Quantitative Computed Tomography Research Articles

Quantitative CT parameters combined with preoperative systemic inflammatory markers for differentiating risk subgroups of thymic epithelial tumors

BackgroundThymic epithelial tumors (TETs) are the most common primary neoplasms of the anterior mediastinum. Different risk subgroups of TETs have different prognosis and therapeutic strategies, therefore, preoperative identification of different risk subgroups is of high clinical significance. This study aims to explore the diagnostic efficiency of quantitative computed tomography (CT) parameters combined with preoperative systemic inflammatory markers in differentiating low-risk thymic epithelial tumors (LTETs) from high-risk thymic epithelial tumors (HTETs).Methods74 Asian patients with TETs confirmed by biopsy or postoperative pathology between January 2013 and October 2022 were collected retrospectively and divided into two risk subgroups: LTET group (type A, AB and B1 thymomas) and HTET group (type B2, B3 thymomas and thymic carcinoma). Statistical analysis were performed between the two groups in terms of quantitative CT parameters and preoperative systemic inflammatory markers. Multivariate logistic regression analysis was used to determine the independent predictors of risk subgroups of TETs. The area under curve (AUC) and optimal cut-off values were calculated by receiver operating characteristic (ROC) curves.Results47 TETs were in LTET group, while 27 TETs were in HTET group. In addition to tumor size and CT value of the tumor on plain scan, there were statistical significance comparing in CT value of the tumor on arterial phase (CTv-AP) and venous phase (CTv-VP), and maximum enhanced CT value (CEmax) of the tumor between the two groups (for all, P < 0.05). For systemic inflammatory markers, HTET group was significantly higher than LTET group (for all, P < 0.05), including platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR) and systemic immune-inflammation index (SII). Multivariate logistic regression analysis showed that NLR (odds ratio [OR] = 2.511, 95% confidence interval [CI]: 1.322–4.772, P = 0.005), CTv-AP (OR = 0.939, 95%CI: 0.888–0.994, P = 0.031) and CTv-VP (OR = 0.923, 95%CI: 0.871–0.979, P = 0.008) were the independent predictors of risk subgroups of TETs. The AUC value of 0.887 for the combined model was significantly higher than NLR (0.698), CTv-AP (0.800) or CTv-VP (0.811) alone. The optimal cut-off values for NLR, CTv-AP and CTv-VP were 2.523, 63.44 Hounsfeld Unit (HU) and 88.29HU, respectively.ConclusionsQuantitative CT parameters and preoperative systemic inflammatory markers can differentiate LTETs from HTETs, and the combined model has the potential to improve diagnostic efficiency and to help the patient management.

Computed tomography-based imaging biomarker identifies coal workers’ pneumoconiosis

Rationale: The increase in the incidence and the diagnostic limitations of pneumoconiosis have emerged as a public health concern. This study aimed to conduct a computed tomography (CT)- based quantitative analysis to understand differences in imaging results of pneumoconiosis according to disease severity.Methods: According to the International Labor Organization (ILO) guidelines, coal workers’ pneumoconiosis (CWP) are classified into five categories. CT images were obtained only at full inspiration and were quantitatively evaluated for airway structural variables such as bifurcation angle (θ), hydraulic diameter (Dh), wall thickness (WT), and circularity (Cr). Parenchymal functional variables include abnormal regions (emphysema, ground–glass opacities, consolidation, semi consolidation, and fibrosis) and blood vessel volume. Through the propensity score matching method, the confounding effects were decreased.Results: Category 4 demonstrated a reduced θ in TriLUL, a thicker airway wall in both the Trachea and Bronint compared to Category 0, and a decreased Cr in Bronint. Category 4 presented with higher abnormal regions except for ground–glass opacity and a narrower pulmonary blood vessel volume. A negative correlation was found between abnormal areas with lower Hounsfield units (HU) than the normal lung and the ratio of forced expiratory volume in 1 s/forced vital capacity, with narrowed pulmonary blood vessel volume which is positively correlated with abnormal areas with upper HU than the normal lung.Conclusion: This study provided valuable insight into pneumoconiosis progression through a comparison of quantitative CT images based on severity. Furthermore, as there has been paucity of studies on the pulmonary blood vessel volume of the CWP, in this study, a correlation between reduced pulmonary blood vessel volume and regions with low HU values holds significant importance.

Multi-view information fusion using multi-view variational autoencoder to predict proximal femoral fracture load.

