Noncontrast Chest Chest Computed Tomography Research Articles

Bone mineral density as an individual prognostic biomarker in NSCLC patients treated with immune checkpoint inhibitors.

Immune checkpoint inhibitors (ICIs) have left a deep impression in the treatment of non-small cell lung cancer (NSCLC), however, not all patients benefit from it. The purpose of this study was to investigate the prognostic value of baseline bone mineral density (BMD) derived from chest computed tomography (CT) scans in NSCLC patients treated with ICIs. This study included patients with advanced NSCLC who underwent ICI treatment at the Wuhan Union Hospital from March 2020 to October 2022. Baseline BMD was evaluated at non-contrast chest CT at the level of first lumbar vertebra. Patients were divided into BMD-lower group and BMD-higher group according to the optimal cutoff value calculated by X-tile software. Baseline characteristics of the two groups were compared and variables between the two groups were balanced by propensity score matching (PSM) analysis. We calculated the objective response rate (ORR) and disease control rate (DCR) of the two groups and analyzed overall survival (OS) and progression-free survival (PFS) using BMD and other clinical indexes through Cox regression models and Kaplan-Meier survival curves. A total of 479 patients were included in this study, and all patients were divided into BMD-lower group (n=270) and BMD-higher group (n=209). After PSM analysis, each group consisted of 150 patients. ORR (43.3% vs. 43.5% before PSM, P = 0.964; 44.7% vs. 44.7% after PSM, P = 1.000) and DCR (91.1% vs. 94.3% before PSM, P = 0.195; 93.3% vs. 96.7% after PSM, P =0.190) were similar in two groups. There was no statistically significant relationship between BMD degree and PFS before (16.0 months vs. 18.0 months, P = 0.067) and after PSM analysis (17.0 months vs. 19.0 months, P = 0.095). However, lower BMD was associated with shorter OS both before (20.5 months vs. 23.0 months, P< 0.001) and after PSM analysis (20.0 months vs. 23.0 months, P = 0.008). Lower baseline BMD is associated with worse clinical outcomes in NSCLC patients treated with ICIs. As a reliable and easily obtained individual prognostic biomarker, BMD can become a routine detection indicator before immunotherapy.

Temporal Evolution of CT Findings in COVID-19 Patients: An Observational Study

Introduction: Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), presents a broad spectrum of clinical manifestations, from asymptomatic cases to severe pneumonia and acute respiratory distress syndrome (ARDS). Chest computed tomography (CT) has become a critical diagnostic tool, especially in regions with high disease prevalence. This study aims to assess the common CT findings, patterns of lung involvement, and severity of disease in RT-PCR positive COVID-19 patients to better understand and manage this pervasive disease. Materials and Methods: This observational cross-sectional study was conducted at Dr. Kamakshi Memorial Hospital, Chennai, from 2020 to 2021. The study included RT-PCR positive COVID-19 patients aged 18 to 95 years who presented to the fever clinic or COVID casualty and were referred to the radiology department for chest CT evaluation. Exclusion criteria included pregnancy, age under 18 years, and refusal to consent. Non-contrast chest CT scans were performed using a TOSHIBA Aquilion Lightening 16-slice CT machine. Scans were acquired in a single inspiratory breath-hold from the lung apex to the costophrenic angle. CT findings were analyzed and reported by two experienced radiologists, with discrepancies resolved through consensus. Results: Out of 349 patients, 213 (61%) were male and 136 (39%) were female, with a mean age of 47.7 years. The distribution of CT findings showed significant variability among the four groups. Group A had the highest percentage of normal CT scans (22%) and ground-glass opacities (52%). Group B exhibited a reduction in normal CT scans (9%) and an increase in ground-glass opacities (57%). Group C showed further decrease in normal CT scans (10.6%) with increased crazy paving (17.3%) and reticulation (14.6%). Group D had similar normal CT scans (10.8%) but significantly higher incidences of reticulation (24.3%) and ground-glass opacities (64%). Conclusion: This study highlights the critical role of chest CT in monitoring the progression of COVID-19 pneumonia. The findings demonstrate a clear temporal evolution of lung involvement, from ground-glass opacities in the early stages to more complex patterns such as crazy paving and reticulation in later stages.

Elastofibroma Dorsi Detected Incidentally on Chest Computed Tomography: The Prevalence and Reporting Rate in Radiology Reports.

