Development and validation of a machine learning-derived radiomics model for diagnosis of osteoporosis and osteopenia using quantitative computed tomography

• Published in: BMC medical imaging 2022-08, Vol.22 (1), p.1-140, Article 140
• Main Authors: Xie, Qianrong, Chen, Yue, Hu, Yimei, Zeng, Fanwei, Wang, Pingxi, Xu, Lin, Wu, Jianhong, Li, Jie, Zhu, Jing, Xiang, Ming, Zeng, Fanxin
• Format: Article
• Language: English
• Subjects: Accuracy; Age; Alkaline phosphatase; Alzheimer's disease; Bone density; Calibration; Combined clinical-radiomic model; Computed tomography; Confidence intervals; CT imaging; Decision analysis; Diagnosis; Feature extraction; Fractures; Gender; Homocysteine; Machine learning; Medical diagnosis; Multiple myeloma; Nomograms; Osteopenia; Osteoporosis; Patients; Performance evaluation; Phosphatases; Quantitative computed tomography; Radiomics; Redundancy; Regression models; Reproducibility; Risk analysis; Risk factors; Signatures; Software; Statistical analysis; Tomography; Training; Vertebrae
• ISSN: 1471-2342; 1471-2342
• DOI: 10.1186/s12880-022-00868-5

Background To develop and validate a quantitative computed tomography (QCT) based radiomics model for discriminating osteoporosis and osteopenia. Methods A total of 635 patients underwent QCT were retrospectively included from November 2016 to November 2019. The patients with osteopenia or osteoporosis (N = 590) were divided into a training cohort (N = 414) and a test cohort (N = 176). Radiomics features were extracted from the QCT images of the third lumbar vertebra. Minimum redundancy and maximum relevance and least absolute shrinkage and selection operator were used for data dimensional reduction, features selection and radiomics model building. Multivariable logistic regression was applied to construct the combined clinical-radiomic model that incorporated radiomics signatures and clinical characteristics. The performance of the combined clinical-radiomic model was evaluated by the area under the curve of receiver operator characteristic curve (ROC-AUC), accuracy, specificity, sensitivity, positive predictive value, and negative predictive value. Results The patients with osteopenia or osteoporosis were randomly divided into training and test cohort with a ratio of 7:3. Six more predictive radiomics signatures, age, alkaline phosphatase and homocysteine were selected to construct the combined clinical-radiomic model for diagnosis of osteoporosis and osteopenia. The AUC of the combined clinical-radiomic model was 0.96 (95% confidence interval (CI), 0.95 to 0.98) in the training cohort and 0.96 (95% CI 0.92 to 1.00) in the test cohort, which were superior to the clinical model alone (training-AUC = 0.81, test-AUC = 0.79). The calibration curve demonstrated that the radiomics nomogram had good agreement between prediction and observation and decision curve analysis confirmed clinically useful. Conclusions The combined clinical-radiomic model that incorporates the radiomics score and clinical risk factors, can serve as a reliable and powerful tool for discriminating osteoporosis and osteopenia. Keywords: Combined clinical-radiomic model, Osteoporosis, Osteopenia, Quantitative computed tomography