BACKGROUND: The longitudinal relation between coronary artery disease (CAD) polygenic risk score (PRS) and long-term plaque progression and high-risk plaque (HRP) features is unknown.
OBJECTIVE: The goal of this study was to investigate the impact of CAD PRS on long-term coronary plaque progression and HRP.
METHODS: Patients underwent CAD PRS measurement and prospective serial coronary computed tomography angiography (CTA) imaging. Coronary CTA scans were analyzed with a previously validated artificial intelligence-based algorithm (atherosclerosis imaging-quantitative computed tomography imaging). The relationship between CAD PRS and change in percent atheroma volume (PAV), percent noncalcified plaque progression, and HRP prevalence was investigated in linear mixed-effect models adjusted for baseline plaque volume and conventional risk factors.
RESULTS: A total of 288 subjects (mean age 58 ± 7 years; 60% male) were included in this study with a median scan interval of 10.2 years. At baseline, patients with a high CAD PRS had a more than 5-fold higher PAV than those with a low CAD PRS (10.4% vs 1.9%; P < 0.001). Per 10 years of follow-up, a 1 SD increase in CAD PRS was associated with a 0.69% increase in PAV progression in the multivariable adjusted model. CAD PRS provided additional discriminatory benefit for above-median noncalcified plaque progression during follow-up when added to a model with conventional risk factors (AUC: 0.73 vs 0.69; P = 0.039). Patients with high CAD PRS had an OR of 2.85 (95% CI: 1.14-7.14; P = 0.026) and 6.16 (95% CI: 2.55-14.91; P < 0.001) for having HRP at baseline and follow-up compared with those with low CAD PRS.
CONCLUSIONS: Polygenic risk is strongly associated with future long-term plaque progression and HRP in patients suspected of having CAD.

BACKGROUND: There is a corresponding increase in the prevalence of osteoporosis and related fractures with the aging population on the rise. Furthermore, osteoporotic vertebral compression fractures (OVCF) may contribute to higher patient mortality rates. It is essential to conduct research on risk factors for OVCF and provide a theoretical basis for preventing such fractures.
METHODS: We retrospectively recruited patients who had spine CT for OVCF or back pain. Demographic and CT data were collected. Quantitative computed tomography (QCT) software analyzed the CT data, using subcutaneous fat and paraspinal muscles as reference standards for BMD processing. BMD of cortical and cancellous bones in each patient\'s vertebral body was determined.
RESULTS: In this study, 144 patients were divided into non-OVCF (96) and OVCF (48) groups. Non-OVCF patients had higher cortical BMD of 382.5 ± 52.4 to 444.6 ± 70.1 mg/cm3, with T12 having the lowest BMD (p < 0.001, T12 vs. L2). Cancellous BMD ranged from 128.5 ± 58.4 to 140.9 ± 58.9 mg/cm3, with L3 having the lowest BMD. OVCF patients had lower cortical BMD of 365.0 ± 78.9 to 429.3 ± 156.7 mg/cm3, with a further decrease in T12 BMD. Cancellous BMD ranged from 71.68 ± 52.07 to 123.9 ± 126.2 mg/cm3, with L3 still having the lowest BMD. Fractured vertebrae in OVCF patients (T12, L1, and L2) had lower cortical bone density compared to their corresponding vertebrae without fractures (p < 0.05).
CONCLUSIONS: T12 had the lowest cortical BMD and L3 had the lowest cancellous BMD in OVCF patients, with T12 also having the highest incidence of osteoporotic fractures. These findings suggest that reduction in cortical BMD has a greater impact on OVCF than reduction in cancellous BMD, along with biomechanical factors.

UNASSIGNED: Osteoporosis diagnosis often utilizes quantitative computed tomography (QCT). This study explored the validity of applying lumbar bone mineral density (LBMD) standards to thoracic vertebrae (T8-T10) for osteoporosis detection during CT lung cancer screenings. This study investigated the utility of thoracic BMD (BMD-T8-T10) for detecting osteoporosis in older persons during CT lung cancer screening.
UNASSIGNED: We studied 701 participants who underwent QCT scans for both LBMD and BMD-T8-T10. Osteoporosis was diagnosed using ACR criteria based on LBMD. We determined BMD-T8-T10 thresholds via a receiver operating characteristic (ROC) curve and translated BMD-T8+T9+T10 to LBMD (TTBMD) using linear regression. Kappa test was used to evaluate the accuracy of BMD-T8-T10 thresholds and TTBMD in diagnosing osteoporosis.
UNASSIGNED: Raw BMD-T8-T10 poorly identified osteoporosis (kappa = 0.51). ROC curve analysis identified BMD-T8-T10 thresholds for osteopenia (138 mg/cm3) and osteoporosis (97 mg/cm3) with areas under the curve of 0.97 and 0.99, respectively. We normalized BMD-T8-T10 to TTBMD based on the formula: TTBMD = 0.9 × BMD-T8-T10 - 2.56. These thresholds (kappa = 0.74) and TTBMD performed well in detecting osteoporosis/osteopenia (kappa = 0.74).
UNASSIGNED: Both calculating BMD-T8-T10 threshold (138.0 mg/cm3 for osteopenia and 97 mg/cm3 for osteoporosis) and normalizing BMD-T8-T10 to LBMD demonstrated good performance in identifying osteoporosis in older adults during CT lung cancer screening.