Background Elastofibroma dorsi (ED) is an uncommon benign connective-tissue tumor, usually seen in the subscapular region of women after the fifth decade. The study aimed to determine the prevalence, radiological characteristics, and the rates of mentionin the initial radiology reports of ED incidentally detected by chest computed tomography (CT) imaging in a large population. Methodology This study was conducted at Hitit University, Erol Olçok Training and Research Hospital Radiology Department in Çorum, Turkey. A total of 3,299 patients (1,554 females, 1,745 males)who underwent non-contrast chest CT for various reasons were included in this retrospective study. The presence, side, thickness, and density of ED were investigated in these patients. Differences in gender and laterality were assessed statistically. It was also reviewed whether it was stated in the initial radiology report or not. Results ED was detected in 79 (2.4%) of 3,299 patients, in 60 (75.9%) females and 19 (24.1%) males with a median age of 57.5 years (range, 18-99 years). The risk of ED presence was determined to be 3.65-fold in females than in males. In the cases determined with ED, the median age was 75 years (range, 53-96 years), and ED was not determined in any patient aged <50 years. A statistically significant correlation was determined between age and the presence of ED (p < 0.001). No statistically significant correlation was found between age and ED thickness or density (p = 0.602, p = 0.233, respectively). It was noted that none of the patients were diagnosed in the first radiological report. Conclusions ED can be easily overlooked on cross-sectional examinations because of the similar appearance and density to adjacent structures. Knowledge of the characteristic radiological features of these lesions and increased awareness of radiologists will make early diagnosis possible in asymptomatic cases.

Photon-counting detector CT allows significant reduction in radiation dose while maintaining image quality and noise on non-contrast chest CT

PurposeTo investigate if clinical non-contrast chest CT studies obtained with PCD CT using much lower radiation exposure can achieve the same image quality as with the currently established EID protocol. Materials/methodsA total of seventy-one patients were identified who had a non-contrast chest computed tomography (CT) done on PCD CT and EID CT scanners within a 4-month interval. Five fellowship trained chest radiologists, blinded to the scanner details were asked to review the cases side-by-side and record their preference for images from either the photon-counting-detector (PCD) CT or the energy-integrating detector (EID) CT scanner. ResultsThe median CTDIvol for PCD-CT system was 4.710 mGy and EID system was 7.80 mGy (p < 0.001). The median DLP with the PCD-CT was 182.0 mGy.cm and EID system was 262.60 mGy.cm (p < 0.001). The contrast to noise ratio (CNR) was superior on the PCD-CT system 59.2 compared to the EID-CT 53.3; (p < 0.001). Kappa-statistic showed that there was poor agreement between the readers over the image quality from the PCD and EID scanners (κ = 0.19; 95 % CI: 0.12 – 0.27; p < 0.001). Chi-square analysis revealed that 3 out of 5 readers showed a significant preference for images from the PCDCT (p ≤ 0.012). There was no significant difference in the preferences of two readers between EID-CT and PCD-CT images. ConclusionThe first clinical PCD-CT system allows a significant reduction in radiation exposure while maintaining image quality and image noise using a standardized non-contrast chest CT protocol.

Existing Nongated CT Coronary Calcium Predicts Operative Risk in Patients Undergoing Noncardiac Surgeries (ENCORES).

