ObjectiveTo explore the changing trends of cortical bone of the first lumbarvertebral body (L1cortical bone) measured by the CT spinal bone quantitative system with age, sex, and volume bone mineral density(vBMD), as well as their predictive value in osteoporosis.MethodsA retrospective analysis was conducted on 169 participants who underwent simultaneous chest-abdominal or lumbar spine computed tomography (CT) and quantitative computed tomography (QCT) scans. All participants were stratified by sex (male and female) and further divided into three age groups: 50–59 years, 60–69 years, and ≥ 70 years. Based on QCT results, participants were categorized into two groups: non-osteoporosis (including normal bone mass and osteopenia) and osteoporosis. The average density, average thickness, average area and the total volume of L1cortical bone were measured using a CT spinal bone quantification system. A one-way analysis of variance (ANOVA) was employed to compare differences in the aforementioned parameters among different age and vBMD groups, while receiver operating characteristic (ROC) curve analysis was used to evaluate their efficacy in predicting osteoporosis.ResultsFor all participants and the female subgroup, there were statistically significant differences in the average thickness, average area, and total volume of L1cortical bone among the 50–59, 60–69, and ≥ 70 year age groups, with all parameters showing a decreasing trend with increasing age. Additionally, statistically significant differences were observed in the average thickness, average area, and total volume of L1cortical bone among the normal, osteopenia, and osteoporosis groups, and these parameters exhibited a decreasing trend with decreasing vBMD. In the male subgroup, the area under the ROC curve (AUC) values of the three parameters (average thickness, average area, and total volume) for detecting osteoporosis were 0.77, 0.83, and 0.84, respectively. The average thickness of L1cortical bone demonstrated the highest sensitivity (82.89%), whereas the average area showed the highest specificity (90.00%). A similar trend was observed in the female participants.ConclusionThe quantitative parameters including the average thickness, average area, and total volume of L1cortical bone measured by the CT spinal bone quantitative system appear to show a downward trend with increasing age and decreasing vBMD, and may potentially help in detecting osteoporosis.

Non-contrast chest computed tomography (CT) scans contain a large amount of background redundant information, but their clinical utilization is currently insufficient. This study aimed to assess whether non-contrast CT-derived body composition phenotypes combined with clinical information could opportunistically identify coronary atherosclerosis (CA) risk in middle-aged and older adults. This retrospective study included 1,196 adults who underwent non-contrast chest CT and were divided into a training set (n = 837) and a validation set (n = 359). An external cohort (n = 53) also served as validation. Body composition phenotypes were automatically extracted from chest CT and selected by least absolute shrinkage and selection operator (LASSO) regression. Clinical variables, including demographics, medical history, and laboratory parameters before imaging, were collected and screened by logistic regression. Models were developed using generalized linear model (GLM), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). Performance was evaluated by area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Among all models, the combined GLM achieved AUCs of 0.817 (95

Purpose To develop and validate a predictive model combining pretreatment axial lumbar MRI radiomic features of paravertebral muscles with clinical factors for assessing treatment response to the Non-surgical Spinal Decompression System (SDS) in patients with lumbar disc herniation (LDH). Methods This retrospective dual-center study included 766 LDH patients who underwent SDS. Radiomic features were extracted from segmented multifidus, erector spinae, and psoas major muscles on axial MRI. Six data modalities were constructed and compared using five machine-learning algorithms, with evaluation via five-fold cross-validation and an independent external cohort. Key features were selected by LASSO regression. Model discrimination, calibration, and clinical utility were assessed by ROC curves, calibration plots, and decision curve analysis. SHAP was applied to quantify feature contributions. Results LASSO selected 11 nonzero-coefficient predictors (4 clinical variables, 7 radiomic features). Random forest was the optimal algorithm. The combined clinical-radiomic model achieved internal and external validation AUCs of 0.879 ± 0.039 and 0.835 ± 0.038, significantly outperforming the clinical-only and all single-modality models ( P < 0.05). SHAP showed clinical variables accounted for 53.8% of predictive contribution, with age as the strongest feature. Calibration was good (Hosmer-Lemeshow P > 0.05), and decision curve analysis confirmed positive net benefit across a wide threshold range (0.05–0.85). Conclusion The combined clinical-radiomic model demonstrates good predictive performance for SDS outcomes in LDH patients and holds promise as a clinically useful decision-support tool.

