Quantitative Computed Tomography Research Articles

Imaging in osteogenesis imperfecta: Where we are and where we are going

Osteogenesis imperfecta (OI) is a rare phenotypically and genetically heterogeneous group of inherited skeletal dysplasias. The hallmark features of OI include bone fragility and susceptibility to fractures, bone deformity, and diminished growth, along with a plethora of associated secondary features (both skeletal and extraskeletal). The diagnosis of OI is currently made on clinical grounds and may be confirmed by genetic testing. However, imaging remains pivotal in the evaluation of this disease. The aim of this article is to review the current role played by the various radiologic techniques in the diagnosis and monitoring of OI in the postnatal setting as well as to discuss recent advances and future perspectives in OI imaging. Conventional Radiography and Dual-energy X-ray Absorptiometry (DXA) are currently the two most used imaging modalities in OI. The cardinal radiographic features of OI include generalized osteopenia/osteoporosis, bone deformities, and fractures. DXA is currently the most available technique to assess Bone Mineral Density (BMD), specifically areal BMD (aBMD). However, DXA has important limitations and cannot fully characterize bone fragility in OI based on aBMD. Novel DXA-derived parameters, such as Trabecular Bone Score (TBS), may provide further insight into skeletal changes induced by OI, but evidence is still limited. Techniques like Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) can be useful as problem-solvers or in specific settings, including the evaluation of cranio-cervical abnormalities. Recent evidence supports the use of High-Resolution peripheral Quantitative Computed Tomography (HR-pQCT) as a promising tool to improve the characterization of bone fragility in OI. However, HR-pQCT remains a primarily research technique at present. Quantitative Computed Tomography (QCT) is an alternative to DXA for the determination of BMD at central sites, with distinct advantages but considerably higher radiation exposure. Quantitative Ultrasound (QUS) is a portable, inexpensive, and radiation-free modality that may complement DXA evaluation, providing information on bone quality. However, evidence of usefulness of QUS in OI is poor. Radiofrequency Echographic Multi Spectrometry (REMS) is an emerging non-ionizing imaging method that holds promise for the diagnosis of low BMD and for the prediction of fracture risk, but so far only one published study has investigated its role in OI. To conclude, several different radiologic techniques have proven to be effective in the diagnosis and monitoring of OI, each with their own specificities and peculiarities. Clinicians should be aware of the strategic role of the various modalities in the different phases of the patient care process. In this scenario, the development of international guidelines including recommendations on the role of imaging in the diagnosis and monitoring of OI, accompanied by continuous active research in the field, could significantly improve the standardization of patient care.

Accuracy and precision of internal displacement and strain measurements in long human bones using HR-pQCT and digital volume correlation

ABSTRACT Digital volume correlation (DVC) is a technique for measuring 3D, internal displacements and strains in loaded structures. One application of DVC is the study of internal bone mechanics and the validation of subject-specific finite element (FE) models. While micro-computed tomography (µCT) is a common choice for DVC studies of small samples of bone, long human bones such as the tibia may exceed the spatial limitations of conventional µCT. High resolution peripheral quantitative CT (HR-pQCT) scanners, with their large bore and open-ended design, may be a viable option for long-bone DVC. However, HR-pQCT is afflicted by stitching artefacts, caused by the stacking of scan blocks to form the large scan volume, resulting in erroneous steps in DVC displacements and bands of increased strain error. This study proposed a modified HR-pQCT scanning protocol and stitching methodology to mitigate the effects of stitching artefacts on DVC measurements of displacement and strain. With the application of the proposed scanning/stitching methodology, displacements greater than 11.7µm (0.29 voxels) and strains greater than 1633µε could be repeatedly measured by DVC. These results show that HR-pQCT combined with DVC is suitable for measuring internal bone displacements in long human bones with sub-voxel precision and strains greater than 1633µε.