Preoperative cardiovascular risk stratification before noncardiac surgery is a common clinical challenge. Coronary artery calcium scores from ECG-gated chest computed tomography (CT) imaging are associated with perioperative events. At the time of preoperative evaluation, many patients will not have had ECG-gated CT imaging, but will have had nongated chest CT studies performed for a variety of noncardiac indications. We evaluated relationships between coronary calcium severity estimated from previous nongated chest CT imaging and perioperative major clinical events (MCE) after noncardiac surgery. We retrospectively identified consecutive adults age ≥45 years who underwent in-hospital, major noncardiac surgery from 2016 to 2020 at a large academic health system composed of 4 acute care centers. All patients had nongated (contrast or noncontrast) chest CT imaging performed within 1 year before surgery. Coronary calcium in each vessel was retrospectively graded from absent to severe using a 0 to 3 scale (absent, mild, moderate, severe) by physicians blinded to clinical data. The estimated coronary calcium burden (ECCB) was computed as the sum of scores for each coronary artery (0 to 9 scale). A Revised Cardiac Risk Index was calculated for each patient. Perioperative MCE was defined as all-cause death or myocardial infarction within 30 days of surgery. A total of 2554 patients (median age, 68 years; 49.7% women; median Revised Cardiac Risk Index, 1) were included. The median time interval from nongated chest CT imaging to noncardiac surgery was 15 days (interquartile range, 3-106 days). The median ECCB was 1 (interquartile range, 0-3). Perioperative MCE occurred in 136 (5.2%) patients. Higher ECCB values were associated with stepwise increases in perioperative MCE (0: 2.9%, 1-2: 3.7%, 3-5: 8.0%; 6-9: 12.6%, P<0.001). Addition of ECCB to a model with the Revised Cardiac Risk Index improved the C-statistic for MCE (from 0.675 to 0.712, P=0.018), with a net reclassification improvement of 0.428 (95% CI, 0.254-0.601, P<0.0001). An ECCB ≥3 was associated with 2-fold higher adjusted odds of MCE versus an ECCB <3 (adjusted odds ratio, 2.11 [95% CI, 1.42-3.12]). Prevalence and severity of coronary calcium obtained from existing nongated chest CT imaging improve preoperative clinical risk stratification before noncardiac surgery.

Validation of an AI-based algorithm for measurement of the thoracic aortic diameter in low-dose chest CT

ObjectivesTo evaluate the performance of artificial intelligence (AI) software for automatic thoracic aortic diameter assessment in a heterogeneous cohort with low-dose, non-contrast chest computed tomography (CT). Materials and methodsParticipants of the Imaging in Lifelines (ImaLife) study who underwent low-dose, non-contrast chest CT (August 2017–May 2022) were included using random samples of 80 participants <50y, ≥80y, and with thoracic aortic diameter ≥40 mm. AI-based aortic diameters at eight guideline compliant positions were compared with manual measurements. In 90 examinations (30 per group) diameters were reassessed for intra- and inter-reader variability, which was compared to discrepancy of the AI system using Bland-Altman analysis, paired samples t-testing and linear mixed models. ResultsWe analyzed 240 participants (63 ± 16 years; 50 % men). AI evaluation failed in 11 cases due to incorrect segmentation (4.6 %), leaving 229 cases for analysis. No difference was found in aortic diameter between manual and automatic measurements (32.7 ± 6.4 mm vs 32.7 ± 6.0 mm, p = 0.70). Bland-Altman analysis yielded no systematic bias and a repeatability coefficient of 4.0 mm for AI. Mean discrepancy of AI (1.3 ± 1.6 mm) was comparable to inter-reader variability (1.4 ± 1.4 mm); only at the proximal aortic arch showed AI higher discrepancy (2.0 ± 1.8 mm vs 0.9 ± 0.9 mm, p < 0.001). No difference between AI discrepancy and inter-reader variability was found for any subgroup (all: p > 0.05). ConclusionThe AI software can accurately measure thoracic aortic diameters, with discrepancy to a human reader similar to inter-reader variability in a range from normal to dilated aortas.

A CT-based radiomics integrated model for discriminating pulmonary cryptococcosis granuloma from lung adenocarcinoma-a diagnostic test.

