Quantitative Computed Tomography Research Articles

3D quantitative CT study to assess rotator cuff muscle fatty infiltration.

Fatty infiltration is a degenerative condition of the tendon-muscle unit of rotator cuff muscles, characterized by fatty accumulation within and around the muscles. This study compares a 3D method to the Goutallier classification for evaluating fatty infiltration of the rotator cuff muscles. Employing a retrospective study design, four researchers performed 3D segmentation of the rotator cuff muscles on 65 CT scans of patients with rotator cuff arthropathy. The Goutallier classification was graded on screenshots of the Y view and two axial slices. Measurements, including HU, volume, cross-sectional area, and Goutallier grade, were performed on the 3D segmentations and the respective 2D slices. The inter- and intra-rater variability for 2D and 3D methods were calculated using the Intraclass Correlation Coefficient (ICC). The ICC for 2D and 3D methods was excellent (ICC: 0.90-0.95 and 0.81-0.99, respectively) and good for the Goutallier classification (ICC: 0.62-0.81). Overall, the relative 3D fatty infiltration was 0% for Goutallier grade 0 muscles, 7% for grade 1, 19% for grade 2, 33% for grade 3, and 37% for grade 4. The relative 2D fatty infiltration was 0%, 8%, 25%, 37%, and 43%, respectively. We conclude that 3D segmentation on CT scans is better reproducible and evaluates the muscle entirely. However, the time-intensive nature of the 3D method currently limits its clinical practicality. Quantitive 2D evaluation is excellently reproducible but may overestimate the actual fatty infiltration percentage of the whole muscle.

Medial Calcar Density Measured via Opportunistic Computed Tomography Is Well Represented by the Dorr C Classification.

Introduction The canal-to-calcar isthmus (CC) ratio has been previously correlated with proximal femur osteology, but its relationship with bone density is not well established. Our purpose was to assess the relationship between femoral bone density, measured on opportunistic quantitative CT in Hounsfield units (HU), and CC ratio. Methods A total of 148 THA patients were included. The CC ratio was measured on anteroposterior hip radiographs. Using perioperative CT scans, a 1 cm diameter area was identified on a single mid-coronal slice in the medial calcar just proximal to the intertrochanteric ridge. The mean HU was calculated in this region to represent calcar bone density. Results Twenty-four percent (n = 35) of patients were classified as Dorr A (average CC ratio 0.47 [0.45; 0.48]), 67% (n = 96) as Dorr B (0.62 [0.55; 0.68]), and 11% (n = 17) as Dorr C (0.78 [0.77; 0.80]). There was a significant difference between Dorr A and Dorr C femurs (769 (144) vs. 588 (154) HU) as well as between B and C femurs (718 (166) vs. 588 (154) HU). The CC ratio was correlated with calcar bone density on CT (-0.370). Conclusion CC ratio is correlated with bone density determined by HU measurements on an opportunistic quantitative computed tomography scan, and bone density HU values were able to accurately differentiate bone density in Dorr A and B from Dorr C femurs. These findings suggest that the CC ratio is a reliable measurement to predict bone density in Dorr C femurs. Therefore, arthroplasty surgeons can confidently use the Dorr classification for patients with Dorr C femurs when preoperatively planning for THA.

MRI-based vertebral bone quality score as a novel bone status marker of patients with adolescent idiopathic scoliosis

