Chest Computed Tomography Exams Research Articles

Body physical parameters instead of water equivalent diameter to calculate size-specific dose estimate in adult chest CT

This study aimed to investigate body physical parameters as substitutes for water equivalent diameter (Dw) while calculating size-specific dose estimates (SSDEs) during adult chest computed tomography (CT). A retrospective analysis was conducted on 776 patients. Patients were divided into training set (542 patients) and validation set (234 patients) according to a ratio of 7:3. The correlations between physical parameters and Dw were analyzed. The differences between SSDEsubstitutes and the reference SSDE (SSDEreference) were compared. Strong positive correlations were observed between body mass index (BMI) and Dw as well as between weight and Dw in overall, male, and female patients (all p < 0.001). The correlations between BMI and Dw were stronger than those between weight and Dw in overall, male, and female subjects (all p < 0.001). SSDEweight and SSDEBMI were not significantly different from SSDEreference (p > 0.05). The RMSEs of overall patients between SSDEweight and SSDEreference as well as between SSDEBMI and SSDEreference were 0.237 and 0.2, respectively. The use of sex-specific regression equations for BMI caused a slightly reduction in RMSE. Weight and BMI can be used as surrogate parameters for Dw when calculating SSDE in adult chest CT exams, with BMI being the preferred substitute parameter.

The evaluation of subcutaneous and visceral adipose tissue changes by computed tomography in coronavirus disease 2019 and comparison with quantitative analysis of lung involvement.

This study aims to reveal the relationship between lung involvement and visceral adipose tissue changes between chest-computed tomography (CT) scans taken in short intervals in COVID-19 patients. The retrospective study included 52 patients who tested positive for SARS-CoV-2. All patients had two chest CT exams. Lung involvement measurements were calculated by using an artificial intelligence tool. Visceral and subcutaneous fat tissue was measured at the level of the first lumbar vertebra on chest CT. Additionally, demographic and laboratory data were collected. 52 patients were included (36.5 % female, mean age 50). Visceral fat area and visceral fat thickness changes were significantly positive predictors of total lung involvement changes (p=0.033, p=0.00024). Subcutaneous fat area and subcutaneous fat thickness changes were not associated with lung involvement change (p>0.05). CRP, IL-6, d-dimer, and ferritin levels were higher in patients who need intensive care units. Visceral adipose tissue changes may indicate that it can have a role as a reservoir of virus involvement.

Chest CT utilization in COVID-19: a dosimetric and diagnostic-quality study.

The purpose of this study is to look at the variations in chest computed tomography (CT) use, radiation dose and image quality in the 2019 novel coronavirus (COVID-19) pneumonia patients in Saudi Arabia. This is a retrospective study of 402 patients with COVID-19, who were treated between February and October 2021. Radiation dose was estimated using metrics of volume CT dose index (CTDIvol) and size-specific dose estimate (SSDE). The imaging performance of the CT scanners was evaluated by measuring different parameters, such as resolution and CT number uniformity, with an ACR-CT accreditation phantom. Expert radiologists assessed the diagnostic quality and occurrence of artefacts. For all of the image quality parameters tested, the majority of the scanner sites (80%) were found to be within the suggested acceptance limits. Ground-glass opacities were the most common finding in our patient sample (54%). On chest CT exams with typical appearance of COVID-19 pneumonia, the most respiratory motion artefacts (56.3%) were present, followed by those with indeterminate appearance (32.2%). There were significant differences in CT utilization, CTDIvol and SSDE across the collaborated sites. The use of CT scans and radiation doses varied in the COVID-19 patients, highlighting the optimizations of CT protocols at participating sites.

Imaging findings of COPA Syndrome.

