Standard Chest Computed Tomography Research Articles

Standalone deep learning versus experts for diagnosis lung cancer on chest computed tomography: a systematic review.

To compare the diagnostic performance of standalone deep learning (DL) algorithms and human experts in lung cancer detection on chest computed tomography (CT) scans. This study searched for studies on PubMed, Embase, and Web of Science from their inception until November 2023. We focused on adult lung cancer patients and compared the efficacy of DL algorithms and expert radiologists in disease diagnosis on CT scans. Quality assessment was performed using QUADAS-2, QUADAS-C, and CLAIM. Bivariate random-effects and subgroup analyses were performed for tasks (malignancy classification vs invasiveness classification), imaging modalities (CT vs low-dose CT [LDCT] vs high-resolution CT), study region, software used, and publication year. We included 20 studies on various aspects of lung cancer diagnosis on CT scans. Quantitatively, DL algorithms exhibited superior sensitivity (82%) and specificity (75%) compared to human experts (sensitivity 81%, specificity 69%). However, the difference in specificity was statistically significant, whereas the difference in sensitivity was not statistically significant. The DL algorithms' performance varied across different imaging modalities and tasks, demonstrating the need for tailored optimization of DL algorithms. Notably, DL algorithms matched experts in sensitivity on standard CT, surpassing them in specificity, but showed higher sensitivity with lower specificity on LDCT scans. DL algorithms demonstrated improved accuracy over human readers in malignancy and invasiveness classification on CT scans. However, their performance varies by imaging modality, underlining the importance of continued research to fully assess DL algorithms' diagnostic effectiveness in lung cancer. DL algorithms have the potential to refine lung cancer diagnosis on CT, matching human sensitivity and surpassing in specificity. These findings call for further DL optimization across imaging modalities, aiming to advance clinical diagnostics and patient outcomes. Lung cancer diagnosis by CT is challenging and can be improved with AI integration. DL shows higher accuracy in lung cancer detection on CT than human experts. Enhanced DL accuracy could lead to improved lung cancer diagnosis and outcomes.

Chest CT-based automated vertebral fracture assessment using artificial intelligence and morphologic features.

Spinal degeneration and vertebral compression fractures are common among the elderly that adversely affect their mobility, quality of life, lung function, and mortality. Assessment of vertebral fractures in chronic obstructive pulmonary disease (COPD) is important due to the high prevalence of osteoporosis and associated vertebral fractures in COPD. We present new automated methods for (1) segmentation and labelling of individual vertebrae in chest computed tomography (CT) images using deep learning (DL), multi-parametric freeze-and-grow (FG) algorithm, and separation of apparently fused vertebrae using intensity autocorrelation and (2) vertebral deformity fracture detection using computed vertebral height features and parametric computational modelling of an established protocol outlined for trained human experts. A chest CT-based automated method was developed for quantitative deformity fracture assessment following the protocol by Genant etal. The computational method was accomplished in the following steps: (1) computation of a voxel-level vertebral body likelihood map from chest CT using a trained DL network; (2) delineation and labelling of individual vertebrae on the likelihood map using an iterative multi-parametric FG algorithm; (3) separation of apparently fused vertebrae in CT using intensity autocorrelation; (4) computation of vertebral heights using contour analysis on the central anterior-posterior (AP) plane of a vertebral body; (5) assessment of vertebral fracture status using ratio functions of vertebral heights and optimized thresholds. The method was applied to inspiratory or total lung capacity (TLC) chest scans from the multi-site Genetic Epidemiology of COPD (COPDGene) (ClinicalTrials.gov: NCT00608764) study, and the performance was examined (n=3231). One hundred and twenty scans randomly selected from this dataset were partitioned into training (n=80) and validation (n=40) datasets for the DL-based vertebral body classifier. Also, generalizability of the method to low dose CT imaging (n=236) was evaluated. The vertebral segmentation module achieved a Dice score of.984 as compared to manual outlining results as reference (n=100); the segmentation performance was consistent across images with the minimum and maximum of Dice scores among images being.980 and.989, respectively. The vertebral labelling module achieved 100% accuracy (n=100). For low dose CT, the segmentation module produced image-level minimum and maximum Dice scores of.995 and.999, respectively, as compared to standard dose CT as the reference; vertebral labelling at low dose CT was fully consistent with standard dose CT (n=236). The fracture assessment method achieved overall accuracy, sensitivity, and specificity of 98.3%, 94.8%, and 98.5%, respectively, for 40,050 vertebrae from 3231 COPDGene participants. For generalizability experiments, fracture assessment from low dose CT was consistent with the reference standard dose CT results across all participants. Our CT-based automated method for vertebral fracture assessment is accurate, and it offers a feasible alternative to manual expert reading, especially for large population-based studies, where automation is important for high efficiency. Generalizability of the method to low dose CT imaging further extends the scope of application of the method, particularly since the usage of low dose CT imaging in large population-based studies has increased to reduce cumulative radiation exposure.