To investigate the application of MRI-based vertebral bone quality (VBQ) score in assessing bone mineral density (BMD) for patients with adolescent idiopathic scoliosis (AIS). We reviewed the data of AIS patients between January 2021 and October 2023 with MRI, whole-spine plain radiographs, quantitative computed tomography (QCT) and general information. VBQ score was calculated using T1-weighted MRI. Univariate analysis was applied to present the differences between variables of patients with normal BMD group (QCT Z-score > − 2.0) and low BMD group (QCT Z-score ≤ − 2.0). The correlation between VBQ score and QCT Z-score was analyzed with Pearson correlation test. A multivariate logistic regression model was used to determine the independent factors related to low BMD. Receiver operating characteristic curve (ROC) was drawn to analyze the diagnostic performance of VBQ score in distinguishing low BMD. A total of 136 AIS patients (mean age was 14.84 ± 2.10 years) were included, of which 41 had low BMD. The low BMD group had a significantly higher VBQ score than that in normal group (3.48 ± 0.85 vs. 2.62 ± 0.62, P < 0.001). The VBQ score was significantly negative correlated with QCT Z score (r = − 0.454, P < 0.001). On multivariate analysis, VBQ score was independently associated with low BMD (OR: 4.134, 95% CI 2.136–8.000, P < 0.001). The area under the ROC curve indicated that the diagnostic accuracy of the VBQ score for predicting low BMD was 81%. A sensitivity of 65.9% with a specificity of 88.4% could be achieved for distinguishing low BMD by setting the VBQ score cutoff as 3.18. The novel VBQ score was a promising tool in distinguishing low BMD in patients with AIS and could be useful as opportunistic assessment for screening and complementary evaluation to QCT before surgery.

Four hepatic steatosis indices in predicting quantitative computed tomography-based metabolic dysfunction-associated fatty liver disease

Aim: To evaluate the prediction ability for quantitative computed tomography (QCT)-based metabolic dysfunction-associated fatty liver disease (MAFLD) of four widely known hepatic steatosis algorithms, namely the fatty liver index (FLI), the hepatic steatosis index (HSI), the Framingham Steatosis index (FSI) and the Zhejiang University index (ZJU index). Methods: From July 2020 to June 2022, health checkup subjects who accepted liver fat quantification with QCT at the Health Management Center of the Second Affiliated Hospital of Chongqing Medical University were recruited in this study. MAFLD was diagnosed by using QCT-based liver fat quantification. The prediction performance of FLI, HSI, FSI, and ZJU index on MAFLD was evaluated using the area under the receiver operating characteristic curve (AUC). Results: Of a total of 4,566 subjects enrolled in this study, 48.7% were diagnosed with MAFLD. The AUC values of FLI, HSI, FSI, and ZJU index were 0.819, 0.792, 0.822 and 0.826, respectively. FLI exhibited the highest sensitivity (SN) of 79.42%, while the ZJU index demonstrated the highest specificity (SP) of 75.35%. Conclusions: All four indices (FLI, HSI, FSI, and ZJU index) have acceptable predictive performance for patients with QCT-based MAFLD. Our study suggests that the above indices have a stable ability for detecting MAFLD.

Age and BMI have different effects on subcutaneous, visceral, liver, bone marrow, and muscle adiposity, as measured by CT and MRI.

We analyzed quantitative computed tomography (CT) and chemical shift-encoded magnetic resonance imaging (MRI) data from a Chinese cohort to investigate the effects of BMI and aging on different adipose tissue (AT) depots. In 400 healthy, community-dwelling individuals aged 22 to 83 years, we used MRI to quantify proton density fat fraction (PDFF) of the lumbar spine (L2-L4) bone marrow AT (BMAT), the psoas major and erector spinae (ES) muscles, and the liver. Abdominal total AT, visceral AT (VAT), and subcutaneous AT (SAT) areas were measured at the L2-L3 level using quantitative CT. Partial correlation analysis was used to evaluate the relationship of each AT variable with age and BMI. Multiple linear regression analysis was performed in which each AT variable was evaluated in turn as a function of age and the other five independent AT measurements. Of the 168 men, 29% had normal BMI (<24.0 kg/m2), 47% had overweight (24.0-27.9 kg/m2), and 24% had obesity (≥ 28.0 kg/m2). In the 232 women, the percentages were 46%, 32%, and 22%, respectively. Strong or very strong correlations with BMI were found for total AT, VAT, and SAT in both sexes. BMAT and ES PDFF was strongly correlated with age in women and moderately correlated in men. In both sexes, BMAT PDFF correlated only with age and not with any of the other AT depots. Psoas PDFF correlated only with ES PDFF and not with age or the other AT depots. Liver PDFF correlated with BMI and VAT and weakly with SAT in men. VAT and SAT correlated with age and each other in both sexes. Age and BMI are both associated with adiposity, but their effects differ depending on the type of AT.