Autosomal dominant mutations in the coatomer-associated protein alpha (COPA) gene cause an immune dysregulation disorder associated with pulmonary hemorrhage, lymphoid hyperplasia, arthritis, and glomerulonephritis. To describe the thoracic, musculoskeletal, and renal imaging findings of COPA syndrome with a focus on the evolution of the pulmonary findings. With approval of the Institutional Review Board, consensus retrospective review of findings on chest radiography and computed tomography (CT), musculoskeletal radiography and magnetic resonance imaging (MRI), and renal ultrasound (US) was performed for pediatric COPA syndrome patients. COPA syndrome patients < 18 years of age presenting between 1992 and 2019 were identified from an institutional rheumatology registry. Twelve pediatric COPA syndrome patients (mean age of 6.5 years at first imaging exam; 6 females) were identified. Imaging exams available for review included 45 chest CT exams on 12 patients, 37 musculoskeletal exams on 4 patients, and 10 renal US exams on 5 patients. All 12 had abnormal chest CT exams, with findings including ground-glass opacities (12/12), cysts (8/12), septal thickening (9/12), nodules (8/12), fibrosis (7/12), crazy-paving (2/12), consolidation (1/12), hilar/mediastinal lymphadenopathy (11/12), and chest wall deformity (5/12). Nine had at least one follow-up chest CT, which showed improvement in nodules (7/9), ground-glass opacities (4/9), and lymphadenopathy (9/9), but worsening of septal thickening (3/9), cyst formation (3/9), and fibrosis (3/9). Four had musculoskeletal imaging revealing synovitis (2/4), bone erosions (1/4), tenosynovitis (1/4), enthesitis (1/4), and subcutaneous nodules (1/4). Five had at least one renal US, revealing renal size abnormalities (4/5) and cortical hyperechogenicity (3/5). The most prevalent imaging finding of COPA syndrome is diffuse lung disease related to early childhood-onset recurrent pulmonary hemorrhage and lymphoid hyperplasia that may progress to pulmonary fibrosis. Other imaging findings manifesting later in childhood or adolescence relate to arthritis and glomerulonephritis.

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.

Is there a Correlation between Hepatic Venous Indices and Main Pulmonary Artery Diameter?

To search whether there is a correlation between middle hepatic venous indices (HVIs) obtained by pulsed waved doppler ultrasonography (PW-DUS) and the main pulmonary artery (mPA) diameter, calculated by computed tomography (CT). A descriptive cross-sectional study. Department of Radiology, Balikesir University School of Medicine, Turkey, from February to December 2020. After excluding the cases with suspected COVID-19 from the cases sent to the Radiology Department for chest CT exams with the mPA included in the cross-sections, the volunteers were evaluated with PW-DUS. The study group consisted of 66 cases. Two radiologists measured the velocity values of the A, S, and D waves in the recorded PW Doppler spectra. HVIs (A/S, A/S+D, A/A+S+D) were calculated. The mPA diameter was calculated in the axial plane from the pulmonary trunk, 1cm proximally to the bifurcation manually on the workstation. The correlation between the HIVs and mPA diameter was evaluated. Interoperator reliability was also analysed. Thirty-nine males (59%) and 27 (41%) females were included in the study group. The mean HVI values were 0.50 ±0.20, 0.28 ±0.12, and 0.21 ±0.07 for A/S, A/A+S, and A/A+S+D, respectively. The mean mPA diameter was 24.0 ±3.3 mm. Correlation analysis determined that the HVIs were positively correlated with mPA diameter (r=0.730-0.765-0.751, p<0.001). Inter-observer correlation coefficients were found to be compatible between two radiologists. A significant and strong correlation was found between HVIs and mPA diameter. The mPA diameter that reflects the pulmonary artery systolic pressure (PAP) increased as the HVIs increased. Therefore, PW-DUS may be helpful to evaluate PAP as a quantitative method that is cost-effective, easily accessible and radiation-free. Hepatic veins, Pulmonary artery, Multidetector computed tomography, Pulmonary hypertension, Pulsed doppler ultrasonography.

