Low-dose Chest Computed Tomography Scans Research Articles

Deep learning analysis of epicardial adipose tissue to predict cardiovascular risk in heavy smokers

BackgroundHeavy smokers are at increased risk for cardiovascular disease and may benefit from individualized risk quantification using routine lung cancer screening chest computed tomography. We investigated the prognostic value of deep learning-based automated epicardial adipose tissue quantification and compared it to established cardiovascular risk factors and coronary artery calcium.MethodsWe investigated the prognostic value of automated epicardial adipose tissue quantification in heavy smokers enrolled in the National Lung Screening Trial and followed for 12.3 (11.9–12.8) years. The epicardial adipose tissue was segmented and quantified on non-ECG-synchronized, non-contrast low-dose chest computed tomography scans using a validated deep-learning algorithm. Multivariable survival regression analyses were then utilized to determine the associations of epicardial adipose tissue volume and density with all-cause and cardiovascular mortality (myocardial infarction and stroke).ResultsHere we show in 24,090 adult heavy smokers (59% men; 61 ± 5 years) that epicardial adipose tissue volume and density are independently associated with all-cause (adjusted hazard ratios: 1.10 and 1.38; P < 0.001) and cardiovascular mortality (adjusted hazard ratios: 1.14 and 1.78; P < 0.001) beyond demographics, clinical risk factors, body habitus, level of education, and coronary artery calcium score.ConclusionsOur findings suggest that automated assessment of epicardial adipose tissue from low-dose lung cancer screening images offers prognostic value in heavy smokers, with potential implications for cardiovascular risk stratification in this high-risk population.

Evaluating the diagnostic efficacy of 12-point lung ultrasound in detecting COVID-19 lung lesions: a comparative study with low-dose chest CT scan

Objective:&#x0D; Chest computed tomography (CT) scans are the gold standard for identifying pulmonary involvement in pneumonia-like COVID-19 cases, albeit with certain drawbacks such as radiation exposure and high costs. This research aims to evaluate the diagnostic precision of a 12-point lung ultrasound (LUS) against a low-dose chest CT scan in identifying lung lesions associated with COVID-19.&#x0D; &#x0D; Methods:&#x0D; The study incorporated 100 consecutive patients, aged over 18 years, exhibiting suspected clinical symptoms of COVID-19 or inpatients requiring a low-dose chest CT scan for diagnosing asymptomatic COVID-19 lung lesions. All participants underwent a 12-point LUS, followed by a low-dose chest CT scan. Data analysis was conducted using STATA-16, with descriptive results presented as mean and standard deviation.&#x0D; &#x0D; Results:&#x0D; The study comprised 60 males and 40 females, with an average age of 43.0±16.9 years. The mean distribution of the patients' clinical features was calculated. The LUS demonstrated a sensitivity, specificity, and positive and negative predictive values of 97.5%, 86.4%, 83.3%, and 98%, respectively.&#x0D; Conclusion:&#x0D; The 12-point LUS exhibited high sensitivity and specificity in assessing pulmonary involvement in COVID-19 patients. Therefore, lung ultrasound results, combined with medical history and clinical examination, can serve as an effective triage tool for COVID-19 patients. The LUS, a swift, safe, and effective ionization tool, can potentially replace chest CT scans in scenarios such as CT scan unavailability, intensive care management, and patient follow-up.

The relationship of fat and muscle measurements with emphysema and bronchial wall thickening in smokers.

Differences in body composition in patients with COPD may have important prognostic value and may provide opportunities for patient-specific management. We investigated the relation of thoracic fat and muscle with computed tomography (CT)-measured emphysema and bronchial wall thickening. Low-dose baseline chest CT scans from 1031 male lung cancer screening participants from one site were quantified for emphysema, bronchial wall thickening, subcutaneous fat, visceral fat and skeletal muscle. Body composition measurements were performed by segmenting the first slice above the aortic arch using Hounsfield unit thresholds with region growing and manual corrections. COPD presence and severity were evaluated with pre-bronchodilator spirometry testing. Participants had a median age of 61.5 years (58.6-65.6, 25th-75th percentile) and median number of 38.0 pack-years (28.0-49.5); 549 (53.2%) were current smokers. Overall, 396 (38.4%) had COPD (256 Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1, 140 GOLD 2-3). Participants with COPD had less subcutaneous fat, visceral fat and skeletal muscle (p<0.001 for all). With increasing GOLD stages, subcutaneous (p=0.005) and visceral fat values (p=0.004) were higher, and skeletal muscle was lower (p=0.004). With increasing severity of CT-derived emphysema, subcutaneous fat, visceral fat and skeletal muscle values were lower (p<0.001 for all). With increasing CT-derived bronchial wall thickness, subcutaneous and visceral fat values were higher (p<0.001 for both), without difference in skeletal muscle. All statistical relationships remained when adjusted for age, pack-years and smoking status. COPD presence and emphysema severity are associated with smaller amounts of thoracic fat and muscle, whereas bronchial wall thickening is associated with fat accumulation.

