Unenhanced Chest Research Articles

Psoriasis Is Associated With Myosteatosis but Not Sarcopenia: A Case-Control Study.

Background: The link between psoriasis and body fat (or obesity) has been well established. However, there are no reports detailing the possible relationship between psoriasis and fat infiltration in skeletal muscle, also known as myosteatosis. A recent study reported the possible association between psoriasis, arthritis, and sarcopenia (the loss of skeletal muscle mass or function). The present study aimed to explore the possible associations of chronic plaque psoriasis with myosteatosis and sarcopenia.Methods: We conducted a case-control study. In-patients with chronic plaque psoriasis were retrospectively recruited. Healthy controls were prospectively and continuously recruited. Unenhanced cross-sectional chest computed tomography images at the 12th thoracic vertebral level were analyzed using Mimics software. Skeletal muscle area (SMA), skeletal muscle radiodensity (SMD), and intermuscular adiposity tissue (IMAT) were measured. The skeletal muscle index (SMI) was calculated as SMA/height2. The percentage of IMAT (IMAT%) was calculated as IMAT/SMA × 100%. Myosteatosis was defined by SMD or IMAT%, whereas sarcopenia was defined by SMI. Propensity score matching was performed to adjust for the main confounders. Logistic regression models were used to evaluate the associations of psoriasis with myosteatosis and sarcopenia.Results: We included 155 psoriasis patients and 512 healthy controls. After propensity score matching, we retained 310 controls. The prevalence of sarcopenia was not significantly different between the psoriasis and control groups (men: 9.8% vs. 14.4%, p = 0.244; women: 7.0% vs. 11.7%, p = 0.548). Psoriasis patients were more prone to SMD-defined myosteatosis (men: 39.3% vs. 20.8%; women: 46.5% vs. 16.0%; both p < 0.001) and IMAT%-defined myosteatosis (men: 21.4% vs. 12.5%, p = 0.034; women: 46.5 vs. 28.7%, p = 0.042) than the control group. After adjustment for potential confounders, psoriasis was not significantly associated with sarcopenia (odds ratio [OR] 0.51, 95% confidence interval [CI] 0.25–1.19, p = 0.136). However, psoriasis was associated with SMD-defined myosteatosis (OR 3.16, 95% CI 1.86–5.37, p < 0.001) and IMAT%-defined myosteatosis (OR 1.76, 95% CI 1.04–3.00; p = 0.037).Conclusions: Chronic plaque psoriasis is independently associated with myosteatosis but not sarcopenia. Since fat and muscle are considered endocrine organs and can drive the inflammatory process, further studies detailing the interaction between psoriasis, fat, and skeletal muscle are warranted.

Machine Learning-Based CT Radiomics Analysis for Prognostic Prediction in Metastatic Non-Small Cell Lung Cancer Patients With EGFR-T790M Mutation Receiving Third-Generation EGFR-TKI Osimertinib Treatment.

Background and PurposeAs a third-generation EGFR tyrosine kinase inhibitor (TKI), osimertinib is approved for treating advanced non-small cell lung cancer (NSCLC) patients with EGFR-T790M mutation after progression on first- or second-generation EGFR-TKIs such as gefitinib, erlotinib and afatinib. We aim at exploring the feasibility and effectiveness of using radiomic features from chest CT scan to predict the prognosis of metastatic non-small cell lung cancer (NSCLC) patients with EGFR-T790M mutation receiving second-line osimertinib therapy.MethodsContrast-enhanced and unenhanced chest CT images before osimertinib treatment were collected from 201 and 273 metastatic NSCLC patients with EGFR-T790M mutation, respectively. Radiomic features were extracted from the volume of interest. LASSO regression was used to preliminarily evaluate the prognostic values of different radiomic features. We then performed machine learning-based analyses including random forest (RF), support vector machine (SVM), stepwise regression (SR) and LASSO regression with 5-fold cross-validation (CV) to establish the optimal radiomic model for predicting the progression-free survival (PFS) of osimertinib treatment. Finally, a combined clinical-radiomic model was developed and validated using the concordance index (C-index), decision-curve analysis (DCA) and calibration curve analysis.ResultsDisease progression occurred in 174/273 (63.7%) cases. CT morphological features had no ability in predicting patients’ prognosis in osimertinib treatment. Univariate COX regression followed by LASSO regression analyses identified 23 and 6 radiomic features from the contrast-enhanced and unenhanced CT with prognostic value, respectively. The 23 contrast-enhanced radiomic features were further used to construct radiomic models using different machine learning strategies. Radiomic model built by SR exhibited superior predictive accuracy than RF, SVR or LASSO model (mean C-index of the 5-fold CV: 0.660 vs. 0.560 vs. 0.598 vs. 0.590). Adding the SR radiomic model to the clinical model could remarkably strengthen the C-index of the latter from 0.672 to 0.755. DCA and calibration curve analyses also demonstrated good performance of the combined clinical-radiomic model.ConclusionsRadiomic features extracted from the contrast-enhanced chest CT could be used to evaluate metastatic NSCLC patients’ prognosis in osimertinib treatment. Prognostic models combing both radiomic features and clinical factors had a great performance in predicting patients’ outcomes.