COMPUTED TOMOGRAPHY FEATURES OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE&#x0D; ASSOCIATED WITH LUNG CANCER

Relevance: Chronic obstructive pulmonary disease (COPD) associated with lung cancer is common in smoking patients. Computed tomography (CT) is vital in detecting lung cancer, assessing regional lung parenchymal destruction, and airway remodeling in COPD.&#x0D; ДИАГНОСТИКА&#x0D; 40 Онкология и радиология Казахстана, №1 (63) 2022&#x0D; The study aimed to determine the CT features of COPD associated with lung cancer.&#x0D; Methods: This retrospective study included chest CT exams of 56 patients over the age of 40, with a smoking history of 10 years and more and&#x0D; with suspected COPD. 46.43% of patients had CT signs of lung cancer associated with COPD.&#x0D; Results: Barrel-shaped chest (19.23%), saber-sheath trachea (7.69%), and bronchiectasis (15.38%) were less common in patients with lung&#x0D; cancer. Paraseptal and centrilobular emphysema were diagnosed in 96.15%, bullae – 73.33%. Bronchial narrowing of the lumen and wall thickening were identified for all lobar bronchus, segmental bronchus, and subsegmental bronchus.&#x0D; Conclusion: Paraseptal and centrilobular emphysema and bullae in the upper lobes of lungs were predominant CT features in patients with&#x0D; COPD associated with lung cancer. Lumen narrowing and wall thickening of lobar bronchus, segmental bronchus, and subsegmental bronchus&#x0D; were also revealed.

COLI-Net: Deep learning-assisted fully automated COVID-19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images.

We present a deep learning (DL)‐based automated whole lung and COVID‐19 pneumonia infectious lesions (COLI‐Net) detection and segmentation from chest computed tomography (CT) images. This multicenter/multiscanner study involved 2368 (347′259 2D slices) and 190 (17 341 2D slices) volumetric CT exams along with their corresponding manual segmentation of lungs and lesions, respectively. All images were cropped, resized, and the intensity values clipped and normalized. A residual network with non‐square Dice loss function built upon TensorFlow was employed. The accuracy of lung and COVID‐19 lesions segmentation was evaluated on an external reverse transcription‐polymerase chain reaction positive COVID‐19 dataset (7′333 2D slices) collected at five different centers. To evaluate the segmentation performance, we calculated different quantitative metrics, including radiomic features. The mean Dice coefficients were 0.98 ± 0.011 (95% CI, 0.98–0.99) and 0.91 ± 0.038 (95% CI, 0.90–0.91) for lung and lesions segmentation, respectively. The mean relative Hounsfield unit differences were 0.03 ± 0.84% (95% CI, −0.12 to 0.18) and −0.18 ± 3.4% (95% CI, −0.8 to 0.44) for the lung and lesions, respectively. The relative volume difference for lung and lesions were 0.38 ± 1.2% (95% CI, 0.16–0.59) and 0.81 ± 6.6% (95% CI, −0.39 to 2), respectively. Most radiomic features had a mean relative error less than 5% with the highest mean relative error achieved for the lung for the range first‐order feature (−6.95%) and least axis length shape feature (8.68%) for lesions. We developed an automated DL‐guided three‐dimensional whole lung and infected regions segmentation in COVID‐19 patients to provide fast, consistent, robust, and human error immune framework for lung and pneumonia lesion detection and quantification.

Mix-and-Interpolate: A Training Strategy to Deal With Source-Biased Medical Data.

Till March 31st, 2021, the coronavirus disease 2019 (COVID-19) had reportedly infected more than 127 million people and caused over 2.5 million deaths worldwide. Timely diagnosis of COVID-19 is crucial for management of individual patients as well as containment of the highly contagious disease. Having realized the clinical value of non-contrast chest computed tomography (CT) for diagnosis of COVID-19, deep learning (DL) based automated methods have been proposed to aid the radiologists in reading the huge quantities of CT exams as a result of the pandemic. In this work, we address an overlooked problem for training deep convolutional neural networks for COVID-19 classification using real-world multi-source data, namely, the data source bias problem. The data source bias problem refers to the situation in which certain sources of data comprise only a single class of data, and training with such source-biased data may make the DL models learn to distinguish data sources instead of COVID-19. To overcome this problem, we propose MIx-aNd-Interpolate (MINI), a conceptually simple, easy-to-implement, efficient yet effective training strategy. The proposed MINI approach generates volumes of the absent class by combining the samples collected from different hospitals, which enlarges the sample space of the original source-biased dataset. Experimental results on a large collection of real patient data (1,221 COVID-19 and 1,520 negative CT images, and the latter consisting of 786 community acquired pneumonia and 734 non-pneumonia) from eight hospitals and health institutions show that: 1) MINI can improve COVID-19 classification performance upon the baseline (which does not deal with the source bias), and 2) MINI is superior to competing methods in terms of the extent of improvement.