From pixels to prognosis: unlocking the potential of deep learning in fibrotic lung disease imaging analysis.

The licensing of antifibrotic therapy for fibrotic lung diseases, including idiopathic pulmonary fibrosis (IPF), has created an urgent need for reliable biomarkers to predict disease progression and treatment response. Some patients experience stable disease trajectories, while others deteriorate rapidly, making treatment decisions challenging. High-resolution chest CT has become crucial for diagnosis, but visual assessments by radiologists suffer from low reproducibility and high interobserver variability. To address these issues, computer-based image analysis, called quantitative CT, has emerged. However, many quantitative CT methods rely on human input for training, therefore potentially incorporating human error into computer training. Rapid advances in artificial intelligence, specifically deep learning, aim to overcome this limitation by enabling autonomous quantitative analysis. While promising, deep learning also presents challenges including the need to minimize algorithm biases, ensuring explainability, and addressing accessibility and ethical concerns. This review explores the development and application of deep learning in improving the imaging process for fibrotic lung disease.

Methionine as a regulator of bone remodeling with fasting.

Caloric restriction improves metabolic health but is often complicated by bone loss. We studied bone parameters in humans during a 10-day fast and identified candidate metabolic regulators of bone turnover. Pro-collagen 1 intact N-terminal pro-peptide (P1NP), a bone formation marker, decreased within 3 days of fasting. Whereas dual-energy x-ray absorptiometry measures of bone mineral density were unchanged after 10 days of fasting, high-resolution peripheral quantitative CT demonstrated remodeling of bone microarchitecture. Pathway analysis of longitudinal metabolomics data identified one-carbon metabolism as fasting dependent. In cultured osteoblasts, we tested the functional significance of one-carbon metabolites modulated by fasting, finding that methionine - which surged after 3 days of fasting - affected markers of osteoblast cell state in a concentration-dependent manner, in some instances exhibiting a U-shaped response with both low and high concentrations driving putative antibone responses. Administration of methionine to mice for 5 days recapitulated some fasting effects on bone, including a reduction in serum P1NP. In conclusion, a 10-day fast in humans led to remodeling of bone microarchitecture, potentially mediated by a surge in circulating methionine. These data support an emerging model that points to a window of optimal methionine exposure for bone health.

Post-market surveillance of six COVID-19 point-of-care tests using pre-Omicron and Omicron SARS-CoV-2 variants.

Post-market surveillance of test performance is a critical function of public health agencies and clinical researchers that ensures tests maintaining diagnostic characteristics following their regulatory approval. Changes in product quality, manufacturing processes over time, or the evolution of new variants may impact product performance. During the COVID-19 pandemic, a plethora of point-of-care tests (POCTs) was released onto the Canadian market. This study evaluated the performance characteristics of several of the most widely distributed POCTs in Canada, including four rapid antigen tests (Abbott Panbio, BTNX Rapid Response, SD Biosensor, and Quidel QuickVue) and two molecular tests (Abbott ID NOW and Lucira Check IT). All tests were challenged with 149 SARS-CoV-2 clinical positives, including multiple variants up to and including Omicron XBB.1.5, as well as 29 clinical negatives. Results were stratified based on whether the isolate was Omicron or pre-Omicron as well as by reverse transcriptase quantitative PCR Ct value. The test performance of each POCT was consistent with the manufacturers' claims and showed no significant decline in clinical performance against any of the variants tested. These findings provide continued confidence in the results of these POCTs as they continue to be used to support decentralized COVID-19 testing. This work demonstrates the essential role of post-market surveillance in ensuring reliability in diagnostic tools.IMPORTANCEPost-market surveillance of diagnostic test performance is critical to ensure their reliability after regulatory approval. This is especially critical in the context of the COVID-19 pandemic as the use of point-of-care tests (POCTs) became widespread. Our study focused on four rapid antigen tests (Abbott Panbio, BTNX Rapid Response, SD Biosensor, and Quidel QuickVue) and two molecular tests (Abbott ID NOW and Lucira Check IT) that were widely distributed across Canada, assessing their performance using many SARS-CoV-2 variants, including up to Omicron subvariant XBB.1.5. Overall, we found no significant difference in performance against any variant, reinforcing confidence in their use. As concerns in test efficacy have been raised by news outlets, particularly regarding the BTNX Rapid Response, this work is even more timely and crucial. Our research offers insights into the performance of widely used COVID-19 POCTs but also highlights the necessity for post-market surveillance.

