Increased water content in multifidus muscles of young adults with chronic nonspecific low back pain detected by dual-energy CT and MRI.

OBJECTIVES: To demonstrate the feasibility of better diagnosing young adults with chronic nonspecific low back pain (CNLBP) by measuring water content in paraspinal muscles using water-muscle decomposition technique in dual-energy CT (DECT) and T2-mapping in MRI. METHODS: This prospective cross-sectional study included 110 young individuals (56 with CNLBP at age of 25.7 ± 2.0 years and 54 of asymptomatic at age of 25.1 ± 1.9 years) who underwent both MRI and DECT on the spine. T2 values on T2 mapping in MRI and water density (WD) value on water(muscle) images in DECT were generated at the L1-L4 levels for erector spinae muscle and L2-L5 for multifidus muscle. Pain duration time, Oswestry Disability Index (ODI), Visual Analogue Scale (VAS) were recorded for CNLBP patients. Difference of T2 value and WD between the two patient groups, and correlations between T2 value and WD, and T2 value and WD with clinical indicators were analyzed. RESULTS: Compared with asymptomatic participants, the mean WD of multifidus muscle at L4-L5 and mean T2 values of multifidus muscle at L5 were significantly higher in CNLBP patients (all P < 0.05). T2 values had moderate to strong positive correlations (r = 0.34-0.60, all P < 0.05) with DECT WD in CNLBP patients and healthy volunteers. There was a weak correlation between VAS and WD in L5-level multifidus muscle (r = 0.29, P < 0.05). CONCLUSIONS: The T2 values in MRI and WD in DECT are higher in multifidus muscles of lower vertebra levels for young CNLBP patients, and there exists positive correlation between WD and T2 values, providing useful information for diagnosing CNLBP.

Prediction of osteoporosis using radiomics analysis derived from single source dual energy CT.

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.

Fast kilovoltage (KV)-switching dual-energy CT virtual noncalcium technique using different reconstruction kernels for identifying acute and chronic vertebral compression fractures.

The vertebral compression fractures (VCFs) represent an incidental finding on thoracic and abdominal dual-energy CT examinations (which use STND reconstruction kernel), which are associated with increased mortality. While the BONE reconstruction kernel shows a superior diagnostic accuracy to find fractures. This study showed STND and BONE reconstruction kernel both had excellent diagnostic performance to detect abnormal edema in acute VCFs. PURPOSE: To investigate whether different reconstruction kernels (STND V.S. BONE) affect the diagnostic performance of dual-energy CT virtual noncalcium technique (VNCa) for identifying acute and chronic vertebral compression fractures (VCFs). METHODS: This retrospective study included 31 consecutive patients with 79 VCFs who underwent both a dual-energy CT and a 3-T MR examination of the spine between August 2018 and March 2019. MR images served as the reference standard. Two independent and blinded radiologists evaluated all vertebral bodies for the presence of abnormal edema on color-coded overlay VNCa images. Two additional radiologists performed a quantitative analysis on VNCa images by calculating water content of vertebral bodies. Receiver operating characteristic curve (ROC) analysis was conducted. Area under the curve (AUC) was calculated. RESULTS: MR imaging depicted 44 edematous and 35 nonedematous VCFs. In visual analysis, the AUCSTND and AUCBONE were 0.932 and 0.943. In quantitative analysis, water content results were significantly different between vertebrae with and without bone marrow edema on MR (P < 0.001). And the AUCSTND and AUCBONE were 0.851 and 0.850 respectively. CONCLUSION: Visual and quantitative analysis of dual-energy CT VNCa technique had excellent diagnostic performance for identifying acute and chronic compression fractures; different reconstruction kernels did not matter.

Fast kilovoltage (KV)-switching dual-energy computed tomography hydroxyapatite (HAP)-water decomposition technique for identifying bone marrow edema in vertebral compression fractures.

BACKGROUND: Hydroxyapatite (HAP) is the main component of bone mineral. The utility of using HAP-water 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/cm3 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.