BACKGROUND: Effective tools to evaluate bone quality preoperatively are scarce and the standard method to determine bone quality requires an invasive biopsy. A non-invasive, and preoperatively available method for bone quality assessment would be of clinical value. The purpose of this study is to investigate the associations of bone formation marker, serum bone alkaline phosphatase (BAP), and bone resorption marker, urine collagen cross-linked N-telopeptide (uNTX) to volumetric bone mineral density (vBMD), fluorescent advanced glycation endproducts (fAGEs) and bone microstructure.
METHODS: A cross-secional analysis using prospective data of patients undergoing lumbar spinal fusion was performed. BAP and uNTX were preoperatively collected. Quantitative computed tomography (QCT) was performed at the lumbar spine (vBMD ≤ 120 mg/cm3 osteopenic/osteoporotic). Bone biopsies from the posterior superior iliac spine were obtained and evaluated with multiphoton fluorescence microscopy for fAGEs and microcomputed tomography (µCT) for bone microarchitecture. Correlations between BAP/uNTX to vBMD, fAGEs and µCT parameters were assessed with Spearman\'s ρ. Receiver operating characteristic (ROC) analysis evaluated BAP and uNTX as predictors for osteopenia/osteoporosis. Multivariable linear regression models adjusting for age, sex, BMI, race and diabetes mellitus determined associations between BAP/uNTX and fAGEs.
RESULTS: 127 prospectively enrolled patients (50.4% female, 62.5 years, BMI 28.7 kg/m2) were analyzed. uNTX (ρ=-0.331,p < 0.005) and BAP (ρ=-0.245,p < 0.025) decreased with cortical fAGEs, and uNTX (ρ=-0.380,p < 0.001) decreased with trabecular fAGEs. BAP and uNTX revealed no significant correlation with vBMD. ROC analysis for BAP and uNTX discriminated osteopenia/osteoporosis with AUC of 0.477 and 0.561, respectively. In the multivariable analysis, uNTX decreased with increasing trabecular fAGEs after adjusting for covariates (β = 0.923;p = 0.031).
CONCLUSIONS: This study demonstrated an inverse association of bone turnover markers and fAGEs. Both uNTX and BAP could not predict osteopenia/osteoporosis in the spine. uNTX reflects collagen characteristics and might have a complementary role to vBMD, as a non-invasive tool for bone quality assessment in spine surgery.

The importance of osteoporosis assessment before lumbar surgery is well recognized. The MRI-based Vertebral Bone Quality (VBQ) score is introduced to evaluate bone quality; however, its diagnostic value has not been well documented. The purpose of this meta-analysis was to summarize the diagnostic value of the VBQ score for osteoporosis or osteopenia in patients undergoing lumbar surgery. We comprehensively searched electronic databases for studies exploring the diagnostic accuracy of the VBQ score for osteoporosis/osteopenia in patients with lumbar disease following the PRISMA guidelines. The quality of the included studies was assessed. The VBQ scores were compared between the groups, and the pooled sensitivity, specificity, and summary receiver operating characteristic (ROC) were calculated. Publication bias was assessed, and meta-regression was conducted. We included 17 studies with a total of 2815 patients, with a mean age of 66.4 years and a percentage of females of 72.5%. According to the QUADAS-2 tool, the quality of the included studies was relatively high. The results showed a significantly higher VBQ score in the osteoporosis/osteopenia group compared with the control group. According to the mean VBQ cutoff value of 3.02 ± 0.38 for the diagnosis of osteoporosis, the pooled sensitivity and specificity were 0.76 and 0.74, respectively, and the AUC was 0.81. According to the mean VBQ cutoff value of 2.31 ± 0.18 for the diagnosis of osteopenia, the pooled sensitivity and specificity were 0.78 and 0.58, respectively, and the AUC was 0.76. The MRI-based VBQ score could provide useful information for identifying patients with low bone mass who need further evaluation. Future prospective studies are still needed to evaluate the complementary role of the VBQ score.