Hip fracture occurs when an applied force exceeds the force that the proximal femur can support (the fracture load or "strength") and can have devastating consequences with poor functional outcomes. Proximal femoral strengths for specific loading conditions can be computed by subject-specific finite element analysis (FEA) using quantitative computerized tomography (QCT) images. However, the radiation and availability of QCT limit its clinical usability. Alternative low-dose and widely available measurements, such as dual energy X-ray absorptiometry (DXA) and genetic factors, would be preferable for bone strength assessment. The aim of this paper is to design a deep learning-based model to predict proximal femoral strength using multi-view information fusion. We developed new models using multi-view variational autoencoder (MVAE) for feature representation learning and a product of expert (PoE) model for multi-view information fusion. We applied the proposed models to an in-house Louisiana Osteoporosis Study (LOS) cohort with 931 male subjects, including 345 African Americans and 586 Caucasians. We performed genome-wide association studies (GWAS) to select 256 genetic variants with the lowest p-values for each proximal femoral strength and integrated whole genome sequence (WGS) features and DXA-derived imaging features to predict proximal femoral strength. The best prediction model for fall fracture load was acquired by integrating WGS features and DXA-derived imaging features. The designed models achieved the mean absolute percentage error of 18.04%, 6.84% and 7.95% for predicting proximal femoral fracture loads using linear models of fall loading, nonlinear models of fall loading, and nonlinear models of stance loading, respectively. The proposed models are capable of predicting proximal femoral strength using WGS features and DXA-derived imaging features. Though this tool is not a substitute for predicting FEA using QCT images, it would make improved assessment of hip fracture risk more widely available while avoiding the increased radiation exposure from QCT.

Voxel-Wise Comparison of Co-Registered Quantitative CT and Hyperpolarised Gas Diffusion-Weighted MRI Measurements in IPF

The patterns of idiopathic pulmonary fibrosis (IPF) lung disease that directly correspond to elevated hyperpolarised gas diffusion-weighted (DW) MRI metrics are currently unknown. This study aims to develop a spatial co-registration framework for a voxel-wise comparison of hyperpolarised gas DW-MRI and CALIPER quantitative CT patterns. Sixteen IPF patients underwent 3He DW-MRI and CT at baseline, and eleven patients had a 1-year follow-up DW-MRI. Six healthy volunteers underwent 129Xe DW-MRI at baseline only. Moreover, 3He DW-MRI was indirectly co-registered to CT via spatially aligned 3He ventilation and structural 1H MRI. A voxel-wise comparison of the overlapping 3He apparent diffusion coefficient (ADC) and mean acinar dimension (LmD) maps with CALIPER CT patterns was performed at baseline and after 1 year. The abnormal lung percentage classified with the LmD value, based on a healthy volunteer 129Xe LmD, and CALIPER was compared with a Bland–Altman analysis. The largest DW-MRI metrics were found in the regions classified as honeycombing, and longitudinal DW-MRI changes were observed in the baseline-classified reticular changes and ground-glass opacities regions. A mean bias of −15.3% (95% interval −56.8% to 26.2%) towards CALIPER was observed for the abnormal lung percentage. This suggests DW-MRI may detect microstructural changes in areas of the lung that are determined visibly and quantitatively normal by CT.

Nomogram Based on Body Composition and Prognostic Nutritional Index Predicts Survival After Curative Resection of Gastric Cancer

This study aimed to identify independent prognostic factors for gastric cancer (GC) patients after curative resection using quantitative computed tomography (QCT) combined with prognostic nutritional index (PNI), and to develop a nomogram prediction model for individualized prognosis. This study retrospectively analyzed 119 patients with GC who underwent curative resection from January 2016 to March 2018. The patients' preoperative clinical pathological data were recorded, and all patients underwent QCT scans before and after curative resection to obtain QCT parameters: bone mineral density (BMD), skeletal muscle area (SMA), visceral fat area (VFA), subcutaneous fat area (SFA) and CT fat fraction (CTFF), then relative rate of change in each parameter (ΔBMD, ΔSMA, ΔVFA, ΔSFA, ΔCTFF) was calculated after time normalization. Multivariate Cox proportional hazards was used to establish a nomogram model that based on independent prognostic factors. The concordance index (C-index), area under the time-dependent receiver operating characteristic (ROC) curve and clinical decision curve were used to evaluate the predictive performance and clinical benefit of the nomogram model． RESULTS: This study found that ΔCTFF, ΔVFA, ΔBMD and PNI are independent prognostic factors for overall survival (OS)(hazard ratio: 1.034, 0.895, 0.976, 2.951, respectively, all p<0.05). The established nomogram model could predict the area under the ROC curveof OS at 1, 3and 5 years as 0.816, 0.815 and 0.881, respectively. The C-index was 0.743 (95% CI, 0.684-0.801), and the decision curve analysis showed that this model has good clinical net benefit. The nomogram model based on body composition and PNI is reliable in predicting the individualized survival of underwent curative resection for GC patients.