Chest computed tomography (CT) is a critical tool in the diagnosis of pulmonary cryptococcosis as approximately 30% of normal immunity individuals may not exhibit any significant symptoms or laboratory findings. Pulmonary cryptococcosis granuloma and lung adenocarcinoma can appear similar on noncontrast chest CT. This study evaluates the use of an integrated model that was developed based on radiomic features combined with demographic and radiological features to differentiate pulmonary cryptococcosis nodules from lung adenocarcinomas. Preoperative chest CT images for 215 patients with solid pulmonary nodules with histopathologically confirmed lung adenocarcinoma and cryptococcosis infection were collected from two clinical centers (108 cases in the training set and 107 cases in the test set divided by the different hospitals). Radiomics models were constructed based on nodular lesion volume (LV), 5-mm extended lesion volume (ELV), and perilesion volume (PLV). A demoradiological model was constructed using logistic regression based on demographic information (age, sex) and 12 radiological features (location, number, shape and specific imaging signs). Both models were used to build an integrated model, the performance of which was assessed using the test set. A junior and a senior radiologist evaluated the nodules. Receiver operating characteristic (ROC) curve analysis was conducted, and areas under the curve (AUCs), sensitivity (SEN), and specificity (SPE) of the models were calculated and compared. Among the radiomics models, AUCs of the LV, ELV, and PLV were 0.558, 0.757, and 0.470, respectively. Age, lesion number, and lobular sign were identified as independent discriminative features providing an AUC of 0.77 in the demoradiological model (SEN 0.815, SPE 0.642). The integrated model achieved the highest AUC of 0.801 (SEN 0.759, SPE 0.755), which was significantly higher than that obtained by a junior radiologist (AUC =0.689, P=0.024) but showed no significant difference from that of the senior radiologist (AUC =0.784, P=0.388). An integrated model with radiomics and demoradiological features improves discrimination of cryptococcosis granulomas from solid adenocarcinomas on noncontrast CT. This model may be an effective strategy for machine complementation to discrimination by radiologists, and whole-lung automated recognition methods might dominate in the future.

The value of non-contrast chest CT in the prediction of myocardial injury in patients with the COVID-19 Omicron variant

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Omicron variant associated myocardial injury seriously affected the patient's health. Chest computed tomography (CT) is an essential imaging diagnostic tool for evaluating lung diseases in these patients, but its value in the diagnosis of myocardial injury remains unknown. The purpose of this study was to evaluate the lung lesions in patients with Omicron infection with or without myocardial injury, and to evaluate the predictive value of non-contrast chest CT in such patients with myocardial injury. We enrolled 122 consecutive hospitalized patients with laboratory-confirmed coronavirus disease 2019 (COVID-19) for non-contrast chest CT examination. These patients were divided into two groups according to whether myocardial injury occurred. Myocardial injury was defined as a Troponin I level above the 99th-percentile upper reference limit (0.04 ng/mL). The imaging manifestations of the patients’ lungs were evaluated. Myocardial CT value, left atrium (LA) size, long diameter of left ventricular (LV), and cardiothoracic ratio (CTR) were recorded. Multivariate logistic analysis was performed to identify the predictive factors associated with myocardial injury. Of 122 patients, 61 patients (50%) had myocardial injury. Compared with patients without myocardial injury, there was worse NYHA class, more critical patients, higher incidence of bronchial meteorology, larger area and percentage of lung lesions, diameters of LA, and lower myocardial CT value in the myocardial injury group (P < 0.05). Troponin I concentration in patients with myocardial injury group showed negative correlation with myocardial CT value (r = − 0.319, P = 0.012). The multivariable logistic regression analysis showed that disease severity status (OR 2.279; 95% CI 1.247–4.165, P = 0.007), myocardial CT value (OR 0.849; 95% CI 0.752–0.958, P = 0.008), neutrophil count (OR 1.330; 95% CI 1.114–1.587, P = 0.002) were independent predictors of myocardial injury. The discrimination of the model was good (C-statistic = 0.845, 95% CI 0.775–0.914) and well calibrated with a Hosmer–Lemeshow test for goodness of fit (P = 0.476). Patients infected with Omicron with myocardial injury had more severe lung disease than those without myocardial injury. Non-contrast chest CT can be a useful method of detection of myocardial injury in Omicron infection patients.

Coronary artery calcium identified on non-gated chest CT scans - A wasted opportunity to avoid the tragedy

