Diagnostic performance of coronary calcifications on CT to rule out acute coronary syndrome in the emergency department.

BACKGROUND: At present, the diagnosis of acute coronary syndrome (ACS) can be made by emergency physicians using the usual complementary tests, since the current troponin and electrocardiogram (ECG) protocols have been extensively tested for their safety. However, the detection of coronary calcifications on CT associated with coronary obstruction may be of interest for the diagnostic strategy in the emergency department (ED). The aim of this study was to evaluate a strategy combining a non-ischemic ECG with an initial normal troponin assay and the diagnostic accuracy of chest CT in detecting coronary calcifications to rule out the presence of an acute coronary event in patients presenting with chest pain in the ED. METHODS: This was a retrospective, single-center study carried out in an ED in France and included all patients over 18 years of age presenting with chest pain between 1 June 2021 and 31 December 2021 with a non-ischemic ECG and a negative first troponin assay. The primary endpoint was the diagnostic performance of the combing strategy in ruling out ACS. The secondary endpoints were the sensitivity and specificity of calcifications in acute coronary syndrome, comparison with the diagnostic performance of a second troponin assay and the rate of reconsultation, rehospitalisation and investigations within 2 months of the ED. RESULTS: Of the 280 patients included, 141 didn't have calcifications. A total of 14 events were found with a negative predictive value for the combining strategy of 99.8% [95%CI: 98.2 - 100]. Sensitivity and specificity were 98.4% [95%CI: 83.8 - 100] and 53% [95%CI: 47 - 58.9], respectively. Among patients with no calcification, 8.2% were admitted to hospital and none suffered an acute coronary event. A total of 36 patients (12.8%) consulted a doctor within 2 months, with 23 investigations, all of which were negative in the non-calcification group. CONCLUSIONS: A strategy combining the detection of coronary calcifications on chest CT in patients with a non-ischemic ECG and a single troponin assay is effective to rule out ACS in the ED, and may perform better then ECG and troponin alone.

Value of Hounsfield units measured by chest computed tomography for assessing bone density in the thoracolumbar segment of the thoracic spine.

STUDY DESIGN: A retrospective study. PURPOSE: To investigate the correlation between Hounsfield unit (HU) values measured by chest computed tomography (CT) and dual-energy Xray absorptiometry (DXA) T-scores. HU-based thoracolumbar (T11 and T12) cutoff thresholds were calculated for a cohort of Chinese patients. OVERVIEW OF LITERATURE: For patients with osteoporosis, the incidence of fractures in the thoracolumbar segment is significantly higher than that in other sites. However, most current clinical studies have focused on L1. METHODS: This retrospective study analyzed patients who underwent chest CT and DXA at our hospital between August 2021 and August 2022. Thoracic thoracolumbar segment HU values, lumbar T-scores, and hip T-scores were computed for comparison, and thoracic thoracolumbar segment HU thresholds suggestive of potential bone density abnormalities were established using receiver operating characteristic curves. RESULTS: In total, 470 patients (72.4% women; mean age, 65.5±12.3 years) were included in this study. DXA revealed that of the 470 patients, 90 (19%) had osteoporosis, 180 (38%) had reduced osteopenia, and 200 (43%) had normal bone mineral density (BMD). To differentiate osteoporosis from osteopenia, the HU threshold was established as 105.1 (sensitivity, 54.4%; specificity, 72.2%) for T11 and 85.7 (sensitivity, 69.4%; specificity, 61.1%) for T12. To differentiate between osteopenia and normal BMD, the HU threshold was 146.7 for T11 (sensitivity, 57.5%; specificity, 84.4%) and 135.7 for T12 (sensitivity, 59.5%; specificity, 80%). CONCLUSIONS: This study supports the significance of HU values from chest CT for BMD assessment. Chest CT provides a new method for clinical opportunistic screening of osteoporosis. When the T11 HU is >146.7 or the T12 HU is >135.7, additional osteoporosis testing is not needed unless a vertebral fracture is detected. If the T11 HU is <105.1 or the T12 HU is <85.7, further DXA testing is strongly advised. In addition, vertebral HU values that fall faster than those of the T11 and L1 vertebrae may explain the high incidence of T12 vertebral fractures.