Adipose tissue biomarkers and type 2 diabetes incidence in normoglycemic participants in the MESArthritis Ancillary Study: A cohort study.

Given the central role of skeletal muscles in glucose homeostasis, deposition of adipose depots beneath the fascia of muscles (versus subcutaneous adipose tissue [SAT]) may precede insulin resistance and type 2 diabetes (T2D) incidence. This study was aimed to investigate the associations between computed tomography (CT)-derived biomarkers for adipose tissue and T2D incidence in normoglycemic adults. This study was a population-based multiethnic retrospective cohort of 1,744 participants in the Multi-Ethnic Study of Atherosclerosis (MESA) with normoglycemia (baseline fasting plasma glucose [FPG] less than 100 mg/dL) from 6 United States of America communities. Participants were followed from April 2010 and January 2012 to December 2017, for a median of 7 years. The intermuscular adipose tissue (IMAT) and SAT areas were measured in baseline chest CT exams and were corrected by height squared (SAT and IMAT indices) using a predefined measurement protocol. T2D incidence, as the main outcome, was based on follow-up FPG, review of hospital records, or self-reported physician diagnoses. Participants' mean age was 69 ± 9 years at baseline, and 977 (56.0%) were women. Over a median of 7 years, 103 (5.9%) participants were diagnosed with T2D, and 147 (8.4%) participants died. The IMAT index (hazard ratio [HR]: 1.27 [95% confidence interval [CI]: 1.15-1.41] per 1-standard deviation [SD] increment) and the SAT index (HR: 1.43 [95% CI: 1.16-1.77] per 1-SD increment) at baseline were associated with T2D incidence over the follow-up. The associations of the IMAT and SAT indices with T2D incidence were attenuated after adjustment for body mass index (BMI) and waist circumference, with HRs of 1.23 (95% CI: 1.09-1.38) and 1.29 (95% CI: 0.96-1.74) per 1-SD increment, respectively. The limitations of this study include unmeasured residual confounders and one-time measurement of adipose tissue biomarkers. In this study, we observed an association between IMAT at baseline and T2D incidence over the follow-up. This study suggests the potential role of intermuscular adipose depots in the pathophysiology of T2D. ClinicalTrials.gov NCT00005487.

Long-Term Imaging Follow-Up in DIPNECH: Multicenter Experience.

Diffuse pulmonary neuroendocrine cell hyperplasia (DIPNECH) is a rare pre-invasive disease whose pathophysiology remains unclear. We aimed to assess long-term evolution in imaging of DIPNECH, in order to propose follow-up recommendations. Patients with histologically confirmed DIPNECH from four centers, evaluated between 2001 and 2020, were enrolled if they had at least two available chest computed tomography (CT) exams performed at least 24 months apart. CT exams were analyzed for the presence and the evolution of DIPNECH-related CT findings. Twenty-seven patients, mostly of female gender (n = 25/27; 93%) were included. Longitudinal follow-up over a median 63-month duration (IQR: 31–80 months) demonstrated an increase in the size of lung nodules in 19 patients (19/27, 70%) and the occurrence of metastatic spread in three patients (3/27, 11%). The metastatic spread was limited to mediastinal lymph nodes in one patient, whereas the other two patients had both lymph node and distant metastases. The mean time interval between baseline CT scan and metastatic spread was 70 months (14, 74 and 123 months). Therefore, long-term annual imaging follow-up of DIPNECH might be appropriate to encompass the heterogeneous longitudinal behavior of this disease.

Computed tomography findings of COVID-19 pneumonia in Intensive Care Unit-patients.