BACKGROUND: Exacerbations of chronic obstructive pulmonary disease (COPD) have a significant impact on hospitalizations, morbidity, and mortality of patients. This study aimed to develop a model for predicting acute exacerbation in COPD patients (AECOPD) based on deep-learning (DL) features.
METHODS: We performed a retrospective study on 219 patients with COPD who underwent inspiratory and expiratory HRCT scans. By recording the acute respiratory events of the previous year, these patients were further divided into non-AECOPD group and AECOPD group according to the presence of acute exacerbation events. Sixty-nine quantitative CT (QCT) parameters of emphysema and airway were calculated by NeuLungCARE software, and 2,000 DL features were extracted by VGG-16 method. The logistic regression method was employed to identify AECOPD patients, and 29 patients of external validation cohort were used to access the robustness of the results.
RESULTS: The model 3-B achieved an area under the receiver operating characteristic curve (AUC) of 0.933 and 0.865 in the testing cohort and external validation cohort, respectively. Model 3-I obtained AUC of 0.895 in the testing cohort and AUC of 0.774 in the external validation cohort. Model 7-B combined clinical characteristics, QCT parameters, and DL features achieved the best performance with an AUC of 0.979 in the testing cohort and demonstrating robust predictability with an AUC of 0.932 in the external validation cohort. Likewise, model 7-I achieved an AUC of 0.938 and 0.872 in the testing cohort and external validation cohort, respectively.
CONCLUSIONS: DL features extracted from HRCT scans can effectively predict acute exacerbation phenotype in COPD patients.

BACKGROUND: Quantitative CT imaging, particularly iodine and calcium quantification, is an important CT-based biomarker.
OBJECTIVE: This study quantifies sources of errors in quantitative CT imaging in both single-energy and spectral CT.
METHODS: This work examines the theoretical relationship between CT numbers, linear attenuation coefficient, and material quantification. We derive four understandings: (1) CT numbers are not proportional with element mass in vivo, (2) CT numbers are proportional with element mass only when contained in a voxel of pure water, (3) iodine-water material decomposition is never accurate in vivo, and (4) for error-free material decomposition a voxel must only consist of the basis decomposition vectors. Misinterpretation-based errors are calculated using the National Institute of Standards and Technology (NIST) XCOM database for: tissue chemical compositions, clinical concentrations of hydroxyapatite (HAP), and iodine. Quantification errors are also demonstrated experimentally using phantoms.
RESULTS: In single-energy CT, misinterpretation-induced errors for HAP density in adipose, muscle, lung, soft tissue, and blood ranged from 0-132%, i.e., a mass error of 0-749 mg/cm3. In spectral CT, errors with iodine in the same tissues resulted in a range of < 0.1-33% error, resulting in a mass error of < 0.1-1.2 mg/mL.
CONCLUSIONS: Our work demonstrates material quantification is fundamentally limited when measured in vivo due to measurement conditions differing from assumed and the errors are at or above detection limits for bone mineral density (BMD) and spectral iodine quantification. To define CT-derived biomarkers, the errors we demonstrate should either be avoided or built into uncertainty bounds.
CONCLUSIONS: Improving error bounds in quantitative CT biomarkers, specifically in iodine and BMD quantification, could lead to improvements in clinical care aspects based on quantitative CT.
CONCLUSIONS: CT numbers are only proportional with element mass only when contained in a voxel of pure water, therefore iodine-water material decomposition is never accurate in vivo. Misinterpretation-induced errors ranged from 0-132% for HAP density and < 0.1-33% in spectral CT with iodine. For error-free material decomposition, a voxel must only consist of the basis decomposition vectors.

OBJECTIVE: The effects of daily teriparatide (D-PTH, 20 μg/day), weekly high-dose teriparatide (W-PTH, 56.5 μg/week), or bisphosphonate (BP) on the vertebra and proximal femur were investigated using quantitative computed tomography (QCT).
METHODS: A total of 131 postmenopausal women with a history of fragility fractures were randomized to receive D-PTH, W-PTH, or bisphosphonate (oral alendronate or risedronate). QCT were evaluated at baseline and after 18 months of treatment.
RESULTS: A total of 86 participants were evaluated by QCT (Spine: D-PTH: 25, W-PTH: 21, BP: 29. Hip: PTH: 22, W-PTH: 21, BP: 32. Dropout rate: 30.5 %). QCT of the vertebra showed that D-PTH, W-PTH, and BP increased total vBMD (+34.8 %, +18.2 %, +11.1 %), trabecular vBMD (+50.8 %, +20.8 %, +12.2 %), and marginal vBMD (+20.0 %, +14.0 %, +11.5 %). The increase in trabecular vBMD was greater in the D-PTH group than in the W-PTH and BP groups. QCT of the proximal femur showed that D-PTH, W-PTH, and BP increased total vBMD (+2.8 %, +3.6 %, +3.2 %) and trabecular vBMD (+7.7 %, +5.1 %, +3.4 %), while only W-PTH and BP significantly increased cortical vBMD (-0.1 %, +1.5 %, +1.6 %). Although there was no significant increase in cortical vBMD in the D-PTH group, cortical bone volume (BV) increased in all three treatment groups (+2.1 %, +3.6 %, +3.1 %).
CONCLUSIONS: D-PTH had a strong effect on trabecular bone of vertebra. Although D-PTH did not increase cortical BMD of proximal femur, it increased cortical BV. W-PTH had a moderate effect on trabecular bone of vertebra, while it increased both cortical BMD and BV of proximal femur. Although BP had a limited effect on trabecular bone of vertebra compared to teriparatide, it increased both cortical BMD and BV of proximal femur.

