporotic Fractures: Value Assessment of Chest CT-Based Machine Learning.

Rationale And Objectives: To assess the value of a chest CT-based machine learning model in predicting osteoporotic vertebral fractures (OVFs) of the thoracolumbar vertebral bodies.

Materials And Methods: We monitored 8910 patients aged ≥50 who underwent chest CT (2021-2024), identifying 54 incident OVFs cases. Using propensity score matching, 108 controls were selected.

Prediction of abnormal bone mass with a pericoronary adipose tissue Attenuation model.

Background: The aim is to explore the value of pericoronary adipose tissue (PCAT) attenuation in predicting abnormal bone mass by establishing a prediction model.

Materials And Methods: 361 patients with coronary computed tomography angiography (CCTA) and quantitative computed tomography (QCT) scans were retrospectively recruited. 311 patients from institution 1 from July 2021 to January 2023 were divided into a training cohort (n = 217) and an internal cohort (n = 94).