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Background A chest X-ray is a standard imaging procedure in the diagnostic work-up of patients suspected of having non-traumatic pulmonary disease. Compared to a chest X-ray, an ultra-low-dose (ULD) chest computed tomography (CT) scan provides substantially more detailed information on pulmonary conditions. To what extent this translates into an improvement in patient outcomes and health care efficiency is yet unknown. The OPTimal IMAging strategy in patients suspected of non-traumatic pulmonary disease at the emergency department: chest X-ray or ultra-low-dose chest CT (OPTIMACT) study is a multicenter, pragmatic, non-inferiority randomized controlled trial designed to evaluate replacement of chest X-ray by ULD chest CT in the diagnostic work-up of such patients, in terms of patient-related health outcomes and costs. During randomly assigned periods of 1 calendar month, either conventional chest X-ray or ULD chest CT scan was used as the imaging strategy. This paper presents in detail the statistical analysis plan of the OPTIMACT trial, developed prior to data analysis. Methods/results Functional health at 28 days is the primary clinical outcome. Functional health at 28 days is measured by the physical component summary scale of the Short Form (SF)-12 questionnaire version 1. Secondary outcomes are mental health (mental component summary scale of the SF-12), length of hospital stay, mortality within 28 days, quality-adjusted life year equivalent during the first 28 days (derived from the EuroQol five-dimension, five-level instrument), correct diagnoses at emergency department discharge as compared to the final post hoc diagnosis at day 28, number of patients in follow-up because of incidental findings on chest X-ray or ULD chest CT, and health care costs. Conclusions After this pragmatic trial we will have precise estimates of the effectiveness of replacing chest X-ray with ULD chest CT in terms of patient-related health outcomes and costs. Trial registration Netherlands National Trial Register: NTR6163 . Registered on 6 December 2016.

Purpose This study aimed to develop a calibration approach for low‐dose CT (LDCT) used in lung cancer screening to enable coronary artery calcium (CAC) scores that reflect those obtained from conventional electrocardiogram (ECG)‐gated calcium scoring CT (CSCT). Methods An electron density phantom‐based calibration curve was developed to align Hounsfield unit (HU) values measured on LDCT with those from ECG‐gated CSCT. To evaluate the efficacy of the proposed method, 139 patients undergoing physical examination who received both LDCT and CSCT on the same day were retrospectively enrolled. Agatston and volume scores were quantified for the right coronary artery, left anterior descending artery, left circumflex artery, and left main artery. The Bland‐Altman analysis and two‐sample t‐test were performed to compare CAC scores measured on CSCT and LDCT before and after HU‐based calibration. Results Compared with the mean bias observed in Bland‐Altman analysis between CSCT and LDCT, the mean bias between CSCT and calibration LDCT (cLDCT) was substantially reduced. Based on two‐sample t‐test, statistically significant differences in total Agatston scores and total volume scores calculated were observed between CSCT and LDCT for the four coronary arteries. In contrast, no significant differences were found between CSCT and cLDCT. Among patients undergoing diastolic reconstruction in CSCT, the p‐values comparing CSCT and cLDCT were 0.94 for the Agatston score and 0.43 for the volume score, whereas corresponding p‐values of 0.95 and 0.63 were observed in patients undergoing systolic reconstruction in CSCT. Conclusion The proposed calibration approach enables consistent HU mapping across protocols and supports simultaneous assessment of coronary calcium burden and pulmonary pathology on a single LDCT scan.

Imaging plays an important role in the detection of coronavirus (COVID-19) pneumonia in both managing the disease and evaluating the complications. Imaging with chest computed tomography (CT) can also have a potential predictive and prognostic role in COVID-19 patient outcomes. The aim of this pictorial review is to describe the role of imaging with chest X-ray (CXR), lung ultrasound (LUS), and CT in the diagnosis and management of COVID-19 pneumonia, the current indications, the scores proposed for each modality, the advantages/limitations of each modality and their role in detecting complications, and the histopathological correlations.

The two most important signs of pulmonary embolism (PE) in a non‐contrast CT scan include wedge‐shaped sub‐pleural opacity surrounded by an air‐space consolidation, along with a ground‐glass opacity in the center consistent with pulmonary infarction, also known as the reverse halo sign and the hyperdense lumen sign. Signs of PE in non‐contrast CT scan include reverse halo sign and hyperdense lumen sign.