Background:Osteoporotic vertebral compression fractures are common fragility fractures in older adults and are associated with substantial disability and healthcare burden. Opportunistic CT may provide a practical no-extra-radiation pathway for fracture-risk assessment, but quantitative vertebral structural parameters, especially cortical parameters, remain insufficiently studied. Purpose:To evaluate the value of vertebral structural parameters derived from opportunistic CT in identifying osteoporotic vertebral compression fractures and to develop a nomogram for individualized risk estimation. Methods:This retrospective single-center study included 298 patients aged 45 years or older who underwent chest or abdominal CT at the Second Affiliated Hospital of Shandong First Medical University between January 2020 and May 2024. Osteoporotic vertebral compression fracture status on sagittal CT was determined by two readers (one radiology resident and one senior physician) by consensus. A high-resolution 3D Dense-U-Net was used for automated vertebral segmentation and extraction of L1 structural parameters. Group comparisons were performed with t tests, one-way analysis of variance, and chi-squared tests as appropriate. Independent predictors were identified with univariate and multivariate logistic regression, and the nomogram was evaluated with receiver operating characteristic and calibration analyses. Results:Of the 298 participants (182 men and 116 women; mean age, 62.33 ± 9.56 years), 134 had osteoporotic vertebral compression fractures and 164 did not. In multivariate analysis, L1 cortical average area (OR, 0.99; 95% CI, 0.99-1.00; P = 0.002) and L1 cortical average thickness (OR, 0.22; 95% CI, 0.13-0.38; P < 0.001) were independent predictors. The nomogram achieved an area under the curve of 0.867 (95% CI, 0.817-0.918) in the training cohort and 0.804 (95% CI, 0.709-0.899) in the validation cohort. Conclusion:Quantitative vertebral structural parameters derived from opportunistic CT, particularly cortical parameters, showed good performance for identifying osteoporotic vertebral compression fracture risk in this single-center cohort. These findings support the potential value of deep learning-assisted quantitative assessment for opportunistic screening, although external validation is still required before broader clinical implementation.

Rationale and Objectives: Osteoporotic vertebral compression fractures (OVFs), particularly at the thoracolumbar spinal junction spanning levels T11 to L2, represent a significant and debilitating global health challenge. The objective of the present research was to establish and verify a robust radiomics model using routine non-contrast computed tomography (CT) to predict thoracolumbar OVFs risk. Materials and Methods: In this retrospective cohort study, 80 patients with new thoracolumbar OVFs were propensity-matched 1:2 with 160 controls. A 3D U-Net automatically segmented T11-L2 cortical and cancellous bone. Following minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) feature selection, 13 cortical and 16 cancellous radiomics features were extracted. Logistic regression models were developed using corresponding radscores. The best-performing model was compared to volumetric bone mineral density (vBMD) and evaluated via ROC curves, decision curve analysis (DCA), and calibration plots. Results: A total of 240 patients (147 females, 93 males) were enrolled, with no significant age or sex differences between groups. The combined cortical and cancellous radscore model (vertebral model) attained an AUC of 0.825 and 0.840 for OVF prediction, outperforming the vBMD model (AUC: 0.752/0.735). DCA and calibration plots verified its outstanding predictive performance. Notably, integrating vBMD with the vertebral model did not yield a statistically significant improvement (p > 0.05). The selected features highlighted crucial microstructural insights, with cancellous features reflecting trabecular heterogeneity and cortical features indicating mineralization uniformity and integrity. Conclusion: Our findings demonstrate that radiomics derived from routine non-contrast CT, leveraging deep learning for automated bone compartment segmentation, offers a superior and practical tool for Early identification of high-risk individuals for thoracolumbar OVFs. This approach provides valuable, complementary information beyond vBMD, potentially enhancing clinical decision-making and reducing the burden of osteoporosis.