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Coronary artery calcium (CAC) is an independent predictor of cardiovascular events. While it is traditionally performed utilizing gating with specific acquisition parameters, CAC can be identified in non-gated standard chest computed tomography (CT). This study aimed to assess CAC on chest CTs, evaluating its correlation with coronary lesions on coronary angiography (CAG) and prognosis. Methods We retrospectively reviewed patients (pts) who underwent CAG due to acute coronary syndrome (ACS) who had undergone a prior non-gated non-contrast chest CT. CAC was qualitatively evaluated by visual assessment (mild/moderate/severe) and quantitatively assessed using Agatson score and stratified by terciles. Evaluation was performed by an investigator blinded to CAG report. Results We included 114 pts after reviewing 1000 CAGs: 67% male, mean age 68 years, 78% hypertension, 62% dyslipidemia, 38% chronic kidney disease, 38% diabetes. The mean time difference between CT and CAG was 23 months. CAG was performed due to unstable angina in 33% of pts, NSTEMI in 52% and STEMI in 16%. Significant lesions were found in 57% (69% performed PCI and 17% surgical revascularization). CAC was visual classified as mild, moderate and severe in 31%, 33% and 16% of pts, respectively. Moderate or severe CAC was an independent predictor of significant lesions on CAG [OR 22, 95%CI 8–61, p&amp;lt;0.001] and all-cause mortality [OR 4, 95%CI 2–9, p = 0.001]. Pts with severe CAC had higher peak troponin than those with mild/moderate CAC (1780 vs 315 ng/L, p = 0.024). Quantitative CAC score accurately predicted significant lesions (AUC 0.81, p&amp;lt;0.001; figure 1A), with higher scores in this subgroup (1308 vs 120, p&amp;lt;0.001) and strongly correlated with SYNTAX score (p&amp;lt;0.001). Survival analysis stratified by severity of CAC assessment is shown in figure 1B and 1C. The most severely calcified artery in the CT often matched the culprit vessel of future ACS, with 79%, 60% and 50% concordance for left anterior descending, circumflex, and right coronary artery, respectively. While significant CAC was identified in 80% of CTs, formal reporting was as low as 25%, even with severe CAC, where only 2/18 reports mentioned it. Furthermore, only 62% pts were on statin therapy at the time of CAG. Conclusion CAC evaluation in chest CTs was feasible and strongly associated with the extent/severity of coronary artery disease on CAG, as well as mortality. Notwithstanding, CAC underreporting was frequent and statin therapy underused, suggesting a simple and common opportunity for preventive care.

Type A Aortic Dissection and Non-Contrast Computed Tomography.

Non-contrast enhanced chest computed tomography (CT) carries a low sensitivity for acute aortic dissection; CT Angiography remains the gold standard. We highlight the potential utility of non-contrast CT for detection of aortic dissection in a case of a young, immunocompromised man presenting with acute abdominal pain and renal injury. Given elevated creatinine, an initial non-contrast chest CT demonstrated subtle findings suggestive of aortic dissection (aneurysmal dilation of the proximal ascending aorta as well as displaced calcified intimal flap/intraluminal high linear density in the thoracic descending and distal abdominal aorta). Subsequent CT angiography confirmed the presence of an extensive type A aortic dissection. He underwent emergent exploratory laparotomy and hemiarch repair. Displaced calcified intimal flaps, intraluminal high-densities, intramural hematoma, and aneurysmal aortic dilation are common non-contrast computed tomography imaging findings that suggest aortic dissection.

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.

Incremental Value of Noncontrast Chest Computed Tomography-derived Parameters in Predicting Subclinical Carotid Atherosclerosis: From the PERSUADE Study.

To investigate the incremental value of noncontrast chest computed tomography (CT)-derived parameters, such as coronary artery calcium score (CACS) and epicardial adipose tissue volume (EATv), in predicting subclinical carotid atherosclerosis above traditional risk factors in community-based asymptomatic populations of northern China. A total of 2195 community-based asymptomatic individuals were enrolled from Jidong Oilfield in accordance with the PERSUADE study. CACS and EATv were measured on noncontrast chest CT. Demographics and ideal cardiovascular health score (ICHS) were collected through questionnaires. We recalculated the ideal cardiovascular health risk score (ICHRS) (ICHRS=14-ICHS) and standardized the parameters as log-CACS and body mass index adjusted EATv (i-EATv). Subclinical carotid atherosclerosis was assessed by Doppler sonography and defined as any prevalence of average carotid intima-media thickness ≥1.00 mm, appearance of carotid plaque, and carotid arterial stenosis in the areas of extracranial carotid arteries on both sides. A total of 451 (20.55%) individuals presented subclinical carotid atherosclerosis. CACS and EATv were significantly greater in the subclinical group, while ICHS was lower. In multivariate logistic regression, ICHRS (odds ratio [OR]=1.143, 95% confidence interval [CI]: 1.080-1.210, P <0.001), log-CACS (OR=1.701, 95% CI: 1.480-1.955, P <0.001), and i-EATv (OR=1.254, 95% CI: 1.173-1.341, P <0.001) were found to be independent risk predictors for subclinical carotid atherosclerosis. In receiver-operating characteristic curve analysis, when combined with male sex and age level, the area under the curve of the ICHRS basic model increased from 0.627 (95% CI: 0.599-0.654) to 0.757 (95% CI: 0.732-0.781) ( P <0.0001). Further adding log-CACS and i-EATv, the area under the curve demonstrated a statistically significant improvement (0.788 [95% CI: 0.765-0.812] vs. 0.757 [95% CI: 0.732-0.781], P <0.0001). Noncontrast chest CT-derived parameters, including CACS and EATv, could provide significant incremental improvement for predicting subclinical carotid atherosclerosis beyond the conventional risk assessment model based on ICHRS.