Quantitative CT features on admission combined with laboratory biomarkers for predicting severe acute pancreatitis

To assess the association of quantitative computed tomography (CT) features on admission with acute pancreatitis (AP) severity, and to explore the performance of combined CT and laboratory markers for predicting severe AP (SAP). Data from 208 AP patients were reviewed retrospectively. Pancreas volume, the area of extrapancreatic inflammation, extrapancreatic fluid collection volume, and number were calculated based on CT images on admission. Laboratory biomarkers within 24 h of admission were collected. Interobserver agreement for CT measurements was measured by calculating interclass correlation coefficient (ICC). The associations of quantitative CT features with AP severity were evaluated. Predictive models for SAP were constructed based on CT and laboratory markers. Performances of single marker and the models were evaluated using receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). Pancreas volume, area of extrapancreatic inflammation, extrapancreatic fluid collection volume, and number were significantly different between severe and non-severe AP groups. In predicting SAP, the AUCs of quantitative CT indicators ranged from 0.72 to 0.79; the AUCs of laboratory biomarkers were between 0.53 and 0.66. The combined model of area of extrapancreatic inflammation, serum calcium, and haematocrit yielded an AUC of 0.84, significantly higher than that of the laboratory model, single CT, or laboratory marker. Interobserver agreements for quantitative CT indicators were excellent, with ICC ranging from 0.91 to 0.98. Quantitative CT features on admission were significantly associated with AP severity; the combination of extrapancreatic inflammation area, serum calcium, and haematocrit could be taken as a new method for predicting SAP.

Quantification of diffuse parenchymal lung disease in non-small cell lung cancer patients with definitive concurrent chemoradiation therapy for predicting radiation pneumonitis.

We sought to quantify diffuse parenchymal lung disease (DPLD) extent using quantitative computed tomography (CT) analysis and to investigate its association with radiation pneumonitis (RP) development in non-small cell lung cancer (NSCLC) patients receiving definitive concurrent chemoradiation therapy (CCRT). A total of 82 NSCLC patients undergoing definitive CCRT were included in this prospective cohort study. Pretreatment CT scans were analyzed using quantitative CT analysis software. Low-attenuation area (LAA) features based on lung density and texture features reflecting interstitial lung disease (ILD) were extracted from the whole lung. Clinical and dosimetric factors were also evaluated. RP development was assessed using the Common Terminology Criteria for Adverse Events version 5.0. Univariable and multivariable logistic regression analyses were performed to identify independent risk factors for grade ≥3 (≥GR3) RP. RP was identified in 68 patients (73.9%), with nine patients (10.9%) experiencing ≥GR3 RP. Univariable logistic regression analysis identified excess kurtosis and high-attenuation area (HAA)_volume (cc) as significantly associated with ≥GR3 RP. Multivariable logistic regression analysis showed that the combined use of imaging features and clinical factors (forced expiratory volume in 1 second [FEV1], forced vital capacity [FVC], and CHEMO regimen) demonstrated the best performance (area under the receiver operating characteristic curve = 0.924) in predicting ≥GR3 RP. Quantified imaging features of DPLD obtained from pretreatment CT scans would predict the occurrence of RP in NSCLC patients undergoing definitive CCRT. Combining imaging features with clinical factors could improve the accuracy of the predictive model for severe RP.

Bone mineral density differences between femurs of scoliotic patients undergoing quantitative computed tomography analysis

PurposeScoliosis is a cause of loading imbalance between the lower limbs, which can result in BMD differences between the two femurs. We investigated the discrepancy in BMD values assessed by quantitative computed tomography (QCT) between femurs in patients with and without scoliosis, also assessing if this difference can be related to spine convexity.MethodsAbdominal CT examinations were retrospectively reviewed. An ‘‘asynchronous’’ calibration of CT images was performed to obtain BMD values from QCT. Scoliosis was evaluated on the antero-posterior CT localizer to calculate the Cobb angle. Differences between aBMD and vBMD of femurs were assessed in both scoliotic and non-scoliotic subjects.ResultsFinal study cohort consisted of 263 subjects, 225 of them without scoliosis (85.6%) and 38 with scoliosis (14.4%). No significant differences were found in the general population without scoliosis, except for vBMD at the neck. Comparison of femurs in scoliotic patients showed statistically significant differences at neck aBMD −0.028 g/cm2, p = 0.004), total femur aBMD (−-0.032 g/cm2, p = 0.008) and total femur vBMD (−-8.9 mg/cm3, p = 0.011), with lower BMD values on the convexity side. In 10 cases (26%) a change in the final T-score diagnosis was observed.ConclusionQCT analysis demonstrated a difference in both areal and volumetric BMD between the two femurs of scoliotic patients, in relation to the side of the scoliotic curve. If these data will be confirmed by larger studies, bilateral femoral DXA acquisition may be proposed for these patients.