Background: In December 2019, a cluster of unknown etiology pneumonia cases occurred in Wuhan, China leading to identification of the responsible pathogen as SARS-coV-2. Since then, the coronavirus disease 2019 (COVID-19) has spread to the entire world. Computed Tomography (CT) is frequently used to assess severity and complications of COVID-19 pneumonia. The purpose of this study is to compare the CT patterns and clinical characteristics in intensive care unit (ICU) and non- ICU patients with COVID-19 pneumonia.Design and Methods: This retrospective study included 218 consecutive patients (136 males; 82 females; mean age 63±15 years) with laboratory-confirmed SARS-coV-2. Patients were categorized in two different groups: (a) ICU patients and (b) non-ICU inpatients. We assessed the type and extent of pulmonary opacities on chest CT exams and recorded the information on comorbidities and laboratory values for all patients.Results: Of the 218 patients, 23 (20 males: 3 females; mean age 60 years) required ICU admission, 195 (118 males: 77 females, mean age 64 years) were admitted to a clinical ward. Compared with non-ICU patients, ICU patients were predominantly males (60% versus 83% p=0.03), had more comorbidities, a positive CRP (p=0.04) and higher LDH values (p=0.008). ICU patients’ chest CT demonstrated higher incidence of consolidation (p=0.03), mixed lesions (p=0.01), bilateral opacities (p<0.01) and overall greater lung involvement by consolidation (p=0.02) and GGO (p=0.001).Conclusions: CT imaging features of ICU patients affected by COVID-19 are significantly different compared with non-ICU patients. Identification of CT features could assist in a stratification of the disease severity and supportive treatment. Significance for public health The major implication of our study is the differences in CT and laboratory findings in COVID-19 patients in ICU and non-ICU settings. Although the prospective impact of these findings was not assessed, recognition of these findings may help physicians anticipate the disease course and triage the patient to early ICU management. Our study highlights the importance of structured and quantitative reporting format where radiologists explicitly describe the specific pattern of pulmonary opacities (such as GGO, consolidation, and mixed) as well as lung areas and the number of lung lobes involved with COVID-19 pneumonia. To

Automated coronary calcium scoring using deep learning with multicenter external validation

Coronary artery disease (CAD), the most common manifestation of cardiovascular disease, remains the most common cause of mortality in the United States. Risk assessment is key for primary prevention of coronary events and coronary artery calcium (CAC) scoring using computed tomography (CT) is one such non-invasive tool. Despite the proven clinical value of CAC, the current clinical practice implementation for CAC has limitations such as the lack of insurance coverage for the test, need for capital-intensive CT machines, specialized imaging protocols, and accredited 3D imaging labs for analysis (including personnel and software). Perhaps the greatest gap is the millions of patients who undergo routine chest CT exams and demonstrate coronary artery calcification, but their presence is not often reported or quantitation is not feasible. We present two deep learning models that automate CAC scoring demonstrating advantages in automated scoring for both dedicated gated coronary CT exams and routine non-gated chest CTs performed for other reasons to allow opportunistic screening. First, we trained a gated coronary CT model for CAC scoring that showed near perfect agreement (mean difference in scores = −2.86; Cohen’s Kappa = 0.89, P < 0.0001) with current conventional manual scoring on a retrospective dataset of 79 patients and was found to perform the task faster (average time for automated CAC scoring using a graphics processing unit (GPU) was 3.5 ± 2.1 s vs. 261 s for manual scoring) in a prospective trial of 55 patients with little difference in scores compared to three technologists (mean difference in scores = 3.24, 5.12, and 5.48, respectively). Then using CAC scores from paired gated coronary CT as a reference standard, we trained a deep learning model on our internal data and a cohort from the Multi-Ethnic Study of Atherosclerosis (MESA) study (total training n = 341, Stanford test n = 42, MESA test n = 46) to perform CAC scoring on routine non-gated chest CT exams with validation on external datasets (total n = 303) obtained from four geographically disparate health systems. On identifying patients with any CAC (i.e., CAC ≥ 1), sensitivity and PPV was high across all datasets (ranges: 80–100% and 87–100%, respectively). For CAC ≥ 100 on routine non-gated chest CTs, which is the latest recommended threshold to initiate statin therapy, our model showed sensitivities of 71–94% and positive predictive values in the range of 88–100% across all the sites. Adoption of this model could allow more patients to be screened with CAC scoring, potentially allowing opportunistic early preventive interventions.

AI detection of mild COVID-19 pneumonia from chest CT scans.

