Session Type: Poster Session A
Session Time: 9:00AM-11:00AM
Background/Purpose: To discriminate osteoporosis and osteopenia using a quantitative computed tomography (QCT) radiomics signatures and clinical variables.
Methods: This retrospective study enrolled 635 patients with QCT images and clinical characteristics from November 2016 to November 2019. 851 radiomics features extracted from the QCT images of the third lumbar vertebra of each patients. Minimum redundancy and maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) were used for features selection. A radiomic nomogram was constructed based on the radiomics signatures and clinical characteristics for diagnosing osteoporosis and osteopenia. We systematically evaluated the diagnostic performance of the combined radiomics model, clinical model and radiomics model. Moreover, we investigated the value of the radiomics score in normal group and abnormal group.
Results: A total of 6 optimal radiomics features were selected to construct quantitative computed tomography (QCT) based signatures. The individualized nomogram included these features and 3 clinical characteristics (age, alkaline phosphatase, homocysteine) achieved good discrimination performance in both the training cohort (N=414; area under the curve (AUC) 0.96, 95% CI 0.95–0.98) and the validation cohort (N=176; AUC 0.96, 95% CI 0.92–1.00). The alone radiomics score also demonstrated significant differences in osteoporosis and osteopenia (P < 0.001).
Conclusion: This study presents a radiomics nomogram that incorporates the radiomics score and clinical risk factors, which can serve as a reliable and powerful tool for discriminating osteoporosis and osteopenia.
To cite this abstract in AMA style:Xie Q, Chen Y, Hu Y, Zeng F, Wang P, Xu L, Wu J, Li J, Zhu J, Xiang M, Zeng F. A Machine Learning-derived Radiomics Nomogram for Diagnosis of Osteoporosis and Osteopenia [abstract]. Arthritis Rheumatol. 2020; 72 (suppl 10). https://acrabstracts.org/abstract/a-machine-learning-derived-radiomics-nomogram-for-diagnosis-of-osteoporosis-and-osteopenia/. Accessed November 28, 2020.
« Back to ACR Convergence 2020
ACR Meeting Abstracts - https://acrabstracts.org/abstract/a-machine-learning-derived-radiomics-nomogram-for-diagnosis-of-osteoporosis-and-osteopenia/