How are Lung Volume and Respiratory Muscles Affected in Non-Severe Patients With Covid-19?

Aim: Coronavirus-2019 (Covid-19) primarily affects the respiratory system, and how it affects respiratory muscles and lung volume is still not fully understood. Our study aimed to assess the time-dependent changes that may occur in lungs and respiratory muscle sizes on chest computed tomography (CT) in adult coronavirus disease 2019 (COVID-19) patients.Material and Methods: The clinical and radiological records of 101 adult patients who had at least two non-contrast chest CT images in stage 1 (0-4 days) and stage 6 (&amp;gt;28 days) were collected retrospectively. ImFusion Suite program were used to calculate lung volumes, and the cross-sectional areas of the pectoral and intercostal muscles were also calculated using with “ImageJ” program.Results: One-hundred one patients (51 females, 50.49%) were included in the study. ANCOVA revealed a significant stage*side interaction effect regarding cross-sectional area (CSA) of intercostal muscles [(p=.010; η²p=.064)]. There was no significant difference between the two stages in terms of CSA of pectoralis major and minor muscles [(p=.314; η2p=.010), (p=.644; η²p=.002)] respectively and lung volume [(p=.340; η²p=.009)].Conclusion: Covid-19 pneumonia causes an atrophy in respiratory muscles. However, it seems to have a nonsignificant effect on auxiliary respiratory muscles and lung volume. Further investigation of respiratory muscles and respiratory muscle training to reduce the risk of serious complications during viral infections are required.

Automatic coronary artery calcium scoring on routine chest computed tomography (CT): comparison of a deep learning algorithm and a dedicated calcium scoring CT.

The aim of this study was to investigate the reliability and accuracy of automatic coronary artery calcium (CAC) scoring and risk classification in non-gated, non-contrast chest computed tomography (CT) of different slice thicknesses using a deep learning algorithm. This retrospective study was performed at 2 tertiary hospitals. Paired, dedicated calcium-scoring CT scans and non-gated, non-contrast chest CT scans taken within a month from the same patients were included. Chest CT images were grouped according to the slice thickness (group A: 1 mm; group B: 3 mm). For internal scans, the CAC score manually measured on dedicated calcium scoring CT images was used as the gold standard. The deep learning algorithm for group A was trained using 150 chest CT scans and tested using 144 scans, and that for group B was trained using 170 chest CT scans and tested using 144 scans. The intraclass correlation coefficient (ICC) was used to evaluate the correlation between the algorithm and the gold standard. Agreement between the deep learning algorithm, the manual results on chest CT, and the gold standard was determined by Bland-Altman analysis. Cardiac risk categories were compared. External validation was performed on 334 paired scans from a different organization. A total of 608 internal paired scans (1 mm: 294; 3 mm: 314) of 406 individuals and 334 external paired scans (1 mm: 117; 3 mm: 117) of 117 individuals were included in the analysis. The ICCs between the deep learning algorithm and the gold standard were excellent in both group A (0.90; 95% CI: 0.85-0.93) and group B (0.94; 95% CI: 0.92-0.96). The Bland-Altman plots showed good agreement in both groups. For the cardiovascular risk category, the deep learning algorithm accurately classified 71% of cases in group A and 81% of cases in group B. The Kappa values for risk classification were 0.72 in group A and 0.82 in group B. External validation yielded equally good results. The automatic calculation of CAC score and cardiovascular risk stratification on non-gated chest CT using a deep learning algorithm was reliable and accurate on both 1 and 3 mm scans. Chest CT with a slice thickness of 3 mm was slightly more accurate in CAC detection and risk classification.