ObjectivesAn artificial intelligence model was adopted to identify mild COVID-19 pneumonia from computed tomography (CT) volumes, and its diagnostic performance was then evaluated.MethodsIn this retrospective multicenter study, an atrous convolution-based deep learning model was established for the computer-assisted diagnosis of mild COVID-19 pneumonia. The dataset included 2087 chest CT exams collected from four hospitals between 1 January 2019 and 31 May 2020. The true positive rate, true negative rate, receiver operating characteristic curve, area under the curve (AUC) and convolutional feature map were used to evaluate the model.ResultsThe proposed deep learning model was trained on 1538 patients and tested on an independent testing cohort of 549 patients. The overall sensitivity was 91.5% (195/213; p < 0.001, 95% CI: 89.2–93.9%), the overall specificity was 90.5% (304/336; p < 0.001, 95% CI: 88.0–92.9%) and the general AUC value was 0.955 (p < 0.001).ConclusionsA deep learning model can accurately detect COVID-19 and serve as an important supplement to the COVID-19 reverse transcription–polymerase chain reaction (RT-PCR) test.Key Points • The implementation of a deep learning model to identify mild COVID-19 pneumonia was confirmed to be effective and feasible. • The strategy of using a binary code instead of the region of interest label to identify mild COVID-19 pneumonia was verified. • This AI model can assist in the early screening of COVID-19 without interfering with normal clinical examinations. Supplementary InformationThe online version contains supplementary material available at 10.1007/s00330-021-07797-x.

Diaphragmatic excursion: Quantitative measure to assess adequacy of expiratory phase CT chest images

ObjectiveTo evaluate diaphragmatic excursion as a quantitative metric for change in lung volume between inspiratory and expiratory chest computed tomography (CT) images. MethodsA 12-month retrospective review identified 226 chest CT exams with inspiratory and expiratory phase imaging, 63 in individuals referred with diagnosis of asthma by ICD9/10 code. Exams acquired in the supine position at 1.25 mm slice thickness in each phase were included (n = 30, mean age = 62, M = 15, F = 15). Diaphragmatic excursion was calculated as the difference between axial slices through the lungs on inspiration and expiration, using the lung apex as the cranial bound, and the hemidiaphragm caudally. Inspiratory and expiratory lung and tracheal volumes were calculated through volumetric segmentation. Tracheal morphology was assessed at 1 cm above the level of the aortic arch, and 1 cm above the carina. ResultsInspiratory and expiratory lung volumes were higher in men (mean I = 5 + 1.6 L, E = 3.1 + 1.2 L) than women (mean I = 3.6 + 0.8 L, E = 2.4 + 0.7 L), p = .005 and p = .047, respectively. Average inspiratory and expiratory tracheal volumes were higher in men (I = 61 + 17 mL, E = 43 + 14) than women (I = 44 + 14, E = 30 + 8), p = .006 and p = .005. Average change in lung and tracheal volume between inspiratory and expiratory scans did not significantly differ between men and women. Average diaphragmatic excursion was 2.5 cm between inspiratory and expiratory scans (2.7 cm in men, 2.3 cm in women; p = .5). There was a strong positive correlation between diaphragmatic excursion and change in lung (r = .84) and tracheal volume (r = .79). A moderate correlation was also found between change in tracheal volume and change in lung volume (r = 0.67). Change in tracheal morphology between inspiratory and expiratory imaging was associated with change in tracheal volume at both 1 cm above the aortic arch (p = .04) and 1 cm above the carina (p = .008); there was no association with diaphragmatic excursion or lung volume. ConclusionsDiaphragmatic excursion is a quantitative measure of expiratory effort as validated by both lung and tracheal volumes in asthma patients, and may be more accurate than qualitative assessment based on tracheal morphology.

A model based on CT radiomic features for predicting RT-PCR becoming negative in coronavirus disease 2019 (COVID-19) patients