Rationale and design of the CONFIRM2 (Quantitative COroNary CT Angiography Evaluation For Evaluation of Clinical Outcomes: An InteRnational, Multicenter Registry) study

BackgroundIn the last 15 years, large registries and several randomized clinical trials have demonstrated the diagnostic and prognostic value of coronary computed tomography angiography (CCTA). Advances in CT scanner technology and developments of analytic tools now enable accurate quantification of coronary artery disease (CAD), including total coronary plaque volume and low attenuation plaque volume. The primary aim of CONFIRM2, (Quantitative COroNary CT Angiography Evaluation For Evaluation of Clinical Outcomes: An InteRnational, Multicenter Registry) is to perform comprehensive quantification of CCTA findings, including coronary, non-coronary cardiac, non-cardiac vascular, non-cardiac findings, and relate them to clinical variables and cardiovascular clinical outcomes. DesignCONFIRM2 is a multicenter, international observational cohort study designed to evaluate multidimensional associations between quantitative phenotype of cardiovascular disease and future adverse clinical outcomes in subjects undergoing clinically indicated CCTA. The targeted population is heterogenous and includes patients undergoing CCTA for atherosclerotic evaluation, valvular heart disease, congenital heart disease or pre-procedural evaluation. Automated software will be utilized for quantification of coronary plaque, stenosis, vascular morphology and cardiac structures for rapid and reproducible tissue characterization. Up to 30,000 patients will be included from up to 50 international multi-continental clinical CCTA sites and followed for 3–4 years. SummaryCONFIRM2 is one of the largest CCTA studies to establish the clinical value of a multiparametric approach to quantify the phenotype of cardiovascular disease by CCTA using automated imaging solutions.

Predicting Bone Adaptation in Astronauts during and after Spaceflight.

A method was previously developed to identify participant-specific parameters in a model of trabecular bone adaptation from longitudinal computed tomography (CT) imaging. In this study, we use these numerical methods to estimate changes in astronaut bone health during the distinct phases of spaceflight and recovery on Earth. Astronauts (N = 16) received high-resolution peripheral quantitative CT (HR-pQCT) scans of their distal tibia prior to launch (L), upon their return from an approximately six-month stay on the international space station (R+0), and after six (R+6) and 12 (R+12) months of recovery. To model trabecular bone adaptation, we determined participant-specific parameters at each time interval and estimated their bone structure at R+0, R+6, and R+12. To assess the fit of our model to this population, we compared static and dynamic bone morphometry as well as the Dice coefficient and symmetric distance at each measurement. In general, modeled and observed static morphometry were highly correlated (R2> 0.94) and statistically different (p < 0.0001) but with errors close to HR-pQCT precision limits. Dynamic morphometry, which captures rates of bone adaptation, was poorly estimated by our model (p < 0.0001). The Dice coefficient and symmetric distance indicated a reasonable local fit between observed and predicted bone volumes. This work applies a general and versatile computational framework to test bone adaptation models. Future work can explore and test increasingly sophisticated models (e.g., those including load or physiological factors) on a participant-specific basis.

Abstract 16606: Diagnostic Performance of AI-QCT ISCHEMIA for Abnormal FFR Across Calcified Plaque Subgroups

Introduction: Coronary atherosclerosis and vascular morphology imaging by quantitative CT angiography (AI-QCT) provides detailed plaque and vessel measures of the coronary tree. These features combined in a tool (AI-QCT ISCHEMIA ) accurately estimate vessel level ischemia. We investigated the diagnostic performance of AI-QCT ISCHEMIA for abnormal invasive fractional flow reserve (FFR) across subgroups of calcified plaque. Methods: Symptomatic patients within the CREDENCE (N=305) and PACIFIC (N=208) studies underwent CCTA, myocardial perfusion imaging (MPI; single photon emission computed tomography [SPECT] and/or positron emission tomography [PET]), fractional flow reserve by CT (FFR-CT ) and invasive coronary angiography in conjunction with unbiased 3-vessel invasive FFR measurements as reference standard. The diagnostic performance of the non-invasive tests was compared on vessel-level across the tertiles of calcified plaque volume, on an intention-to-diagnose basis. Results: Within the CREDENCE study, AI-QCT ISCHEMIA had an area under the receiver operating characteristics curve (AUC ROC) of 0.887, 0.850, 0.816 across tertile 1-3 of calcified plaque, respectively, which was significantly higher than FFR-CT and MPI. Within the PACIFIC study, the AUC ROC was 0.811, 0.860, 0.817 across the calcified plaque tertiles 1-3, which was significantly better than SPECT, and comparable to FFR-CT ad PET. The specificity of AI-QCT ISCHEMIA was lower with increased calcified plaque, being approximately 70% for the most calcified vessels amongst both cohorts. Conclusion AI-QCT ISCHEMIA , a tool to assess coronary ischemia based on quantitative coronary plaque and vascular morphology retained good diagnostic performance for invasive FFR across calcified plaque subgroups.