Detection of Incidental Esophageal Cancers on Chest CT by Deep Learning.

ObjectiveTo develop a deep learning-based model using esophageal thickness to detect esophageal cancer from unenhanced chest CT images.MethodsWe retrospectively identified 141 patients with esophageal cancer and 273 patients negative for esophageal cancer (at the time of imaging) for model training. Unenhanced chest CT images were collected and used to build a convolutional neural network (CNN) model for diagnosing esophageal cancer. The CNN is a VB-Net segmentation network that segments the esophagus and automatically quantifies the thickness of the esophageal wall and detect positions of esophageal lesions. To validate this model, 52 false negatives and 48 normal cases were collected further as the second dataset. The average performance of three radiologists and that of the same radiologists aided by the model were compared.ResultsThe sensitivity and specificity of the esophageal cancer detection model were 88.8% and 90.9%, respectively, for the validation dataset set. Of the 52 missed esophageal cancer cases and the 48 normal cases, the sensitivity, specificity, and accuracy of the deep learning esophageal cancer detection model were 69%, 61%, and 65%, respectively. The independent results of the radiologists had a sensitivity of 25%, 31%, and 27%; specificity of 78%, 75%, and 75%; and accuracy of 53%, 54%, and 53%. With the aid of the model, the results of the radiologists were improved to a sensitivity of 77%, 81%, and 75%; specificity of 75%, 74%, and 74%; and accuracy of 76%, 77%, and 75%, respectively.ConclusionsDeep learning-based model can effectively detect esophageal cancer in unenhanced chest CT scans to improve the incidental detection of esophageal cancer.

Impact of Morphotype on Image Quality and Diagnostic Performance of Ultra-Low-Dose Chest CT.

Objectives: The image quality of an Ultra-Low-Dose (ULD) chest CT depends on the patient’s morphotype. We hypothesize that there is a threshold beyond which the diagnostic performance of a ULD chest CT is too degraded. This work assesses the influence of morphotype (Body Mass Index BMI, Maximum Transverse Chest Diameter MTCD and gender) on image quality and the diagnostic performance of a ULD chest CT. Methods: A total of 170 patients from three prior prospective monocentric studies were retrospectively included. Renewal of consent was waived by our IRB. All the patients underwent two consecutive unenhanced chest CT acquisitions with a full dose (120 kV, automated tube current modulation) and a ULD (135 kV, fixed tube current at 10 mA). Image noise, subjective image quality and diagnostic performance for nine predefined lung parenchyma lesions were assessed by two independent readers, and correlations with the patient’s morphotype were sought. Results: The mean BMI was 26.6 ± 5.3; 20.6% of patients had a BMI > 30. There was a statistically significant negative correlation of the BMI with the image quality (ρ = −0.32; IC95% = (−0.468; −0.18)). The per-patient diagnostic performance of ULD was sensitivity, 77%; specificity, 99%; PPV, 94% and NPV, 65%. There was no statistically significant influence of the BMI, the MTCD nor the gender on the per-patient and per-lesion diagnostic performance of a ULD chest CT, apart from a significant negative correlation for the detection of emphysema. Conclusions: Despite a negative correlation between the BMI and the image quality of a ULD chest CT, we did not find a correlation between the BMI and the diagnostic performance of the examination, suggesting a possible use of the ULD protocol in obese patients.

Compromised Lung Volume and Hemostatic Abnormalities in COVID-19 Pneumonia: Results from an Observational Study on 510 Consecutive Patients.

Background: Hemostatic abnormalities have been described in COVID-19, and pulmonary microthrombosis was consistently found at autopsy with concomitant severe lung damage. Methods: This is a retrospective observational cross-sectional study including consecutive patients with COVID-19 pneumonia who underwent unenhanced chest CT upon admittance at the emergency room (ER) in one large academic hospital. QCT was used for the calculation of compromised lung volume (%CL). Clinical data were retrieved from patients’ files. Laboratory data were obtained upon presentation at the ER. Aim: The aim of this study was to evaluate the correlation between hemostatic abnormalities and lung involvement in patients affected by COVID-19 pneumonia as described using computer-aided quantitative evaluation of chest CT (quantitative CT (QCT)). Results: A total of 510 consecutive patients (68% males), aged 67 years in median, diagnosed with COVID-19 pneumonia, who underwent unenhanced CT scan upon admission to the ER, were included. In all, 115 patients had %CL > 23%; compared to those with %CL < 23%, they showed higher levels of D-dimer, fibrinogen, and CRP, greater platelet count, and longer PT ratio. Via multivariate regression analysis, BMI ≥ 30 kg/m2, D-dimer levels > 500 ng/mL, CRP > 5.0 ng/mL and PT ratio > 1.2 were found to be independent predictors of a %CL > 23% (adjusted odds ratios (95% confidence intervals): 2.1 (1.1–4.0), 3.1 (1.6–5.8), 2.4 (1.3–4.5), and 3.4 (1.4–8.5), respectively). Conclusions: Hemostatic abnormalities in patients affected by COVID-19 correlate with the severity of lung injury as measured by %CL. Our results underline the pathogenetic role of hemostasis in COVID-19 pneumonia beyond the presence of clinically evident thromboembolic complications.