IntroductionRoutine prenatal ultrasound assessment of the corpus callosum (CC) does not reflect information on fibrous connections. The primary purpose was to construct detailed reference ranges of quantitative characteristics of the foetal CC based on cortical connectivity information. Secondary goals were to examine for sex differences and assess the validity of the measurement technique for cases with CC dysplasia.Material and methodsPregnant women referred to a tertiary centre for sonographic examination were recruited to undergo a detailed foetal scan from 19 to 40 weeks. The foetal CC was divided into 5 distinct segments using the Hofer & Frahm classification technique. The thickness of each segment and the overall length of the CC were measured. Additionally, a segmental evaluation was conducted on partial agenesis of the CC. The inter- and intraobserver variability were assessed by interclass correlation coefficients. Regression analysis was used to determine the association between the biological measurements and gestational age.ResultsA total of 852 foetuses (403 males and 449 females) were included in the final analysis. Intra- and interobserver reliability coefficients ranged from 0.86 to 0.98 and 0.84 to 0.97, respectively. Reference ranges were established for the thickness and length of its segments. We observed that the biometric measurements of the foetal CC showed a curvilinear increase with gestational age. There was a statistically significant sex effect for the CC. At the average gestation age 29.6 weeks, the genu, anterior midbody, posterior midbody, and isthmus of male foetuses were 0.06280 mm, 0.04435 mm, 0.01731 mm, and 0.01556 mm, respectively, thicker than those of female foetuses, whereas the splenium of the female foetus was 0.06583 mm thicker than the male foetus.ConclusionsThe study uncovers distinct patterns of thickness and length growth in the foetal CC and establishes precise reference ranges. These findings can aid in evaluating normal brain development and conducting comprehensive assessments of CC abnormalities.

Purpose:There was a strong correlation between adrenal adenoma and osteoporosis, the primary objective of this research was to establish and authenticate a radiomics nomogram using CT scan of adrenal adenoma to screen abnormal bone mineral density (BMD) opportunistically. Methods and Materials:A total of 161 patients with adrenal adenomas who underwent thoracoabdominal CT and quantitative CT (QCT) were enrolled retrospectively. The radiomics features were chosen from the cross-sectional CT images of adrenal adenomas and the nomogram models that including patient's clinical and radiomics features were then established. The receiver operating characteristic (ROC) curve was performed to evaluate the performance of the model and the decision curve analysis (DCA) was used to assess the clinical usefulness. Results:To build a radiomics model, 11 radiomics features based on CT scans of adrenal adenomas were selected and showed good performance in distinguishing abnormal BMD from normal BMD. Moreover, the radiomics nomogram model demonstrated excellent ability to identify abnormal BMD of adrenal adenoma patients with area under the curve (AUC) of 0.87 (95% CI, 0.80-0.93) in training cohort and 0.85 (95% CI, 0.74-0.96) in validation cohort. The accuracy, sensitivity, specificity of the nomogram model were 79.7%, 78.3%, 81.1% in training cohort, and 72.9%, 67.7%, 82.4% in validation cohort respectively. Conclusion:The radiomics nomogram based on clinical and radiomics features of adrenal adenoma CT images had a satisfying predictive ability and can be an opportunistic effective tool for identifying bone mass change.