A new computed tomography score-based staging for melioidosis pneumonia to predict progression.

Background: Melioidosis pneumonia, caused by the bacterium Burkholderia pseudomallei, is a serious infectious disease prevalent in tropical regions. Chest computed tomography (CT) has emerged as a valuable tool for assessing the severity and progression of lung involvement in melioidosis pneumonia. However, there persists a need for the quantitative assessment of CT characteristics and staging methodologies to precisely anticipate disease progression. This study aimed to quantitatively extract CT features and evaluate a CT score-based staging system in predicting the progression of melioidosis pneumonia. Methods: This study included 97 patients with culture-confirmed melioidosis pneumonia who presented between January 2002 and December 2021. Lung segmentation and annotation of lesions (consolidation, nodules, and cavity) were used for feature extraction. The features, including the involved area, amount, and intensity, were extracted. The CT scores of the lesion features were defined by the feature importance weight and qualitative stage of melioidosis pneumonia. Gaussian process regression (GPR) was used to predict patients with severe or critical melioidosis pneumonia according to CT scores. Results: The melioidosis pneumonia stages included acute stage (0-7 days), subacute stage (8-28 days), and chronic stage (>28 days). In the acute stage, the CT scores of all patients ranged from 2.5 to 6.5. In the subacute stage, the CT scores for the severe and mild patients were 3.0-7.0 and 2.0-5.0, respectively. In the chronic stage, the CT score of the mild patients fluctuated approximately between 2.5 and 3.5 in a linear distribution. Consolidation was the most common type of lung lesion in those with melioidosis pneumonia. Between stages I and II, the percentage of severe scans with nodules dropped from 72.22% to 47.62% (P<0.05), and the percentage of severe scans with cavities significantly increased from 16.67% to 57.14% (P<0.05). The GPR optimization function yielded area under the receiver operating characteristic curves of 0.71 for stage I, 0.92 for stage II, and 0.87 for all stages. Conclusions: In patients with melioidosis pneumonia, it is reasonable to divide the period (the whole progression of melioidosis pneumonia) into three stages to determine the prognosis.

Lung Ultrasonography in the Evaluation of Late Sequelae of COVID-19 Pneumonia-A Comparison with Chest Computed Tomography: A Prospective Study.

The onset of the COVID-19 pandemic allowed physicians to gain experience in lung ultrasound (LUS) during the acute phase of the disease. However, limited data are available on LUS findings during the recovery phase. The aim of this study was to evaluate the utility of LUS to assess lung involvement in patients with post-COVID-19 syndrome. This study prospectively enrolled 72 patients who underwent paired LUS and chest CT scans (112 pairs including follow-up). The most frequent CT findings were ground glass opacities (83.3%), subpleural lines (72.2%), traction bronchiectasis (37.5%), and consolidations (31.9%). LUS revealed irregular pleural lines as a common abnormality initially (56.9%), along with subpleural consolidation >2.5 mm ≤10 mm (26.5%) and B-lines (26.5%). A strong correlation was found between LUS score, calculated by artificial intelligence percentage involvement in ground glass opacities described in CT (r = 0.702, p < 0.05). LUS score was significantly higher in the group with fibrotic changes compared to the non-fibrotic group with a mean value of 19.4 ± 5.7 to 11 ± 6.6, respectively (p < 0.0001). LUS might be considered valuable for examining patients with persistent symptoms after recovering from COVID-19 pneumonia. Abnormalities identified through LUS align with CT scan findings; thus, LUS might potentially reduce the need for frequent chest CT examinations.

Anesthetic Management of One-Lung Ventilation in Patients With Tracheal Bronchus: A Narrative Review.

Tracheal bronchus is a rare congenital tracheal abnormality that generally refers to the right upper lobe bronchus of the lung that originates from the trachea. Tracheal bronchus is usually asymptomatic and is often accidentally detected by fiberoptic bronchoscopy or computed tomography for other conditions. Depending on the location of the tracheal bronchial opening and possible anatomical variations, the management of 1-lung ventilation in patients with tracheal bronchus is a significant challenge for anesthesiologists. To provide a reference for anesthesiologists to better manage anesthesia in such patients, we review the pathophysiology, definition, and Conacher classification of tracheal bronchus and then discuss the diagnosis of tracheal bronchus and management of 1-lung ventilation during anesthesia according to the Conacher classification.