Reduced muscle mass as predictor of intensive care unit hospitalization in COVID-19 patients.

PurposeTo evaluate if reduced muscle mass, assessed with Computed Tomography (CT), is a predictor of intensive care unit (ICU) hospitalization in COVID-19 patients.MethodsIn this Institution Review Board approved study, we retrospectively evaluated COVID-19 patients treated in our tertiary center from March to November 2020 who underwent an unenhanced chest CT scan within three weeks from hospitalization.We recorded the mean Hounsfield Unit (Hu) value of the right paravertebral muscle at the level of the 12th thoracic vertebra, the hospitalization unit (ICU and COVID-19 wards), clinical symptoms, Barthel Index, and laboratory findings.Logistic regression analysis was applied to assess if muscle loss (Hu<30) is a predictor of ICU admission and outcome.Fisher’s exact and Student’s tests were applied to evaluate if differences between patients with and without muscle loss occurred (p<0.05).ResultsOne-hundred-fifty patients matched the inclusion criteria (46 females; mean age±SD 61.3±15 years-old), 36 treated in ICU. Patients in ICU showed significantly lower Hu values (29±24 vs 39.4±12, p = 0.001). Muscle loss was a predictor of ICU admission (p = 0.004).Patients with muscle loss were significantly older (73.4±10 vs 56.4±14 years), had lower Barthel Index scores (54.4±33 vs 85.1±26), red blood-cell count (3.9±1 vs 4.6±1×1012L−1), and Hb levels (11.5±2 vs 13.2±2g/l) as well as higher white blood-cell count (9.4±7 vs 7.2±4×109L−1), C-reactive protein (71.5±71 vs 44±48U/L), and lactate dehydrogenase levels (335±163 vs 265.8±116U/L) (p<0.05, each).ConclusionsMuscle loss seems to be a predictor of ICU hospitalization in COVID-19 patients and radiologists reporting chest CT at admission should note this finding in their reports.

Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features.

Pulmonary parenchymal and vascular damage are frequently reported in COVID-19 patients and can be assessed with unenhanced chest computed tomography (CT), widely used as a triaging exam. Integrating clinical data, chest CT features, and CT-derived vascular metrics, we aimed to build a predictive model of in-hospital mortality using univariate analysis (Mann–Whitney U test) and machine learning models (support vectors machines (SVM) and multilayer perceptrons (MLP)). Patients with RT-PCR-confirmed SARS-CoV-2 infection and unenhanced chest CT performed on emergency department admission were included after retrieving their outcome (discharge or death), with an 85/15% training/test dataset split. Out of 897 patients, the 229 (26%) patients who died during hospitalization had higher median pulmonary artery diameter (29.0 mm) than patients who survived (27.0 mm, p < 0.001) and higher median ascending aortic diameter (36.6 mm versus 34.0 mm, p < 0.001). SVM and MLP best models considered the same ten input features, yielding a 0.747 (precision 0.522, recall 0.800) and 0.844 (precision 0.680, recall 0.567) area under the curve, respectively. In this model integrating clinical and radiological data, pulmonary artery diameter was the third most important predictor after age and parenchymal involvement extent, contributing to reliable in-hospital mortality prediction, highlighting the value of vascular metrics in improving patient stratification.

Pneumomediastinum As A New Rare Complicance Of Covid-19 Pneumonia: CT Findings

The Covid-19 Coronavirus infectious pandemic began since December 2019, mainly causing bilateral interstitial pneumonia in affected patients. The heterogeneity of the course and clinical frameworks of the pathology represents an important problem for health providers, who are faced with both the acute phase of the disease and the possible complications. An optimal management of patients was, initially, very hard. So, the use of computed tomography (CT) imaging in the diagnosis and follow-up had rapidly grown, and radiological patterns along the disease course were increasingly understood. Many typical imaging features of this disease were described: bilateral multilobar ground-glass opacity (GGO), with a prevalent peripheral or posterior distribution, mainly in the lower lobes; consolidative areas of the lungs; septal thickenings; bronchiectasis; pleural thickenings and subpleural involvements. Unenhanced chest CT is considered the first-line imaging tool for the diagnosis, the severity assessment, and monitoring of Covid-19 pneumonia [1]. Spontaneous pneumomediastinum (SPM) is a rare complication of Covid-19 pneumomia. The major risk factors for development of SPM are male gender, advanced age and preexisting lung disease. SPM was considered as a potential predictive factor for adverse outcome and probable cause of unexplained deterioration of clinical conditions in Covid-19 pneumonia [2,3] (Figure 1).