Automated identification of pulmonary arteries and veins depicted in non-contrast chest CT scans.

We present a novel integrative computerized solution to automatically identify and differentiate pulmonary arteries and veins depicted on chest computed tomography (CT) without iodinated contrast agents. We first identified the central extrapulmonary arteries and veins using a convolutional neural network (CNN) model. Then, a computational differential geometry method was used to automatically identify the tubular-like structures in the lungs with high densities, which we believe are the intrapulmonary vessels. Beginning with the extrapulmonary arteries and veins, we progressively traced the intrapulmonary vessels by following their skeletons and differentiated them into arteries and veins. Instead of manually labeling the numerous arteries and veins in the lungs for machine learning, this integrative strategy limits the manual effort only to the large extrapulmonary vessels. We used a dataset consisting of 120 chest CT scans acquired on different subjects using various protocols to develop, train, and test the algorithms. Our experiments on an independent test set (n = 15) showed promising performance. The computer algorithm achieved a sensitivity of ∼98% in labeling the pulmonary artery and vein branches when compared with a human expert's results, demonstrating the feasibility of our computerized solution in pulmonary artery/vein labeling.

Chest imaging for COVID-19: a single-center comparative results in pediatric patients.

Chest computed tomography (CT) appears to be an important radiological modality for the diagnosis of COVID-19 in adults. Studies comparing the findings of such children with those of other viral infections have not been reported either. The aim of this study was to present comparative imaging findings of 75 pediatric COVID-19 patients and four patients with other viral upper respiratory tract infections. We also aimed to demonstrate the possible association between the radiological and laboratory findings in the COVID group. From 11 March 2020 to 20 June 2020, 79 children (aged < 18 years) were enrolled. COVID-19 was detected by RT-PCR or antibody testing. A plain chest X-ray was obtained from all subjects. Non-contrast chest CT was performed for symptomatic patients. Seventy-five patients had COVID-19 and 4 were infected with other pathogens i.e. adenovirus, rhinovirus, parainfluenza virus B, respiratory syncytial virus. The ages of the patients (36 M, 43 F) ranged from 7 months to 17 years old. The sensitivity of chest X-ray (as compared to RT-PCR) was 10.67% (95 CI%: 4.72 - 19.94%). From 23 chest CT`s five of them were normal and nine of them had only nodules ( < 5mm). The sensitivity of CT was 78.26% (95CI%: 54.30 - 92.54%), false-negative rate was 21.7%. The sensitivity of chest CT was found to be low and any significant correlations could have not been depicted, between the radiological parameters and the presence of lymphopenia. Clinical follow-up combined with corresponding pathogen detection, and chest CT of the symptomatic COVID-19 patients might be a feasible/prompt protocol in children.

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.

Development and implementation of optimized chest CT protocol in COVID-19: A clinical audit.

Several studies have justified use of chest computed tomography (CT) in diagnosis, evaluation of severity, treatment response, and complications of coronavirus disease 2019 (COVID-19) pneumonia. Increased utilization of CT in patients with known or suspected COVID-19 pneumonia has resulted in concerns of overuse, lack of protocol optimization, and radiation exposure. The study was conducted to develop and implement optimized protocol for chest CT for reducing radiation dose in adult patients suspected or diagnosed to have COVID-19 infection. The study was conducted in the department of radiology of a rural tertiary care teaching hospital in western India. Clinical audit was used as a tool to impart and assess the impact of optimized chest CT protocol. The pre-intervention audit included radiation dosimetry data, number of phases and length of scan of 50 adult patients, undergoing non-contrast chest CT scans in March 2021. A brief educational intervention outlining the parameters of optimized protocol was conducted on April 1, 2021.The post-intervention audit consisted of two cycles for 109 and 67 chest CT scans in the months April and May 2021. The optimized protocol was found clinically adequate with a good inter-rater reliability. The compliance to the optimized protocol was weak in audit cycle 2, which improved significantly in audit cycle 3 after reinforcement. The mean (SD) per scan Computed Tomography Dose Index-Volume (CTDI-vol) reduced significantly across audit cycles [22.06 (12. 31) Vs. 10.58 (7.58) Vs. 4.51 (2.90) milli Gray, respectively, P < 0.001]. Similar findings were noted for Dose Length Product (DLP). Clinical audit of chest CT protocol and resultant radiation doses provided adequate feedback for dose optimization. A simple educational intervention helped achieve dose optimization.