Background:There is an urgent need for a convenient and incidental method to assess the bone health status of the population, especially in primary-level hospitals lacking specialized bone density testing equipment. This study aims to investigate the association between multiple vertebral Hounsfield Unit (HU) value clusters and bone mass subtypes using an unsupervised learning approach, providing a practical tool for incidental osteoporosis screening in clinical settings. Materials and Methods:This retrospective study included subjects who underwent chest CT and quantitative CT (QCT) from January 2023 to December 2024. Vertebral HU values (T7-T12) were measured on chest CT images. Intergroup comparisons (normal, osteopenia, and osteoporosis) in clinical findings and CT values were performed using Pearson χ2 test and one-way analysis of variance. An unsupervised k-means clustering was applied to vertebral CT values across the cohort. Results:The study comprised 455 participants (260 males, 195 females) with a median age of 60 years (interquartile range, 51-67 years), who were classified into three groups: normal bone mass, 253 cases; osteopenia, 152 cases; osteoporosis, 50 cases. Among 455 participants, age inversely correlated with bone mass. Vertebrae HU values (T7-T12) exhibited significant stepwise declines from normal to osteopenia to osteoporosis (OP) groups. The clustering analysis revealed five distinct subtypes: cluster 1 strongly correlated with OP (45 of 72 cases), cluster 4 with osteopenia (107 of 146 cases), and clusters 2, 3, and 5 with normal bone mass (31 of 31 cases; 90 of 107 cases; 97 of 99 cases). Conclusion:Unsupervised clustering of T7-T12 vertebral HU values effectively stratifies bone mass subtypes, offering an efficient, CT-based screening method for skeletal health assessment, especially valuable in resource-limited primary-level hospitals lacking dedicated bone densitometry.

Background:Diabetic osteoporosis (DOP) can cause abnormal brain neural activity, but its mechanism is still unclear. This study aims to further explore the abnormal functional connectivity between different brain regions based on the team's previous research. Methods:Resting-state functional magnetic resonance imaging (rs-fMRI) data were obtained from 14 participants diagnosed with type 2 diabetes mellitus (T2DM) and osteoporosis. For comparison, data from 13 T2DM patients without osteoporosis were analyzed. The seed regions for functional connectivity (FC) analysis were chosen according to brain areas previously reported to exhibit abnormal regional homogeneity (ReHo). Results:DOP patients exhibited significantly decreased BMD, T-scores, MoCA scores, and osteocalcin (OC) levels compared to controls (p<0.05). FC analysis revealed: 1) Reduced connectivity between the left middle temporal gyrus (increased ReHo) and middle occipital gyrus; 2) Enhanced connectivity between the right angular gyrus (increased ReHo) and left Rolandic operculum; 3) Weakened the left precuneus (increased ReHo) and right superior/left middle frontal gyri. These alterations correlated with deficits in visual processing, working memory, and executive function. Conclusion:Distinct FC reorganization in DOP patients reflects synergistic effects of metabolic and skeletal pathologies on neural networks, potentially mediating cognitive decline through visual pathway disruption and prefrontal-default mode network decoupling. The findings highlight neuroimaging biomarkers for metabolic bone disease-related cognitive disorders.

The aim is to explore the value of pericoronary adipose tissue (PCAT) attenuation in predicting abnormal bone mass by establishing a prediction model. 361 patients with coronary computed tomography angiography (CCTA) and quantitative computed tomography (QCT) scans were retrospectively recruited. 311 patients from institution 1 from July 2021 to January 2023 were divided into a training cohort (n = 217) and an internal cohort (n = 94). The external cohort comprised 50 patients from institution 2 from January 2023 to August 2023. Clinical variables and PCAT attenuation of the major epicardial vessels were obtained. Univariate and multivariate logistic regression analyses were used to identify factors with statistical significance. Model 1 was constructed based on clinical variables. Model 2 was constructed by combining the clinical variables with the PCAT attenuation. The performances of the models were assessed using receiver operating characteristic curve analysis, calibration curves and decision curve analysis (DCA). Age, gender, coronary artery disease reporting and data system (CAD-RADS), statins and RCAPCAT were found to be significant predictors of abnormal bone mass. The area under the curve (AUC) of Model 2 was superior to that of Model 1 in the training cohort (AUC: 0.959 vs. 0.920), internal (AUC: 0.943 vs. 0.890) and external validation cohorts (AUC: 0.889 vs. 0.812). The calibration curves and DCA indicated that Model 2 had the higher clinical value. The model incorporating clinical factors and RCAPCAT has good performance in predicting bone mass abnormalities.