Chest Computed Tomography for Lymphoma Staging: A Wasted Opportunity for Cardiovascular Risk Stratification?

Patients with a lymphoma diagnosis undergo non-gated chest computed tomography (CT) scans as part of cancer diagnosis or staging. Although coronary artery calcification (CAC) is traditionally evaluated on dedicated cardiac CT, CAC can also be detected on standard chest CT. This exploratory study aimed to determine the prognostic value of CAC detected on non-gated chest CT and to report its use on clinical practice. Consecutive patients with a lymphoma diagnosis who performed non-contrasted non-gated chest CT for cancer diagnosis or staging were included and retrospectively evaluated. Coronary artery calcification was evaluated by quantitative (Agatston score) and qualitative (visual) assessment. Fifty-seven patients were included in this study (mean age 61±15 years; 58% male). Coronary artery calcification was identified in 22 patients (39%), most of them with multi-vessel involvement. Coronary artery calcification was qualitatively classified as mild, moderate and severe in 11%, 19% and 9% patients, respectively. This study suggested that moderate or severe CAC was an independent predictor of all-cause mortality (odds ratio 3, 95% confidence interval 2-11; p=0.04) after adjusting for cardiovascular risk factors and lymphoma staging. Regarding quantitative evaluation, a higher CAC score was also associated with higher mortality. While significant CAC was identified in 22 patients, it was only reported in four patients. The preliminary findings of this hypothesis-generating study support the investigation of CAC identified by chest CT for diagnosis/staging of cancer as a risk modifier in the global risk assessment of patients with lymphoma. The unrecognition and underreporting of this finding may represent a wasted opportunity to detect subclinical coronary atherosclerosis in these patients and may help in guiding preventive cardiology care.

Coronary artery calcium score predicts outcome in patients with COVID-19

Abstract Funding Acknowledgements Type of funding sources: None. Aim Coronary artery calcium (CAC) score is a proven prognostic marker of cardiovascular risk. The aim of this study was to evaluate the prognostic value of CAC score on standard chest computed tomography (CT) in patients admitted with COVID-19. Methods We enrolled 144 consecutive patients (mean age 67± 13, 60.7% male) hospitalized with COVID-19, who on admission underwent chest CT to detect and assess pulmonary involvement. CAC score was calculated by two investigators blinded to patients clinical data. Patients were followed-up for 12 months after their admission. Results 106 patients had image quality sufficient for analysis. The median CAC score was 249 Agatston units (interquartile range 658). 33 deaths were recorded in the study group during the follow-up period. The median values of CAC of patients who died during the study period and of the survivors were 592 (interquartile range 1492) and 142 (interquartile range 400), respectively. The difference in CAC between these two subgroups was statistically significant (p = 0,0001). The area under the ROC curve for CAC score for predicting all-cause mortality was 0.738 (95% CI 0,644 to 0,819). The criterion with the highest prognostic accuracy was CAC score >143 Agatston units (positive and negative predictive values were 44,4% and 88,4%, respectively). The presence of this criterion was associated with 3,82 times higher relative risk of mortality (95%CI 1.60 – 9,11; p = 0.0025). Conclusion CAC score measurement based on standard chest CT performed on admission in patients with COVID-19 to detect pulmonary involvement is a prognostic marker of increased mortality during the 12-month follow-up.

Low-Dose Chest CT Protocols for Imaging COVID-19 Pneumonia: Technique Parameters and Radiation Dose.