Can Hounsfield units on chest CT characterize breast nodules as cystic or solid?

We report the results of our retrospective analysis of the ability of standard chest computed tomography (CT) scans to correctly differentiate cystic from solid lesions. MModal Catalyst identified 27 women who had an ultrasound of the breast that was recommended because of a chest CT finding between January 1, 2010, and December 31, 2017. All images were reviewed by a radiologist fellowship trained in both breast imaging and cardiothoracic radiology (MS). Ultrasound characterization of lesion density as cystic or solid was considered the gold standard for this study. Analysis of CT scans was performed to identify lesions of interest corresponding to ultrasound abnormality; average, minimum, and maximum Hounsfield units (HUs) were measured. If masses had any solid component, they were considered solid. Twenty masses were solid, and 7 masses were cystic on ultrasound. Thirteen studies were performed without contrast and 14 were performed with contrast. On non-contrast studies, the average HU for cystic lesions was 19 compared to 38 HU for solid (P=0.007). On contrast studies, the average HU for cystic lesions was 16 compared to 53 HU for solid (P=0.002). Cystic lesions did not change with contrast significantly. Solid lesions enhanced with contrast; average HU 38 without contrast to 53 HU with contrast. Chest CT accurately diagnosed breast masses as cystic or solid with or without contrast.

The Suitable Population for Opportunistic Low Bone Mineral Density Screening Using Computed Tomography.

Objective: To explore the suitable population of CT value for predicting low bone mineral density (low-BMD). Methods: A total of 1268 patients who underwent chest CT examination and DXA within one-month period retrospectively analyzed. The CT attenuation values of trabecular bone were measured in mid-sagittal plane from thoracic vertebra 7 (T7). Receiver operating characteristic (ROC) curves were used to evaluate the ability to diagnose low-BMD. Results: The AUC for diagnosing low BMD was larger in women than in men (0.894 vs 0.744, p < 0.05). The AUC increased gradually with the increase of age but decreased gradually with the increase in height and weight (p < 0.05). In females, when specificity was adjusted to approximately 90%, a threshold of 140.25 HU has a sensitivity of 69.3%, which is higher than the sensitivity of 36.5% in males for distinguishing low-BMD from normal. At the age of 70 or more, when specificity was adjusted to approximately 90%, a threshold of 126.31 HU has a sensitivity of 76.1%, which was higher than that of other age groups. Conclusion: For patients who had completed chest CTs, the CT values were more effective in predicting low-BMD in female, elderly, lower height, and lower weight patients.

Comparative Evaluation of Chest Ultrasonography and Computed Tomography as Predictors of Malignant Pleural Effusion: A Prospective Study.

Malignant pleural effusion (MPE) is a manifestation of advanced cancer that requires a prompt and accurate diagnosis. Ultrasonography (US) and computed tomography (CT) are valuable imaging techniques for evaluating pleural effusions; however, their relative predictive ability for a malignant origin remains debatable. This prospective study aimed to compare chest US with CT findings as predictors of malignancy in patients with undiagnosed exudative pleural effusion. Fifty-four adults with undiagnosed exudative pleural effusions underwent comprehensive clinical evaluation including chest US, CT, and histopathologic biopsy. Blinded radiologists evaluated the US and CT images for features suggestive of malignancy, based on predefined criteria. Diagnostic performance measures were calculated using histopathology as a reference standard. Of the 54 patients, 33 (61.1%) had MPEs confirmed on biopsy. No significant differences between US and CT were found in detecting parietal pleural abnormalities, lung lesions, chest wall invasion, or liver metastasis. US outperformed CT in identifying diaphragmatic pleural thickening ≥10 mm (33.3% vs. 6.1%, p < 0.001) and nodularity (45.5% vs. 3%, p < 0.001), whereas CT was superior for mediastinal thickening (48.5% vs. 15.2%, p = 0.002). For diagnosing MPE, diaphragmatic nodularity detected by US had 45.5% sensitivity and 100% specificity, whereas CT mediastinal thickening had 48.5% sensitivity and 90.5% specificity. Both US and CT demonstrate reasonable diagnostic performance for detecting MPE, with particular imaging findings favoring a malignant origin. US may be advantageous for evaluating diaphragmatic pleural involvement, whereas CT is more sensitive to mediastinal abnormalities.