Post-discharge chest CT findings and pulmonary function tests in severe COVID-19 patients

PurposeTo evaluate chest computed tomography (CT) and pulmonary function test (PFT) findings in severe COVID-19 patients after discharge and correlate CT pulmonary involvement with PFT results. MethodsCOVID-19 patients admitted to our hospital between February 25 and May 2, 2020, were retrospectively included according to the following criteria: (a) COVID-19 defined as severe based on the WHO interim guidance (i.e., clinical signs of pneumonia plus respiratory rate > 30 breaths/min, severe respiratory distress, and/or SpO2 < 90 % on room air); (b) chest radiograph in the acute setting; (c) post-discharge unenhanced chest CT; and (d) post-discharge comprehensive PFT. Imaging findings were retrospectively evaluated in consensus by two readers, and volume of abnormal lung was measured on CT using 3D Slicer software. Differences between demographics, comorbidities, acute radiographic findings, PFT, and post-discharge clinical and laboratory data of patients with normal and abnormal CT findings were assessed by Mann-Whitney or Fisher tests, and the compromised lung volume-PFT association by Pearson correlation after removing possible outliers. ResultsAt a median of 105 days from symptom onset, 74/91 (81 %) patients had CT abnormalities. The most common CT pattern was combined ground-glass opacity and reticular pattern (46/74, 62 %) along with architectural distortion (68/74, 92 %) and bronchial dilatation (66/74, 89 %). Compromised lung volume had a median value of 15 % [11–23], was higher in dyspneic patients, and negatively correlated with the percentage of predicted DLCO, VA, and FVC values (r = -0.39, -0.5, and -0.42, respectively). These PFT parameters were significantly lower in patients with CT abnormalities. Impairment of DLCO and KCO was found in 12 (13 %) cases, possibly implying an underlying pulmonary vasculopathy in this subgroup of patients. ConclusionsMost severe COVID-19 survivors still had physiologically relevant CT abnormalities about three months after the disease onset, with an impairment of diffusion capacity on PFT. A pulmonary vasculopathy was suggested in a minor proportion of patients.

Pulmonary embolism in COVID-19: Ancillary findings on chest CT angiography.

Pulmonary embolism in COVID-19: Ancillary findings on chest CT angiography.

CT-based radiomics combined with signs: a valuable tool to help radiologist discriminate COVID-19 and influenza pneumonia

BackgroundIn this COVID-19 pandemic, the differential diagnosis of viral pneumonia is still challenging. We aimed to assess the classification performance of computed tomography (CT)-based CT signs and radiomics features for discriminating COVID-19 and influenza pneumonia.MethodsA total of 154 patients with confirmed viral pneumonia (COVID-19: 89 cases, influenza pneumonia: 65 cases) were collected retrospectively in this study. Pneumonia signs and radiomics features were extracted from the initial unenhanced chest CT images to build independent and combined models. The predictive performance of the radiomics model, CT sign model, the combined model was constructed based on the whole dataset and internally invalidated by using 1000-times bootstrap. Diagnostic performance of the models was assessed via receiver operating characteristic (ROC) analysis.ResultsThe combined models consisted of 4 significant CT signs and 7 selected features and demonstrated better discrimination performance between COVID-19 and influenza pneumonia than the single radiomics model. For the radiomics model, the area under the ROC curve (AUC) was 0.888 (sensitivity, 86.5%; specificity, 78.4%; accuracy, 83.1%), and the AUC was 0.906 (sensitivity, 86.5%; specificity, 81.5%; accuracy, 84.4%) in the CT signs model. After combining CT signs and radiomics features, AUC of the combined model was 0.959 (sensitivity, 89.9%; specificity, 90.7%; accuracy, 90.3%).ConclusionsCT-based radiomics combined with signs might be a potential method for distinguishing COVID-19 and influenza pneumonia with satisfactory performance.

February 2021 Imaging Case of the Month: An Indeterminate Solitary Nodule

No abstract available. Article truncated after first page. Clinical History: A 43 -year-old woman with no past medical history presented to the Emergency Room with complaints of right chest wall pain extending into the right upper quadrant. The patient was a non-smoker, denied any allergies, and was not taking any prescription medications. Physical examination showed the patient to be afebrile with normal heart and respiratory rates and blood pressure = 110/75 mmHg. Her room air oxygen saturation was 99%. The patient’s complete blood count and serum chemistries showed normal values. Her liver function testing and renal function testing parameters were also within normal limits. Which of the following represents an appropriate next step for the patient’s management? 1. Perform abdominal ultrasound 2. Perform chest radiography 3. Perform unenhanced chest CT 4. More than one of the above 5. None of the above …

Normative reference values of major thoracic arterial vasculature in Turkiye.