Assessment of whole-body and regional body fat using abdominal quantitative computed tomography in Chinese women and men

BackgroundBeing overweight or obese has become a serious public health concern, and accurate assessment of body composition is particularly important. More precise indicators of body fat composition include visceral adipose tissue (VAT) mass and total body fat percentage (TBF%). Study objectives included examining the relationships between abdominal fat mass, measured by quantitative computed tomography (QCT), and the whole-body and regional fat masses, measured by dual energy X-ray absorptiometry (DXA), as well as to derive equations for the prediction of TBF% using data obtained from multiple QCT slices.MethodsWhole-body and regional fat percentage were quantified using DXA in Chinese males (n = 68) and females (n = 71) between the ages of 24 and 88. All the participants also underwent abdominal QCT measurement, and their VAT mass and visceral fat volume (VFV) were assessed using QCT and DXA, respectively.ResultsDXA-derived TBF% closely correlated with QCT abdominal fat percentage (r = 0.89–0.93 in men and 0.76–0.88 in women). Stepwise regression showed that single-slice QCT data were the best predictors of DXA-derived TBF%, DXA android fat percentage and DXA gynoid fat percentage. Cross-validation analysis showed that TBF% and android fat percentage could be accurately predicted using QCT data in both sexes. There were close correlations between QCT-derived and DXA-derived VFV (r = 0.97 in men and 0.93 in women).ConclusionClinicians can assess the TBF% and android and gynoid fat percentages of Chinese women and men by analysing existing abdominal CT-derived data using the QCT technique.

Phantomless Computed Tomography-Based Quantitative Bone Mineral Density Assessment: A Literature Review

The global surge in aging populations has intensified osteoporosis challenges, necessitating improved diagnostic methods. While dual X-ray absorptiometry (DXA) is the conventional standard in assessing bone mineral density (BMD), the exploration of quantitative computed tomography (QCT) has been proposed, particularly phantomless techniques. Challenges in single-energy CT, emphasizing internal calibration standards like subcutaneous fat, are discussed. Advances in PL-QCT, notably with automatic region of interest (ROI) selection, show improved accuracy. Dual-energy CT (DECT) introduces new dimensions for musculoskeletal analysis. Despite advancements, challenges persist, including interindividual variability and patient-specific factors. Evolving single-energy CT and DECT techniques show promise in refining BMD assessment and osteoporosis diagnosis, enhancing patient care. Continued research and integration into clinical practice are vital for realizing these advancements’ full benefits. In this review, we evaluate and summarize current evidence on the feasibility and different approaches to achieve analysis of BMD with phantomless QCT.

The importance of nonobstructive plaque characteristics in symptomatic and asymptomatic coronary artery disease

BackgroundWe examined obstructive and nonobstructive plaque volumes in populations with subclinical and clinically manifested coronary artery disease (CAD) using quantitative computed tomography (QCT). Methods855 participants with CAD (274 asymptomatic individuals, 254 acute chest pain patients without acute coronary syndrome (ACS), and 327 patients with ACS) underwent QCT of proximal coronary segments to assess participant-level plaque volumes of dense calcium, fibrous, fibrofatty, and necrotic core tissue. ResultsNonobstructive (<50% stenosis) plaque volumes were greater than obstructive plaque volumes, irrespective of population (all p<0.0001): Asymptomatic individuals (mean (95% CI)): 218 [190-250] vs. 16 [12-22] mm3; acute chest pain patients without ACS: 300 [263-341] vs. 51 [41-62] mm3; patients with ACS: 370 [332-412] vs. 159 [139-182] mm3. After multivariable adjustment, nonobstructive fibrous and fibrofatty tissue volumes were greater in acute chest pain patients without ACS compared to asymptomatic individuals (fibrous tissue: 122 [107-139] vs. 175 [155-197] mm3, p<0.01; fibrofatty tissue: 44 [38-50] vs. 71 [63-80] mm3, p<0.01. Necrotic core tissue was greater in ACS patients (29 [26-33] mm3) compared to both asymptomatic individuals (15 [13-18] mm3, p<0.0001) and acute chest pain patients without ACS (21 [18-24] mm3, p<0.05). Nonobstructive dense calcium volumes did not differ between the three populations: 29 [24-36], 29 [23-35], and 41 [34-48] mm3, p>0.3 respectively. ConclusionNonobstructive CAD was the predominant contributor to total atherosclerotic plaque volume in both subclinical and clinically manifested CAD. Nonobstructive fibrous, fibrofatty and necrotic core tissue volumes increased with worsening clinical presentation, while nonobstructive dense calcium tissue volumes did not.