RATIONALE AND OBJECTIVES:To assess the value of a chest CT-based machine learning model in predicting osteoporotic vertebral fractures (OVFs) of the thoracolumbar vertebral bodies. MATERIALS AND METHODS:We monitored 8910 patients aged ≥50 who underwent chest CT (2021-2024), identifying 54 incident OVFs cases. Using propensity score matching, 108 controls were selected. The 162 patients were randomly assigned to training (n=113) and testing (n=49) cohorts. Clinical models were developed through logistic regression. Radiomics features were extracted from the thoracolumbar vertebral bodies (T11-L2), with top 10 features selected via minimum-redundancy maximum-relevancy and the least absolute shrinkage and selection operator to construct a Radscore model. Nomogram model was established combining clinical and radiomics features, evaluated using receiver operating characteristic curves, decision curve analysis (DCA) and calibration plots. RESULTS:Volumetric bone mineral density (vBMD) (OR=0.95, 95%CI=0.93-0.97) and hemoglobin (HGB) (OR=0.96, 95%CI=0.94-0.98) were selected as independent risk factors for clinical model. From 2288 radiomics features, 10 were selected for Radscore calculation. The Nomogram model (Radscore + vBMD + HGB) achieved area under the curve (AUC) of 0.938/0.906 in training/testing cohorts, outperforming both Radscore (AUC=0.902/0.871) and clinical (AUC=0.802/0.820) models. DCA and calibration plots confirmed the Nomogram model's superior prediction capability. CONCLUSION:Nomogram model combined with radiomics and clinical features has high predictive performance, and its predictive results for thoracolumbar OVFs can provide reference for clinical decision making.

Background:Currently, dual-energy X-ray absorptiometry (DEXA) and quantitative CT (QCT) are commonly used in clinical practice to measure bone mineral density (BMD), offering diagnostic value but involving radiation and inability to visualize bone microstructure. This study aims to assess lumbar spine bone microstructure changes in normal, osteopenic, and osteoporotic groups using IVIM-DWI and IDEAL-IQ sequences to provide useful information for clinical practice. Methods:A total of 346 patients (50-87 years, 232 females, 114 males) underwent spinal DEXA and MRI. Based on the BMD obtained from DEXA, the patients were stratified into: normal (n=79), osteopenia (n=92), and OP (n=175) groups. Then to evaluated the results of IVIM-DWI and IDEAL-IQ and extracted quantitative parameters from regions of interest covering the L1 to L4 vertebrae. Group comparisons used One-way analysis of variance and the Kruskal‒Wallis H-test. Receiver operating characteristic (ROC) and Spearman's analyses evaluated diagnostic performance and correlations. Results:Significant differences existed in the ADCslow, f, FF and R2* between groups (P<0.05). BMD was weakly positively correlated with ADCslow, f, and R2* (r=0.494, 0.153, 0.182, 0.029, P<0.001) but a negative correlation with FF (r=-0.402, P<0.001). BMD and the ADCslow and R2* decreased but FF increased with age (P<0.05 for all), whereas no significant association existed between age and ADCfast or f value (P>0.05). FF had the highest areas under the curve (AUCs) (0.624, 0.831 and 0.747) and sensitivity (72.2%, 70.9% and 81.5%) in differentiating normal from osteopenia patients, normal from osteoporosis patients, and osteopenia from osteoporosis patients, respectively. ADCslow and f had the highest specificity (88%) in differentiating between normal and osteopenia patients, while ADCslow had the highest specificity (91.4%) in differentiating between normal and osteoporosis patients. Conclusion:Quantitative parameters extracted from IVIM-DWI and IDEAL-IQ have the potential to become good biomarkers for diagnosing OP.