The aim of this study was to determine normative reference values for major thoracic arterial vasculature in Turkiye and to evaluate differences according to age and gender. Low-dose unenhanced chest computerized tomography images acquired with pre-diagnosis of COVID-19 between March and June 2020 were evaluated retrospectively. Patients with known chronic lung parenchymal disease, pleural effusion, pneumothorax, chronic diseases such as diabetes, hypertension, obesity, and chronic heart diseases (coronary artery disease, atherosclerosis, congestive heart failure, valve replacement, and arrhythmia) were excluded from the study. The ascending aorta diameter (AAD), descending aorta diameter (DAD), aortic arch diameter (ARCAD), main pulmonary artery diameter (MPAD), right pulmonary artery diameter (RPAD), and the left pulmonary artery diameter (LPAD) were measured in the same sections by standardized methods. The variability of parameters according to age (<40 years; ≥40 years) and gender (male to female) was evaluated by statistical methods. The Student's t test was used to compare the normal distribution according to the given quantitative age and gender, while the data that did not fit the normal distribution were compared with the Mann-Whitney U test. The conformity of the data to the normal distribution was tested with the Kolmogorov-Smirnov, Shapiro-Wilk test, and graphical examinations. Totally 777 cases between the ages of 18-96 (43.80±15.98) were included in the study. Among these, 52.8% (n=410) were male and 47.2% (n=367) were female. Mean diameters were 28.52±5.13 mm (12-48 mm in range) for AAD, 30.83±5.25 mm (12-52 mm in range) for ARCAD, DAD 21.27±3.57 mm (11-38 mm in range) for DAD; 23.27±4.03 mm (14-40 mm in range) for MPAD, 17.27±3.19 mm (10-30 mm in range) for RPAD, and 17.62±3.06 mm (10-37 mm in range) for LPAD. Statistically significantly higher values were obtained in all diameters for cases over 40 years of age. Similarly, higher values were obtained in all diameters for males compared to females. The diameters of all thoracic main vascular structures are larger in men than in women and increase with age.

Early extrapulmonary prognostic features in chest computed tomography in COVID-19 pneumonia: Bone mineral density is a relevant predictor for the clinical outcome - A multicenter feasibility study.

BackgroundBesides throat-nose swab polymerase chain reaction (PCR), unenhanced chest computed tomography (CT) is a recommended diagnostic tool for early detection and quantification of pulmonary changes in COVID-19 pneumonia caused by the novel corona virus. Demographic factors, especially age and comorbidities, are major determinants of the outcome in COVID-19 infection. This study examines the extra pulmonary parameter of bone mineral density (BMD) from an initial chest computed tomography as an associated variable of pre-existing comorbidities like chronic lung disease or demographic factors to determine the later patient's outcome, in particular whether treatment on an intensive care unit (ICU) was necessary in infected patients.MethodsWe analyzed 58 PCR-confirmed COVID-19 infections that received an unenhanced CT at admission at one of the included centers. In addition to the extent of pulmonary involvement, we performed a phantomless assessment of bone mineral density of thoracic vertebra 9–12.ResultsIn a univariate regression analysis BMD was found to be a significant predictor of the necessity for intensive care unit treatment of COVID-19 patients. In the subgroup requiring intensive care treatment within the follow-up period a significantly lower BMD was found.In a multivariate logistic regression model considering gender, age and CT measurements of bone mineral density, BMD was eliminated from the regression analysis as a significant predictor.ConclusionPhantomless assessed BMD provides prognostic information on the necessity for ICU treatment in course of COVID-19 pneumonia. We recommend using the measurement of BMD in an initial CT image to facilitate a potentially better prediction of severe patient outcomes within the 22 days after an initial CT scan.Consequently, in the present sample, additional bone density analysis did not result in a prognostic advantage over simply considering age. Significantly larger patient cohorts with a more homogenous patient age should be performed in the future to illustrate potential effects.Clinical relevanceWhile clinical capacities such as ICU beds and ventilators are more crucial than ever to help manage the current global corona pandemic, this work introduces an approach that can be used in a cost-effective way to help determine the amount of these rare clinical resources required in the near future.

Correlation of liver-to-spleen ratio, lung CT scores, clinical, and laboratory findings of COVID-19 patients with two consecutive CT scans.