Chest computed tomography (CT) plays a vital role in the early diagnosis, treatment, and follow-up of COVID-19 pneumonia during the pandemic. However, this raises concerns about excessive exposure to ionizing radiation. This study aimed to survey radiation doses in low-dose chest CT (LDCT) and ultra-low-dose chest CT (ULD) protocols used for imaging COVID-19 pneumonia relative to standard CT (STD) protocols so that the best possible practice and dose reduction techniques could be recommended. A total of 564 articles were identified by searching major scientific databases, including ISI Web of Science, Scopus, and PubMed. After evaluating the content and applying the inclusion criteria to technical factors and radiation dose metrics relevant to the LDCT protocols used for imaging COVID-19 patients, data from ten articles were extracted and analyzed. Technique factors that affect the application of LDCT and ULD are discussed, including tube current (mA), peak tube voltage (kVp), pitch factor, and iterative reconstruction (IR) algorithms. The CTDIvol values for the STD, LDCT, and ULD chest CT protocols ranged from 2.79-13.2 mGy, 0.90-4.40 mGy, and 0.20-0.28 mGy, respectively. The effective dose (ED) values for STD, LDCT, and ULD chest CT protocols ranged from 1.66-6.60 mSv, 0.50-0.80 mGy, and 0.39-0.64 mSv, respectively. Compared with the standard (STD), LDCT reduced the dose reduction by a factor of 2-4, whereas ULD reduced the dose reduction by a factor of 8-13. These dose reductions were achieved by applying scan parameters and techniques such as iterative reconstructions, ultra-long pitches, and fast spectral shaping with a tin filter. Using LDCT, the cumulative radiation dose of serial CT examinations during the acute period of COVID-19 may have been inferior or equivalent to that of conventional CT.

Automated analysis of lung lesions in COVID-19: comparison of standard and low-dose CT

Introduction. Chest computed tomography (CT) plays a prominent role in determining the extent of pulmonary parenchymal lesions in COVID-19. At the same time, subjectivity of lung lesion volume assessment using 0-4 CT scale in COVID-19 and gradual introduction of low-dose CT (LDCT) requires an investigation of semi-automated lung segmentation accuracy in LDCT compared to CT.Study Objective. To compare the accuracy of affected lung tissue volume calculation between CT and LDCT in COVID-19 using a semi-automatic segmentation program.Material and Methods. The retrospective study was performed on data from the earlier prospective multicenter study registered at ClinicalTrials.gov, NCT04379531. CT and LDCT data were processed in 3D Slicer software with Lung CT Segmenter and Lung CT Analyzer extensions, and the volume of affected lung tissue and lung volume were determined by thresholding.Results. The sample size was 84 patients with signs of COVID-19-associated pneumonia. Mean age was 50.6 ± 13.3 years, and the median body mass index (BMI) was 28.15 [24.85; 31.31] kg/m2. The effective doses were 10.1 ± 3.26 mSv for the standard CT protocol and 2.64 mSv [1.99; 3.67] for the developed LDCT protocol. The analysis of absolute lung lesion volume in cubic centimeters with Wilcoxon Signed Ranks Test revealed a statistically significant difference between CT and LDCT (p-value < 0.001). No statistically significant differences were found in the relative values of lung tissue lesion volume (lesion volume/lung volume) between CT and LDCT using Wilcoxon Signed Ranks Test (p-value = 0.95).Conclusion. The reliability of developed LDCT protocol in COVID-19 for the semi-automated calculation of affected tissue percentage was comparable to the standard chest CT protocol when using 3D Slicer with Lung CT Segmenter and Lung CT Analyzer extensions.

Importance of incidental coronary artery calcification in early diagnosis of obstructive coronary artery disease.

The early diagnosis of coronary artery disease (CAD) enables early intervention for the modifiable risk factors of the disease. Coronary artery calcification (CAC) detected incidentally on standard noncontrast chest computed tomography (CT) provides an opportunity for the early diagnosis of CAD. The purpose of this study was to demonstrate that CAC should be routinely reported when evaluating thoracic CT examinations. Routine reporting of CAC will contribute to the early diagnosis of CAD. The present study included 279 patients who underwent conventional coronary angiography (CAG) and CT within one month before undergoing CAG. The CAG and CT images of the patients were evaluated retrospectively. The levels of coronary artery stenosis were determined in reference to the CAG images. The CAC scores of the patients were calculated using the Weston method based on their chest CT images. The mean age of the patients was 63.2 ± 11.5 (range, 41-93) years, and 172 (61.6%) of them were men. The Weston score (WS) was 0 in 18.9% of the patients with obstructive CAD (OCAD), whereas it was ≥ 7 in 27.9% of patients. All patients with a WS of ≥ 7 had OCAD. All patients without luminal stenosis or < 50% stenosis had a WS of < 7. The CAC score is useful for the diagnosis of CAD and OCAD. If CAC is identified on standard noncontrast chest CT, it should be scored and reported accordingly. The WS can be used for CAC scoring.

