Myocardial extracellular volume assessment at CT in hospitalized COVID-19 patients with regards to pulmonary embolism.

PURPOSE: To evaluate myocardial status through the assessment of extracellular volume (ECV) calculated at computed tomography (CT) in patients hospitalized for novel coronavirus disease (COVID-19), with regards to the presence of pulmonary embolism (PE) as a risk factor for cardiac dysfunction. METHOD: Hospitalized patients with COVID-19 who underwent contrast-enhanced CT at our institution were retrospectively included in this study and grouped with regards to the presence of PE. Unenhanced and portal venous phase scans were used to calculate ECV by placing regions of interest in the myocardial septum and left ventricular blood pool. ECV values were compared between patients with and without PE, and correlations between ECV values and clinical or technical variables were subsequently appraised. RESULTS: Ninety-four patients were included, 63/94 of whom males (67%), with a median age of 70 (IQR 56-76 years); 28/94 (30%) patients presented with PE. Patients with PE had a higher myocardial ECV than those without (33.5%, IQR 29.4-37.5% versus 29.8%, IQR 25.1-34.0%; p = 0.010). There were no correlations between ECV and patients' age (p = 0.870) or sex (p = 0.122), unenhanced scan voltage (p = 0.822), portal phase scan voltage (p = 0.631), overall radiation dose (p = 0.569), portal phase scan timing (p = 0.460), and contrast agent dose (p = 0.563). CONCLUSIONS: CT-derived ECV could help identify COVID-19 patients at higher risk of cardiac dysfunction, especially when related to PE, to potentially plan a dedicated, patient-tailored clinical approach.

The impact of the SARS-COV-2 pandemic on the quality of breast cancer care in EUSOMA-certified breast centres.

AIMS: We analysed the impact of the SARS-CoV-2 pandemic (COVID-19) on the quality of breast cancer care in certified EUSOMA (European Society of Breast Cancer Specialists) breast centres. MATERIALS AND METHODS: The results of the EUSOMA quality indicators were compared, based on pseudonymised individual records, for the periods 1 March 2020 till 30 June 2020 (first COVID-19 peak in most countries in Europe) and 1 March 2019 till 30 June 2019. In addition, a questionnaire was sent to the participating Centres for investigating the impact of the COVID-19 pandemic on the organisation and the quality of breast cancer care. RESULTS: Forty-five centres provided data and 31 (67%) responded to the questionnaire. The total number of new cases dropped by 19% and there was a small significant higher tumour (p = 0.003) and lymph node (p = 0.011) stage at presentation. Comparing quality indicators (12,736 patients) by multivariable analysis showed mostly non-significant differences. Surgery could be performed in a COVID-free zone in 94% of the centres, COVID testing was performed before surgery in 96% of the centres, and surgical case load was reduced in 55% of the centres. Modifications of the indications for neoadjuvant endocrine therapy, chemotherapy, and targeted therapy were necessary in 23%, 23%, and 10% of the centres; changes in indications for adjuvant endocrine, chemo-, targeted, immune, and radiotherapy in 3%, 19%, 3%, 6%, and 10%, respectively. CONCLUSION: Quality of breast cancer care was well maintained in EUSOMA breast centres during the first wave of the COVID-19 pandemic. A small but significantly higher tumour and lymph node stage at presentation was observed.

The impact of the SARS-COV-2 pandemic on the quality of breast cancer care in EUSOMA certified breast centers

Aims We analyzed the impact of the SARS-CoV-2 pandemic (COVID-19) on the quality of breast cancer care in certified EUSOMA breast centers. Materials and methods The results of the EUSOMA quality indicators (QIs) were compared, based on pseudonymized individual records, for the periods 1 March 2020 till 30 June 2020 (first COVID19 peak in most countries in Europe) and 1 March 2019 till 30 June 2019. In addition, a questionnaire was sent to the participating Centres for investigating the impact of the COVID-19 pandemic on the organization and the quality of breast cancer care. Results Forty-five Centers provided data and 31 (67%) responded to the questionnaire. There was a small significant higher tumour (p=0.003) and lymph node (p=0.011) stage at presentation. Comparing QIs (12736 patients) by multivariable analysis showed non-significant differences. Surgery could be performed in a COVID-free zone in 94% of the Centres, COVID testing was performed before surgery in 96% of the Centres and surgical case load was reduced in 55% of the Centres. Modifications of the indications for neoadjuvant endocrine therapy, chemotherapy and targeted therapy were necessary in 23%, 23% and 10% of the Centres;changes in indications for adjuvant endocrine, chemo-, targeted, immune and radiotherapy in 3%, 19%, 3%, 6% and 10%, respectively. Conclusion Quality of breast cancer care was well maintained in EUSOMA breast Centres during the first wave of the COVID-19 pandemic. A small but significantly higher tumour and lymph node stage at presentation was observed.

