Background: Osteoporosis is a common, progressive disease related to low bone mineral density (BMD).If it can be diagnosed at an early stage, osteoporosis is treatable. Quantitative computed tomography (QCT) is one of the current reference standards of BMD measurement, but dual-energy computed tomography (DECT) is considered to be a potential alternative. This study aimed to evaluate the feasibility and accuracy of phantomless in vivo DECT-based BMD quantification in comparison with QCT.Methods: A total of 128 consecutive participants who underwent DECT lumbar examinations between July 2018 and February 2019 were retrospectively analyzed. The density of calcium (water), hydroxyapatite (water), calcium (fat), and hydroxyapatite (fat) [D Ca(Wa) , D HAP(Wa) , D Ca(Fat) and D HAP(Fat) , respectively] were measured along with BMD in the trabecular bone of lumbar level 1-2 by DECT and QCT. Linear regression analysis was performed to assess the relationship between DECT-and QCT-derived BMD at both the participant level and the vertebral level. Linear regression models were quantitatively evaluated with adjusted R-square, normalized mean squared error (NMSE) and relative error (RE). Bland-Altman analysis was conducted to assess agreement between measurements. P<0.05 was considered statistically significant.Results: Strong correlations were observed between DECT-and QCT-derived BMD at both the participant level and the vertebral level (adjusted R 2 =0.983-0.987; NMSE = 1.6-2.1%; RE linear =0.6-0.9%).Bland-Altman plots indicated high agreement between both measurements. D Ca(Fat) and D HAP(Fat) showed relatively similar and optimal predictive capability for QCT-derived BMD (both: adjusted R 2 =0.987, NMSE =1.6%, RE linear =0.6%).Conclusions: Fast kVp switching DECT enabled accurate phantomless in vivo BMD quantification of the lumbar spine. D Ca(Fat) and D HAP(Fat) had relatively similar and optimal predictive capability.

Background: Hydroxyapatite (HAP) is the main component of bone mineral. The utility of using HAPwater decomposition technique with fast kilovoltage (KV)-switching dual-energy computed tomography (DECT) to detect abnormal edema in vertebral compression fractures (VCFs) has not been widely reported.Methods: A total of 31 consecutive patients with 80 VCFs who underwent DECT and magnetic resonance imaging (MRI) of the spine were retrospectively enrolled in our study between October 2018 and January 2019. VCFs in MR examinations served as the standard of reference. Two radiologists blindly and independently evaluated color-coded overlay virtual nonhydroxyapatite (VNHAP) images for the presence of abnormal edema. The inter-reader agreement, specificity, sensitivity, accuracy, and predictive values of VNHAP images for edema detection were calculated. The diagnostic accuracy of two readers was compared using McNemar's test. Two additional radiologists performed a quantitative analysis on VNHAP images, receiver operating characteristic (ROC) curve analysis was conducted, and the threshold was calculated.Results: MRI depicted 45 edematous and 35 nonedematous VCFs. For visual analysis, the VNHAP technique showed a sensitivity of 93.3%, a specificity of 97.1%, a positive predictive value (PPV) of 97.7%, a negative predictive value (NPV) of 91.9%, and an accuracy of 95.0%. The inter-reader agreement was almost perfect (k=0.90). The diagnostic accuracy of the two readers showed no significant differences in the assessment of VNHAP images (P=1.00). Significant differences in CT numbers between vertebrae with and without bone marrow edema were found by quantitative analysis (P<0.01). The area under the curve (AUC) of the VNHAP images was estimated to be 0.917. The threshold of 1,003.2 mg/cm 3 yielded a sensitivity of 88.9% and a specificity of 82.9% for the differentiation of fresh and old VCFs.Conclusions: Fast KV-switching DECT HAP-water decomposition technique had excellent diagnostic performance for identifying acute and chronic VCFs in visual and quantitative analyses.

Background With the aging population of society, the incidence rate of osteoporosis is increasing year by year. Early diagnosis of osteoporosis plays a significant role in the progress of disease prevention. As newly developed technology, computed tomography (CT) radiomics could discover radiomic features difficult to recognize visually, providing convenient, comprehensive and accurate osteoporosis diagnosis. This study aimed to develop and validate a clinical-radiomics model based on the monochromatic imaging of single source dual-energy CT for osteoporosis prediction. Methods One hundred sixty-four participants who underwent both single source dual-energy CT and quantitative computed tomography (QCT) lumbar-spine examination were enrolled in a study cohort including training datasets (n = 114 [30 osteoporosis and 84 non-osteoporosis]) and validation datasets (n = 50 [12 osteoporosis and 38 non-osteoporosis]). One hundred seven radiomics features were extracted from 70-keV monochromatic CT images. With QCT as the reference standard, a radiomics signature was built by using least absolute shrinkage and selection operator (LASSO) regression on the basis of reproducible features. A clinical-radiomics model was constructed by incorporating the radiomics signature and a significant clinical predictor (age) using multivariate logistic regression analysis. Model performance was assessed by its calibration, discrimination and clinical usefulness. Results The radiomics signature comprised 14 selected features and showed good calibration and discrimination in both training and validation cohorts. The clinical-radiomics model, which incorporated the radiomics signature and a significant clinical predictor (age), also showed good discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.938 (95% confidence interval, 0.903–0.952) in the training cohort and an AUC of 0.988 (95% confidence interval, 0.967–0.998) in the validation cohort, and good calibration. The clinical-radiomics model stratified participants into groups with osteoporosis and non-osteoporosis with an accuracy of 94.0% in the validation cohort. Decision curve analysis (DCA) demonstrated that the radiomics signature and the clinical-radiomics model were clinically useful. Conclusions The clinical-radiomics model incorporating the radiomics signature and a clinical parameter had a good ability to predict osteoporosis based on dual-energy CT monoenergetic imaging.

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