Ultra-Low-Dose Computed Tomography Chest for Screening of COVID-19 Patients: Assessment of Radiation Dose Reduction

Abstract Introduction: Chest computed tomography (CT) scans have a significant role in the identification and treatment of COVID-19 disease. In earlier studies, the introduction of chest CT has been a highly sensitive method in testing COVID-19 pneumonia. With a radiation dose comparable to a chest X-ray, ultra-low-dose CT has been promised to be a successful method for lowering radiation dose and motion artifact. Materials and Methods: The research was carried out at a tertiary care facility’s radiology department between August 1 and August 31, 2020. A sum of 109 individuals aged over 18 years old and been called for a noncontrast CT scan chest and received were included. Results: The minimum age was 18 and the maximum was 87, where 14.67% of them are between 21 and 40 years, 40.37% of them are between 41 and 60 years and 44.96% of them are between 61 and 87 years. Male’s mean age is 55.89 years and female’s mean age is 60.14 years. The overall mean age is 57.02 years and standard deviation is 13.49 years. The average body mass index was 26.51 ± 3.5 kg/m2. In the standard dose and ultra-low-dose groups, the mean effective dose values were 6.70 ± 0.85 and 0.33 ± 0.09 mSv, respectively (P &lt; 0.001). Conclusion: When identifying lung infiltrations among the patients admitted for elective or moderately urgent surgical or medical operations at the time of the COVID-19 pandemic, ultra-low-dose chest CT is an effective method that exposes patients to less radiation than standard dose CT.

Three-dimensional computed tomography reconstruction invideo-assisted thoracoscopic segmentectomy (DRIVATS): Aprospective, multicenter randomized controlled trial.

ObjectiveAnatomical segmentectomy has been proven to be a viable surgical treatment for small-size peripheral lung nodules. Three-dimensional (3D) reconstruction computed tomography (CT) has been proposed as an effective approach to overcome the challenges of encountering pulmonary anatomical variations when performing segmentectomy. Therefore, to further investigate the usefulness of preoperative 3D reconstruction CT in segmentectomy, we will conduct this prospective, multicenter randomized controlled DRIVATS study to compare the use of 3D reconstruction CT with standard chest CT in video-assisted segmentectomy (ClinicalTrials.gov ID: NCT04004494).MethodsThis study began in July 2019 and a total of 190 patients will be accrued from three clinical centers within 4 years. The main inclusion criteria are patients with a single peripheral nodule 0.8–2 cm with at least one of the following requirements: (i) histology of adenocarcinoma in situ; (ii) nodule has ≥50% ground-glass appearance on CT; (iii) radiologic surveillance confirms a long doubling time (≥400 days). Surgical procedures include segmental resection of the lesion and mediastinal lymph node sampling (subsegmental resection or combined subsegmental resection will not be included in this study). The primary endpoint is operative time. The secondary endpoints include incidence of change of surgical plan, intraoperative blood loss, conversion rate, operative accident event, incidence of postoperative complications, postoperative hospital stay, length of hospitalization, duration of chest tube placement, postoperative 30-day mortality, dissection of lymph nodes, overall survival, disease-free survival, preoperative lung function, and postoperative lung function.DiscussionThis multicenter DRIVATS study aims to verify the usefulness of preoperative 3D reconstruction CT compared with standard chest CT in segmentectomy. If successfully completed, this multicenter prospective study will provide a higher level of evidence for the use of 3D reconstruction CT in segmentectomy.