Changes in bone density and structure of proximal humerus with aging in Chinese women

AimsThis study aimed to investigate age- and menopause-related differences in bone mineral density (BMD), bone structure and estimated bone strength at surgical neck of humerus in Chinese female sample. MethodsWe conducted a cross-sectional cohort study of 171 Chinese women. Bone mass, indices of geometric properties and estimated mechanical strength of the surgical neck were evaluated by quantitative computed tomography (QCT). Comparisons were performed across menstrual status categories. Age-related changes in QCT-derived bone parameters were calculated. ResultsThe age-related difference of BMD and cortical thickness was 40.25 % and 32.86 % between the age of 20 and 86 years. Progressive periosteal and endosteal expansion was associated linearly with age. Estimated mechanical strength indexes showed significant quadratic associations with age, with their peak occurred at the age of 46–55 years. The quartile of women with the greatest medullary diameter also had the lowest valve of BMD and cortical thickness and the greatest in skeletal width. Compared to premenopausal individuals, perimenopausal women were distinguished by lower cortical thickness (18.63 %) and BMD (20.05 %). The continued decrease in cortical thickness and BMD was noted after menopause. The medullary and periosteal diameter increased by 17.98 % and 9.34 % respectively in perimenopausal period, but not after menopause. The accelerated loss of the maximum and polar section modulus was observed in late postmenopausal women. ConclusionsThe increase in bone size only occurred during the menopause transition. Obvious loss of resistance to bending was in late postmenopausal period.

Enhancing Osteoporosis Management: A Thorough Examination of Surgical Techniques and Their Effects on Patient Outcomes.

Managing osteoporotic fractures in older individuals is a difficult task in orthopedic surgery. It requires a careful approach that combines advanced diagnostic methods, customized surgical treatments, and comprehensive rehabilitation strategies. This article presents the results of an analysis carried out at the University Emergency Hospital, Bucharest. The analysis specifically examines the treatment of osteoporotic fractures using different osteosynthesis techniques. Although diagnostic tools like dual-energy X-ray absorptiometry (DXA) and Fracture Risk Assessment Tool (FRAX) have improved, a considerable number of fractures still happen in people who do not have obvious osteoporosis. This emphasizes the importance of using additional diagnostic measures such as high-resolution peripheral quantitative computed tomography (HR-pQCT) and quantitative computed tomography (QCT) to improve the accuracy of predictions. The study demonstrates the intricate nature of surgical decision-making and the significance of adjusting techniques to meet the specific needs of each patient. An instance of osteosynthesis failure resulting from the inappropriate choice of method highlighted the crucial significance of a thorough preoperative assessment. The discussion highlights the importance of early mobilization and rehabilitation in reducing the risks associated with prolonged immobilization and improving patient recovery. This paper strongly supports the use of evidence-based and patient-centered methods in the management of osteoporotic fractures. It emphasizes the importance of utilizing the most recent advancements in diagnostic and surgical technologies. Promising advancements in orthopedic medicine lie in the future, particularly in the integration of interdisciplinary research and personalized medicine. These advancements have the potential to enhance patient outcomes in this population that is at high risk.