PurposeGiven the lack of information about abdominal imaging findings and correlation with clinical features of COVID-19, we aimed to evaluate the changes in hepatic attenuation during the course of disease. Our aim was to correlate the liver-to-spleen ratio (L/S), clinical, laboratory findings, and lung CT scores of patients with COVID-19 who had two consecutive chest CTs.MethodsA retrospective search was performed between March 1, 2020 and April 26, 2020 to identify patients who had positive RT-PCR tests and two unenhanced chest CTs. Scans that were obtained at hospital admission and follow-up were reviewed to assess L/S and lung CT scores. Patients were divided into two groups based on lung CT scores (non-progressive vs progressive). Patient demographics, laboratory findings, length of hospital stay, and survival were noted from electronic medical records.ResultsTwenty patients in the progressive group and 7 patients in the non-progressive group were identified. The mean L/S of the progressive group (1.13 ± 0.3) was lower than that of the non-progressive group (1.21 ± 0.29) at hospital admission but there was no significant difference between the two groups (p = 0.547). L/S at follow-up was significantly different between the groups as the mean L/S values of the progressive and non-progressive groups were 1.02 ± 0.23 and 1.25 ± 0.29, respectively (p = 0.009). L/S was negatively correlated with AST and ALT (r = − 0.46, p = 0.016 and r = − 0.534, p = 0.004, respectively). There were significant differences between the two groups in terms of WBC, neutrophil, lymphocyte, monocyte, and platelet counts that were obtained at hospital admission. Length of hospital stay was significantly longer in patients in the progressive group (p = 0.035).ConclusionsDecrease in L/S may be observed in patients with elevated lung CT scores at follow-up. WBC, neutrophil, lymphocyte, monocyte, and platelet counts at hospital admission may predict the progression of COVID-19.

Third-generation iterative reconstruction on a dual-source, high-pitch, low-dose chest CT protocol with tin filter for spectral shaping at 100kV: a study on a small series of COVID-19 patients.

ObjectivesTo investigate the role of third-generation iterative reconstruction (ADMIRE) in dual-source, high-pitch chest CT protocol with spectral shaping at 100 kVp in Coronavirus disease 2019 (COVID-19).MethodsConfirmed COVID-19 inpatients undergoing to unenhanced chest CT were scanned with a dual-energy acquisition (DECT, 90/150Sn kV) and a dual-source, high-pitch acquisition with tin-filtered 100 kVp (LDCT). On the DECT with ADMIRE 3 (DECT3) were evaluated the pulmonary findings and their extension (25-point score). Two radiologists in consensus evaluated with 5-point scales the overall image quality, the anatomical structures, and the elementary findings on LDCT reconstructed with filtered backprojection (LDCT0), with ADMIRE 3 (LDCT3) and 5 (LDCT5), and on DECT3. The signal-to-noise ratio (SNR), the body mass index, the exposure times, and the radiation doses were recorded.ResultsSeventy-five patients (57 M/18F; median age: 63 y.o.) were included, with median pulmonary extension of 13/25 points. The imaging findings were detected in proportion comparable to the available literature. The ADMIRE significantly improved the SNR in LDCT (p < 0.00001) with almost no significant differences in overweight patients. The LDCT had median effective dose of 0.39 mSv and acquisition time of 0.71 s with significantly less motion artifacts than DECT (p < 0.00001). The DECT3 and LDCT3 provided the best image quality and depiction of pulmonary anatomy and imaging findings, with significant differences among all the series (p < 0.00001).ConclusionThe LDCT with spectral shaping and ADMIRE3 provided acceptable image quality in the evaluation of patients with COVID-19, with significantly reduced radiation dose and motion artifacts.

Deep learning-based triage and analysis of lesion burden for COVID-19: a retrospective study with external validation

BackgroundPrompt identification of patients suspected to have COVID-19 is crucial for disease control. We aimed to develop a deep learning algorithm on the basis of chest CT for rapid triaging in fever clinics.MethodsWe trained a U-Net-based model on unenhanced chest CT scans obtained from 2447 patients admitted to Tongji Hospital (Wuhan, China) between Feb 1, 2020, and March 3, 2020 (1647 patients with RT-PCR-confirmed COVID-19 and 800 patients without COVID-19) to segment lung opacities and alert cases with COVID-19 imaging manifestations. The ability of artificial intelligence (AI) to triage patients suspected to have COVID-19 was assessed in a large external validation set, which included 2120 retrospectively collected consecutive cases from three fever clinics inside and outside the epidemic centre of Wuhan (Tianyou Hospital [Wuhan, China; area of high COVID-19 prevalence], Xianning Central Hospital [Xianning, China; area of medium COVID-19 prevalence], and The Second Xiangya Hospital [Changsha, China; area of low COVID-19 prevalence]) between Jan 22, 2020, and Feb 14, 2020. To validate the sensitivity of the algorithm in a larger sample of patients with COVID-19, we also included 761 chest CT scans from 722 patients with RT-PCR-confirmed COVID-19 treated in a makeshift hospital (Guanggu Fangcang Hospital, Wuhan, China) between Feb 21, 2020, and March 6, 2020. Additionally, the accuracy of AI was compared with a radiologist panel for the identification of lesion burden increase on pairs of CT scans obtained from 100 patients with COVID-19.FindingsIn the external validation set, using radiological reports as the reference standard, AI-aided triage achieved an area under the curve of 0·953 (95% CI 0·949–0·959), with a sensitivity of 0·923 (95% CI 0·914–0·932), specificity of 0·851 (0·842–0·860), a positive predictive value of 0·790 (0·777–0·803), and a negative predictive value of 0·948 (0·941–0·954). AI took a median of 0·55 min (IQR: 0·43–0·63) to flag a positive case, whereas radiologists took a median of 16·21 min (11·67–25·71) to draft a report and 23·06 min (15·67–39·20) to release a report. With regard to the identification of increases in lesion burden, AI achieved a sensitivity of 0·962 (95% CI 0·947–1·000) and a specificity of 0·875 (95 %CI 0·833–0·923). The agreement between AI and the radiologist panel was high (Cohen's kappa coefficient 0·839, 95% CI 0·718–0·940).InterpretationA deep learning algorithm for triaging patients with suspected COVID-19 at fever clinics was developed and externally validated. Given its high accuracy across populations with varied COVID-19 prevalence, integration of this system into the standard clinical workflow could expedite identification of chest CT scans with imaging indications of COVID-19.FundingSpecial Project for Emergency of the Science and Technology Department of Hubei Province, China.