A randomized phase III trial of postoperative surveillance for pathological stage II and IIIA non-small cell lung cancer (JCOG2012, PHOENIX).

The goal of postoperative surveillance following non-small cell lung cancer surgery is to detect recurrence and second primary malignancies while curative treatment is still possible. Although several guidelines recommend that patients have computed tomography (CT) scans every 6 months for the first 2 years after resection, then once a year, there is no evidence that it is effective for survival, especially in locally advanced non-small cell lung cancer. In October 2022, we launched a multi-institutional, randomized controlled phase III trial for pathological stage II and IIIA non-small cell lung cancer patients to confirm the non-inferiority of less intensive surveillance with less frequent CT scans versus standard surveillance in terms of overall survival. The primary endpoint is overall survival. We intend to enroll 1100 patients from 45 institutions over 4 years. The trial has been registered in the Japan Registry of Clinical Trials under the code jRCT1030220361 (https://jrct.niph.go.jp/latest-detail/jRCT1030220361).

A chest CT-based nomogram for predicting survival in acute myeloid leukemia.

BACKGROUND: The identification of survival predictors is crucial for early intervention to improve outcome in acute myeloid leukemia (AML). This study aim to identify chest computed tomography (CT)-derived features to predict prognosis for acute myeloid leukemia (AML). METHODS: 952 patients with pathologically-confirmed AML were retrospectively enrolled between 2010 and 2020. CT-derived features (including body composition and subcutaneous fat features), were obtained from the initial chest CT images and were used to build models to predict the prognosis. A CT-derived MSF nomogram was constructed using multivariate Cox regression incorporating CT-based features. The performance of the prediction models was assessed with discrimination, calibration, decision curves and improvements. RESULTS: Three CT-derived features, including myosarcopenia, spleen_CTV, and SF_CTV (MSF) were identified as the independent predictors for prognosis in AML (P < 0.01). A CT-MSF nomogram showed a performance with AUCs of 0.717, 0.794, 0.796 and 0.792 for predicting the 1-, 2-, 3-, and 5-year overall survival (OS) probabilities in the validation cohort, which were significantly higher than the ELN risk model. Moreover, a new MSN stratification system (MSF nomogram plus ELN risk model) could stratify patients into new high, intermediate and low risk group. Patients with high MSN risk may benefit from intensive treatment (P = 0.0011). CONCLUSIONS: In summary, the chest CT-MSF nomogram, integrating myosarcopenia, spleen_CTV, and SF_CTV features, could be used to predict prognosis of AML.

Exploring the link between a novel approach for computer aided lung sound analysis and imaging biomarkers: a cross-sectional study.

BACKGROUND: Computer Aided Lung Sound Analysis (CALSA) aims to overcome limitations associated with standard lung auscultation by removing the subjective component and allowing quantification of sound characteristics. In this proof-of-concept study, a novel automated approach was evaluated in real patient data by comparing lung sound characteristics to structural and functional imaging biomarkers. METHODS: Patients with cystic fibrosis (CF) aged > 5y were recruited in a prospective cross-sectional study. CT scans were analyzed by the CF-CT scoring method and Functional Respiratory Imaging (FRI). A digital stethoscope was used to record lung sounds at six chest locations. Following sound characteristics were determined: expiration-to-inspiration (E/I) signal power ratios within different frequency ranges, number of crackles per respiratory phase and wheeze parameters. Linear mixed-effects models were computed to relate CALSA parameters to imaging biomarkers on a lobar level. RESULTS: 222 recordings from 25 CF patients were included. Significant associations were found between E/I ratios and structural abnormalities, of which the ratio between 200 and 400 Hz appeared to be most clinically relevant due to its relation with bronchiectasis, mucus plugging, bronchial wall thickening and air trapping on CT. The number of crackles was also associated with multiple structural abnormalities as well as regional airway resistance determined by FRI. Wheeze parameters were not considered in the statistical analysis, since wheezing was detected in only one recording. CONCLUSIONS: The present study is the first to investigate associations between auscultatory findings and imaging biomarkers, which are considered the gold standard to evaluate the respiratory system. Despite the exploratory nature of this study, the results showed various meaningful associations that highlight the potential value of automated CALSA as a novel non-invasive outcome measure in future research and clinical practice.