Plasma metabolomics and quantitative interstitial abnormalities in ever-smokers

BackgroundQuantitative interstitial abnormalities (QIA) are an automated computed tomography (CT) finding of early parenchymal lung disease, associated with worse lung function, reduced exercise capacity, increased respiratory symptoms, and death. The metabolomic perturbations associated with QIA are not well known. We sought to identify plasma metabolites associated with QIA in smokers. We also sought to identify shared and differentiating metabolomics features between QIA and emphysema, another smoking-related advanced radiographic abnormality.MethodsIn 928 former and current smokers in the Genetic Epidemiology of COPD cohort, we measured QIA and emphysema using an automated local density histogram method and generated metabolite profiles from plasma samples using liquid chromatography–mass spectrometry (Metabolon). We assessed the associations between metabolite levels and QIA using multivariable linear regression models adjusted for age, sex, body mass index, smoking status, pack-years, and inhaled corticosteroid use, at a Benjamini–Hochberg False Discovery Rate p-value of ≤ 0.05. Using multinomial regression models adjusted for these covariates, we assessed the associations between metabolite levels and the following CT phenotypes: QIA-predominant, emphysema-predominant, combined-predominant, and neither- predominant. Pathway enrichment analyses were performed using MetaboAnalyst.ResultsWe found 85 metabolites significantly associated with QIA, with overrepresentation of the nicotinate and nicotinamide, histidine, starch and sucrose, pyrimidine, phosphatidylcholine, lysophospholipid, and sphingomyelin pathways. These included metabolites involved in inflammation and immune response, extracellular matrix remodeling, surfactant, and muscle cachexia. There were 75 metabolites significantly different between QIA-predominant and emphysema-predominant phenotypes, with overrepresentation of the phosphatidylethanolamine, nicotinate and nicotinamide, aminoacyl-tRNA, arginine, proline, alanine, aspartate, and glutamate pathways.ConclusionsMetabolomic correlates may lend insight to the biologic perturbations and pathways that underlie clinically meaningful quantitative CT measurements like QIA in smokers.

Bone Mineral Density Derived from Cardiac CT Scans: Using Contrast Enhanced Scans for Opportunistic Screening

Purpose: Osteoporosis is under-diagnosed and often co-exists with other diseases. Very low bone mineral density (BMD) indicates risk of osteoporosis and opportunistic screening for low BMD in CT-scans has been suggested. In a non-contrast enhanced thoracic CT scan, the scan-field-of-view includes vertebrae enabling BMD estimation. However, many CT scans are obtained by administration of contrast material. If the impact of contrast enhancement on BMD measurements could be quantified, considerably more patients are eligible for screening. Methods: This study investigated the impact of intravenous contrast on thoracic BMD measurements in cardiac CT scans pre- and post-contrast, including different contrast trigger levels of 130 and 180 Hounsfield units (HU). BMD was measured using quantitative CT with asynchronous calibration. Results: In 195 participants undergoing cardiac CT (mean age 57±9 years, 37 % females) contrast increased mean thoracic BMD from 116±33 mg/cm3 (non-enhanced CT) to 130±38 mg/cm3 (contrast-enhanced CT) (p<0.001). Using clinical cut-off values for very low (<80 mg/cm3) and low BMD (<120 mg/cm3) showed that 24 % (47/195 participants) were misclassified when BMD was measured on contrast-enhanced CT-scans. Of the misclassified patients, 6 % (12/195 participants) were categorized as having low BMD despite having very low BMD on the non-enhanced images. Contrast-CT using a higher contrast trigger level showed a significant increase in BMD compared to the lower trigger level (119±32 vs. 135±40 mg/cm3, p<0.01). Conclusion: For patients undergoing cardiac CT, using contrast-enhanced images to assess BMD entails substantial overestimation. Contrast protocol trigger levels also affect BMD measurements. Adjusting for these factors is needed before contrast-enhanced images can be used clinically. Mini abstract: Osteoporosis is under-diagnosed. Contrast-enhanced CT made to examine other diseases might be utilized simultaneously for bone mineral density (BMD) screening. These scans, however, likely entails overestimation of BMD due to the effect of contrast. Adjusting for this effect is needed before contrast-enhanced images can be implemented clinically for BMD screening.