High-resolution peripheral quantitative computed tomography for the assessment of acro-osteolysis and calcinosis in patients with systemic sclerosis

ObjectiveTo assist the development of future treatments in systemic sclerosis (SSc), the development of reliable outcome measures is pivotal. We aimed to evaluate the use of high-resolution peripheral quantitative CT (HR-pQCT) for visualization and gradation of acro-osteolysis (AO) and calcinosis compared to conventional hand radiographs (CR) in patients with SSc. MethodsHR-pQCT scans of the 2nd to 4th fingers, CR, nail fold capillaroscopy, and a clinical examination were conducted. Images were reviewed for the presence and degree of AO and calcinosis according to semiquantitative grading scales. ResultsForty patients were included. Fourteen had AO according to CR, whereas HR-pQCT revealed AO in 18 patients. The sensitivity and specificity of classifying patients as having AO by HR-pQCT when CR was used as reference were 93% (95% CI: 66–99%) and 80% (95% CI: 59–93%), respectively. By CR and with HR-pQCT as reference, the sensitivity and specificity were 72% (95% CI: 47–90%) and 95% (95% CI: 76–99%). Patients with AO had more or larger calcifications than patients without AO according to the proposed HR-pQCT grading system, with a median grade of 2 (IQR: 1–3) versus 0 (IQR: 0–1) (P<0.01). Grade 3 changes were observed exclusively in patients with AO (n=6/14, 42.9%). Assessment of AO and calcinosis by HR-pQCT demonstrated moderate to excellent test-retest reliability. ConclusionHR-pQCT allowed precise and reliable classification and grading of acro-osteolysis and acral calcinosis. The modality could prove helpful for detecting and monitoring these lesions as well as facilitating early diagnosis and guide treatment of these patients.

Towards the adoption of quantitative computed tomography in the management of interstitial lung disease.

The shortcomings of qualitative visual assessment have led to the development of computer-based tools to characterise and quantify disease on high-resolution computed tomography (HRCT) in patients with interstitial lung diseases (ILDs). Quantitative CT (QCT) software enables quantification of patterns on HRCT with results that are objective, reproducible, sensitive to change and predictive of disease progression. Applications developed to provide a diagnosis or pattern classification are mainly based on artificial intelligence. Deep learning, which identifies patterns in high-dimensional data and maps them to segmentations or outcomes, can be used to identify the imaging patterns that most accurately predict disease progression. Optimisation of QCT software will require the implementation of protocol standards to generate data of sufficient quality for use in computerised applications and the identification of diagnostic, imaging and physiological features that are robustly associated with mortality for use as anchors in the development of algorithms. Consortia such as the Open Source Imaging Consortium have a key role to play in the collation of imaging and clinical data that can be used to identify digital imaging biomarkers that inform diagnosis, prognosis and response to therapy.

Quantitative computed tomography analysis of body composition changes in paediatric patients with acute lymphoblastic leukaemia.

Children with acute lymphoblastic leukaemia (ALL) are at risk for obesity and cardiometabolic diseases. To gain insight into body composition changes among children with ALL, we assessed quantitative computed tomography (QCT) data for specific body compartments (subcutaneous adipose tissue [SAT], visceral adipose tissue [VAT], total adipose tissue [TAT], lean tissue [LT], LT/TAT and VAT/SAT at lumbar vertebrae L1 and L2) at diagnosis and at off-therapy for 189 children with ALL and evaluated associations between body mass index (BMI) Z-score and clinical characteristics. BMI Z-score correlated positively with SAT, VAT and TAT and negatively with LT/TAT and VAT/SAT. At off-therapy, BMI Z-score, SAT, VAT and TAT values were higher than at diagnosis, but LT, LT/TAT and VAT/SAT were lower. Patients aged ≥10 years at diagnosis had higher SAT, VAT and TAT and lower LT and LT/TAT than patients aged 2.0-9.9 years. Female patients had lower LT and LT/TAT than male patients. Black patients had less VAT than White patients. QCT analysis showed increases in adipose tissue and decreases in LT during ALL therapy when BMI Z-scores increased. Early dietary and physical therapy interventions should be considered, particularly for patients at risk for obesity.