Changes in the organs of the gastrointestinal tract in a new coronavirus infection (COVID-19): computed tomography semiotics

Introduction. A new coronavirus infection (COVID-19) is a pathogenetically complex disease characterized by the involvement of the main human organs and systems in the pathological process, including the organs of the gastrointestinal tract (gastrointestinal tract).Purpose: to study the gastroenterological manifestations of COVID-19 in patients of mild and moderate severity based on the analysis of complex data, including computed tomography examination.Material and methods. The study included 80 patients hospitalized with a confirmed diagnosis of COVID-19 (19 patients with mild and 61 with moderate severity) who underwent a comprehensive original examination upon admission to the hospital and outpatient at week 8 of dynamic follow-up. In the complex therapy of 41 patients, the drug rebamipid was used. The scope of the examination included questionnaires, traditional clinical, clinical and laboratory, biochemical, instrumental and radiation studies, unique methods for studying microbiota and intestinal permeability. All patients also underwent computed tomography (CT) of the chest organs, followed by analysis and postprocessing of images of the upper abdominal cavity. The study also included a group of 43 inpatient patients with moderate severity of the disease, a group of 17 practically healthy patients was used as a control.Results and conclusions. A method of qualitative and quantitative assessment of the state of parenchymal and hollow organs of the upper abdominal cavity according to CT data is proposed. It was found that with moderate severity of COVID-19, quantitative criteria (attenuation coefficient of the liver, pancreas, bile) were subject to more pronounced deviations, as was the diameter of the colon. Additional diagnostic capabilities of the standard chest CT protocol in patients with COVID-19 are associated with postprocessor analysis of images of abdominal organs.

Human-level COVID-19 diagnosis from low-dose CT scans using a two-stage time-distributed capsule network

Reverse transcription-polymerase chain reaction is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagnosis, and false negative rate is relatively high. Imaging, in particular chest computed tomography (CT), can assist with diagnosis and assessment of this disease. Nevertheless, it is shown that standard dose CT scan gives significant radiation burden to patients, especially those in need of multiple scans. In this study, we consider low-dose and ultra-low-dose (LDCT and ULDCT) scan protocols that reduce the radiation exposure close to that of a single X-ray, while maintaining an acceptable resolution for diagnosis purposes. Since thoracic radiology expertise may not be widely available during the pandemic, we develop an Artificial Intelligence (AI)-based framework using a collected dataset of LDCT/ULDCT scans, to study the hypothesis that the AI model can provide human-level performance. The AI model uses a two stage capsule network architecture and can rapidly classify COVID-19, community acquired pneumonia (CAP), and normal cases, using LDCT/ULDCT scans. Based on a cross validation, the AI model achieves COVID-19 sensitivity of 89.5%pm 0.11, CAP sensitivity of 95%pm 0.11, normal cases sensitivity (specificity) of 85.7%pm 0.16, and accuracy of 90%pm 0.06. By incorporating clinical data (demographic and symptoms), the performance further improves to COVID-19 sensitivity of 94.3%pm 0.05, CAP sensitivity of 96.7%pm 0.07, normal cases sensitivity (specificity) of 91%pm 0.09 , and accuracy of 94.1%pm 0.03. The proposed AI model achieves human-level diagnosis based on the LDCT/ULDCT scans with reduced radiation exposure. We believe that the proposed AI model has the potential to assist the radiologists to accurately and promptly diagnose COVID-19 infection and help control the transmission chain during the pandemic.

Influence of cellular models and individual factor in the biological response to chest CT scan exams

BackgroundWhile computed tomography (CT) exams are the major cause of medical exposure to ionising radiation, there is increasing evidence that the potential radiation-induced risks must be documented. We investigated the impact of cellular models and individual factor on the deoxyribonucleic acid double-strand breaks (DSB) recognition and repair in human fibroblasts and mammary epithelial cells exposed to current chest CT scan conditions.MethodTwelve human primary fibroblasts and four primary human mammary epithelial cell lines with different levels of radiosensitivity/susceptibility were exposed to a standard chest CT scan exam using adapted phantoms. Cells were exposed to a single helical irradiation (14.4 mGy) or to a topogram followed, after 1 min, by one single helical examination (1.1 mGy + 14.4 mGy). DSB signalling and repair was assessed through anti-γH2AX and anti-pATM immunofluorescence.ResultsChest CT scan induced a significant number of γH2AX and pATM foci. The kinetics of both biomarkers were found strongly dependent on the individual factor. The topogram may also influence the biological response of radiosensitive/susceptible fibroblasts to irradiation. Altogether, our findings show that a chest CT scan exam may result in 2 to 3 times more unrepaired DSB in cells from radiosensitive/susceptible patients.ConclusionsBoth individual and tissue factors in the recognition and repair of DSB after current CT scan exams are important. Further investigations are needed to better define the radiosensitivity/susceptibility of individual humans.