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.

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.

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.

Significance of Normal Lung Volume on Quantitative CT Imaging Analysis in Group 1 and Group 3 Pulmonary Hypertension

Significance of Normal Lung Volume on Quantitative CT Imaging Analysis in Group 1 and Group 3 Pulmonary Hypertension

Utilizing 3-dimensional models to assess keel bone damage in laying hens throughout the lay cycle

The global egg industry is rapidly transitioning to cage-free egg production from conventional cages. Hens housed in cage-free systems have an increased prevalence of keel damage that could lead to reduced egg production and compromised well-being. The objective of this study was to determine the effects of dietary supplementation of n-3 fatty acids and vitamin D3 on keel damage in hens housed in multi-tier aviary systems (AV). Brown hens were placed in 4 AV system rooms after rearing at 17 wk of age (woa) with each room containing 576 birds. At 12 woa, rooms were randomly assigned to a dietary treatment of flaxseed oil, fish oil, vitamin D3, or control. Focal birds (36 per treatment) were longitudinally examined for keel damage using quantitative computed tomography (QCT) at nine timepoints from 16 to 52 woa. Three-dimensional digital twins of the keels were created from the QCT scans and visually assessed for damage. An overall keel severity score was recorded as well as the location, direction, and severity of each deviation or fracture. Severity was ranked on a 0 to 5 scale with 0 being no damage and 5 being severe. Damage scores were analyzed utilizing odds ratios with main effects of age and treatment. At 16 woa, 80% of hens had overall keel scores of 0 and 20% had scores of 1. At 52 woa, all hens had damage, with 31% having a score of 1, 61% scored 2 to 3, and 8% scored 4 to 5. Most fractures were not observed until peak lay. Dietary treatments did not affect likelihood of fracture incidences, but younger birds had lower odds of incurring keel fractures than older birds (P < 0.0001). The initial incidences of keel deviations occurred earlier than fractures, with most birds obtaining a keel deviation by 28 woa. Keel damage was not able to be prevented, but the age at which keel fractures and deviations initiate appear to be different, with deviations occurring during growth and fractures during lay.

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 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 Computed Tomography and Response to Pronation in COVID-19 ARDS.

The use of prone position (PP) has been widespread during the COVID-19 pandemic. Whereas 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 (CT) scan results and gas exchange response to PP in invasively ventilated subjects with COVID-19 ARDS is unknown. The present study aimed to compare baseline quantitative CT results between subjects responding to PP in terms of oxygenation or CO2 clearance and those who did not. This was a single-center, retrospective observational study including critically ill, invasively ventilated subjects with COVID-19-related ARDS admitted to the ICUs of Niguarda Hospital between March 2020-November 2021. Blood gas samples were collected before and after PP. Subjects in whom the PaO2 /FIO2 increase was ≥ 20 mm Hg after PP were defined as oxygen responders. CO2 responders were defined when the ventilatory ratio (VR) decreased during PP. Automated quantitative CT analyses were performed to obtain tissue mass and density of the lungs. One hundred twenty-five subjects were enrolled, of which 116 (93%) were O2 responders and 51 (41%) CO2 responders. No difference in quantitative CT characteristics and oxygen were observed between responders and non-responders (tissue mass 1,532 ± 396 g vs 1,654 ± 304 g, P = .28; density -544 ± 109 HU vs -562 ± 58 HU P = .42). Similar findings were observed when dividing the population according to CO2 response (tissue mass 1,551 ± 412 g vs 1,534 ± 377 g, P = .89; density -545 ± 123 HU vs -546 ± 94 HU, P = .99). Most subjects with COVID-19-related ARDS improved their oxygenation at the first pronation cycle. The study suggests that baseline quantitative CT scan data were not associated with the response to PP in oxygenation or CO2 in mechanically ventilated subjects with COVID-19-related ARDS.

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.