CT features based preoperative predictors of aggressive pathology for clinical T1 solid renal cell carcinoma and the development of nomogram model

BackgroundWe aimed to identify preoperative predictors of aggressive pathology for cT1 solid renal cell carcinoma (RCC) by combining clinical features with qualitative and quantitative CT parameters, and developed a nomogram model.MethodsWe conducted a retrospective study of 776 cT1 solid RCC patients treated with partial nephrectomy (PN) or radical nephrectomy (RN) between 2018 and 2022. All patients underwent four-phase contrast-enhanced CT scans and the CT parameters were obtained by two experienced radiologists using region of interest (ROI). Aggressive pathology was defined as patients with nuclear grade III-IV; upstage to pT3a; type II papillary renal cell carcinoma (pRCC), collecting duct or renal medullary carcinoma, unclassified RCC or sarcomatoid/rhabdoid features. Univariate and multivariate logistic analyses were used to determine significant predictors and develop the nomogram model. To evaluate the accuracy and clinical utility of the nomogram model, we used the receiver operating characteristic (ROC) curve, calibration plot, decision curve analysis (DCA), risk stratification, and subgroup analysis.ResultsOf the 776 cT1 solid RCC patients, 250 (32.2%) had aggressive pathological features. The interclass correlation coefficient (ICC) of CT parameters accessed by two reviewers ranged from 0.758 to 0.982. Logistic regression analyses showed that neutrophil-to-lymphocyte ratio (NLR), distance to the collecting system, CT necrosis, tumor margin irregularity, peritumoral neovascularity, and RER-NP were independent predictive factors associated with aggressive pathology. We built the nomogram model using these significant variables, which had an area under the curve (AUC) of 0.854 in the ROC curve.ConclusionsOur research demonstrated that preoperative four-phase contrast-enhanced CT was critical for predicting aggressive pathology in cT1 solid RCC, and the constructed nomogram was useful in guiding patient treatment and postoperative follow-up.

Bone microarchitecture and strength assessment in adults with osteogenesis imperfecta using HR-pQCT: normative comparison and challenges.

Data on bone microarchitecture in osteogenesis imperfecta (OI) are scarce. The aim of this cross-sectional study was to assess bone microarchitecture and strength in a large cohort of adults with OI using high-resolution peripheral quantitative computed tomography (HR-pQCT) and to evaluate challenges of using HR-pQCT in this cohort. Second-generation HR-pQCT scans were obtained at the distal radius and tibia in 118 men and women with Sillence OI type I, III, or IV using an extremity-length-dependent scan protocol. In total, 102 radius and 105 tibia scans of sufficient quality could be obtained, of which 11 radius scans (11%) and 14 tibia scans (13%) had a deviated axial scan angle as compared with axial angle data of 13 young women. In the scans without a deviated axial angle and compared with normative HR-pQCT data, Z-scores at the radius for trabecular bone mineral density (BMD), number, and separation were -1.6 ± 1.3, -2.5 ± 1.4, and -2.7 (IQR: 2.7), respectively. They were -1.4 ± 1.5 and -1.1 ± 1.2 for stiffness and failure load and between ±1 for trabecular thickness and cortical bone parameters. Z-scores were significantly lower for total and trabecular BMD, stiffness, failure load, and cortical area and thickness at the tibia. Additionally, local microarchitectural inhomogeneities were observed, most pronounced being trabecular void volumes. In the scans with a deviated axial angle, the proportion of Z-scores <-4 or >4 was significantly higher for trabecular BMD and separation (radius) or most total and trabecular bone parameters (tibia). To conclude, especially trabecular bone microarchitecture and bone strength were impaired in adults with OI. HR-pQCT may be used without challenges in most adults with OI, but approximately 12% of the scans may have a deviated axial angle in OI due to bone deformities or scan positioning limitations. Furthermore, standard HR-pQCT parameters may not always be reliable due to microarchitectural inhomogeneities nor fully reflect all inhomogeneities.