Ultrashort echo time MRI of the lung in children and adolescents: comparison with non-enhanced computed tomography and standard post-contrast T1w MRI sequences

ObjectivesTo compare the diagnostic value of ultrashort echo time (UTE) magnetic resonance imaging (MRI) for the lung versus the gold standard computed tomography (CT) and two T1-weighted MRI sequences in children.MethodsTwenty-three patients with proven oncologic disease (14 male, 9 female; mean age 9.0 + / − 5.4 years) received 35 low-dose CT and MRI examinations of the lung. The MRI protocol (1.5-T) included the following post-contrast sequences: two-dimensional (2D) incoherent gradient echo (GRE; acquisition with breath-hold), 3D volume interpolated GRE (breath-hold), and 3D high-resolution radial UTE sequences (performed during free-breathing). Images were evaluated by considering image quality as well as distinct diagnosis of pulmonary nodules and parenchymal areal opacities with consideration of sizes and characterisations.ResultsThe UTE technique showed significantly higher overall image quality, better sharpness, and fewer artefacts than both other sequences. On CT, 110 pulmonary nodules with a mean diameter of 4.9 + / − 2.9 mm were detected. UTE imaging resulted in a significantly higher detection rate compared to both other sequences (p < 0.01): 76.4% (84 of 110 nodules) for UTE versus 60.9% (67 of 110) for incoherent GRE and 62.7% (69 of 110) for volume interpolated GRE sequences. The detection of parenchymal areal opacities by the UTE technique was also significantly higher with a rate of 93.3% (42 of 45 opacities) versus 77.8% (35 of 45) for 2D GRE and 80.0% (36 of 45) for 3D GRE sequences (p < 0.05).ConclusionThe UTE technique for lung MRI is favourable in children with generally high diagnostic performance compared to standard T1-weighted sequences as well as CT.Key Points• Due to the possible acquisition during free-breathing of the patients, the UTE MRI sequence for the lung is favourable in children.• The UTE technique reaches higher overall image quality, better sharpness, and lower artefacts, but not higher contrast compared to standard post-contrast T1-weighted sequences.• In comparison to the gold standard chest CT, the detection rate of small pulmonary nodules small nodules ≤ 4 mm and subtle parenchymal areal opacities is higher with the UTE imaging than standard T1-weighted sequences.

Lung Ultrasound Assessment of Focal and Non-focal Lung Morphology in Patients With Acute Respiratory Distress Syndrome.

Background: The identification of phenotypes based on lung morphology can be helpful to better target mechanical ventilation of individual patients with acute respiratory distress syndrome (ARDS). We aimed to assess the accuracy of lung ultrasound (LUS) methods for classification of lung morphology in critically ill ARDS patients under mechanical ventilation.Methods: This was a post hoc analysis on two prospective studies that performed LUS and chest computed tomography (CT) scanning at the same time. Expert panels from the two participating centers separately developed two LUS methods for classifying lung morphology based on LUS aeration scores from a 12-region exam (Amsterdam and Lombardy method). Moreover, a previously developed LUS method based on anterior LUS scores was tested (Piedmont method). Sensitivity and specificity of all three LUS methods was assessed in the cohort of the other center(s) by using CT as the gold standard for classification of lung morphology.Results: The Amsterdam and Lombardy cohorts consisted of 32 and 19 ARDS patients, respectively. From these patients, 23 (45%) had focal lung morphology while others had non-focal lung morphology. The Amsterdam method could classify focal lung morphology with a sensitivity of 77% and a specificity of 100%, while the Lombardy method had a sensitivity and specificity of 100 and 61%. The Piedmont method had a sensitivity and specificity of 91 and 75% when tested on both cohorts. With both the Amsterdam and Lombardy method, most patients could be classified based on the anterior regions alone.Conclusion: LUS-based methods can accurately classify lung morphology in invasively ventilated ARDS patients compared to gold standard chest CT. The anterior LUS regions showed to be the most discriminant between focal and non-focal lung morphology, although accuracy increased moderately when lateral and posterior LUS regions were integrated in the method.