Blood oxygenation state in COVID-19 patients: Unexplored role of 2,3-bisphosphoglycerate.

BACKGROUND: COVID-19 reduces lung functionality causing a decrease of blood oxygen levels (hypoxemia) often related to a decreased cellular oxygenation (hypoxia). Besides lung injury, other factors are implicated in the regulation of oxygen availability such as pH, partial arterial carbon dioxide tension (PaCO2), temperature, and erythrocytic 2,3-bisphosphoglycerate (2,3-BPG) levels, all factors affecting hemoglobin saturation curve. However, few data are currently available regarding the 2,3-BPG modulation in SARS-CoV-2 affected patients at the hospital admission. MATERIAL AND METHODS: Sixty-eight COVID-19 patients were enrolled at hospital admission. The lung involvement was quantified using chest-Computer Tomography (CT) analysed with automatic software (CALIPER). Haemoglobin concentrations, glycemia, and routine analysis were evaluated in the whole blood, while partial arterial oxygen tension (PaO2), PaCO2, pH, and HCO3- were assessed by arterial blood gas analysis. 2,3-BPG levels were assessed by specific immunoenzymatic assays in RBCs. RESULTS: A higher percentage of interstitial lung disease (ILD) and vascular pulmonary-related structure (VRS) volume on chest-CT quantified with CALIPER had been found in COVID-19 patients with a worse disease outcome (R = 0.4342; and R = 0.3641, respectively). Furthermore, patients with lower PaO2 showed an imbalanced acid-base equilibrium (pH, p = 0.0208; PaCO2, p = 0.0496) and a higher 2,3-BPG levels (p = 0.0221). The 2,3-BPG levels were also lower in patients with metabolic alkalosis (p = 0.0012 vs. no alkalosis; and p = 0.0383 vs. respiratory alkalosis). CONCLUSIONS: Overall, the data reveal a different pattern of activation of blood oxygenation compensatory mechanisms reflecting a different course of the COVID-19 disease specifically focusing on 2,3-BPG modulation.

Incidental detection of ground glass nodules and primary lung cancer in patients with breast cancer: prevalence and long-term follow-up on chest computed tomography.

Background: Patients with breast cancer have a higher risk of developing lung cancer than the general population. The study aimed to evaluate the prevalence of ground glass nodule (GGN) and risk factors for GGN growth in patients with breast cancer and to evaluate the prevalence and pathologic features of lung cancer. Methods: We retrospectively reviewed the clinical data and chest computed tomography (CT) of 1,384 patients diagnosed with breast cancer who underwent chest CT between January 2008 and December 2022. We evaluated the prevalence of GGNs and their size changes on follow-up chest CT with volume doubling time (VDT) and identified independent risk factors associated with the growth of GGN using multivariable logistic regression analyses. Furthermore, the prevalence and pathologic features of lung cancer were also evaluated. Results: We detected persistent GGNs in 69 of 1,384 (5.0%) patients. The initial diameter of GGNs was 6.3±3.6 mm on average, with primarily (85.5%) pure GGNs. Among them, 27 (39.1%) exhibited interval growth with a median VDT of 1,006.0 days (interquartile range, 622.0-1,528.0 days) during the median 959.0 days (interquartile range, 612.0-1,645.0 days) follow-up period. Older age (P=0.026), part-solid nodules (P=0.006), and total number of GGNs (≥2) (P=0.007) were significant factors for GGN growth. Lung cancer was confirmed in 13 of 1,384 patients (0.9%), all with adenocarcinoma, including one case of minimally invasive adenocarcinoma. The cancers demonstrated a high rate of epidermal growth factor receptor (EGFR) mutation (69.2%). Conclusions: Persistent GGNs in breast cancer patients with high-risk factors should be adequately monitored for early detection and treatment of lung cancer.

