Quantitative Computed Tomography Research Articles

Exploring the nexus between fatigue, body composition, and muscle strength in hemodialysis patients

BackgroundFatigue is a relatively prevalent condition among hemodialysis patients, resulting in diminished health-related quality of life and decreased survival rates. The purpose of this study was to investigate the relationship between fatigue and body composition in hemodialysis patients.MethodsThis cross-sectional study included 92 patients in total. Fatigue was measured by Functional Assessment of Chronic Illness Therapy – Fatigue (FACIT-F) (cut-off ≤ 34). Body composition was measured based on quantitative computed tomography (QCT), parameters including skeletal muscle index (SMI), intermuscular adipose tissue (IMAT), and bone mineral density (BMD). Handgrip strength was also collected. To explore the relationship between fatigue and body composition parameters, we conducted correlation analyses and binary logistic regression.ResultsThe prevalence of fatigue was 37% (n = 34), abnormal bone density was 43.4% (n = 40). There was a positive correlation between handgrip strength and FACIT-F score (r = 0.448, p < 0.001). Age (r = − 0.411, p < 0.001), IMAT % (r = − 0.424, p < 0.001), negatively associated with FACIT-F score. Multivariate logistic regression analysis shows that older age, lower serum phosphorus, higher IMAT% are associated with a high risk of fatigue.ConclusionThe significantly increased incidence and degree of fatigue in hemodialysis patients is associated with more intermuscular adipose tissue in paraspinal muscle.

Distinguishing thymic cysts from low-risk thymomas via [18F]FDG PET/CT

BackgroundThymic cysts are a rare benign disease that needs to be distinguished from low-risk thymoma. [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) is a non-invasive imaging technique used in the differential diagnosis of thymic epithelial tumours, but its usefulness for thymic cysts remains unclear. Our study evaluated the utility of visual findings and quantitative parameters of [18F]FDG PET/CT for differentiating between thymic cysts and low-risk thymomas.MethodsPatients who underwent preoperative [18F]FDG PET/CT followed by thymectomy for a thymic mass were retrospectively analyzed. The visual [18F]FDG PET/CT findings evaluated were PET visual grade, PET central metabolic defect, and CT shape. The quantitative [18F]FDG PET/CT parameters evaluated were PET maximum standardized uptake value (SUVmax), CT diameter (cm), and CT attenuation in Hounsfield units (HU). Findings and parameters for differentiating thymic cysts from low-risk thymomas were assessed using Pearson’s chi-square test, the Mann-Whitney U-test, and receiver operating characteristics (ROC) curve analysis.ResultsSeventy patients (18 thymic cysts and 52 low-risk thymomas) were finally included. Visual findings of PET visual grade (P < 0.001) and PET central metabolic defect (P < 0.001) showed significant differences between thymic cysts and low-risk thymomas, but CT shape did not. Among the quantitative parameters, PET SUVmax (P < 0.001), CT diameter (P < 0.001), and CT HU (P = 0.004) showed significant differences. In ROC analysis, PET SUVmax demonstrated the highest area under the curve (AUC) of 0.996 (P < 0.001), with a cut-off of equal to or less than 2.1 having a sensitivity of 100.0% and specificity of 94.2%. The AUC of PET SUVmax was significantly larger than that of CT diameter (P = 0.009) and CT HU (P = 0.004).ConclusionsAmong the [18F]FDG PET/CT parameters examined, low FDG uptake (SUVmax ≤ 2.1, equal to or less than the mediastinum) is a strong diagnostic marker for a thymic cyst. PET visual grade and central metabolic defect are easily accessible findings.

Quantitative analysis of lung lesions using unenhanced chest computed tomography images.

Chest radiograph and computed tomography (CT) scans can accidentally reveal pulmonary nodules. Malignant and benign pulmonary nodules can be difficult to distinguish without specific imaging features, such as calcification, necrosis, and contrast enhancement. However, these lesions may exhibit different image texture characteristics which cannot be assessed visually. Thus, a computer-assisted quantitative method like histogram analysis (HA) of Hounsfield unit (HU) values can improve diagnostic accuracy, reducing the need for invasive biopsy. In this exploratory control study, nonenhanced chest CT images of 20 patients with benign (10) and cancerous (10) lesion were selected retrospectively. The appearances of benign and malignant lesions were very similar in chest CT images, and only pathology report was used to discriminate them. Free hand region of interest (ROI) was inserted inside the lesion for all slices of each lesion. Mean, minimum, maximum, and standard deviations of HU values were recorded and used to make HA. HA showed that the most malignant lesions have a mean HU value between 30 and 50, a maximum HU less than 150, and a minimum HU between -30 and 20. Lesions outside these ranges were mostly benign. Quantitative CT analysis may differentiate malignant from benign lesions without specific malignancy patterns on unenhanced chest CT image.