Unenhanced computed tomography (CT) utility for triage at the emergency department during COVID-19 pandemic.

BackgroundUnenhanced chest computed tomography (CT) can assist in the diagnosis and classification of coronavirus disease 2019 (COVID-19), complementing to the reverse-transcription polymerase chain reaction (RT-PCR) tests; the performance of which has yet to be validated in emergency department (ED) setting. The study sought to evaluate the diagnostic performance of chest CT in the diagnosis and management of COVID-19 in ED.MethodsThis retrospective single-center study included 155 patients in ED who underwent both RT-PCR and chest CT for suspected COVID-19 from March 1st to April 1st, 2020. The clinical information, CT images and laboratory reports were reviewed and the performance of CT was assessed, using the RT-PCR as standard reference. Moreover, an adjudication committee retrospectively rated the probability of COVID-19 before and after the CT calculating the net reclassification improvement (NRI). Their final diagnosis was considered as reference. The proportion of patients with negative RT-PCR test that was directed to the referent hospital based on positive CT findings was also assessed.ResultsAmong 155 patients, 42% had positive RT-PCR results, and 46% had positive CT findings. Chest CT showed a sensitivity of 84.6%, a specificity of 80.0% and a diagnostic accuracy of 81.9% in suggesting COVID-19 with RT-PCR as reference. Concurrently, corresponding values of 89.4%, 84.3% and 86.5% were retrieved with the adjudication committee diagnosis as reference. For the subgroup of patients with age > 65, specificity and sensitivity were 50% and 80.8%, respectively. In patients with negative RT-PCR results, 20% (18/90) had positive chest CT finding and 22% (4/18) of those were eventually considered as COVID-19 positive according to the adjudication committee. After CT, the estimated probability of COVID-19 changed in 10/104 (11%) patients with available data: 4 (4%) were downgraded, 6 (6%) upgraded. The NRI was 1.92% (NRI event −2.08% + NRI non-event 5.36%). No patient with negative RT-PCR but positive CT was eventually directed to hospital.ConclusionChest CT showed promising sensitivity for diagnosing COVID-19 across all patients' subgroups. However, CT did not modify the estimated probability of COVID-19 infection in a substantial proportion of patients and its utility as an emergency department triage tool warrants further analyses.

Implications of Infection Prevention and Control Measures From a Patient With Atypical 2019 Novel Coronavirus Pneumonia