3D-DXA Based Finite Element Modelling for Femur Strength Prediction: Evaluation Against QCT.

Osteoporosis is characterised by the loss of bone density resulting in an increased risk of fragility fractures. The clinical gold standard for diagnosing osteoporosis is based on the areal bone mineral density (aBMD) used as a surrogate for bone strength, in combination with clinical risk factors. Finite element (FE) analyses based on quantitative computed tomography (QCT) have been shown to estimate bone strength better than aBMD. However, their application in the osteoporosis clinics is limited due to exposure of patients to increased X-rays radiation dose. Statistical modelling methods (3D-DXA) enabling the estimation of 3D femur shape and volumetric bone density from dual energy X-ray absorptiometry (DXA) scan have been shown to improve osteoporosis management. The current study used 3D-DXA based FE analyses to estimate femur strength from the routine clinical DXA scans and compared its results against 151 QCT based FE analyses, in a clinical cohort of 157 subjects. The linear regression between the femur strength predicted by QCT-FE and 3D-DXA-FE models correlated highly (coefficient of determination R2 = 0.86) with a root mean square error (RMSE) of 397 N. In conclusion, the current study presented a 3D-DXA-FE modelling tool providing accurate femur strength estimates noninvasively, compared to QCT-FE models.

Association between high-density lipoprotein cholesterol and lumbar bone mineral density in Chinese: a large cross-sectional study

BackgroundThe association between lipid and bone metabolism, particularly the role of high-density lipoprotein cholesterol (HDL-C) in regulating bone mineral density (BMD), is of significant interest. Despite numerous studies, findings on this relationship remain inconclusive, especially since evidence from large, sexually diverse Chinese populations is sparse. This study, therefore, investigates the correlation between HDL-C and lumbar BMD in people of different genders using extensive population-based data from physical examinations conducted in China.MethodsData from a cross-sectional survey involving 20,351 individuals aged > = 20 years drawn from medical records of health check-ups at the Health Management Centre of the Henan Provincial People’s Hospital formed the basis of this study. The primary objective was to determine the correlation between HDL-C levels and lumbar BMD across genders. The analysis methodology included demographic data analysis, one-way ANOVA, subgroup analyses, multifactorial regression equations, smoothed curve fitting, and threshold and saturation effect analyses.ResultsMultifactorial regression analysis revealed a significant inverse relationship between HDL-C levels and lumbar BMD in both sexes, controlling for potential confounders (Male: β = -8.77, 95% CI -11.65 to -5.88, P < 0.001; Female: β = -4.77, 95% CI -8.63 to -0.90, P = 0.015). Subgroup and threshold saturation effect analyses indicated a stronger association in males, showing that increased HDL-C correlates with reduced lumbar BMD irrespective of age and body mass index (BMI). The most significant effect was observed in males with BMI > 28 kg/m2 and HDL-C > 1.45 mmol/L and in females with a BMI between 24 and 28 kg/m2.ConclusionElevated HDL-C is associated with decreased bone mass, particularly in obese males. These findings indicate that individuals with high HDL-C levels should receive careful clinical monitoring to mitigate osteoporosis risk.Trial registrationThe research protocol received ethics approval from the Ethics Committee at Beijing Jishuitan Hospital, in conformity with the Declaration of Helsinki guidelines (No. 2015-12-02). These data are a contribution of the China Health Quantitative CT Big Data Research team, registered at clinicaltrials.gov (code: NCT03699228).