Clinical Correlates of Incidental Probable Benign Pulmonary Nodules with Diameters Less than 8 mm in a Healthy Korean Cohort: A Retrospective Study

Incidental pulmonary nodules detected via computed tomography (CT) are usually small, solid nodules (diameters less than 8 mm) that are likely benign and are difficult to biopsy. Additional features of the benignity of these small nodules may help determine the need and periodicity of further follow-up and should be identified. This study was conducted to examine the clinical factors associated with benign solid pulmonary nodules measuring less than 8 mm in diameter. This retrospective study enrolled participants who underwent low-dose chest CT scans for 3 consecutive years during routine health check-ups at a university hospital in Korea. We chose a 2-year study period to ensure that the nodule(s) were benign, which meant there was no interval change over this period. Participants were stratified into two groups: no nodule (n = 56) and nodule(s) (n = 355). Multivariable logistic regression analyses were performed to explore associations (adjusted odds ratio [aOR], 95% confidence interval [CI], p-value) between variables and nodule(s). In this study cohort, elevated levels of low-density lipoprotein (LDL) cholesterol were positively associated factors with the presence of benign pulmonary nodule(s) (aOR: 1.10, 95% CI:1.00–1.20, p = 0.0488), whereas current cigarette smoking was negatively associated with nodules (aOR: 0.26, 95% CI: 0.08–0.81, p = 0.0202). Therefore, an elevated LDL cholesterol level was the only factor that was positively associated with the presence of benign small pulmonary nodules.

P1.17-07 Impact of Lung Cancer Screening with Low-Dose Chest Computed Tomography Scan in an Elderly Population: A Retrospective Cohort Study

P1.17-07 Impact of Lung Cancer Screening with Low-Dose Chest Computed Tomography Scan in an Elderly Population: A Retrospective Cohort Study

MA19.03 Sex Differences in the Prediction of Future Lung Cancer Risk Based on a Single Low-Dose Chest Computed Tomography Scan

MA19.03 Sex Differences in the Prediction of Future Lung Cancer Risk Based on a Single Low-Dose Chest Computed Tomography Scan

Evaluation of the relationship between γ-H2AX biomarker levels and dose received after radiation exposure in abdominal-pelvic and chest CT scans.

As one of the most informative diagnostic radiation instruments, computed tomography (CT) has seen considerable improvement since its implementation in the 1970s; however, the possibility of low-dose radiation risk after CT procedures is still challenging and little is known about the biological effects of CT exposure on patients. As a result, this research aimed to look at the biological and cytogenetic effects of low-dose abdominal-pelvic and chest CT scans on adults, focusing on the number of γ-H2AX foci formation. Blood tests were taken before and 10 min after CT exams on patients aged 25-55 who were undergoing abdominal-pelvic and chest CT exams with very low-ionizing radiation exposure (TLD doses of 15.67-63.45 mGy). Blood lymphocytes that had been isolated, fixed, and stained were dyed with γ-H2AX antibodies. Finally, the percentage of phosphorylation of histone H2AX as an indicator of double-strand breaks was determined using a cytometry technique. Our findings showed that after CT examination, the mean value of γ-H2AX foci in patients increased (P < 0.0001). A statistically significant correlation between dose radiation and the number of γ-H2AX foci was also found (P = 0.047, r = 0.4731). The current study also found a pattern of elevated γ-H2AX foci in patients over 40 years of age relative to younger patients. A Significant activation of γ-H2AX foci was found in lymphocytes of peripheral blood samples of patients after CT compared to before CT scan. This increase in γ-H2AX foci levels in blood cells may be a useful quantitative biomarker of low-level radiation exposure in humans.

Impact of Low-dose Chest CT Screening on the Association Between Rurality and Lung Cancer Outcomes.