Usefulness of Computed Tomography for Evaluating the Effects of Bronchial Thermoplasty in Japanese Patients with Severe Asthma.

Background: Bronchial thermoplasty (BT) improves clinical outcomes and quality of life for patients with severe asthma and has shown sustained reductions in airway narrowing and air trapping in previous CT studies. However, there is a lack of a comprehensive analysis, including CT evaluation, of clinical outcomes in Japanese patients who have undergone BT for severe asthma. This study aimed to evaluate the impact of BT in Japanese asthma patients, with a focus on the CT metric "WA at Pi10" to assess airway disease. Methods: Twelve patients with severe persistent asthma who underwent BT were assessed using ACQ6, AQLQ, pulmonary function tests, FeNO measurement, blood sampling, and chest CT before BT and one year after the third procedure for the upper lobes. Results: The median age of the patient was 62.0 years, 7/12 (58.3%) were male, 4/12 (33.3%) used regular oral corticosteroids, and 8/12 (66.7%) received biologics. Median FEV1% was 73.6%, and median peripheral eosinophil count was 163.8/µL. After one year of BT, ACQ6 scores improved from 2.4 to 0.8 points (p = 0.007), and AQLQ scores improved from 4.3 to 5.8 points (p < 0.001). Significant improvements were also observed in asthma exacerbations, unscheduled visits due to exacerbations, FeNO, and √WA at Pi10 (p < 0.05). The baseline mucus score on the CT findings was negatively correlated with FEV1 (r = -0.688, p = 0.013) and with the maximum mid-expiratory flow rate (r = -0.631, p = 0.028), and positively correlated with the peripheral blood eosinophil count (r = -0.719, p = 0.008). Changes in √WA at Pi10 after one year were positively correlated with changes in the mucus score (r = 0.742, p = 0.007). Conclusion: This study has limitations, including its single-arm observational design and the small sample size. However, BT led to a symptomatic improvement in patients with severe asthma. The validated "√WA at Pi10" metric on CT effectively evaluated the therapeutic response in Japanese asthma patients after BT.

Latent class analysis of chest CT abnormalities to define subphenotypes in patients with MPO-ANCA-positive microscopic polyangiitis.

BACKGROUND: Patients with microscopic polyangiitis (MPA) and positive myeloperoxidase antineutrophil cytoplasmic antibody (MPO-ANCA) may present with various abnormalities in chest computed tomography (CT). This study aimed to identify subphenotypes using latent class analysis (LCA) and to explore the relationship between the subphenotypes and clinical patterns, as well as compare the clinical characteristics of these subphenotypes in patients with MPO-ANCA-positive MPA (MPO-MPA). METHODS: The study identified subphenotypes using LCA based on chest CT findings in 178 patients with MPO-MPA and pulmonary involvement from June 2014 to August 2022. RESULTS: LCA identified 27 participants (15.2%) in class 1, 43 (24.1%) in class 2, 35 (19.7%) in class 3, and 73 (41.0%) in class 4. Class 1 was characterized by prominent inflammatory exudation, class 2 by fibrosis and architectural distortion, class 3 by predominantly bronchiectasis, and class 4 by lesions mixed with inflammation and fibrosis. Class 1 had the highest level of extrapulmonary disease activity, with 77.8% of patients experiencing diffuse alveolar hemorrhage. Class 2 had the lowest level of extrapulmonary disease activity, with 41.9% of patients showing usual interstitial pneumonia. Class 3 patients were more likely to have complications involving the ear, nose, and throat, as well as pulmonary infections before treatment, and they exhibited the best outcomes. The characteristics and outcomes of class 4 were intermediate among the four classes. CONCLUSIONS: These findings suggest that bronchiectasis may represent a unique pattern of pulmonary involvement in MPO-MPA, highlighting the importance of screening for bronchiectasis in MPO-MPA and identifying optimal management strategies.

Relationships between Size-specific Dose Estimate and Signal to Noise Ratio under Chest CT Examinations with Tube Current Modulation.