Evaluation of bone-related mechanical properties in female patients with long-term remission of Cushing's syndrome using quantitative computed tomography-based finite element analysis.

Hypercortisolism in Cushing's syndrome (CS) is associated with bone loss, skeletal fragility, and altered bone quality. No studies evaluated bone geometric and strain-stress values in CS patients after remission thus far. Thirty-two women with CS in remission (mean age [±SD] 51 ± 11; body mass index [BMI], 27 ± 4 kg/m2; mean time of remission, 120 ± 90 months) and 32 age-, BMI-, and gonadal status-matched female controls. Quantitative computed tomography (QCT) was used to assess volumetric bone mineral density (vBMD) and buckling ratio, cross-sectional area, and average cortical thickness at the level of the proximal femur. Finite element (FE) models were generated from QCT to calculate strain and stress values (maximum principal strain [MPE], maximum strain energy density [SED], maximum Von Mises [VM], and maximum principal stress [MPS]). Areal BMD (aBMD) and trabecular bone score (TBS) were assessed by dual-energy X-ray absorptiometry (2D DXA). Trabecular vBMD at total hip and trochanter were lower in CS as compared with controls (P < .05). Average cortical thickness was lower, and buckling ratio was greater in CS vs controls (P < .01). All strain and stress values were higher in CS patients vs controls (P < .05). 2D DXA-derived measures were similar between patients and controls (P > .05). Prior hypercortisolism predicted both VM (β .30, P = .014) and MPS (β .30, P = .015), after adjusting for age, BMI, menopause, delay to diagnosis, and duration of remission. Women with prior hypercortisolism have reduced trabecular vBMD and impaired bone geometrical and mechanical properties, which may contribute to an elevated fracture risk despite long-term remission.

Assessment of pulmonary fibrosis severity at autopsy in patients after COVID-19: comparison with quantitative CT scan data in the acute phase of the disease

INTRODUCTION: After the COVID-19 pandemic, there is increasing evidence that many patients show fibrous changes in lung tissue accompanied by functional lung disorders. Objective data on the histopathogenesis of such changes is still insufficient. Prospective studies are required to fully assess the consequences of these clinical manifestations.OBJECTIVE: Evaluation of the capabilities of digital processing of histological preparations of lung tissue and their comparison with quantitative CT data of lung patients in the acute phase of COVID-19.MATERIALS AND METHODS: The study included data from patients after COVID-19 (7 women and 3 men aged 47 to 93 years) who died after the acute phase of COVID-19 from extrapulmonary causes. The control group included data from 7 people (5 women and 2 men aged 35 to 93 years) who died shortly after hospitalization from extrapulmonary causes (myocardial infarction or acute cerebral stroke), with no signs of lung diseases, including autopsy results. Digital processing of histological preparations of lung tissue obtained during autopsy was carried out, and their comparison with the results of quantitative semi-automatic processing of CT data.Statistics. Beta regression (mgcv library) was used. The model was characterized by a pseudodetermination coefficient R2. The association was considered statistically significant at p&lt;0.05.RESULTS: A reliable dependence of the severity of fibrous changes in histological samples on the results of quantitative analysis of CT images of patients in the acute period of COVID-19 was demonstrated.DISCUSSION: Extrapolation of lung autopsy data through quantitative CT assessment is one of the ways to pre-diagnose and identify groups of patients who require specific treatment of post-COVID-19 pulmonary fibrosis.CONCLUSION. Computerized digital processing of histological images made it possible to correctly compare the histopathological examination data with the CT picture in COVID-19, which could potentially have a prognostic value in the search for more effective treatment strategies.

SSDL-an automated semi-supervised deep learning approach for patient-specific 3D reconstruction of proximal femur from QCT images.