Assessment of bone marrow fat by 3-Tesla magnetic resonance spectroscopy in patients with chronic kidney disease.

Proton magnetic resonance spectroscopy (1H MRS) is an imaging method for quantification of bone marrow fat. It has been used for evaluation of bone marrow changes in patients with chronic disorders, such as chronic kidney disease (CKD). In these patients, there is a high turnover state, with an excessive amount of non-mineralized component of bone, leading to skeletal fragility and subsequent increased fracture risk. Thirty CKD patients underwent magnetic resonance spectroscopy (MRS) and quantitative computed tomography (QCT), and eight healthy controls underwent MRS at lumbar spine. Proton density fat fraction (PDFF) and volumetric bone mineral density (vBMD) of L1-L3 were determined from MRS and QCT respectively. CKD patients were divided into three groups according to glomerular filtration rate (GFR); for each patient, blood levels of parathyroid hormone (PTH) were also reported. Paired t-tests, Pearson's correlation coefficients and analysis of variance were applied. The mean age of patients was 59.6±11.5 years, mean GFR value was 21.5±8.8 mL/min and mean PTH value was 149.2±53.1 pg/mL. PDFF at L1-L3 levels was significantly higher in CKD patients compared to controls (71.4±8.7 vs. 55.5±7.6; P<0.001) and showed an inverse correlation with vBMD (r=-0.71; P<0.001). PDFF significantly increased from CKD group 1 to CKD group 3 (P=0.002) and was inversely correlated with GFR (r=-0.53; P=0.003). There was no significant association between PDFF and PTH values (P>0.05). In CKD patients, PDFF assessed by MRS at lumbar spine is higher than in healthy population, correlates with bone loss assessed by QCT and significantly increases with the worsening of renal function. MRS is a reliable and highly repeatable tool for PDFF quantification in CKD patients.

Low-dose quantitative CT myocardial flow measurement using a single volume scan: phantom and animal validation.

To validate a low-dose, single-volume quantitative CT myocardial flow technique in a cardiovascular flow phantom and a swine animal model of coronary artery disease. A cardiovascular flow phantom was imaged dynamically over different flow rates (0.97 to ) using 15mL of contrast per injection. Six swine () were also imaged dynamically, with different left anterior descending coronary artery balloon stenoses assessed under intracoronary adenosine stress, using of contrast per injection. The resulting images were used to simulate dynamic bolus tracking and peak volume scan acquisition. After which, first-pass single-compartment modeling was performed to derive quantitative flow, where the pre-contrast myocardial attenuation was assumed to be spatially uniform. The accuracy of CT flow was then assessed versus ultrasound and microsphere flow in the phantom and animal models, respectively, using regression analysis. Single-volume quantitative CT flow measurements in the phantom () were related to reference ultrasound flow measurements () by (Pearson's ; ). In the animal model (), they were related to reference microsphere flow measurements () by (Pearson's ; ). The effective dose per CT measurement was 1.21mSv. The single-volume quantitative CT flow technique only requires bolus tracking data, spatially uniform pre-contrast myocardial attenuation, and a single volume scan acquired near the peak aortic enhancement for accurate, low-dose, myocardial flow measurement (in mL/min/g) under rest and adenosine stress conditions.

Determination of lipid-rich plaques by artificial intelligence-enabled quantitative computed tomography using near-infrared spectroscopy as reference

Background and aimsArtificial intelligence quantitative CT (AI-QCT) determines coronary plaque morphology with high efficiency and accuracy. Yet, its performance to quantify lipid-rich plaque remains unclear. This study investigated the performance of AI-QCT for the detection of low-density noncalcified plaque (LD-NCP) using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS). MethodsThe INVICTUS Registry is a multi-center registry enrolling patients undergoing clinically indicated coronary CT angiography and IVUS, NIRS-IVUS, or optical coherence tomography. We assessed the performance of various Hounsfield unit (HU) and volume thresholds of LD-NCP using maxLCBI4mm ≥ 400 as the reference standard and the correlation of the vessel area, lumen area, plaque burden, and lesion length between AI-QCT and IVUS. ResultsThis study included 133 atherosclerotic plaques from 47 patients who underwent coronary CT angiography and NIRS-IVUS The area under the curve of LD-NCP<30HU was 0.97 (95% confidence interval [CI]: 0.93–1.00] with an optimal volume threshold of 2.30 mm3. Accuracy, sensitivity, and specificity were 94% (95% CI: 88–96%], 93% (95% CI: 76–98%), and 94% (95% CI: 88–98%), respectively, using <30 HU and 2.3 mm3, versus 42%, 100%, and 27% using <30 HU and >0 mm3 volume of LD-NCP (p < 0.001 for accuracy and specificity). AI-QCT strongly correlated with IVUS measurements; vessel area (r2 = 0.87), lumen area (r2 = 0.87), plaque burden (r2 = 0.78) and lesion length (r2 = 0.88), respectively. ConclusionsAI-QCT demonstrated excellent diagnostic performance in detecting significant LD-NCP using maxLCBI4mm ≥ 400 as the reference standard. Additionally, vessel area, lumen area, plaque burden, and lesion length derived from AI-QCT strongly correlated with respective IVUS measurements.