Predicting Osteoporosis and Osteopenia by Fusing Deep Transfer Learning Features and Classical Radiomics Features Based on Single-Source Dual-energy CT Imaging

Rationale and ObjectivesTo develop and validate a predictive model for osteoporosis and osteopenia prediction by fusing deep transfer learning (DTL) features and classical radiomics features based on single-source dual-energy computed tomography (CT) virtual monochromatic imaging. MethodsA total of 606 lumbar vertebrae with dual-energy CT imaging and quantitative CT (QCT) evaluation were included in the retrospective study and randomly divided into the training (n=424) and validation (n=182) cohorts. Radiomics features and DTL features were extracted from 70-keV monochromatic CT images, followed by feature selection and model construction, radiomics and DTL features models were established. Then, we integrated the selected two types of features into a features fusion model. We developed a two-level classifier for the hierarchical pairwise classification of each vertebra. All the vertebrae were first classified into osteoporosis and non-osteoporosis groups, then non-osteoporosis group was classified into osteopenia and normal groups. QCT was used as reference. The predictive performance and clinical usefulness of three models were evaluated and compared. ResultsThe area under the curve (AUC) of the features fusion, radiomics and DTL models for the classification between osteoporosis and non-osteoporosis were 0.981, 0.999, 0.997 in the training cohort and 0.979, 0.943, 0.848 in the validation cohort. Furthermore, the AUCs of the abovementioned models for the differentiation between osteopenia and normal were 0.994, 0.971, 0.996 in the training cohort and 0.990, 0.968, 0.908 in the validation cohort. The overall accuracy of the abovementioned models for two-level classifications was 0.979, 0.955, 0.908 in the training cohort and 0.918, 0.885, 0.841 in the validation cohort. Decision curve analysis (DCA) showed that all models had high clinical value. ConclusionThe feature fusion model can be used for osteoporosis and osteopenia prediction with improved predictive ability over a radiomics model or a DTL model alone.

A nomogram to predict severe COVID-19 patients with increased pulmonary lesions in early days.

This study aimed to predict severe coronavirus disease 2019 (COVID-19) progression in patients with increased pneumonia lesions in the early days. A simplified nomogram was developed utilizing artificial intelligence (AI)-based quantified computed tomography (CT). From 17 December 2019 to 20 February 2020, a total of 246 patients were confirmed COVID-19 infected in Jingzhou Central Hospital, Hubei Province, China. Of these patients, 93 were mildly ill and had follow-up examinations in 7 days, and 61 of them had enlarged lesions on CT scans. We collected the neutrophil-to-lymphocyte ratio (NLR) and three quantitative CT features from two examinations within 7 days. The three quantitative CT features of pneumonia lesions, including ground-glass opacity volume (GV), semi-consolidation volume (SV), and consolidation volume (CV), were automatically calculated using AI. Additionally, the variation volumes of the lesions were also computed. Finally, a nomogram was developed using a multivariable logistic regression model. To simplify the model, we classified all the lesion volumes based on quartiles and curve fitting results. Among the 93 patients, 61 patients showed enlarged lesions on CT within 7 days, of whom 19 (31.1%) developed any severe illness. The multivariable logistic regression model included age, NLR on the second time, an increase in lesion volume, and changes in SV and CV in 7 days. The personalized prediction nomogram demonstrated strong discrimination in the sample, with an area under curve (AUC) and the receiver operating characteristic curve (ROC) of 0.961 and a 95% confidence interval (CI) of 0.917-1.000. Decision curve analysis illustrated that a nomogram based on quantitative AI was clinically useful. The integration of CT quantitative changes, NLR, and age in this model exhibits promising performance in predicting the progression to severe illness in COVID-19 patients with early-stage pneumonia lesions. This comprehensive approach holds the potential to assist clinical decision-making.

