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.

Associations between paraspinal muscles fatty infiltration and lumbar vertebral bone mineral density - An investigation by fast kVp switching dual-energy CT and QCT.

Purpose: To investigate the relationship between paraspinal muscles fat content and lumbar bone mineral density (BMD). Methods: A total of 119 participants were enrolled in our study (60 males, age: 50.88 ± 17.79 years, BMI: 22.80 ± 3.80 kg·m-2; 59 females, age: 49.41 ± 17.69 years, BMI: 22.22 ± 3.12 kg·m-2). Fat content of paraspinal muscles (erector spinae (ES), multifidus (MS), and psoas (PS)) were measured at (ES L1/2-L4/5; MS L2/3-L5/S1; PS L2/3-L5/S1) levels using dual-energy computed tomography (DECT). Quantitative computed tomography (QCT) was used to assess BMD of L1 and L2. Linear regression analysis was used to assess the relationship between BMD of the lumbar spine and paraspinal muscles fat content with age, sex, and BMI. The variance inflation factor (VIF) was used to detect the degree of multicollinearity among the variables. P < .05 was considered to indicate a statistically significant difference. Results: The paraspinal muscles fat content had a fairly significant inverse association with lumbar BMD after controlling for age, sex, and BMI (adjusted R 2 = 0.584-0.630, all P < .05). Conclusion: Paraspinal muscles fat content was negatively associated with BMD.Paraspinal muscles fatty infiltration may be considered as a potential marker to identify BMD loss.

Grey Matter Hypertrophy and Atrophy in Early-Blind Adolescents: A Surface-Based Morphometric Study.

Objective: This study is aimed at exploring the regional changes in brain cortical morphology (thickness, volume, and surface area) in the early-blind adolescents (EBAs) by using the surface-based morphometric (SBM) method. Methods: High-resolution structural T1-weighted images (T1WI) of 23 early-blind adolescents (EBAs) and 21 age- and gender-matched normal-sighted controls (NSCs) were acquired. Structural indices, including cortical thickness (CT), cortical volume (CV), and surface area (SA), were analyzed by using FreeSurfer software, and the correlations between structural indices and the blindness duration were computed by Pearson correlation analysis. Results: Compared to controls, EBAs had significantly reduced CV and SA mainly in the primary visual cortex (V1) and decreased CV in the left vision-related cortices (r-MFC). There were no regions that EBAs had a significantly larger CV or SA than NSCs. EBAs had significantly increased CT in the V1 and strongly involved the visual cortex (right lateral occipital gyrus, LOG.R) and the left superior temporal gyrus (STG.L), while it had decreased CT in the left superior parietal lobule (SPL.L) and the right lingual gyrus (LING.R). Additionally, no correlation was found between cortical morphometric measures and clinical variables in the EBA group. Conclusions: SBM is a useful method for detecting human brain structural abnormalities in blindness. The results showed that these structural abnormalities in the visual cortex and visual-related areas outside the occipital cortex in the EBAs not only may be influenced by neurodevelopment, degeneration, plasticity, and so on but also involved the interaction of these factors after the early visual deprivation.

Dual-Energy Computed Tomography for the Diagnosis of Mediastinal Lymph Node Metastasis in Lung Cancer Patients: A Preliminary Study.

OBJECTIVE: This study explored the feasibility of dual-energy computed tomography (DECT) for the diagnosis of mediastinal lymph node (LN) metastasis in patients with lung cancer. METHODS: Forty-two consecutive patients with lung cancer, who underwent DECT, were included in this retrospective study. The attenuation value (Hounsfield unit) in virtual monochromatic images and the iodine concentration in the iodine map were measured at mediastinal LNs. The slope of the spectral attenuation curve (K) and normalized iodine concentration (in thoracic aorta) were calculated. The measurement results were statistically compared using 2 independent samples t test. Receiver operating characteristic curve analysis, net reclassification improvement, and integrated discrimination improvement were used to evaluate the diagnostic performance of DECT for mediastinal LN metastasis. RESULTS: A total of 74 mediastinal LNs were obtained, including 33 metastatic LNs and 41 nonmetastatic LNs. The attenuation value at the lower energy levels of virtual monochromatic images (40-90 keV), K, and normalized iodine concentration demonstrated a significant difference between metastatic LNs and nonmetastatic LNs. The attenuation value at 40 keV was the most favorable biomarker for the diagnosis of mediastinal LN metastasis (area under curve, 0.91; sensitivity, 0.94; specificity, 0.81), which showed a much better performance than the LN diameter-based evaluation method (area under curve, 0.72; sensitivity, 0.66; specificity, 0.82; net reclassification improvement, 0.359; integrated discrimination improvement, 0.330). CONCLUSIONS: Dual-energy computed tomography is a promising diagnostic approach for the diagnosis of mediastinal LN metastasis in patients with lung cancer, which may help clinicians implement personalized treatment strategies.

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.

In vivo quantification of bone mineral density of lumbar vertebrae using fast kVp switching dual-energy CT: correlation with quantitative computed tomography.

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) [DCa(Wa), DHAP(Wa), DCa(Fat) and DHAP(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 R2 =0.983-0.987; NMSE = 1.6-2.1%; RElinear =0.6-0.9%). Bland-Altman plots indicated high agreement between both measurements. DCa(Fat) and DHAP(Fat) showed relatively similar and optimal predictive capability for QCT-derived BMD (both: adjusted R2 =0.987, NMSE =1.6%, RElinear =0.6%). CONCLUSIONS: Fast kVp switching DECT enabled accurate phantomless in vivo BMD quantification of the lumbar spine. DCa(Fat) and DHAP(Fat) had relatively similar and optimal predictive capability.