Skeletal Muscle Ultrasound Radiomics and Machine Learning for the Earlier Detection of Type 2 Diabetes Mellitus

作者：Sameed Khan, Chad L. Klochko, Sydney Cooper, Brendan M. Franz, Lauren Wolf, Adam Alessio, Steven B. Soliman · 发表于：Journal of Medical Ultrasound · 年份：2024 · DOI：10.4103/jmu.jmu_12_24 · 被引用次数：6 · 研究领域：Medicine

Background: Studies have demonstrated that a qualitatively and quantitatively assessed hyperechoic deltoid muscle on ultrasound (US) was accurate for the earlier detection of type 2 diabetes (T2D). We aim to demonstrate the utility of automated skeletal muscle US radiomics and machine learning for the earlier detection of T2D and prediabetes (PreD) as a supplement to traditional hemoglobin A1c (HbA1c) testing. Methods: A sample of 1191 patients who underwent shoulder US was collected with five cohorts: 171 “normal” (without T2D), 69 “screening” (negative pre-US, but positive HbA1c post-US), 190 “risk” (negative, but clinically high-risk and referred for HbA1c), 365 with “PreD” (pre-US), and 396 with “diabetes” (pre-US). Analysis was performed on deltoid muscle US images. Automatic detection identified the deltoid region of interest. Radiomics features, race, age, and body mass index were input to a gradient-boosted decision tree model to predict if the patient was either low-risk or moderate/high-risk for T2D. Results: Combining selected radiomics and clinical features resulted in a mean area under the receiver operating characteristic (AUROC) of 0.86 with 71% sensitivity and 96% specificity. In a subgroup of only patients with obesity, combining radiomics and clinical features achieved an AUROC of 0.92 with 82% sensitivity and 95% specificity. Conclusion: US radiomics and machine learning yielded promising results for the detection of T2D using skeletal muscle. Given the inc...