Quantitative CT in IPF: Is It Time to Act?

Quantitative CT in IPF: Is It Time to Act?

Quantitative CT Analysis of Hepatocellular Carcinoma Nodules in Liver Transplant Candidates: Impact of Volume of Interest and Contrast Phase on Recurrence Prediction

Quantitative CT Analysis of Hepatocellular Carcinoma Nodules in Liver Transplant Candidates: Impact of Volume of Interest and Contrast Phase on Recurrence Prediction

Quantitative CT scan and response to pronation in COVID-19 ARDS.

Background: The use of the prone position (PP) has been widespread during the COVID-19 pandemic. While it has demonstrated benefits, including improved oxygenation and lung aeration, the factors influencing the response in terms of gas exchange to PP remain unclear. In particular, the association between baseline quantitative Computed Tomography (qCT) scan results and gas exchange response to PP in intubated, mechanically ventilated subjects with COVID-19 ARDS is unknown. The present study aimed to compare baseline qCT results between subjects responding to PP in terms of oxygenation or carbon dioxide (CO2) clearance and those who did not.Methods: This was a single-center, retrospective observational study, including critically ill, intubated, mechanically ventilated subjects with COVID-19 related acute respiratory distress syndrome admitted to the ICUs of Niguarda Hospital between March 2020 and November 2021. Blood-gas samples were collected before and after PP. Subjects in whom the PaO2/FiO2 increase was ≥ 20 mmHg after PP were defined as Oxygen responders (Oxy-R). CO2-responders (CO2R) were defined when the ventilatory ratio (VR) decreased during PP. Automated qCT analyses were performed to obtain tissue mass and density of the lungs.Results: One hundred twenty-five subjects were enrolled, of which 116 (93%) were Oxy-R and 51 (41%) CO2R. No difference in qCT characteristics and oxygen were observed between Oxy-R and Oxygen Non Responders (Tissue mass 1532 ±396 vs. 1654 ±304 g, p= .28; density -544±109 vs. -562±58 HU, p= .42). Similar findings were observed when dividing the population according to CO2 response (Tissue mass 1551±412 vs. 1534±377 g, p= .89; density -545±123 vs. -546±94 HU, p= .99).Conclusions: Most COVID-19 related ARDS subjects improve their oxygenation at the first pronation cycle. The study suggests that baseline qCT scan data are not associated with the response to PP in oxygenation or CO2 in mechanically ventilated COVID-19 related ARDS subjects.

Short-Term Changes in Weight, Body Composition, and Metabolic Biomarkers After Laparoscopic Sleeve Gastrectomy in Patients with Obesity: A Comparative Prospective Study.

To investigate the changes in weight, body composition, and metabolic biomarkers in patients with obesity after laparoscopic sleeve gastrectomy (LSG) and compare those changes between patients with and without metabolic syndrome (MS). This retrospective longitudinal study included 76 patients who underwent LSG, among whom 32 had complete 1-year postoperative body composition and metabolic biomarkers. Body composition was measured by quantitative CT. Weight changes were compared between the MS and non-MS groups at 1-, 3-, 6-, and 12-month post-LSG in all patients; changes in body compositions and metabolic biomarkers from one day pre-LSG to 12-month post-LSG were also compared in those 32 patients. MS occurred in 46% (35/76) of all patients and 44% (14/32) of patients with complete follow-up data. Excess weight loss was lower in the MS group than that in the non-MS group at 1-, 3-, 6-, and 12-month post-LSG; the 12-month difference was significant (MS vs. non-MS: 0.91 ± 0.22 vs. 1.07 ± 0.42, P = 0.04). The greatest rate of visceral fat area (VFA) change occurred 12-month post-LSG in both the non-MS [0.62(0.55,0.7)] and MS [0.6(0.51,0.63)] groups. The most significant reduction in ectopic fat occurred in liver fat (LF) [non-MS, 0.45(0.22,0.58); MS, 0.39(0.23,0.58)]. LGS significantly improves weight, body composition, and metabolic biomarkers in populations with obesity, regardless of whether they have MS. Among the body composition, VFA and LF were the most significantly improved body composition measurements.