Deep Learning (DL) techniques have recently been used in medical image segmentation and the reconstruction of 3D anatomies of a human body. In this work, we propose a semi-supervised DL (SSDL) approach utilizing a CNN-based 3D U-Net model for femur segmentation from sparsely annotated quantitative computed tomography (QCT) slices. Specifically, QCT slices at the proximal end of the femur forming ball and socket joint with acetabulum were annotated for precise segmentation, where a segmenting binary mask was generated using a3D U-Net model to segment the femur accurately. A total of 5474 QCT slices were considered for training among which 2316 slices were annotated. 3D femurs were further reconstructed from segmented slices employing polynomial spline interpolation. Both qualitative and quantitative performance of segmentation and 3D reconstruction were satisfactory with more than 90% accuracy achieved for allof the standardperformance metrics considered. The spatial overlap index and reproducibility validation metric for segmentation-Dice Similarity Coefficient was 91.8% for unseen patients and 99.2% for validated patients. An average relative error of 12.02% and 10.75% for volume and surface area, respectively, were computed for 3D reconstructed femurs. The proposed approach demonstrates its effectiveness in accurately segmenting and reconstructing 3D femur from QCT slices.

Early bone loss in patients with obstructive sleep apnea: a cross-sectional study

BackgroundObstructive sleep apnea (OSA) and osteoporosis are both prevalent diseases with shared pathophysiological mechanisms and risk factors. However, the association between the two diseases is seldom studied. This study aimed to identify the link between OSA and bone metabolism.MethodsMale participants aged 30–59-years who visited the sleep clinic were continuously recruited. Polysomnography was used to evaluate sleep and respiratory conditions. Blood samples were collected to detect metabolic, inflammatory and bone turnover indicators. High-resolution peripheral quantitative computer tomography was used to measure the non-dominant lateral radius and tibia.ResultsNinety subjects were recruited. The cortical area (Ct.Ar) of tibia of the severe OSA group was significantly higher than that of the mild and moderate OSA groups (P = 0.06 and P = 0.048). There were significant differences between the four groups in terms of total volumetric bone mineral density (vBMD) (F = 2.990, P = 0.035), meta trabecular vBMD (F = 3.696, P = 0.015), trabecular thickness (Tb.Th) (F = 7.060, P = 0.000) and cortical thickness (Ct.Th) (F = 4.959, P = 0.003). The mean values of the OSA groups were lower than control group. Hypopnea index and percentage of total sleep time with SpO2 < 90% were both positively correlated with alkaline phosphatase (R = 0.213, P = 0.044; R = 0.212, P = 0.045). Sleep efficiency was correlated with multiple indicators of the radius.ConclusionsIn non-elderly male populations, OSA patients tended to have lower vBMD, Tb.Th and Ct.Th than non-OSA patients. The negative effect of OSA may mainly affect the osteogenesis process, and is presumed to be related to sleep-related hypoxemia and sleep efficiency.

Association of serum osteocalcin with bone microarchitecture and muscle mass in Beijing community-dwelling postmenopausal women.

Osteoporosis is a systemic skeletal disease with increasing bone fragility and prone to fracture. Osteocalcin (OC), as the most abundant non collagen in bone matrix, has been extensively used in clinic as a biochemical marker of osteogenesis. Two forms of OC were stated on circulation, including carboxylated osteocalcin (cOC) and undercarboxylated osteocalcin (ucOC). OC was not only involved in bone mineralization, but also in the regulation of muscle function. This study explored the relationship between serum OC, cOC, ucOC levels and bone mineral density (BMD), bone microarchitecture, muscle mass and physical activity in Chinese postmenopausal women. 216 community-dwelling postmenopausal women were randomized enrolled. All subjects completed biochemical measurements, including serum β-isomer of C-terminal telopeptides of type I collagen (β-CTX), N-terminal propeptide of type 1 procollagen (P1NP), alkaline phosphatase (ALP), OC, cOC and ucOC. They completed X-ray absorptiometry (DXA) scan to measure BMD, appendicular lean mass (ALM) and trabecular bone score (TBS). They completed high resolution peripheral quantitative CT (HR-pQCT) to assess peripheral bone microarchitectures. Serum OC, cOC and ucOC were elevated in osteoporosis postmenopausal women. In bone geometry, serum ucOC was positively related with total bone area (Tt.Ar) and trabecular area(Tb.Ar). In bone volumetric density, serum OC and ucOC were negatively associated with total volume bone mineral density (Tt.vBMD) and trabecular volume bone mineral density (Tb.vBMD). In bone microarchitecture, serum OC and ucOC were negatively correlative with Tb.N and Tb.BV/TV, and were positively correlated with Tb.Sp. Serum OC and ucOC were positively associated with Tb.1/N.SD. Serum OC was negatively related with Tb.Th. Serum ucOC was positively associated with ALM. The high level of serum OC was the risk factor of osteoporosis. ALM was the protective factor for osteoporosis. All forms of serum OC were negatively associated with BMD. Serum OC and ucOC mainly influenced microstructure of trabecular bone in peripheral skeletons. Serum ucOC participated in modulating both bone microstructure and muscle mass.