Applying Compressed Sensing Volumetric Interpolated Breath-Hold Examination and Spiral Ultrashort Echo Time Sequences for Lung Nodule Detection in MRI.

This prospective study aimed to investigate the ability of spiral ultrashort echo time (UTE) and compressed sensing volumetric interpolated breath-hold examination (CS-VIBE) sequences in magnetic resonance imaging (MRI) compared to conventional VIBE and chest computed tomography (CT) in terms of image quality and small nodule detection. Patients with small lung nodules scheduled for video-assisted thoracoscopic surgery (VATS) for lung wedge resection were prospectively enrolled. Each patient underwent non-contrast chest CT and non-contrast MRI on the same day prior to thoracic surgery. The chest CT was performed to obtain a standard reference for nodule size, location, and morphology. The chest MRI included breath-hold conventional VIBE and CS-VIBE with scanning durations of 11 and 13 s, respectively, and free-breathing spiral UTE for 3.5–5 min. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and normal structure visualizations were measured to evaluate MRI quality. Nodule detection sensitivity was evaluated on a lobe-by-lobe basis. Inter-reader and inter-modality reliability analyses were performed using the Cohen κ statistic and the nodule size comparison was performed using Bland–Altman plots. Among 96 pulmonary nodules requiring surgery, the average nodule diameter was 7.7 ± 3.9 mm (range: 4–20 mm); of the 73 resected nodules, most were invasive cancer (74%) or pre-invasive carcinoma in situ (15%). Both spiral UTE and CS-VIBE images achieved significantly higher overall image quality scores, SNRs, and CNRs than conventional VIBE. Spiral UTE (81%) and CS-VIBE (83%) achieved a higher lung nodule detection rate than conventional VIBE (53%). Specifically, the nodule detection rate for spiral UTE and CS-VIBE reached 95% and 100% for nodules >8 and >10 mm, respectively. A 90% detection rate was achieved for nodules of all sizes with a part-solid or solid morphology. Spiral UTE and CS-VIBE under-estimated the nodule size by 0.2 ± 1.4 mm with 95% limits of agreement from −2.6 to 2.9 mm and by 0.2 ± 1.7 mm with 95% limits of agreement from −3.3 to 3.5 mm, respectively, compared to the reference CT. In conclusion, chest CT remains the gold standard for lung nodule detection due to its high image resolutions. Both spiral UTE and CS-VIBE MRI could detect small lung nodules requiring surgery and could be considered a potential alternative to chest CT; however, their clinical application requires further investigation.

Additional Value of Non-contrast Chest CT in the Prediction of Adverse Cardiovascular Events in Patients With Novel Coronavirus Disease 2019 (COVID-19).

Background: Coronavirus disease 2019 (COVID-19) has outbroken in China and subsequently spread worldwide since the end of 2019. Chest computed tomography (CT) plays an important role in the diagnosis of lung diseases, but its value in the diagnosis of cardiac injury remains unknown.Methods: We enrolled 241 consecutive hospitalized patients (aged 61 ± 16 years, 115 males) with laboratory-confirmed COVID-19 at Renmin Hospital of Wuhan University from January 11 to March 2, 2020. They were divided into two groups according to whether major adverse cardiovascular events (MACEs) occurred during the follow-up. The anteroposterior diameter of the left atrium (LAD), the length of the left ventricle (LV), and cardiothoracic ratio (CTR) were measured. The values of myocardial CT were also recorded.Results: Of 241 patients, 115 patients (47.7%) had adverse cardiovascular events. Compared with no MACEs, patients with MACEs were more likely to have bilateral lesions (95.7% vs. 86.5%, p = 0.01). In multivariable analysis, bronchial wall thickening would increase the odds of MACEs by 13.42 (p = 0.01). LAD + LV and CTR was the best predictor for MACEs (area under the curve = 0.88, p < 0.001) with a sensitivity of 82.6% and a specificity of 80.2%. Plasma high-sensitivity troponin I levels in patients with cardiac injury showed a moderate negative correlation with minimum CT value (R2 = −0.636, p < 0.001).Conclusions: Non-contrast chest CT can be a useful modality for detection cardiac injury and provide additional value to predict MACEs in COVID-19 patients.