BackgroundCoronavirus disease 2019 (COVID-19) has emerged as a global pandemic. According to the diagnosis and treatment guidelines of China, negative reverse transcription-polymerase chain reaction (RT-PCR) is the key criterion for discharging COVID-19 patients. However, repeated RT-PCR tests lead to medical waste and prolonged hospital stays for COVID-19 patients during the recovery period. Our purpose is to assess a model based on chest computed tomography (CT) radiomic features and clinical characteristics to predict RT-PCR negativity during clinical treatment.MethodsFrom February 10 to March 10, 2020, 203 mild COVID-19 patients in Fangcang Shelter Hospital were retrospectively included (training: n = 141; testing: n = 62), and clinical characteristics were collected. Lung abnormalities on chest CT images were segmented with a deep learning algorithm. CT quantitative features and radiomic features were automatically extracted. Clinical characteristics and CT quantitative features were compared between RT-PCR-negative and RT-PCR-positive groups. Univariate logistic regression and Spearman correlation analyses identified the strongest features associated with RT-PCR negativity, and a multivariate logistic regression model was established. The diagnostic performance was evaluated for both cohorts.ResultsThe RT-PCR-negative group had a longer time interval from symptom onset to CT exams than the RT-PCR-positive group (median 23 vs. 16 days, p < 0.001). There was no significant difference in the other clinical characteristics or CT quantitative features. In addition to the time interval from symptom onset to CT exams, nine CT radiomic features were selected for the model. ROC curve analysis revealed AUCs of 0.811 and 0.812 for differentiating the RT-PCR-negative group, with sensitivity/specificity of 0.765/0.625 and 0.784/0.600 in the training and testing datasets, respectively.ConclusionThe model combining CT radiomic features and clinical data helped predict RT-PCR negativity during clinical treatment, indicating the proper time for RT-PCR retesting.

Abstract 17257: Does Repeat Imaging Validate Visual Coronary Artery Calcium Scoring on Non-Electrocardiogram Gated Chest Computed Tomography?

Aim: To investigate if estimation of the amount of calcium in the coronary system utilizing a visual coronary artery calcium scoring (CACS) system (Kirsch et al. Detection of coronary calcium during standard chest computed tomography correlates with multi-detector computed tomography coronary artery calcium score) in non - electrocardiogram (ECG) gated chest computed tomography (CT) scans maintains its strong correlation with the Agatston CACS on dedicated ECG gated cardiac CT exams across two or more points in time per patient. Methods: A visual scoring system (Weston score, WCS) was previously developed assigning a score (0-3, total score 0-12) for each coronary vessel including the left main trunk. Predetermined score cutoffs in both Agatston and Weston scoring methods were designated for risk stratification. Following institutional review board approval, we retrospectively analyzed 127 total body CT scans on 53 low-risk patients referred by executive health who had received repeat CT scans between 2007 and 2018 and compared the traditional Agatston CACS with the Weston score. Analysis: The data were gathered from a total of 53 executive health patients ages 33 to 71, with an average age of 51. 83.6% were male and 16.4% were female. 118 scans were examined, taken between 307 and 2,792 days apart, with an average of 937 days between scans. The Weston and Agatston scores predicted the same risk classification for 79.7% of the first set of scans, with 10.2% uprisked and 10.2% downrisked by their Weston scores. For the second set of scans, 78.0% had the same classification, with 10.2% uprisked and 11.9% downrisked by their Weston scores. Weston scores predicted 100% of scans within one risk classification of the corresponding Agatston score across all pairs of scans.The average CACS utilizing the Agatston scoring method was 54.3, with a standard deviation of 148.6 due to a large positive skew in the data. The corresponding Weston scores had an average of 1.1, with a standard deviation of 2.14. Conclusion: A visual CACS system (WCS) performed on non-gated CT scans allows risk stratification for cardiac events in a similar manner as the Agatston CACS on ECG-gated CT scans even at separate points in time.

SU‐F‐I‐45: An Automated Technique to Measure Image Contrast in Clinical CT Images

Purpose:To develop and validate an automated technique for measuring image contrast in chest computed tomography (CT) exams.Methods:An automated computer algorithm was developed to measure the distribution of Hounsfield units (HUs) inside four major organs: the lungs, liver, aorta, and bones. These organs were first segmented or identified using computer vision and image processing techniques. Regions of interest (ROIs) were automatically placed inside the lungs, liver, and aorta and histograms of the HUs inside the ROIs were constructed. The mean and standard deviation of each histogram were computed for each CT dataset. Comparison of the mean and standard deviation of the HUs in the different organs provides different contrast values. The ROI for the bones is simply the segmentation mask of the bones. Since the histogram for bones does not follow a Gaussian distribution, the 25th and 75th percentile were computed instead of the mean. The sensitivity and accuracy of the algorithm was investigated by comparing the automated measurements with manual measurements. Fifteen contrast enhanced and fifteen non‐contrast enhanced chest CT clinical datasets were examined in the validation procedure.Results:The algorithm successfully measured the histograms of the four organs in both contrast and non‐contrast enhanced chest CT exams. The automated measurements were in agreement with manual measurements. The algorithm has sufficient sensitivity as indicated by the near unity slope of the automated versus manual measurement plots. Furthermore, the algorithm has sufficient accuracy as indicated by the high coefficient of determination, R2, values ranging from 0.879 to 0.998.Conclusion:Patient‐specific image contrast can be measured from clinical datasets. The algorithm can be run on both contrast enhanced and non‐enhanced clinical datasets. The method can be applied to automatically assess the contrast characteristics of clinical chest CT images and quantify dependencies that may not be captured in phantom data.

