Opportunistic application of machine learning for bone health stratification in type 2 diabetes mellitus
Year: 2026
Type: article
Abstract: Purpose The primary objective of this study was to compare the performance of various machine learning (ML) models in the opportunistic screening of abnormal bone mineral density (BMD) among type 2 diabetes mellitus (T2DM) patients using routine non-contrast-enhanced chest CT. Materials and methods A total of 249 T2DM patients (age range, 40–97 years; mean ± standard deviation, 60.93 ± 11.49 years) who underwent chest CT with quantitative CT (QCT) analysis were retrospectively enrolled between January 2021 and June 2024. Patients were randomly split at a 7:3 ratio into a training set (n=175) and an internal test set (n=74). Semi-automatic segmentation of the L1 vertebral body was performed using 3D Slicer, and 2273 radiomic features were extracted via the Research Portal V1.1 workstation. Six radiomics-based ML models were constructed; their predictive performance and stability for distinguishing normal versus abnormal BMD were evaluated and compared. Results Among the T2DM patients included, the incidence of abnormal BMD was 40.56% (101/249). Fifteen radiomic features were selected using the minimum redundancy-maximum relevance (MRMR) algorithm and incorporated into six ML models (RF, SVMRadial, SVMLinear, NNet, xgbLinear and KNNs). In nested cross-validation, the SVMLinear model achieved the better predictive performance (accuracy: 0.906 ± 0.032; area under the curve [AUC]: 0.945 ± 0.028), followed by the NNet model (accuracy: 0.906 ± 0.031; AUC: 0.941 ± 0.029). In terms of stability, the SVMLinear model exhibited greater robustness against data perturbation (relative standard deviation [RSD] of mean accuracy: 3.54%; RSD of mean AUC: 3.04%). Conclusions The ML models enable opportunistic prediction of BMD status from chest CT in patients with T2DM. Specifically, the L1-vertebra radiomics-based SVMLinear model achieves a favorable balance of discrimination ability and stability, and may serve as a useful tool for opportunistic clinical screening for abnormal BMD in this patient population.
Source: Frontiers in Endocrinology
Authors: Hui Yang, Xin Chen, Cheng Jia, Qianqian Yao, Min Liu
Institution: Shandong First Medical University
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7225781219