Background: In vertebrae, the amount of cortical bone has been estimated at 30-60%, but 45-75% of axial load on a vertebral body is borne by cortical bone (1). Rationale and Objectives: The purpose of this study is to investigate the accuracy, sensitivity, specificity, positive predictive value and negative predictive value of vertebral body cortical thickness in predicting osteoporosis (OP) by analyzing the relationship between vertebral body cortical thickness and bone mineral density (BMD) in different age and gender groups. The optimal diagnostic cut-off value of vertebral body cortical thickness in predicting OP was analyzed. Materials and Methods: The data of 150 patients (50-89 years old) who underwent chest or abdominal Quantitative computed tomography (QCT) scan (obtained in one scan) in our hospital from July 2021 to July 2022 were retrospectively analyzed. The average volume bone mineral density (vBMD) of L1 -L2 vertebral bodies was obtained and grouped according to BMD, age, and gender. According to BMD, the patients were divided into three groups: osteoporosis, osteopenia and normal. According to age, the patients were divided into three groups: 50-59 years, 60-69 years and >= 70 years. The axial images of T11, T12 and L1 were reconstructed with 1.25 mm slice thickness by AW4.7 workstation provided by General Electric Co (GE) Company. The images were imported into the computed tomography (CT) Spine Bone Quantification System software for spine analysis, and the vertebral body cortical thickness values were obtained. CT Spine Bone Quantification System is a software for quantitative analysis and separation of cortical bone and cancellous bone. Results: A total of 150 patients were enrolled in this study, including 49 patients in the osteoporosis group, 51 patients in the osteopenia group, and 50 patients in the normal group. The cortical thickness values of T11, T12 and L1 were positively correlated with BMD, and the correlation coefficient was 0.750 at T11. According to the receiver operating characteristic (ROC) curve analysis of T11, T12, L1 cortical thickness value and BMD, OP was diagnosed when T11 < 2.75 mm, T12 < 3.06 mm, and L1 < 2.67 mm. The sensitivity was 83.67%, 87.76%, 75.51%, respectively. The specificity was 79.21%, 71.29% and 90.10%, respectively, and the difference was statistically significant. Conclusion: Vertebral body cortical thickness is correlated with BMD and age. According to the cut-off value of different vertebral bodies, OP can be predicted when T11 < 2.75 mm or T12 < 3.06 mm or L1 < 2.67 mm.

Osteoporosis is a major health concern for postmenopausal women, and the effect of simvastatin (Sim) on bone metabolism is controversial. This study aimed to investigate the effect of simvastatin on the bone microstructure and bone mechanical properties in ovariectomized (OVX) mice. 24 female C57BL/6J mice (8-week-old) were randomly allocated into three groups including the OVX + Sim group, the OVX group and the control group. At 8 weeks after operation, the L4 vertebral bones were dissected completely for micro-Computed Tomography (micro-CT) scanning and micro-finite element analysis (µFEA). The differences between three groups were compared using ANOVA with a LSD correction, and the relationship between bone microstructure and mechanical properties was analyzed using linear regression. Bone volume fraction, trabecular number, connectivity density and trabecular tissue mineral density in the OVX + Sim group were significantly higher than those in the OVX group (P < 0.05). For the mechanical properties detected via µFEA, the OVX + Sim group had lower total deformation, equivalent elastic strain and equivalent stress compared to the OVX group (P < 0.05). In the three groups, the mechanical parameters were significantly correlated with bone volume fraction and trabecular bone mineral density. The findings suggested that simvastatin had a potential role in the treatment of osteoporosis. The results of this study could guide future research on simvastatin and support the development of simvastatin-based treatments to improve bone health.

Background: Polysaccharides from Grifola frondosa (GFP) have gained worldwide attention owing to their promising biological activities and potential health benefits. Purpose: This study aimed to investigate the effects of GFP on alleviation of osteoporosis in ovariectomized (OVX) mice and examine the underlying mechanism. Method: A mouse model of postmenopausal osteoporosis was established by OVX method, Forty eight C57BL/6 female mice were randomly divided into Normal group, OVX alone (Model group, n = 8), OVX + 10 mg/kg GFP (GFP-L group, n = 8), OVX + 20 mg/kg GFP (GFP-M group, n = 8), OVX + 40 mg/kg GFP (GFP-H group, n = 8), OVX + 10 mg/kg Estradiol valerate (Positive group, n = 8). Results: The results showed that compared with Model group, the concentrations of interleukin (IL)-1 beta, interleukin (IL) -6 and Tumor necrosis factor- alpha (TNF- alpha) were significantly reduced, the activity of superoxide dismutase (SOD) and glutathione (GSH) were significantly increased, the content of myeloperoxidase (MPO) and malondialdehyde (MDA) were significantly reduced, and the proteins levels of PINK1, Parkin, Beclin-1 and LC3-II were significantly decreased in the GFP groups. Conclusion: This study demonstrates that GFP alleviates ovariectomy-induced osteoporosis via reduced secretion of inflammatory cytokines, improvement in the oxidative stress status in the body, and inhibition of the PINK1/ Parkin signaling pathway.