DeepmdQCT: A multitask network with domain invariant features and comprehensive attention mechanism for quantitative computer tomography diagnosis of osteoporosis

In the medical field, the application of machine learning technology in the automatic diagnosis and monitoring of osteoporosis often faces challenges related to domain adaptation in drug therapy research. The existing neural networks used for the diagnosis of osteoporosis may experience a decrease in model performance when applied to new data domains due to changes in radiation dose and equipment. To address this issue, in this study, we propose a new method for multi domain diagnostic and quantitative computed tomography (QCT) images, called DeepmdQCT. This method adopts a domain invariant feature strategy and integrates a comprehensive attention mechanism to guide the fusion of global and local features, effectively improving the diagnostic performance of multi domain CT images. We conducted experimental evaluations on a self-created OQCT dataset, and the results showed that for dose domain images, the average accuracy reached 91%, while for device domain images, the accuracy reached 90.5%. our method successfully estimated bone density values, with a fit of 0.95 to the gold standard. Our method not only achieved high accuracy in CT images in the dose and equipment fields, but also successfully estimated key bone density values, which is crucial for evaluating the effectiveness of osteoporosis drug treatment. In addition, we validated the effectiveness of our architecture in feature extraction using three publicly available datasets. We also encourage the application of the DeepmdQCT method to a wider range of medical image analysis fields to improve the performance of multi-domain images.

Radiomics-based quantitative contrast-enhanced CT analysis of abdominal lymphadenopathy to differentiate tuberculosis from lymphoma

Radiomics-based quantitative contrast-enhanced CT analysis of abdominal lymphadenopathy to differentiate tuberculosis from lymphoma

Quantitative Computed Tomography Lung COVID Scores with Laboratory Markers: Utilization to Predict Rapid Progression and Monitor Longitudinal Changes in Patients with Coronavirus 2019 (COVID-19) Pneumonia.

Coronavirus disease 2019 (COVID-19), is an ongoing issue in certain populations, presenting rapidly worsening pneumonia and persistent symptoms. This study aimed to test the predictability of rapid progression using radiographic scores and laboratory markers and present longitudinal changes. This retrospective study included 218 COVID-19 pneumonia patients admitted at the Chungnam National University Hospital. Rapid progression was defined as respiratory failure requiring mechanical ventilation within one week of hospitalization. Quantitative COVID (QCOVID) scores were derived from high-resolution computed tomography (CT) analyses: (1) ground glass opacity (QGGO), (2) mixed diseases (QMD), and (3) consolidation (QCON), and the sum, quantitative total lung diseases (QTLD). Laboratory data, including inflammatory markers, were obtained from electronic medical records. Rapid progression was observed in 9.6% of patients. All QCOVID scores predicted rapid progression, with QMD showing the best predictability (AUC = 0.813). In multivariate analyses, the QMD score and interleukin(IL)-6 level were important predictors for rapid progression (AUC = 0.864). With >2 months follow-up CT, remained lung lesions were observed in 21 subjects, even after several weeks of negative reverse transcription polymerase chain reaction test. AI-driven quantitative CT scores in conjugation with laboratory markers can be useful in predicting the rapid progression and monitoring of COVID-19.