Quantitative computed tomography assessment of bone mineral density in adolescent idiopathic scoliosis: correlations with Cobb angle, vertebral rotation, and Risser sign.

Adolescent idiopathic scoliosis (AIS) is a prevalent spinal disorder that can potentially influence bone mineral density (BMD), thereby increasing the susceptibility to osteoporosis and fractures. Early identification of reduced bone mass in AIS patients is crucial for clinicians to develop effective preventive strategies against fractures. This study aims to elucidate the correlation between BMD, as measured by quantitative computed tomography (QCT), and various clinical parameters in AIS, including the Cobb angle, vertebral rotation, and the Risser sign. By revealing the potential influences of these factors on BMD, our findings aim to assist clinicians in making informed and timely decisions regarding AIS management, particularly in situations where QCT is unavailable. A cross-sectional study was conducted on 129 adolescents with AIS who were enrolled at The Third People's Hospital of Chengdu, Sichuan, China, between 2021 and 2023. QCT was employed to assess BMD and vertebral rotation. The Cobb angle and Risser sign were determined through radiographic evaluation, while anthropometric and biochemical data were also collected. Statistical analyses, including Pearson and Spearman rank correlation and regression models, were used to investigate the associations between BMD and clinical measures. A significant negative correlation was found between BMD and Cobb angle (coefficient =-0.663; P<0.001), as well as between BMD and vertebral rotation angle (coefficient =-0.442; P<0.001) in patients with AIS. BMD was positively correlated with increased height (coefficient =0.355; P<0.001) and BMI (coefficient =0.199; P=0.02). A significant association was detected between BMD and the Risser sign (P=0.002). No significant sex-based differences in BMD were observed (P=0.052). No significant correlations were observed between BMD and levels of potassium (K), calcium (Ca), inorganic phosphate (P), and iron (Fe) (P>0.05 all). The binary logistic regression analysis identified Cobb angle as a risk factor of lower BMD presence in AIS patients (coefficient =0.072; OR=1.075; P<0.001). Furthermore, the receiver operating characteristic (ROC) analysis of the combined model for predicting low BMD in AIS patients yielded an area under the curve (AUC) value of 0.900, with an optimal threshold determined as 0.398. The sensitivity and specificity were calculated as 0.816 and 0.900, respectively, indicating a robust predictive capacity. This study highlights the significant inverse correlation observed between BMD measured by QCT and both Cobb angle and vertebral rotation angle in patients with AIS. Furthermore, a notable variation in BMD was found across different Risser sign categories, with BMD values generally increasing as Risser sign levels increased. Additionally, our findings indicate that Cobb angle serves as a risk factor for low BMD presence. Moreover, a combined model was developed to predict the likelihood of low BMD occurrence in AIS patients.

(364) - Quantitative CT Scan Analysis of Healthy Lung Transplant Recipients Correlates with Lung Function in a Multicenter Prospective Cohort

(364) - Quantitative CT Scan Analysis of Healthy Lung Transplant Recipients Correlates with Lung Function in a Multicenter Prospective Cohort

Interstitial Lung Disease, Quantitative CT Analysis and Artificial Intelligence Applications, Radiomics

Interstitial Lung Disease, Quantitative CT Analysis and Artificial Intelligence Applications, Radiomics

Development and validation of a semi-automated and unsupervised method for femur segmentation from CT

Quantitative computed tomography (QCT)-based in silico models have demonstrated improved accuracy in predicting hip fractures with respect to the current gold standard, the areal bone mineral density. These models require that the femur bone is segmented as a first step. This task can be challenging, and in fact, it is often almost fully manual, which is time-consuming, operator-dependent, and hard to reproduce. This work proposes a semi-automated procedure for femur bone segmentation from CT images. The proposed procedure is based on the bone and joint enhancement filter and graph-cut algorithms. The semi-automated procedure performances were assessed on 10 subjects through comparison with the standard manual segmentation. Metrics based on the femur geometries and the risk of fracture assessed in silico resulting from the two segmentation procedures were considered. The average Hausdorff distance (0.03 ± 0.01 mm) and the difference union ratio (0.06 ± 0.02) metrics computed between the manual and semi-automated segmentations were significantly higher than those computed within the manual segmentations (0.01 ± 0.01 mm and 0.03 ± 0.02). Besides, a blind qualitative evaluation revealed that the semi-automated procedure was significantly superior (p < 0.001) to the manual one in terms of fidelity to the CT. As for the hip fracture risk assessed in silico starting from both segmentations, no significant difference emerged between the two (R2 = 0.99). The proposed semi-automated segmentation procedure overcomes the manual one, shortening the segmentation time and providing a better segmentation. The method could be employed within CT-based in silico methodologies and to segment large volumes of images to train and test fully automated and supervised segmentation methods.