Lung cancer mortality is higher among rural United States populations compared with nonrural ones. Little is known about screening low-dose chest computed tomography (LDCT) outcomes in rural settings. This retrospective cohort study examined all patients (n=1805) who underwent screening LDCT in a prospective registry from March 1, 2015, through December 31, 2019, in a majority-rural health care system. We assessed the proportion of early-stage lung cancers (American Joint Committee on Cancer stage I-II) diagnosed among LDCT-screened patients, and analyzed overall survival after early-stage lung cancer diagnosis according to residency location. The screening cohort had a median age of 63 and median 40-pack-year smoking history; 62.4% had a rural residence, 51.2% were female, and 62.7% completed only 1 LDCT scan. Thirty-eight patients were diagnosed with lung cancer (2.1% of the cohort), of which 65.8% were early-stage. On multivariable analysis, rural (vs nonrural) residency was not associated with a lung cancer diagnosis (adjusted hazard ratio 1.59; 95% CI, 0.74-3.40; P =0.24). At a median follow-up of 37.1 months (range, 3.3 to 67.2 months), 88.2% of rural versus 87.5% of nonrural patients with screen-diagnosed early-stage lung cancer were alive ( P =0.93). In a majority-rural United States population undergoing LDCT, most screen-detected lung cancers were early-stage. There were no significant differences observed between rural and nonrural patients in lung cancer diagnosis rate or early-stage lung cancer survival. Increased implementation of LDCT might blunt the historical association between rural United States populations and worse lung cancer outcomes.

Fully automatic coronary calcium scoring in non-ECG-gated low-dose chest CT: comparison with ECG-gated cardiac CT.

To validate an artificial intelligence (AI)-based fully automatic coronary artery calcium (CAC) scoring system on non-electrocardiogram (ECG)-gated low-dose chest computed tomography (LDCT) using multi-institutional datasets with manual CAC scoring as the reference standard. This retrospective study included 452 subjects from three academic institutions, who underwent both ECG-gated calcium scoring computed tomography (CSCT) and LDCT scans. For all CSCT and LDCT scans, automatic CAC scoring (CAC_auto) was performed using AI-based software, and manual CAC scoring (CAC_man) was set as the reference standard. The reliability and agreement of CAC_auto was evaluated and compared with that of CAC_man using intraclass correlation coefficients (ICCs) and Bland-Altman plots. The reliability between CAC_auto and CAC_man for CAC severity categories was analyzed using weighted kappa (κ) statistics. CAC_auto on CSCT and LDCT yielded a high ICC (0.998, 95% confidence interval (CI) 0.998-0.999 and 0.989, 95% CI 0.987-0.991, respectively) and a mean difference with 95% limits of agreement of 1.3 ± 37.1 and 0.8 ± 75.7, respectively. CAC_auto achieved excellent reliability for CAC severity (κ = 0.918-0.972) on CSCT and good to excellent but heterogenous reliability among datasets (κ = 0.748-0.924) on LDCT. The application of an AI-based automatic CAC scoring software to LDCT shows good to excellent reliability in CAC score and CAC severity categorization in multi-institutional datasets; however, the reliability varies among institutions. • AI-based automatic CAC scoring on LDCT shows excellent reliability with manual CAC scoring in multi-institutional datasets. • The reliability for CAC score-based severity categorization varies among datasets. • Automatic scoring for LDCT shows a higher false-positive rate than automatic scoring for CSCT, and most common causes of a false-positive are image noise and artifacts for both CSCT and LDCT.

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.

Evaluating coronary artery calcification with low-dose chest CT reconstructed by different kernels