Rationale and Objectives The aim of this study was to develop and validate a novel computed tomography (CT)-based fracture risk assessment model (FRCT) specifically tailored for patients suffering from chronic obstructive pulmonary disease (COPD). Methods We conducted a retrospective analysis encompassing a cohort of 284 COPD patients, extracting data on demographics, clinical profiles, pulmonary function tests, and CT-based bone quantification metrics. The Boruta feature selection algorithm was employed to identify key variables for model construction, resulting in a user-friendly nomogram. Results Our analysis revealed that 37.32% of the patients suffered fragility fractures post-follow-up. The FRCT model, integrating age, cancellous bone volume, average cancellous bone density, high-density lipoprotein levels, and prior fracture incidence, demonstrated superior predictive accuracy over the conventional fracture risk assessment tool (FRAX), with a C-index of 0.773 in the training group and 0.797 in the validation group. Calibration assessments via the Hosmer-Lemeshow test confirmed the model's excellent fit, and decision curve analysis underscored the FRCT model's substantial positive net benefit. Conclusion The FRCT model, leveraging opportunistic CT screening, offers a highly accurate and personalized approach to fracture risk prediction in COPD patients, surpassing the capabilities of existing tools. This model is poised to become an indispensable asset for clinicians in managing osteoporotic fracture risks within the COPD population.

Rationale and Objectives: To evaluate the performance of machine learning analysis based on proximal femur of abdominal computed tomography (CT) scans in screening for abnormal bone mass in femur. Materials and Methods: 222 patients aged 50 years or older who underwent abdominal CT and dual -energy X-ray absorptiometry scans within 14 days were retrospectively enrolled. The patients were randomly assigned to a training cohort ( n = 155) and a testing cohort ( n = 67) in a ratio of 7:3. A total of 2288 candidate radiomic features were extracted from the volume region of interest - the left proximal femur of the abdominal CT scans. The most valuable radiomic features were selected using minimum -Redundancy Maximum -Relevancy and the least absolute shrinkage and selection operator to construct the radiomics model. The predictive performance was assessed with receiver operating characteristic curve. Results: 13 features were chosen to establish the radiomics model. The radiomics model using logistic regression displayed excellent prediction performance in distinguishing normal bone mass and abnormal bone mass, with the area under the curve (AUC), accuracy, sensitivity and specificity of 0.917 (95% CI, 0.867-0.967), 0.826, 0.935 and 0.780 in the training cohort. The testing cohort indicated a better performance with AUC, accuracy, sensitivity and specificity of 0.963 (95% CI, 0.919-0.999), 0.851, 0.923 and 0.889. Conclusion: The radiomics model based on proximal femur of abdominal CT scans had a high predictive performance to identify abnormal bone mass in femur, which can be used as a tool for opportunistic osteoporosis screening. (c) 2023 The Association of University Radiologists. Published by Elsevier Inc. This is an open access article under the CC BY -NC -ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Purpose: The aim of this study was to analyze lumbar fascial changes in patients with lower back pain using lumbar spine soft tissue-based MR imaging radiomics. Methods: We retrospectively analyzed the lumbar MRI of 380 patients with low back pain. Patients were randomly assigned to either the training (n = 187) or validation (n = 193) cohorts. Seven different machine learning algorithms were used to establish classification prediction models, and their prediction efficiency were evaluated with the best performance was selected as the final classification prediction model. Multivariate logistic regression analysis was used to create radiomics model and combined nomogram model and their predictive performance were evaluated using receiver operating characteristic (ROC) curves. Results:Among the seven machine learning models, the Lasso model exhibited the highest diagnostic efficiency with an AUC of 0.835. The radiomics nomogram integrated clinical and radiomics signature features, demonstrating strong performance in both the training and validation sets with AUC values of 0.97 and 0.96, respectively. AUC and DCA indicated that the radiomics nomogram model effectively diagnosed lumbar fasciitis. Conclusion: We developed an nomogram model that integrated clinical and radiomics features to help clinicians identify and predict low back fasciitis through soft tissue magnetic resonance.