Automated vertebral bone mineral density measurement with phantomless internal calibration in chest LDCT scans using deep learning.

To develop and evaluate a fully automated method based on deep learning and phantomless internal calibration for bone mineral density (BMD) measurement and opportunistic low BMD (osteopenia and osteoporosis) screening using chest low-dose CT (LDCT) scans. A total of 1175 individuals were enrolled in this study, who underwent both chest LDCT and BMD examinations with quantitative computed tomography (QCT), by two different CT scanners (Siemens and GE). Two convolutional neural network (CNN) models were employed for vertebral body segmentation and labeling, respectively. A histogram technique was applied for vertebral BMD calculation using paraspinal muscle and surrounding fat as references. 195 cases (by Siemens scanner) as fitting cohort were used to build the calibration function. 698 cases as validation cohort I (VCI, by Siemens scanner) and 282 cases as validation cohort II (VCII, by GE scanner) were performed to evaluate the performance of the proposed method, with QCT as the standard for analysis. The average BMDs from the proposed method were strongly correlated with QCT (in VCI: r = 0.896, in VCII: r = 0.956, p < 0.001). Bland-Altman analysis showed a small mean difference of 1.1 mg/cm3, and large interindividual differences as seen by wide 95% limits of agreement (-29.9 to +32.0 mg/cm3) in VCI. The proposed method measured BMDs were higher than QCT measured BMDs in VCII (mean difference = 15.3 mg/cm3, p < 0.001). Osteoporosis and low BMD were diagnosed by proposed method with AUCs of 0.876 and 0.903 in VCI, 0.731 and 0.794 in VCII, respectively. The AUCs of the proposed method were increased to over 0.920 in both VCI and VCII after adjusting the cut-off. Without manual selection of the region of interest of body tissues, the proposed method based on deep learning and phantomless internal calibration has the potential for preliminary screening of patients with low BMD using chest LDCT scans. However, the agreement between the proposed method and QCT is insufficient to allow them to be used interchangeably in BMD measurement. This study proposed an automated vertebral BMD measurement method based on deep learning and phantomless internal calibration with paraspinal muscle and fat as reference.

Shaping the Future in Rare Lung Diseases: From Imaging to Patient Management

This symposium was held during the 2023 European Respiratory Society (ERS) International Congress in Milan, Italy. The main objective was to discuss unmet needs in the diagnosis and management of rare lung diseases, with a particular emphasis on alpha 1 antitrypsin deficiency (AATD) and idiopathic pulmonary fibrosis (IPF). Presentations focused on promising approaches to meet these needs, including the feasibility of genomic diagnosis, the development of improved biomarkers of disease progression, such as quantitative CT and novel blood biomarkers, the use of digital lung auscultation, and increased screening for AATD in vulnerable populations. The overarching message from the symposium was that advancements in technology, multidisciplinary collaboration, and partnerships between academic institutions, patient associations, and industry are crucial to the continued improvement of patient management in rare diseases, and that the education of healthcare professionals is vital to enhance the understanding and awareness of these conditions.

Association between total cholesterol and lumbar bone density in Chinese: a study of physical examination data from 2018 to 2023