METHODS: A retrospective cohort study.
OBJECTIVE: This study aimed to assess the reliability of quantitative computed tomography (QCT) in measuring bone mineral density (BMD) of instrumented vertebrae and investigate the effect of less paraspinal muscle damage on BMD changes after lumbar interbody fusion.
BACKGROUND: Patients always experience a decrease in vertebral BMD after lumbar interbody fusion. However, to the best of our knowledge, no study has analyzed the effect of paraspinal muscles on BMD changes.
METHODS: This retrospective analysis included a total of 155 patients who underwent single-level lumbar fusion, with 81 patients in the traditional group and 74 patients in the Wiltse group (less paraspinal muscle damage). QCT was used to measure the volumetric BMD (vBMD), Hounsfield unit value, and cross-sectional area of the paraspinal muscles at the upper instrumented vertebrae (UIV), vertebrae one segment above the UIV (UIV+1), and the vertebrae one segment above the UIV+1 (UIV+2). Statistical analyses were performed.
RESULTS: No significant differences in general data were observed between the two groups (p>0.05). Strong correlations were noted between the preoperative and 1-week postoperative vBMD of each segment (p<0.01), with no significant difference between the two time points in both groups (p>0.05). Vertebral BMD loss was significantly higher in UIV+1 and UIV+2 in the traditional group than in the Wiltse group (-13.6%±19.1% vs. -4.2%±16.5%, -10.8%±20.3% vs. -0.9%±37.0%; p<0.05). However, no statistically significant difference was observed in the percent vBMD changes in the UIV segment between the two groups (37.7%±70.1% vs. 36.1%±78.7%, p>0.05).
CONCLUSIONS: QCT can reliably determine BMD in the instrumented spine after lumbar interbody fusion. With QCT, we found that reducing paraspinal muscle destruction through the Wiltse approach during surgery can help preserve the adjacent vertebral BMD; however, it does not help increase the BMD in the instrumented vertebrae.

BACKGROUND: Establishing good screw-bone structural stability is conducive to reducing the risk of postoperative screw loosening. Screw insertion torque is an objective index for evaluating screw-bone structural stability. Therefore, accurate prediction of screw insertion torque can improve the preoperative evaluation of patients, optimize the surgical plan, and improve the surgical effect. At present, the correlation between different bone assessment methods and screw insertion torque is unclear.
OBJECTIVE: The aim of this study was to evaluate the correlation between different bone assessment methods and screw insertion torque and to optimize the predictive performance of screw insertion torque through mathematical modeling combined with different radiology methods.
METHODS: Prospective cross-sectional study.
METHODS: 77 patients with preoperatively available DXA, CT and MRI data who underwent spinal fixation surgeries between October 2022 and September 2023 and 357 sets of screw data were included in this analysis.
METHODS: Spinal, vertebrae-specific and screw trajectory\'s BMD were measured preoperatively by different imaging modalities. Intraoperative screw insertion torque was measured using an electronic torque wrench.
METHODS: Pearson linear correlation, scatter plots and univariate linear regression were used to evaluate the correlation between different bone evaluation methods and screw insertion torque. Different bone evaluation methods were fitted into the prediction model of screw torque and the related equations were obtained.
RESULTS: Screw insertion torque had the strongest positive correlation with the volumetric bone mineral density (vBMD) of the screw trajectory (Pedicle screw insertion torque (PSIT): R = 0.618, p<.001; Terminal screw insertion torque (TSIT): R = 0.735, p<.001). A weak negative correlation was found between the screw insertion torque and level specific vertebral bone quality (VBQ) (PSIT: R = -0.178, p=.001; TSIT: R = -0.147, p=.006). We also found that the PSIT was strongly correlated with the TSIT (R = 0.812, p<.001).
CONCLUSIONS: Compared to other bone quality assessment methods, screw trajectory vBMD may be better predict the magnitude of screw insertion torque. In addition, we further optimized preoperative assessments by constructing a mathematical model to better predict screw insertion torque. In conclusion, clinicians should select appropriate preoperative bone quality assessment methods, identify potential low-torque patients, optimize surgical plans, and ultimately improve screw insertion accuracy and reduce postoperative screw loosening rate.