Abstract Background Osteoporosis (OP) is a common chronic metabolic bone disease characterized by decreased bone mineral content and microstructural damage, leading to increased fracture risk. Traditional methods for measuring bone density have limitations in accurately distinguishing vertebral bodies and are influenced by vertebral degeneration and surrounding tissues. Therefore, novel methods are needed to quantitatively assess changes in bone density and improve the accurate diagnosis of OP. Methods This study aimed to explore the applicative value of the iterative decomposition of water and fat with echo asymmetry and least-squares estimation-iron (IDEAL-IQ) sequence combined with intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for the diagnosis of osteoporosis. Data from 135 patients undergoing dual-energy X-ray absorptiometry (DXA), IDEAL-IQ, and IVIM-DWI were prospectively collected and analyzed. Various parameters obtained from IVIM-DWI and IDEAL-IQ sequences were compared, and their diagnostic efficacy was evaluated. Results Statistically significant differences were observed among the three groups for FF, R2*, f, D, DDC values, and BMD values. FF and f values exhibited negative correlations with BMD values, with r=-0.313 and − 0.274, respectively, while R2*, D, and DDC values showed positive correlations with BMD values, with r = 0.327, 0.532, and 0.390, respectively. Among these parameters, D demonstrated the highest diagnostic efficacy for osteoporosis (AUC = 0.826), followed by FF (AUC = 0.713). D* exhibited the lowest diagnostic performance for distinguishing the osteoporosis group from the other two groups. Only D showed a significant difference between genders. The AUCs for IDEAL-IQ, IVIM-DWI, and their combination were 0.74, 0.89, and 0.90, respectively. Conclusions IDEAL-IQ combined with IVIM-DWI provides valuable information for the diagnosis of osteoporosis and offers evidence for clinical decisions. The superior diagnostic performance of IVIM-DWI, particularly the D value, suggests its potential as a more sensitive and accurate method for diagnosing osteoporosis compared to IDEAL-IQ. These findings underscore the importance of integrating advanced imaging techniques into clinical practice for improved osteoporosis management and highlight the need for further research to explore the full clinical implications of these imaging modalities.

Abstract Background The aim of this study was to evaluate the dynamic effects on bone mass of chemotherapy and surgery in lung cancer patients by computed tomography (CT). Methods This was a retrospective study, 147 patients with lung cancer from June to December 2021 in our hospital were finally selected. Data consisted of cycle of chemotherapy and surgery. CT scans before chemotherapy and cycle 1-6 after chemotherapy were performed.The CT values of the T11-L1 vertebral body were recorded and compared. Results The mean CT values of T11-L1 vertebral body in cycle 1, 5and 6 were lower than that in pre-chemotherapy ( P =0.007＜0.05， P =0.038＜0.05， P =0.048＜0.05). There was no significant difference among the rest groups (all P＞0.05）. The CT value of T11 was higher than those of L1 in pre-chemotherapy and cycle 1, 2 after chemotherapy ( P ＜0.001， P =0.042< 0.05， P =0.015< 0.05). There was no statistically significant difference in CT values among the T11,T12 and L1 in cycle 3, 4, 5 and 6 (all P ＞0.05). There was no statistical significant difference in the mean CT values of the T11-L1 vertebral body between operation group and non-operation group in pre-chemotherapy and same cycle after chemotherapy (all P ＞0.05). Conclusions Osteodeficiency exacerbated by ongoing chemotherapy and unaffected by operation in lung cancer patients indicates the need for the development of antiosteoporosis treatment and osteoporosis prophylaxis for lung cancer patients.