Objective To evaluate the incremental value of non‐gated chest CT coronary artery calcium score in enhancing GRACE score prediction of major adverse cardiovascular events (MACE) after percutaneous coronary intervention (PCI) in patients with acute coronary syndrome (ACS). Methods A retrospective cohort study was conducted on 324 ACS patients undergoing PCI and non‐gated chest CT. Patients were divided into MACE (n = 100) and non‐MACE (n = 224) groups with a median follow‐up of 18.7 months. The predictive performance of the GRACE score, Agatston score, and combined clinical composite model was evaluated using receiver operating characteristic (ROC) curves and survival analysis based on optimal cutoff values. Results Model 3 (GRACE + CACS) demonstrated AUC values of 0.798 and 0.827 in the training and testing cohorts, respectively, significantly outperforming Model 1 (GRACE) (training AUC = 0.702; testing AUC = 0.758). Model 4, incorporating clinical features, demonstrated optimal predictive performance (training set AUC = 0.806; testing set AUC = 0.857). The AUC differences were statistically significant (p < 0.05). Survival curves revealed the highest MACE incidence (94.4%, p < 0.01) in the high‐risk combined Ga1 group (GRACE ≥ 140 and Agatston ≥ 400). Conclusion The non‐gated chest CT coronary calcification score significantly enhances the predictive value of the GRACE score for major adverse coronary events (MACE) after coronary intervention. When combined with clinical indicators, the predictive power is further improved. Sensitivity analysis confirms the robustness of this finding, providing a reliable tool for clinical risk stratification. This study confirms that the combined GRACE score and coronary artery calcium score significantly improves the predictive efficacy of the risk of major adverse cardiovascular events (MACE) after percutaneous coronary intervention (PCI) in patients with acute coronary syndrome (ACS), and the predictive value is even better when combined with clinical characteristics.

The study aimed to assess the image quality and diagnostic performance of low-dose Chest Computed Tomography (LDCCT) in detecting pulmonary infections in patients with hematologic malignancies. A total of 164 neutropenic patients underwent 256 consecutive CT examinations, comparing 149 LDCCT and 107 Standard-Dose Chest CT (SDCCT) between May 2015 and June 2019. LDCCT demonstrated a 47% reduction in radiation dose while maintaining acceptable image noise and quality compared to SDCCT. However, LDCCT exhibited lower sensitivity in detecting consolidation (27.5%) and ground glass opacity (64.4%) compared to SDCCT (45.8% and 82.2%, respectively) with all the respective p-values from unadjusted and adjusted for sex, age, and BMI analyses being lower than 0.006 and the corresponding Odds Ratios of detection ranging from 0.30 to 0.34. Similar trends were observed for nodules ≥3 mm and ground glass halo in nodules but were not affected by sex, age and BMI. No significant differences were found for cavitation in nodules, diffuse interlobular septal thickening, pleural effusion, pericardial effusion, and lymphadenopathy. In conclusion, LDCCT achieved substantial dose reduction with satisfactory image quality but showed limitations in detecting specific radiologic findings associated with pulmonary infections in neutropenic patients compared to SDCCT.

Background: Many survivors of severe COVID-19 pneumonia experience lingering respiratory issues. There is limited research on follow-up chest imaging findings in patients with COVID-19 ARDS, particularly in relation to their mMRC dyspnea scores and pulmonary function tests (PFTs). This study addresses this gap by investigating the clinical characteristics, mMRC dyspnea scores, PFTs, and chest CT findings of COVID-19 ARDS patients at the 6 months post-recovery. By analyzing these variables together, we aim to gain a better understanding of the long-term health consequences of COVID-19 ARDS. Methods: This prospective observational study included 56 subjects with COVID-19 ARDS with dyspnea at the six-month follow-up visits. These patients were evaluated by chest CT, mMRC dyspnea scale, and PFT. The CT severity score was calculated individually for each of the four major imaging findings - ground glass opacities (GGOs), parenchymal/atelectatic bands, reticulations/septal thickening, and consolidation - using a modified CT severity scoring system. Statistics were carried out to find any association between individual CT chest findings and the mMRC dyspnea scale and forced vital capacity (FVC). p values < 0.05 were considered statistically significant. Results: Our study population had a mean age of 55.86 ± 9.60 years, with 44 (78.6%) being men. Grades 1, 2, 3, and 4 on the mMRC dyspnea scale were seen in 57.1%, 30.4%, 10.7%, and 1.8% of patients respectively. Common CT findings observed were GGOs (94.6%), reticulations/septal thickening (96.4%), parenchymal/atelectatic bands (92.8%), and consolidation (14.3%). The mean modified CT severity scores for GGOs, reticulations/septal thickening, parenchymal/atelectatic bands, and consolidation were 10.32 ± 5.51 (range: 0–21), 7.66 ± 4.33 (range: 0–19), 4.77 ± 3.03 (range: 0–14) and 0.29 ± 0.91 (range 0–5) respectively. Reticulations/septal thickening (p = 0.0129) and parenchymal/atelectatic bands (p = 0.0453) were associated with an increased mMRC dyspnea scale. Parenchymal/atelectatic bands were also associated with abnormal FVC (<80%) (p = 0.0233). Conclusion: Six-month follow-up chest CTs of COVID-19 ARDS survivors with persistent respiratory problems showed a statistically significant relationship between increased mMRC dyspnea score and imaging patterns of reticulations/septal thickening and parenchymal/atelectatic bands; while parenchymal/atelectatic bands also showed a statistically significant correlation with reduced FVC.