Surgical stabilization for degenerative lesions of the lumbar spine in patients with reduced bone mineral density: a systematic literature review

Objective. To analyze methods contributing to the improvement of clinical and radiological outcomes of surgical stabilization of the lumbar spine in conditions of reduced bone mineral density (BMD).Material and Methods. A search for publications presenting the results of clinical studies of surgical treatment of patients with degenerative lesions of the lumbar spine and reduced BMD was conducted in English and Russian in the MEDLINE and Russian Science Citation Index (RSCI) databases using key words “osteoporosis”, “osteopenia”, “spinal surgery”, “spinal fusion” and related terms in titles and abstracts with a search depth of 20 years. After applying the inclusion and exclusion criteria, 17 clinical studies were selected for analysis.Results. Among the large number of techniques and implant-associated factors affecting the success of surgical intervention for degenerative lesions of the lumbar spine and reduced BMD, high-quality clinical studies have proven the effectiveness of expandable screws, spinal fusion with cortical screw placement and cement augmentation with polymethyl methacrylate.Conclusion. The combination of techniques for cortical pedicle screw placement, cement augmentation and the use of expandable screws makes it possible to achieve results in surgical treatment of patients with degenerative lesions of the lumbar spine with reduced BMD comparable with those in patients with normal BMD. Of particular importance is the preoperative assessment of the regional state of bone tissue, which should be carried out using quantitative CT or MRI studies.

Nomograms combining computed tomography-based body composition changes with clinical prognostic factors to predict survival in locally advanced cervical cancer patients.

To explore the value of body composition changes (BCC) measured by quantitative computed tomography (QCT) for evaluating the survival of patients with locally advanced cervical cancer (LACC) underwent concurrent chemoradiotherapy (CCRT), nomograms combined BCC with clinical prognostic factors (CPF) were constructed to predict overall survival (OS) and progression-free survival (PFS). Eighty-eight patients with LACC were retrospectively selected. All patients underwent QCT scans before and after CCRT, bone mineral density (BMD), subcutaneous fat area (SFA), visceral fat area (VFA), total fat area (TFA), paravertebral muscle area (PMA) were measured from two sets of computed tomography (CT) images, and change rates of these were calculated. Multivariate Cox regression analysis showed ΔBMD, ΔSFA, SCC-Ag, LNM were independent factors for OS (HR = 3.560, 5.870, 2.702, 2.499, respectively, all P < 0.05); ΔPMA, SCC-Ag, LNM were independent factors for PFS (HR = 2.915, 4.291, 2.902, respectively, all P < 0.05). Prognostic models of BCC combined with CPF had the highest predictive performance, and the area under the curve (AUC) for OS and PFS were 0.837, 0.846, respectively. The concordance index (C-index) of nomograms for OS and PFS were 0.834, 0.799, respectively. Calibration curves showed good agreement between the nomograms' predictive and actual OS and PFS, decision curve analysis (DCA) showed good clinical benefit of nomograms. CT-based body composition changes and CPF (SCC-Ag, LNM) were associated with survival in patients with LACC. The prognostic nomograms combined BCC with CPF were able to predict the OS and PFS in patients with LACC reliably.

A Controlled Variable Study of the Biomechanical Properties of the Proximal Femur before and after Cancellous Bone Removal.