Ultra-low-dose chest CT performance for the detection of viral pneumonia patterns during the COVID-19 outbreak period: a monocentric experience.

Ultra low dose chest computed tomography (CT) acquisitions have been used for selected emergency room patients with acute dyspnea or minor thoracic trauma. The purpose of this study was to evaluate the diagnostic performance of ultra-low-dose (ULD) chest CT for detecting viral pneumonia patterns compared to standard (STD) dose chest CT. All consecutive adult patients with two non-enhanced chest CT acquisitions, one STD and one ULD, for suspicion of viral pneumonia between March 5th and April 2nd 2020 were included. CT results were divided into two groups: non-viral pneumonia CT or compatible with viral pneumonia CT based on viral pneumonia CT patterns: ground-glass opacity (GGO), consolidation, crazy paving, air bronchogram signs and fibrous stripes. The diagnostic performance of ULD CT for suspicion of viral pneumonia was evaluated. For CTs compatible with viral pneumonia, CT pattern detection on ULD CT was assessed and STD CT was used as a reference. The study included 380 patients with 97 CTs (25.5%) compatible with viral pneumonia. The mean effective doses (EDs) were 1.66 (1.29; 2.18) mSv for STD and 0.20 (0.18; 0.22) mSv for ULD CT (P<0.001). The sensitivity and specificity of ULD CT for viral pneumonia detection were 98.9% and 99.0%, respectively. GGO, consolidation and fibrous stripes were equally visible in STD and ULD in 100% (n=97), 36% (n=35) and 23% (n=22) of compatible viral pneumonia-CT patients, respectively. Air bronchogram sign detection was equivalent, concerning 23% (n=22) of patients in STD and 22% (n=21) in ULD. Crazy paving was visible in 24% (n=23) of patients in STD and only 8% (n=8) in ULD (P=0.003). In comparison to STD dose chest CT, ULD chest CT, with a mean reduction dose of 88.0%, has comparable diagnostic performance for detecting viral pneumonia on CT.

Coronary artery calcium on standard chest computed tomography predicts cardiovascular events after liver transplantation

AimsCardiac complications are a leading cause of mortality after orthotopic liver transplantation (LT) and pre-operative risk stratification is challenging. We evaluated whether coronary artery calcium (CAC) score calculated on a standard (non-thin layer, non-ECG gated) chest computed tomography (CT) predicted cardiac outcome after LT. MethodsWe included a consecutive series of LT recipients who underwent pre-operative cardiac evaluation including stress-testing or cardiac catheterization in high-risk patients. Patients with a history of coronary artery disease or coronary revascularization were excluded. The CAC score was calculated from the chest CT routinely performed before LT. CAC values were not available at the time of pre-transplant cardiac evaluation and did not affect LT eligibility. The primary end-point included peri-operative arrhythmic cardiac arrest and sustained ventricular arrhythmias; heart failure, myocardial infarction and cardiac death within 1-year after LT. ResultsThe study population consisted of 301 patients (median age 56 years, 76% males). At chest CT, 49% had CAC = 0; 27% had CAC = 1–99, 15% had CAC = 100–399 and 9% CAC > 400. The primary end-point incidence increased from 7% in patients with CAC = 0 to 27% in patients with CAC > 400 (p = 0.007). At multivariable analysis including traditional risk factors, CAC remained an independent predictor of cardiac events (p = 0.01). ConclusionsCAC score calculated on a standard chest CT stratified the risk of cardiac events in patients who underwent LT after negative pre-transplant cardiac evaluation. These findings suggest that evaluation of CAC from a standard chest CT performed for other reasons can be used as an early cardiac risk stratification tool before LT.

Utilization of Quantitative Computed Tomography Assessment to Identify Bronchiolitis Obliterans Syndrome After Single Lung Transplantation.