PurposeTo understand the reliability of low-dose chest computed tomography (LDCT) in coronary artery calcification (CAC) assessment and evaluate the performance of different reconstruction kernels against the standard cardiac computed tomography (CaCT) as reference. Materials and methodsPatients from the NELCIN-B3 screening program who underwent CaCT and LDCT scans were analyzed retrospectively. LDCT were reconstructed with smooth, standard, and sharp kernels (Group B1, B2 and B3) to compare against standard CaCT (Group A). The image quality was evaluated by noise value, signal-to-noise ratio (SNR), and contrast to noise ratio (CNR); moreover, radiation dose was recorded for both scans. Coronary artery calcification scores (CACS) were measured with volume, mass and Agatston standards. Agatston score was divided into four cardiovascular risk categories (0, 1–99, 100–399, and >400). The agreement in CACS and risk classification between LDCT and CaCT was analyzed by intra-group correlation coefficient (ICC) and Kappa test. ResultsThe sensitivity of diagnosing CAC with LDCT was 98.5% (330/335) regardless of reconstruction kernels. Group B1 demonstrated the highest agreement in raw CACS (ICC volume 0.932; mass 0.904; Agatston 0.906; all p < 0.001) and risk classification (kappa 0.757, 95% CI 0.70–0.82). Smooth-kernel reconstruction achieved lower image noise, better SNR and CNR than other kernels. The effective radiation dose in of LDCT was 41.2% lower than that of the calcium scan (p < 0.001). ConclusionReconstructing LDCT with a smooth kernel in LDCT could provide a reliable imaging method to detect and quantitatively evaluate CAC, potentially expanding the application of LDCT lung screening to incidental findings of cardiovascular disease.

Accuracy of non-gated low-dose non-contrast chest CT with tin filtration for coronary artery calcium scoring

ObjectiveThe study investigated the accuracy of coronary artery calcium scores (CACS) and the potential for reducing radiation dose using non-gated low-dose non-contrast chest computed tomography (CT) scanning with tin filtration for one-stop screening of the lungs and heart. MethodsA prospective study was conducted,193 Patients received two scans for determining CACS, including an ECG-gated CT at 120 kV (ECG-gated CT), followed by a non-gated low-dose chest CT using 100 kV with tin filtration (non-gated Sn100 kV-LDCT). The Agatston score (AS), risk stratification, and radiation dose were compared between the scan types. ResultsThere was good consistency in the AS from both an ECG-gated CT and a non-gated low-dose chest CT scan, which had a high correlation (r = 0.970). The Kappa value of risk stratification of the two scan types was 0.549. The area under the ROC curve (AUC) of the CACS was used to develop a new risk stratification standard for non-gated Sn100 kV-LDCT evaluation of CACS. In comparison to the CACS measured by ECG-gated CT, non-gated Sn100 kV-LDCT had an AUC of 0.951 and an optimal critical value of 4.6 in the low-risk category. The AUC of low-medium risk was 0.966, and the optimal critical value was 41.2. The AUC of the medium-high risk category was 0.968, and the optimal critical value was 230. The consistency in CACS measured by ECG-gated CT and non-gated Sn100 kV-LDCT had a Kappa value of 0.831. The Effective dose (ED) of non-gated Sn100 kV-LDCT and ECG-gated CT was 0.056 ± 0.017 mSv and 0.685 ± 0.455 mSv, respectively (p < 0.05). ConclusionThe Agatston score of CACS using non-gated low-dose chest CT was accurate, but there was an underestimation in risk stratification. This study developed a new risk stratification standard for non-gated Sn100 kV-LDCT evaluation of CACS, which is in closer agreement with CACS derived from ECG-gated CT scans.

Creating a training set for artificial intelligence from initial segmentations of airways

Airways segmentation is important for research about pulmonary disease but require a large amount of time by trained specialists. We used an openly available software to improve airways segmentations obtained from an artificial intelligence (AI) tool and retrained the tool to get a better performance. Fifteen initial airway segmentations from low-dose chest computed tomography scans were obtained with a 3D-Unet AI tool previously trained on Danish Lung Cancer Screening Trial and Erasmus-MC Sophia datasets. Segmentations were manually corrected in 3D Slicer. The corrected airway segmentations were used to retrain the 3D-Unet. Airway measurements were automatically obtained and included count, airway length and luminal diameter per generation from the segmentations. Correcting segmentations required 2–4 h per scan. Manually corrected segmentations had more branches (p < 0.001), longer airways (p < 0.001) and smaller luminal diameters (p = 0.004) than initial segmentations. Segmentations from retrained 3D-Unets trended towards more branches and longer airways compared to the initial segmentations. The largest changes were seen in airways from 6th generation onwards. Manual correction results in significantly improved segmentations and is potentially a useful and time-efficient method to improve the AI tool performance on a specific hospital or research dataset.

Comparing Lung CT in COVID-19 Pneumonia and Acute Heart Failure: An Imaging Conundrum.