The 2019 novel coronavirus (2019-nCoV) pneumonia broke out in Wuhan, China, at the end of December 2019.1Wang D. Hu B. Hu C. et al.Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China [published online ahead of print February 7, 2020].https://doi.org/10.1001/jama.2020.1585Google Scholar,2Lu R. Zhao X. Li J. et al.Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding.Lancet. 2020; 395: 565-574Abstract Full Text Full Text PDF PubMed Scopus (6913) Google Scholar A 50-year-old man presented to the hospital with a 6-day history of fever and fatigue without chills, runny nose, dyspnea, cough, and diarrhea. His temperature rose slightly to 38.3°C after admission, and a physical examination of his chest revealed coarse breath sounds in both lungs. Laboratory studies found that the white blood cell count values (5.35×109/L) and the serum concentration of procalcitonin (0.08 ng/mL; normal range, <0.15 ng/mL) were normal. The percentage of lymphocytes decreased slightly to 15.26% (normal range, 20%-40%). The erythrocyte sedimentation rate increased significantly to 54 mm/h (normal range, 0-20 mm/h). On admission, a swab test and chest computed tomography (CT) scanning were performed. Real-time fluorescence polymerase chain reaction (RT-PCR) of the patient’s pharyngeal swab was negative for the 2019-nCoV nucleic acid assay. However, unenhanced chest CT revealed multiple bilateral and peripheral cloudlike high-density shadow opacities in both lungs (Figure A- C). Therefore, the patient was hospitalized in a general ward with a diagnosis of “community-acquired pneumonia.” Three days after admission, the patient still had fever, with a body temperature of up to 39.1°C, and had new symptoms such as dyspnea, cough, and myalgia. Repeat chest CT revealed that the multiple outer bands of the lungs and the subpleural patchy ground-glass opacities were obviously enlarged as compared with previous chest CT (Figure D-F). The second RT-PCR 2019-nCoV nucleic acid assay was positive. Finally, the patient received a diagnosis of “2019-nCoV pneumonia.” A detailed epidemiological investigation revealed that the patient had a history of visiting relatives in Wuhan, China, and a history of close contact with many people. A total of 8 people with whom he was in close contact have received a diagnosis of 2019-nCoV pneumonia. In addition, the groups of close contacts have implemented centralized isolation medical observations, including clinicians and caregivers. Therefore, we may obtain some implications for infection prevention and control measures from this case. First, detailed and objective epidemiological investigations are extremely important for the prevention and control of infectious diseases. Second, reasonable and standardized management, isolation, and treatment of patients with suspected 2019-nCoV pneumonia can effectively reduce cross infection. Third, we should pay attention to the significance of chest CT imaging in the diagnosis of 2019-nCoV pneumonia. Although this patient’s first swab test presented with negative results of RT-PCR for 2019-nCoV, the first chest CT revealed the characteristics of viral pneumonia, such as bifocal extrazonal distribution, bilateral, multifocal.3Lin X. Gong Z. Xiao Z. Xiong J. Fan B. Liu J. Novel coronavirus pneumonia outbreak in 2019: computed tomographic findings in two cases.Korean J Radiol. 2020; 21: 365-368Crossref PubMed Scopus (78) Google Scholar,4Pan Y. Guan H. Zhou S. et al.Initial CT findings and temporal changes in patients with the novel coronavirus pneumonia (2019-nCoV): a study of 63 patients in Wuhan, China [published online ahead of print February 13, 2020].https://doi.org/10.1007/s00330-020-06731-xGoogle Scholar Repeat chest CT revealed a great progression of cloudlike high-density shadow opacities in both lungs as compared with previous chest CT. It may not be extremely reasonable to rely on only 1 nucleic acid test results to diagnose 2019-nCoV pneumonia. In these cases, repeat pharyngeal swab testing and patient isolation should be carefully considered.

CO-RADS: A Categorical CT Assessment Scheme for Patients Suspected of Having COVID-19-Definition and Evaluation.

PurposeTo introduce the COVID-19 Reporting and Data System (CO-RADS) for standardized assessment of pulmonary involvement of COVID-19 on non-enhanced chest CT and report its initial interobserver agreement and performance.MethodsThe Dutch Radiological Society (NVvR) developed CO-RADS based on other efforts for standardization, such as Lung-RADS or BI-RADS. CO-RADS assesses the suspicion for pulmonary involvement of COVID-19 on a scale from 1 (very low) to 5 (very high). The system is meant to be used in patients presenting with moderate to severe symptoms of COVID-19. The system was evaluated using 105 chest CTs of patients admitted to the hospital with clinical suspicion of COVID-19 in whom RT-PCR was performed (62 +/- 16 years, 61 men, 53 with positive RT-PCR). Eight observers assessed the scans using CO-RADS. Fleiss’ kappa was calculated, and scores of individual observers were compared to the median of the remaining seven observers. The resulting area under the receiver operating characteristics curve (AUC) was compared to results from RT-PCR and clinical diagnosis of COVID-19.ResultsThere was absolute agreement among observers in 573 (68.2%) of 840 observations. Fleiss’ kappa was 0.47 (95% confidence interval (CI) 0.45-0.47), with the highest kappa for CO-RADS categories 1 (0.58, 95% CI 0.54-0.62) and 5 (0.68, 95% CI 0.65-0.72). The average AUC was 0.91 (95% CI 0.85-0.97) for predicting RT-PCR outcome and 0.95 (95% CI 0.91-0.99) for clinical diagnosis. The false negative rate for CO-RADS 1 was 9/161 (5.6%, 95% CI 1.0-10%), and the false positive rate for CO-RADS 5 was 1/286 (0.3%, 95% CI 0-1.0%).ConclusionsCO-RADS is a categorical assessment scheme for pulmonary involvement of COVID-19 on non-enhanced chest CT providing very good performance for predicting COVID-19 in patients with moderate to severe symptoms and has a substantial interobserver agreement, especially for categories 1 and 5.