To evaluate quantitative chest computed tomography (CT) methods for the detection of air trapping (AT) and to assess its diagnostic performance for the diagnosis of bronchiolitis obliterans syndrome (BOS) in single lung transplant (SLT) patients. Adult patients who had a SLT at a single transplant center and underwent CT scan after transplantation were retrospectively included. CT findings of air trapping were measured by three different methods: expiratory air-trapping index (ATIexp), mean lung density on expiratory acquisition (MLDexp) and expiratory to inspiratory ratio of mean lung density (E/I-ratio(MLD). Sensitivity, specificity and diagnostic accuracy of the three methods for the detection of BOS status evaluated by serial routine measures of pulmonary function tests (gold standard) were assessed. Forty-six SLT patients (52.2% females, mean age 58 ± 6years) were included in the analysis, 12 (26%) patients with a diagnosis of BOS. Quantitative CT diagnosis of AT ranged from 26 to 35%. Sensitivity, specificity and accuracy of each method for the detection of BOS were 85.7%, 84.7% and 85.0% for ATIexp, 78.5%, 93.4% and 90.0% for MLD and 64.2%, 89.1% and 83.3% E/I-ratio(MLD), respectively. Quantitative measures of AT obtained from standard CT are feasible and show high specificity and accuracy for the detection of BOS in SLT patients.

Cancer risk of CT scan in COVID-19: Resolving the dilemma.

Cancer risk of CT scan in COVID-19: Resolving the dilemma.

Opportunistic muscle measurements on staging chest CT for extremity and truncal soft tissue sarcoma are associated with survival.

Computed tomography (CT) measurements of sarcopenia have been proposed as biomarkers associated with outcomes in various cancers and have typically been evaluated at the L3 vertebral level. However, staging imaging for patients with extremity and truncal soft tissue sarcoma (STS) often only includes chest CT imaging which precludes evaluation at L3. Therefore, we sought to evaluate muscle metrics at T12 on standard staging chest CT scans and evaluate for correlation with overall and event-free survival in patients with STS. CT chest imaging for 89 patients with intermediate and high-grade STS (53 male, 36 female; 58.5 ± 19.0 years old, follow-up 37.4 ± 27.1 months) was reviewed on PACS at T12 for skeletal muscle density (SMD) and skeletal muscle index (SMI). Overall survival increased with increased SMD on univariate (hazard ratio [HR] = 0.61 [0.43, 0.86]) and age-adjusted analysis (HR = 0.65 [0.42, 0.89]. Event-free survival also increased with increased SMD in univariate analyses (HR = 0.68 [0.49, 0.95]) but did not maintain significance after adjusting for age (HR = 0.68 [0.43, 1.07]). SMI was not a predictor of overall or event-free survival. Higher SMD measured on routinely obtained staging chest CTs in STS patients is associated with improved survival.

Coronary artery calcification scoring system based on the coronary artery calcium data and reporting system (CAC-DRS) predicts major adverse cardiovascular events or all-cause death in patients with potentially curable lung cancer without a history of cardiovascular disease.

The coronary artery calcium data and reporting system (CAC-DRS) is a novel reporting system based on CAC severity. Lung cancer patients have a high risk of cardiovascular disease (CVD), for which CAC severity may provide additional prognostic information. Using non-gated, non-contrast computed tomography (CT), we evaluated the CAC-DRS for predicting CVD and all-cause death in patients with potentially curable resected lung cancer. We retrospectively studied 309 consecutive patients without a history of CVD (mean age 67.4 ± 8.2years, 61% male) who underwent curative surgery for non-small-cell lung cancer between May 2012 and March 2019 at the Japanese Red Cross Okayama Hospital. Time to incidence of major adverse cardiac events (MACEs) (non-fatal myocardial infarction, non-fatal stroke and cardiovascular death) and all-cause death was analyzed using Fine and Gray and Cox regression models. The CAC-DRS score was assessed using standard chest CT without electrocardiogram gating. During 52-months' median follow-up, 43 patients (13.4%) developed incident MACEs or died from any cause; the pathological cancer stages were Ia (n = 20), Ib (n = 8), IIa (n = 2), IIb (n = 2) and IIIa (n = 11). Patients had a graded increase in incidence of MACEs or all-cause death with increasing categories of CAC-DRS. The CAC-DRS score was significantly associated with incident MACEs or all-cause death after adjusting for confounding factors (hazard ratio 1.18; 95% confidence interval 1.10-1.25, p < 0.01). In conclusion, the CAC-DRS score on non-gated standard CT can predict incident MACEs and/or all-cause death in patients with potentially curable resected lung cancer. Lung cancer survivors with a greater CAC-DRS category may need more active management of cardiovascular risk factors.