PURPOSE: Exploring the relationship between the signal-to-noise ratio (SNR) of organs and size-specific dose estimate (SSDE) in tube current modulation (TCM) chest CT examination. METHODS: Forty patients who received TCM chest CT scanning were retrospectively collected and divided into four groups according to the tube voltage and sexes. We chose to set up the region of interest (ROI) at the tracheal bifurcation and its upper and lower parts in slice images of the heart, aorta, lungs, paracranial muscles, and female breast, and the SNR of each organ was calculated. We also calculated the corresponding axial volume CT dose index (CTDIvolz) and axial size-specific dose estimate (SSDEz). RESULTS: The correlation analysis showed that the correlation between the SNR of the slice images of most organs and SSDEz was more significant than 0.8, and that between the SNR and CTDIvol was more significant than 0.7. The simple linear regression analysis results showed that when the sex is the same, the SNR of the same organ at 100kVp was higher than 120kVp, except for the lung. In multiple regression analysis, the result indicated that the determination coefficients of the SNR and SSDEz of the four groups were 0.934, 0.971, 0.905, and 0.709, respectively. CONCLUSION: In chest CT examinations with TCM, the correlation between the SNR of each organ in slice images and SSDEz was better than that of CTDIvolz. And when the SSDEz was the same, the SNR at 100 kVp was better than that at 120 kVp.

The identification of an optimal body size parameter to adjust skeletal muscle area on chest CT in COVID-19 patients.

Objectives: The most efficient way to adjust skeletal muscle area (SMA) derived from chest CT to body size remains unclear. We hypothesized that vertebral body area (VBA) measurement would allow such efficient adjustment. Methods: We conducted a retrospective observational study of chest CT imaging in a cohort of critically ill COVID-19 patients. We measured paravertebral SMA at T5 level and T5 vertebral body anteroposterior length, width, and area. We used linear regression and multivariable modelling to assess the association of VBA with SMA. Results: In 48 COVID-19 patients in ICU, T5 VBA could be easily derived from simple width and anteroposterior length linear measurements. T5 VBA (measured manually or estimated from width and length) performed similarly to height (R2 of 0.22) as an adjustment variable for SMA, with R2 of 0.23 and 0.22, respectively. Gender had the strongest correlation with SMA (R2 = 0.28). Adding height or age to a model using gender and VBA did not improve correlation. Conclusions: Gender and estimated VBA from simple linear measurements at T5 level on CT images can be utilized for adjustment of SMA without the need for height. Validation of these findings in larger cohorts of critically ill patients is now needed.

Esophageal cancer detection via non-contrast CT and deep learning.

Background: Esophageal cancer is the seventh most frequently diagnosed cancer with a high mortality rate and the sixth leading cause of cancer deaths in the world. Early detection of esophageal cancer is very vital for the patients. Traditionally, contrast computed tomography (CT) was used to detect esophageal carcinomas, but with the development of deep learning (DL) technology, it may now be possible for non-contrast CT to detect esophageal carcinomas. In this study, we aimed to establish a DL-based diagnostic system to stage esophageal cancer from non-contrast chest CT images. Methods: In this retrospective dual-center study, we included 397 primary esophageal cancer patients with pathologically confirmed non-contrast chest CT images, as well as 250 healthy individuals without esophageal tumors, confirmed through endoscopic examination. The images of these participants were treated as the training data. Additionally, images from 100 esophageal cancer patients and 100 healthy individuals were enrolled for model validation. The esophagus segmentation was performed using the no-new-Net (nnU-Net) model; based on the segmentation result and feature extraction, a decision tree was employed to classify whether cancer is present or not. We compared the diagnostic efficacy of the DL-based method with the performance of radiologists with various levels of experience. Meanwhile, a diagnostic performance comparison of radiologists with and without the aid of the DL-based method was also conducted. Results: In this study, the DL-based method demonstrated a high level of diagnostic efficacy in the detection of esophageal cancer, with a performance of AUC of 0.890, sensitivity of 0.900, specificity of 0.880, accuracy of 0.882, and F-score of 0.891. Furthermore, the incorporation of the DL-based method resulted in a significant improvement of the AUC values w.r.t. of three radiologists from 0.855/0.820/0.930 to 0.910/0.955/0.965 (p = 0.0004/<0.0001/0.0068, with DeLong's test). Conclusion: The DL-based method shows a satisfactory performance of sensitivity and specificity for detecting esophageal cancers from non-contrast chest CT images. With the aid of the DL-based method, radiologists can attain better diagnostic workup for esophageal cancer and minimize the chance of missing esophageal cancers in reading the CT scans acquired for health check-up purposes.