BackgroundChest computed tomography (CT) provides an effective modality to evaluate patients with suspected coronavirus disease 2019 (COVID-19). However, overlapping imaging findings with cardiogenic pulmonary edema is not uncommon. Reports comparing the chest CT features of these diseases have not been elaborated. Thus, we aimed to show the difference between the low-dose lung CT findings of COVID-19 pneumonia and comparing them to those with acute heart failure (HF).MethodsThis retrospective analysis enrolled hospitalized patients with COVID-19 (n=10) and acute heart failure (n=9) that exclusively underwent low-dose chest CT scans within 24 hours of admission. Clinical and lung CT characteristics were collected and analyzed.ResultsThe appearance of ground-glass-opacities (GGOs) has been recorded in all individuals in the HF and COVID-19 groups. There was no significant statistical difference between the two groups for rounded morphology, consolidation, crazy paving pattern, lesion distribution, and parenchymal band (P> 0.05). However, diffuse lesions were more frequent in HF cases (55.6% vs. 0%) than in COVID-19 pneumonia, which had a predominantly multifocal pattern. Notably, CT images in HF patients were more likely to have signs of interstitial tissue thickening, such as the interlobular septums, fissures, and peribronchovascular interstitium (55.6% vs 0%, 88.9% vs 20% and 44.4% vs 0%, respectively), as well as cardiomegaly (77.8% vs 0%), increased artery to bronchus ratio (55.6% vs 0%), and pleural effusions (77.8% vs 0%).ConclusionsMajor overlaps of lung CT imaging features existed between COVID-19 pneumonia and acute HF cases. However, signs of fluid redistribution are clues that favor HF over COVID-19 pneumonia.

Dyspnoea, lung function and CT findings 3 months after hospital admission for COVID-19.

The long-term pulmonary outcomes of coronavirus disease 2019 (COVID-19) are unknown. We aimed to describe self-reported dyspnoea, quality of life, pulmonary function and chest computed tomography (CT) findings 3 months following hospital admission for COVID-19. We hypothesised outcomes to be inferior for patients admitted to intensive care units (ICUs), compared with non-ICU patients.Discharged COVID-19 patients from six Norwegian hospitals were enrolled consecutively in a prospective cohort study. The current report describes the first 103 participants, including 15 ICU patients. The modified Medical Research Council (mMRC) dyspnoea scale, the EuroQol Group's questionnaire, spirometry, diffusing capacity of the lung for carbon monoxide (DLCO), 6-min walk test, pulse oximetry and low-dose CT scan were performed 3 months after discharge.mMRC score was >0 in 54% and >1 in 19% of the participants. The median (25th–75th percentile) forced vital capacity and forced expiratory volume in 1 s were 94% (76–121%) and 92% (84–106%) of predicted, respectively. DLCO was below the lower limit of normal in 24% of participants. Ground-glass opacities (GGO) with >10% distribution in at least one of four pulmonary zones were present in 25% of participants, while 19% had parenchymal bands on chest CT. ICU survivors had similar dyspnoea scores and pulmonary function as non-ICU patients, but higher prevalence of GGO (adjusted OR 4.2, 95% CI 1.1–15.6) and lower performance in usual activities.3 months after admission for COVID-19, one-fourth of the participants had chest CT opacities and reduced diffusing capacity. Admission to ICU was associated with pathological CT findings. This was not reflected in increased dyspnoea or impaired lung function.

Performance of Low-Dose Chest CT Scan for Initial Triage of COVID-19

Background: Chest computed tomography (CT) scan is frequently used for diagnosis of coronavirus disease 2019 (COVID-19), especially in regions with limited availability of reverse-transcription polymerase chain reaction test (RT-PCR) test. Low-dose CT of chest offers acceptable image quality with lower radiation dose, particularly important in younger patients. Objectives: We have designed the current study to evaluate the diagnostic efficacy of low-dose chest CT versus early RT-PCR results, for triage of COVID-19 patients. Patients and Methods: From February 20 to April 15, 2020, 163 patients including 100 males (61.3%) with the median age of 65 years (21 to 97), who underwent both RT-PCR and chest CT were registered in the study. Low-dose chest CT protocol was applied with parameters modified from the lung cancer screening protocol. The accuracy of low-dose chest CT for COVID-19 diagnosis was evaluated, considering first RT-PCR results as reference. Results: Of 163 patients, 89 cases (54.6%) were presented with positive initial RT-PCR result. Lymphocyte percentage and lymphocyte count were significantly lower in the positive RT-PCR group (15% versus 19%, and 0.98 vs. 1.3, respectively); while, erythrocyte sedimentation rate (ESR) was significantly higher (53 vs. 22). Positive chest CT findings were present in 133/163 cases (81.6%). The sensitivity, specificity, positive and negative predictive values (PPV and NPV) and accuracy of low-dose chest CT scan were 96.6% (95% confidence interval [CI], 90% - 99%), 36.5% (95% CI, 26% - 49%), 64.7% (95% CI, 56% - 73%), 90% (95% CI, 72% - 97%) and 69.3% (95% CI, 61% - 76%), respectively based on positive RT-PCR results. Conclusion: Low-dose chest CT scan provides both high sensitivity and negative predictive value in diagnosing COVID-19 compared to initial RT-PCR as the gold standard. It can be used as an alternate to standard-dose CT scan in areas with high prevalence of COVID-19 disease and limited availability of RT-PCR for early triage.