Prevalence of alterations in chest computerized tomography in patients with head and neck cancer

to assess the prevalence of abnormalities found by computed tomography (CT) of the chest in patients with squamous cell carcinoma of the head and neck. we retrospectively analyzed chest CT exams of 209 patients with squamous cell carcinoma of the head and neck. The CT findings were stratified as inflammatory / infectious, parenchymal, nodular uncharacteristic and nodular metastatic/tumoral. alterations were diagnosed in 66.6% of patients. Of these, 25.3% represented emphysema; 18.8%, uncharacteristic micronodules; 12.9%, metastases; 11.9%, thoracic lymph node enlargements; and in 6.6% we detected active pulmonary tuberculosis or its sequelae, pneumonia or inflammatory / infectious signs and pleural thickening or effusion. the prevalence of exams with alterations and the considerable rate of detected metastases indicate that chest CT should be required for diagnostic and / or staging in cases of head and neck cancer.

Prospective estimation of organ dose in CT under tube current modulation.

Computed tomography (CT) has been widely used worldwide as a tool for medical diagnosis and imaging. However, despite its significant clinical benefits, CT radiation dose at the population level has become a subject of public attention and concern. In this light, optimizing radiation dose has become a core responsibility for the CT community. As a fundamental step to manage and optimize dose, it may be beneficial to have accurate and prospective knowledge about the radiation dose for an individual patient. In this study, the authors developed a framework to prospectively estimate organ dose for chest and abdominopelvic CT exams under tube current modulation (TCM). The organ dose is mainly dependent on two key factors: patient anatomy and irradiation field. A prediction process was developed to accurately model both factors. To model the anatomical diversity and complexity in the patient population, the authors used a previously developed library of computational phantoms with broad distributions of sizes, ages, and genders. A selected clinical patient, represented by a computational phantom in the study, was optimally matched with another computational phantom in the library to obtain a representation of the patient's anatomy. To model the irradiation field, a previously validated Monte Carlo program was used to model CT scanner systems. The tube current profiles were modeled using a ray-tracing program as previously reported that theoretically emulated the variability of modulation profiles from major CT machine manufacturers Li et al., [Phys. Med. Biol. 59, 4525-4548 (2014)]. The prediction of organ dose was achieved using the following process: (1) CTDIvol-normalized-organ dose coefficients (horgan) for fixed tube current were first estimated as the prediction basis for the computational phantoms; (2) each computation phantom, regarded as a clinical patient, was optimally matched with one computational phantom in the library; (3) to account for the effect of the TCM scheme, a weighted organ-specific CTDIvol [denoted as CTDIvol organ,weighted] was computed for each organ based on the TCM profile and the anatomy of the "matched" phantom; (4) the organ dose was predicted by multiplying the weighted organ-specific CTDIvol with the organ dose coefficients (horgan). To quantify the prediction accuracy, each predicted organ dose was compared with the corresponding organ dose simulated from the Monte Carlo program with the TCM profile explicitly modeled. The predicted organ dose showed good agreements with the simulated organ dose across all organs and modulation profiles. The average percentage error in organ dose estimation was generally within 20% across all organs and modulation profiles, except for organs located in the pelvic and shoulder regions. For an average CTDIvol of a CT exam of 10 mGy, the average error at full modulation strength (α = 1) across all organs was 0.91 mGy for chest exams, and 0.82 mGy for abdominopelvic exams. This study developed a quantitative model to predict organ dose for clinical chest and abdominopelvic scans. Such information may aid in the design of optimized CT protocols in relation to a targeted level of image quality.