A deep learning-based automatic image quality assessment method for respiratory phase on computed tomography chest images.

Background: Computed tomography (CT) chest scans have become commonly used in clinical diagnosis. Image quality assessment (IQA) for CT images plays an important role in CT examination. It is worth noting that IQA is still a manual and subjective process, and even experienced radiologists make mistakes due to human limitations (fatigue, perceptual biases, and cognitive biases). There are also kinds of biases because of poor consensus among radiologists. Excellent IQA methods can reliably give an objective evaluation result and also reduce the workload of radiologists. This study proposes a deep learning (DL)-based automatic IQA method, to assess whether the image quality of respiratory phase on CT chest images are optimal or not, so that the CT chest images can be used in the patient's physical condition assessment. Methods: This retrospective study analysed 212 patients' chest CT images, with 188 patients allocated to a training set (150 patients), validation set (18 patients), and a test set (20 patients). The remaining 24 patients were used for the observer study. Data augmentation methods were applied to address the problem of insufficient data. The DL-based IQA method combines image selection, tracheal carina segmentation, and bronchial beam detection. To automatically select the CT image containing the tracheal carina, an image selection model was employed. Afterward, the area-based approach and score-based approach were proposed and used to further optimize the tracheal carina segmentation and bronchial beam detection results, respectively. Finally, the score about the image quality of the patient's respiratory phase images given by the DL-based automatic IQA method was compared with the mean opinion score (MOS) given in the observer study, in which four blinded experienced radiologists took part. Results: The DL-based automatic IQA method achieved good performance in assessing the image quality of the respiratory phase images. For the CT sequence of the same patient, the DL-based IQA method had an accuracy of 92% in the assessment score, while the radiologists had an accuracy of 88%. The Kappa value of the assessment score between the DL-based IQA method and radiologists was 0.75, with a sensitivity of 85%, specificity of 91%, positive predictive value (PPV) of 92%, negative predictive value (NPV) of 93%, and accuracy of 88%. Conclusions: This study develops and validates a DL-based automatic IQA method for the respiratory phase on CT chest images. The performance of this method surpassed that of the experienced radiologists on the independent test set used in this study. In clinical practice, it is possible to reduce the workload of radiologists and minimize errors caused by human limitations.

Preoperative Prediction of Shock Impedance for Subcutaneous Implantable Cardioverter Defibrillator Using Chest Computed Tomography.

BACKGROUND: High shock impedance is associated with conversion failure among patients with subcutaneous implantable cardioverter defibrillators (S-ICD). Currently, there is no preoperative assessment method for predicting high shock impedance. This study examined the efficacy of chest computed tomography (CT) as a preoperative evaluation tool to assess the shock impedance of S-ICDs.Methods and Results: The amount of adipose tissue adjacent to the device and anteroposterior diameter at the basal heart region were measured preoperatively using chest CT. We examined the correlation between these measurements and shock impedance at the conversion test. We enrolled 43 patients with S-ICDs (mean [±SD] age 54±15 years; body mass index 23±4 kg/m2; PRAETORIAN score 30-270 points; amount of adipose tissue 1,250±716 cm3), who underwent intraoperative conversion tests by inducing ventricular fibrillation, which was terminated with a 65-J shock. A sufficient concordance correlation coefficient was observed between the shock impedance and the amount of adipose tissue (r=0.616, P<0.01) and anteroposterior diameter (r=0.645, P<0.01). In multiple regression analysis, the amount of adipose tissue (ß=0.439, P=0.009) and anteroposterior diameter (ß=0.344, P=0.038) were identified as independent predictive factors of shock impedance. CONCLUSIONS: The preoperative CT-measured amount of adipose tissue and basal heart anteroposterior diameter are independent predictors of shock impedance. These parameters may be more accurate in identifying higher shock impedance in patients with S-ICDs.