Image quality and clinical usefulness of automatic tube current modulation technology in female chest computed tomography screening.

The study was aimed to evaluate the image quality and radiation dose during female chest computed tomography (CT) screening using organ-based tube current modulation technology X-ray combined applications to reduce exposure (XCARE).Five hundred sixty female individuals undergoing chest CT scan were prospectively enrolled and divided into 4 groups based on body mass index (BMI). Then they were randomly and equally divided into control and experimental subgroup and respectively accepted conventional low-dose and XCARE technology spiral CT scan with same parameters. Signal-to-noise ratio and contrast-to-noise ratio were calculated. The quality of the images was evaluated by 2 radiologists using a 5-point scale.Among experimental subgroups of the 4 BMI groups, Signal-to-noise ratios, CT dose index of volume, dose-length product, effective dose, and contrast-to-noise ratio all displayed significant differences, as well as in control subgroups (P < .001). Both the experimental and control subgroups showed an increasing trend in radiation dose with the increasing of BMI. Parameters of image quality and radiation dose displayed no significant differences between control and experimental subgroups in the 4 groups. In multiple linear regression analysis, age and scanning protocol were not associated with radiation dose (P > .05), while BMI was significantly associated with increased CT dose index of volume (P < .05). The display of the lesions for the patients in the control and experimental subgroups of the 4 groups with different BMIs exhibited no statistically significant difference.The same image quality and radiation dose can be obtained using XCARE technology compared to conventional chest CT scans, which can be used regularly in female patients.Advances in knowledge: Using automatic tube current modulation technology to reduce exposure in breast. In this study, we sought a radiation protection method for sensitive tissue in chest CT screening.

Validation of Deep-Learning Image Reconstruction for Low-Dose Chest Computed Tomography Scan: Emphasis on Image Quality and Noise.

ObjectiveIterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality. The purposes of this study was to evaluate image quality and noise of LDCT scan images reconstructed with DLIR and compare with those of images reconstructed with the adaptive statistical iterative reconstruction-Veo at a level of 30% (ASiR-V 30%).Materials and MethodsThis retrospective study included 58 patients who underwent LDCT scan for lung cancer screening. Datasets were reconstructed with ASiR-V 30% and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The objective image signal and noise, which represented mean attenuation value and standard deviation in Hounsfield units for the lungs, mediastinum, liver, and background air, and subjective image contrast, image noise, and conspicuity of structures were evaluated. The differences between CT scan images subjected to ASiR-V 30%, DLIR-M, and DLIR-H were evaluated.ResultsBased on the objective analysis, the image signals did not significantly differ among ASiR-V 30%, DLIR-M, and DLIR-H (p = 0.949, 0.737, 0.366, and 0.358 in the lungs, mediastinum, liver, and background air, respectively). However, the noise was significantly lower in DLIR-M and DLIR-H than in ASiR-V 30% (all p < 0.001). DLIR had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than ASiR-V 30% (p = 0.027, < 0.001, and < 0.001 in the SNR of the lungs, mediastinum, and liver, respectively; all p < 0.001 in the CNR). According to the subjective analysis, DLIR had higher image contrast and lower image noise than ASiR-V 30% (all p < 0.001). DLIR was superior to ASiR-V 30% in identifying the pulmonary arteries and veins, trachea and bronchi, lymph nodes, and pleura and pericardium (all p < 0.001).ConclusionDLIR significantly reduced the image noise in chest LDCT scan images compared with ASiR-V 30% while maintaining superior image quality.