The biomechanical characteristics of proximal femoral trabeculae are closely related to the occurrence and treatment of proximal femoral fractures. Therefore, it is of great significance to study its biomechanical effects of cancellous bone in the proximal femur. This study examines the biomechanical effects of the cancellous bone in the proximal femur using a controlled variable method, which provide a foundation for further research into the mechanical properties of the proximal femur. Seventeen proximal femoral specimens were selected to scan by quantitative computed tomography (QCT), and the gray values of nine regions were measure to evaluated bone mineral density (BMD) using Mimics software. Then, an intact femur was fixed simulating unilateral standing position. Vertical compression experiments were then performed again after removing cancellous bone in the femoral head, femoral neck, and intertrochanteric region, and data were recorded. According to the controlled variable method, the femoral head, femoral neck, and intertrochanteric trabeculae were sequentially removed based on the axial loading of the intact femur, and the displacement and strain changes of the femur samples under axial loading were recorded. Gom software was used to measure and record displacement and strain maps of the femoral surface. There was a statistically significant difference in anteroposterior displacement of cancellous bone destruction in the proximal femur (p < 0.001). Proximal femoral bone mass explained 77.5% of the strength variation, in addition proximal femoral strength was mainly affected by bone mass at the level of the upper outer, lower inner, lower greater trochanter, and lesser trochanter of the femoral head. The normal stress conduction of the proximal femur was destroyed after removing cancellous bone, the stress was concentrated in the femoral head and lateral femoral neck, and the femoral head showed a tendency to subside after destroying cancellous bone. The trabecular removal significantly altered the strain distribution and biomechanical strength of the proximal femur, demonstrating an important role in supporting and transforming bending moment under the vertical load. In addition, the strength of the proximal femur mainly depends on the bone density of the femoral head and intertrochanteric region.

Qualitative and quantitative spermatic cord abnormalities at CT predict symptomatic scrotal pathology.

To evaluate quantitative and qualitative spermatic cord CT abnormalities and presence of unilateral or bilateral symptomatic scrotal pathology (SSP) at ultrasound. This retrospective study included 122 male patients (mean age 47.8years) undergoing scrotal ultrasound within 24h of contrast-enhanced CT (n = 85), non-contrast CT (NECT, n = 32) or CT-Urogram (n = 5). CECT quantitative analysis assessed differential cord enhancement using maximum Hounsfield unit measurements. Three fellowship trained body radiologists independently assessed qualitative cord abnormalities for both CECT and NECT. Qualitative and quantitative findings were compared with the presence of SSP. Reader performance, interobserver agreement and reader confidence were assessed for NECT and CECT. Quantitative cutoff points were identified which maximized accuracy, specificity, negative predictive value, and other measures. SSP was present in 36/122 patients (29.5%). Positive cases were unilateral in 30 (83.3%) and bilateral in 6 (16.6%). At quantitative assessment, 25% differential cord enhancement had the highest diagnostic accuracy (88.9%), with 90.5% positive predictive value, 88.4% negative predictive value, 96.8% specificity, and 70.4% sensitivity. At qualitative evaluation, CECT reader performance was excellent (aggregate AUC = 0.86; P < .001); NECT was poorly discriminatory, although remained significant (aggregate AUC = 0.67; P = .002). Readers had significantly higher confidence levels with CECT (P < .001). Qualitative inter-observer agreement was high for both CECT and NECT (ICC = 0.981 and 0.963, respectively). Simple quantitative assessment of differential cord enhancement is highly accurate and specific for SSP at CECT. Qualitative abnormalities at CECT and NECT are also both predictors of SSP, however, CECT significantly out-performs non-contrast exams.

Enhancing COPD Classification Using Combined Quantitative CT and Texture-Based Radiomics: A CanCOLD Cohort Study

BackgroundRecent advancements in texture-based CT radiomics have demonstrated its potential for classifying chronic obstructive pulmonary disease (COPD).MethodsParticipants from Canadian Cohort Obstructive Lung Disease (CanCOLD) were investigated. A total of 108 features were included: 8 qCT, 95 texture-based radiomics, and 5 demographics. Machine learning models included demographics along with texture-based radiomics, and/or qCT. Combinations of 5 feature-selection and 5 classification methods were evaluated; a training dataset was used for feature-selection and to train the models, and a testing dataset was used for model evaluation. Models for classifying COPD status and severity were evaluated using the area under the receiver operating characteristic curve (AUC) with DeLong's test for comparison. SHAP analysis was used to investigate the features selected.ResultsA total of 1204 participants were evaluated (n=602 no COPD; n=602 COPD). There were no differences between the groups for sex (p=.77) or BMI (p=.21). For classifying COPD status, the combination of demographics, texture-based radiomics and qCT obtained higher performance (AUC=0.87) compared to demographics and texture-based radiomics (AUC=0.81; p&lt;.05) or qCT (AUC=0.84; p&lt;.05). Similarly, for classifying COPD severity, the combination of demographics, texture-based radiomics and qCT obtained higher performance (AUC=0.81) compared to demographics and texture-based radiomics (AUC=0.72; p&lt;.05) or qCT (AUC=0.79; p=&lt;.05). Texture-based radiomics and qCT features were among the top 5 features selected (15th-percentile-of-the-CT-density-histogram, CT total-airway-count, pack-years, CT grey-level-co-occurrence-matrix zone-distance-entropy, CT low-attenuation-clusters) for classifying COPD status.ConclusionTexture-based radiomics and conventional qCT features in combination improve machine learning models for COPD classification of status and severity.