Introduction: About one-third of acute coronavirus disease 2019 (COVID-19) survivors have suffered from persisting symptoms called long-COVID. Clinical factors such as age and intensity (moderate or acute) of COVID-19 have been found to be associated with long-COVID. Many tissues might be damaged functionally or structurally during acute COVID-19 which can be detected by blood assays and chest computed tomography (CT). We aimed to evaluate the relationship between long-COVID and the initial findings of blood assays and chest CT as possible predictors. Methodology: The study included patients with acute COVID-19. Laboratory tests and chest CT were obtained from each patient at the time of admission to the hospital. Chest CT was evaluated for pneumonic involvement and severity score. Multivariable regression model was created to find the factors that were independently associated with long-COVID. Results: There were 60 (38.2%) patients with long-COVID and 97 (61.8%) without. Baseline demographic, laboratory and chest CT parameters were similar in both groups, except for age, chronic lung disease and chest CT severity score (46.9 ± 15.1 years vs 52.6 ± 15.9 years, p = 0.03; 11.7% vs 3.1%, p = 0.03 and 10.3 ± 9.6 vs 6.5 ± 7.6, p = 0.02, respectively). In multivariable model, chest CT severity score (OR: 1.059, 95% CI: 1.002-1.119, p = 0.04) and age (OR: 0.953, 95% CI: 0.928-0.979, p < 0.001) were independently associated with long-COVID. Conclusions: Chest CT severity score and age were independently associated with long-COVID and may be used to predict the future risk of long-COVID.

Objectives To investigate the clinical utility of deep learning image reconstruction (DLIR) for improving image quality in low‐dose chest CT in comparison with 40% adaptive statistical iterative reconstruction‐Veo (ASiR‐V40%) algorithm. Methods This retrospective study included 86 patients who underwent low‐dose CT for lung cancer screening. Images were reconstructed with ASiR‐V40% and DLIR at low (DLIR‐L), medium (DLIR‐M), and high (DLIR‐H) levels. CT value and standard deviation of lung tissue, erector spinae muscles, aorta, and fat were measured and compared across the four reconstructions. Subjective image quality was evaluated by two blind readers from three aspects: image noise, artifact, and visualization of small structures. Results The effective dose was 1.03 ± 0.36 mSv. There was no significant difference in CT values of erector spinae muscles and aorta, whereas the maximum difference for lung tissue and fat was less than 5 HU among the four reconstructions. Compared with ASiR‐V40%, the DLIR‐L, DLIR‐M, and DLIR‐H reconstructions reduced the noise in aorta by 11.44%, 33.03%, and 56.1%, respectively, and had significantly higher subjective quality scores in image artifacts (all p < 0.001). ASiR‐V40%, DLIR‐L, and DLIR‐M had equivalent score in visualizing small structures (all p > 0.05), whereas DLIR‐H had slightly lower score. Conclusions Compared with ASiR‐V40%, DLIR significantly reduces image noise in low‐dose chest CT. DLIR strength is important and should be adjusted for different diagnostic needs in clinical application.

Some patients with COVID-19 rapidly develop respiratory failure or mortality, underscoring the necessity for early identification of those prone to severe illness. Numerous studies focus on clinical and lab traits, but only few attend to chest computed tomography. The current study seeks to numerically quantify pulmonary lesions using early-phase CT scans calculated through artificial intelligence algorithms in conjunction with clinical and laboratory helps clinicians to early identify the development of severe illness and death in a group of COVID-19 patients. From December 15, 2022, to January 30, 2023, 191 confirmed COVID-19 patients admitted to Xinhua Hospital Affiliated with Shanghai Jiao Tong University School of Medicine were consecutively enrolled. All patients underwent chest CT scans and serum tests within 48 hours prior to admission. Variables significantly linked to critical illness or mortality in univariate analysis were subjected to multivariate logistic regression models post collinearity assessment. Adjusted odds ratio, 95% confidence intervals, sensitivity, specificity, Youden index, receiver-operator-characteristics (ROC) curves, and area under the curve (AUC) were computed for predicting severity and in-hospital mortality. Multivariate logistic analysis revealed that myoglobin (OR = 1.003, 95% CI 1.001-1.005), APACHE II score (OR = 1.387, 95% CI 1.216-1.583), and the infected CT region percentage (OR = 113.897, 95% CI 4.939-2626.496) independently correlated with in-hospital COVID-19 mortality. Prealbumin stood as an independent safeguarding factor (OR = 0.965, 95% CI 0.947-0.984). Neutrophil counts (OR = 1.529, 95% CI 1.131-2.068), urea nitrogen (OR = 1.587, 95% CI 1.222-2.062), SOFA score(OR = 3.333, 95% CI 1.476-7.522), qSOFA score(OR = 15.197, 95% CI 3.281-70.384), PSI score(OR = 1.053, 95% CI 1.018-1.090), and the infected CT region percentage (OR = 548.221, 95% CI 2.615-114,953.586) independently linked to COVID-19 patient severity.