Low-Dose Chest CT for the Diagnosis of COVID-19—A Systematic, Prospective Comparison With PCR

Only limited evidence has been available to date on the accuracy of systematic low-dose chest computed tomography (LDCT) use in the diagnosis of COVID-19 in patients with non-specific clinical symptoms. The COVID-19 Imaging Registry Study Aachen (COVID-19-Bildgebungs-Register Aachen, COBRA) collects data on imaging in patients with COVID-19. Two of the COBRA partner hospitals (RWTH Aachen University Hospital and Dueren Hospital) systematically perform reverse transcriptase polymerase chain reaction (RT-PCR) from nasopharyngeal swabs as well as LDCT in all patients presenting with manifestations that are compatible with COVID-19. In accordance with the COV-RADS protocol, the LDCT scans were prospectively evaluated before the RT-PCR findings were available in order to categorize the likelihood of COVID-19. From 18 March to 5 May 2020, 191 patients with COVID-19 manifestations (117 male, age 65 ± 16 years) underwent RT-PCR testing and LDCT. The mean time from the submission of the sample to the availability of the RT-PCR findings was 491 minutes (interquartile range [IQR: 276-1066]), while that from the performance of the CT to the availability of its findings was 9 minutes (IQR: 6-11). A diagnosis of COVID-19 was made in 75/191 patients (39%). The LDCT was positive in 71 of these 75 patients and negative in 106 of the 116 patients without COVID-19, corresponding to 94.7% sensitivity (95% confidence interval [86.9; 98.5]), 91.4% specificity [84.7; 95.8], positive and negative predictive values of 87.7% [78.5; 93.9] and 96.4% [91.1; 98.6], respectively, and an AUC (area under the curve) of 0.959 [0.930; 0.988]. The initial RT-PCR test results were falsely negative in six patients, yielding a sensitivity of 92.0% [83.4; 97.0]; these six patients had positive LDCT findings. 47.4% of the LDCTs that were negative for COVID-19 (55/116) exhibited pathological pulmonary changes, including infiltrates, that were correctly distinguished from SARS-CoV-2 related changes. In patients with symptoms compatible with COVID-19, LDCT can esablish the diagnosis of COVID-19 with comparable sensitivity to RT-PCR testing. In addition, it offers a high specificity for distinguishing COVID-19 from other diseases associated with the same or similar clinical symptoms. We propose the systematic use of LDCT in addition to RT-PCR testing because it helps correct false-negative RT-PCR results, because its results are available much faster than those of RT-PCRtesting, and because it provides additional diagnostic information useful for treatment planning regardless of the type of the infectious agent.

Use of Low-dose Chest CT Scan in the Evaluation of Breast Composition According to the Recommendations of BI-RADS Atlas-Fifth Edition

Background: Breast cancer and lung cancer are the leading causes of cancer-related mortality in women. Computed tomography (CT) plays an important role in lung cancer examination but an unidentified role in breast examination. Objectives: To investigate the feasibility of breast composition categorization according to the fifth edition of Breast Imaging-Reporting and Data System (BI-RADS) atlas in low-dose CT screening. Patients and Methods: This was a cross-sectional study completed in The 5th Affiliated Hospital of Sun Yat-sen University, Zhuhai, China. We collected the imaging data of 57 women, who underwent low-dose chest CT scan and mammography within one week from 1st October 2013 to 31st March 2015. Two radiologists independently interpreted the mammograms and chest CT scans and classified the breast composition into categories a, b, c, and d. We also summarized the distribution of breast composition categories by collecting, observing, and classifying the chest CT scans from 1916 female examinees from 1st October 2013 to 31st March 2016. Results: Excellent agreement was observed between the two radiologists, using both low-dose CT scan (κ = 0.91) and mammography (κ = 0.86). Agreement between low-dose chest CT scan and mammography was moderate for radiologist A (κ = 0.50) and radiologist B (κ = 0.43). More breasts were classified in categories a and b on the chest CT scan compared to mammography according to both radiologist A (P &lt; 0.01) and radiologist B (P &lt; 0.01). The proportion of non-dense breast tissues (categories a &amp; b) increased with advancing age, while the proportion of dense breast tissues (categories c &amp; d) decreased (P &lt; 0.05). With advancing age, the probability of non-dense breasts increased, while the probability of dense breasts decreased. Conclusions: Based on the findings, it is feasible to categorize breast composition using low-dose chest CT. In the older age group, the probability of non-dense breasts increased.