BackgroundThe impact of total cholesterol (TC) on lumbar bone mineral density (BMD) is a topic of interest. However, empirical evidence on this association from demographic surveys conducted in China is lacking. Therefore, this study aimed to examine the relationship between serum TC and lumbar BMD in a sample of 20,544 Chinese adults between the ages of 20 and 80 years over a period of 5 years, from February 2018 to February 2023. Thus, we investigated the effect of serum TC level on lumbar BMD and its relationship with bone reduction in a Chinese adult population.MethodsThis cross-sectional study used data obtained from the Department of Health Management at Henan Provincial People’s Hospital between February 2018 and February 2023. The aim of this study was to examine the correlation between serum TC and lumbar BMD in individuals of different sexes. The research methodology encompassed population description, analysis of stratification, single-factor and multiple-equation regression analyses, smooth curve fitting, and analysis of threshold and saturation effects. The R and EmpowerStats software packages were used for statistical analysis.ResultsAfter adjusting for confounding variables, a multiple linear regression model revealed a significant correlation between TC and lumbar BMD in men. In subgroup analysis, serum TC was found to have a positive association with lumbar BMD in men, specifically those aged 45 years or older, with a body mass index (BMI) ranging from 24 to 28 kg/m2. A U-shaped correlation arose between serum TC and lumbar BMD was detected in women of different ages and BMI, the inflection point was 4.27 mmol/L for women aged ≥ 45 years and 4.35 mmol/L for women with a BMI of ≥ 28 kg/m2.ConclusionIn this study, Chinese adults aged 20–80 years displayed different effects of serum TC on lumbar BMD in sex-specific populations. Therefore, monitoring BMI and serum TC levels in women of different ages could prevent osteoporosis and osteopenia.Trial registrationThe research protocol was approved by the Ethics Committee of Beijing Jishuitan Hospital, in accordance with the Declaration of Helsinki guidelines (No. 2015-12-02). These data are part of the China Health Quantitative CT Big Data Research team, which has been registered at clinicaltrials.gov (code: NCT03699228).

Correlation between pulmonary quantitative CT measurement indicators and respiratory symptoms in patients with chronic obstructive pulmonary disease in stable stage

Objective: To explore the correlation between pulmonary quantitative CT measurement indicators and respiratory symptoms in patients with stable chronic obstructive pulmonary disease (COPD). Methods: A total of 186 patients with COPD in stable stage who visited in the outpatient department of Beijing Hospital from March 2021 to February 2022 were prospectively included. Demographic data, respiratory symptoms and lung function were collected. The original DICOM data of high-resolution CT (HRCT) were processed using the FACT medical imaging information system and the pulmonary emphysema index pixel index-950 (PI-950) and the airway wall thickness (4-6 T) and the percentage of airway area (4-6 WA%) of the 4-6 generation bronchi which represent the segmental and subsegmental bronchi were measured automatically. According to the modified British medical research council dyspnea scale (mMRC, 0-1 point for low score group, 2-4 points for high score group), chronic obstructive pulmonary disease assessment test (CAT, score<10 points for low score group,≥10 points for high score group), cough, expectoration and wheezing (asymptomatic group and symptomatic group), they were divided into two groups as dependent variables. The relationship between imaging parameters and the above symptoms was evaluated using a logistic regression model. Results: The study ultimately included 186 patients who met the inclusion criteria, including 162 males and 24 females, aged (68.9±9.3) years old. There were 83 patients in the high mMRC group, 120 patients in the high CAT group, 146 patients in the cough group, 154 patients in the expectoration group, and 65 patients in the wheezing group. The age and emphysema parameter PI-950 in the high score group of mMRC were higher than those in the low score group, while the percentage of the forced expiratory volume in 1 second (FEV1) predicted value (FEV1 pred) after medication, the percentage of carbon monoxide diffusion volume (DLCO) predicted value (DLCO pred), and the percentage of the maximum midexpiratory flow (MMEF) predicted value (MMEF pred) after medication were lower than those in the low score group (all P<0.05). The age of the high CAT group was higher than that of the low score group, while FEV1 pred and MMEF pred after medication were lower than those of the low score group (all P<0.05). The proportion of males, patients with smoking history, and smoking index in the cough group were higher than those in the non cough group, while the 4 WA% was lower than that in the non cough group (all P<0.05). The proportion of males, patients with smoking history, smoking index, and PI-950 in the expectoration group were higher than those in the non expectoration group, while FEV1 pred after medication and 4 WA% were lower than those in the non expectoration group (all P<0.05). The 5 WA% and 6 WA% of the wheezing group were higher than those of the non wheezing group, while MMEF pred after medication was lower than that of the non wheezing group (all P<0.05). Multivariate logistic regression analysis showed that after adjusting for demographic characteristics, smoking, combined diseases, lung function and other confounding factors, for every 10% increase in PI-950, the likelihood of developing more severe dyspnea for the patients (high score group according to mMRC) increased by 67.3% (OR=1.673, 95%CI: 1.052-2.658); Every 10% increase in 6WA% increased the likelihood of wheezing by 3.189 times (OR=4.189, 95%CI: 1.070-16.395). No correlation was found between various imaging indicators and cough, expectoration, and CAT scores (P>0.05). Conclusion: Quantitative CT measurement indicators in stable COPD patients can explain the presence and severity of respiratory symptoms, the pulmonary emphysema indicator is associated with dyspnea, and the percentage of proximal airway wall area is associated with wheezing.