Effects of anti-osteoporosis treatment in elderly patients with osteoporosis and lumbar discectomy and fusion.

The effect of anti-osteoporosis treatment in elderly patients with osteoporosis and lumbar discectomy and fusion (LIF) for lumbar degenerative diseases is not well known. This study aimed to evaluate the effect of perioperative anti-osteoporosis treatment in the patients with osteoporosis and LIF. From January to December 2022, patients were divided into three groups according to the inclusive criteria: the normal group (Group A), the osteopenia group (Group B) and the osteoporosis group (Group C). Quantitative computed tomography (QCT), height of the intervertebral space (HIS), segmental sagittal angle (SSA), visual analogue scale (VAS) score and Oswestry Disability Index (ODI) were compared between the groups at the follow-up time. The serum Ca2 + , osteocalcin (OC), propeptide of type I procollagen (PINP) C-terminal cross-linking telopeptide of type I collagen (β-CTX) and 25-OH vitamin D (25-OH VD) levels were compared between the groups at the time of follow-up. Interbody fusion was graded on the X-ray and CT images at the follow-up time. There were 165 patients in this study. There were significant differences in the mean age, mean score, HIS and SSA between the groups at the different follow-up times. There were significant differences in the concentrations of serum Ca2 + , OC, β-CTX, 25-OH VD and PINP at the sixth month after surgery between the groups. There were significant differences in the concentrations of serum Ca2 + , β-CTX and 25-OH VD between the pre-surgery and at six months after surgery in Group B and β-CTX and 25-OH VD in Group C. There was a significant difference in the degree of fusion between Group B and C (χ2= 5.6243, P< 0.05). In elderly patients with LIF and osteoporosis, anti-osteoporosis therapy could reduce bone resorption and thus facilitate fusion. Anti-osteoporosis medication tends to enhance radiological, functional, and fusion short-term outcomes.

A Novel QCT-Based Deep Transfer Learning Approach for Predicting Stiffness Tensor of Trabecular Bone Cubes

ObjectivesThis study was performed to prove the concept that transfer learning techniques, assisted with a generative model, could be used to alleviate the ‘big data’ requirement for training high-fidelity deep learning (DL) models in prediction of stiffness tensor of trabecular bone cubes. Material and methodsTransfer learning approaches of domain adaptation were used, in which a source domain included 1,641 digital trabecular bone cubes synthesized from a generative model, and a target domain included 868 real trabecular bone cubes from human cadaver femurs. Simulated quantitative computed tomography (QCT) images of both the synthesized and real bone cubes were used as input, whereas the stiffness tensor of these cubes determined using finite element simulations were used as output. Three transfer learning algorithms, including instance-based (TrAdaBoostR2 and WANN) and parameter-based (RNN) methods, were used. Two case studies, one with varying sizes of training dataset and the other with a gender-biased training dataset, were performed to evaluate these deep transfer learning models in comparison with a base deep learning (DL) model trained using the dataset from the target domain. ResultsThe results indicated that these deep transfer learning models were robust both to sample size and to the gender-biased training dataset, whereas the base DL model was very sensitive to such changes. Among the three transfer learning algorithms, the prediction accuracy of the RNN-based deep transfer learning model was the best (0.92-0.96%) and comparable to that of the base DL model trained using the dataset from the target domain. ConclusionThis study proved the proposed concept and confirmed that high fidelity QCT-based deep learning models could be obtained for prediction of stiffness tensor of trabecular bone cubes.