Occupational burnout among radiation therapy technologists in Italy before and during COVID-19 pandemic.

INTRODUCTION: Radiation therapy technologists (RTTs) are exposed to high stress levels which may lead to burnout, which could be further increased by the current pandemic. The aim of our study was to assess burnout and stress among Italian RTTs before and during the pandemic. METHODS: The Italian Association of Radiation Therapy and Medical Physics Technologists (AITRO) and the Italian Federation of Scientific Radiographers Societies (FASTeR) proposed a national online survey, including the Maslach Burnout Inventory assessing emotional exhaustion (EE), depersonalisation (DP), and personal accomplishment (PA) to RTTs before and during the pandemic. Multivariate regression analyses and χ2 tests were used for data analysis. RESULTS: We obtained 367 answers, 246 before and 121 during the pandemic. RTTs before and during the pandemic showed high EE and DP, intermediate PA. Median EE was 37 (interquartile range [IQR] 31-46] before and 37 (IQR 30-43) during the pandemic, median DP was 16 (IQR 13-21) and 15 (IQR 12-20), respectively. PA was 31 (IQR 28-34) and 32 (IQR 28-34), respectively. Through multivariate analysis, being female and having children led to higher EE scores before and during the pandemic (p≤0.026). Only the presence of workplace stress management courses was related to lower DP before and being female was related to higher DP during the pandemic (p<0.001). Being female, having children, and working with paediatric patients were related to lower PA before and during the pandemic (p≤0.015). CONCLUSION: Our study highlighted high burnout levels for RTTs regardless of the pandemic. Future interventions aimed at preventing burnout should be implemented in their work environment, independently of the impact of exceptional events.

Axillary lymphadenopathy at the time of COVID-19 vaccination: ten recommendations from the European Society of Breast Imaging (EUSOBI).

Unilateral axillary lymphadenopathy is a frequent mild side effect of COVID-19 vaccination. European Society of Breast Imaging (EUSOBI) proposes ten recommendations to standardise its management and reduce unnecessary additional imaging and invasive procedures: (1) in patients with previous history of breast cancer, vaccination should be performed in the contralateral arm or in the thigh; (2) collect vaccination data for all patients referred to breast imaging services, including patients undergoing breast cancer staging and follow-up imaging examinations; (3) perform breast imaging examinations preferentially before vaccination or at least 12 weeks after the last vaccine dose; (4) in patients with newly diagnosed breast cancer, apply standard imaging protocols regardless of vaccination status; (5) in any case of symptomatic or imaging-detected axillary lymphadenopathy before vaccination or at least 12 weeks after, examine with appropriate imaging the contralateral axilla and both breasts to exclude malignancy; (6) in case of axillary lymphadenopathy contralateral to the vaccination side, perform standard work-up; (7) in patients without breast cancer history and no suspicious breast imaging findings, lymphadenopathy only ipsilateral to the vaccination side within 12 weeks after vaccination can be considered benign or probably-benign, depending on clinical context; (8) in patients without breast cancer history, post-vaccination lymphadenopathy coupled with suspicious breast finding requires standard work-up, including biopsy when appropriate; (9) in patients with breast cancer history, interpret and manage post-vaccination lymphadenopathy considering the timeframe from vaccination and overall nodal metastatic risk; (10) complex or unclear cases should be managed by the multidisciplinary team.

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.

Epicardial fat inflammation response to COVID-19 therapies.

OBJECTIVE: Adipose tissue plays a role in the novel coronavirus disease 2019 (COVID-19). Epicardial adipose tissue (EAT), a unique visceral fat, presents with a high degree of inflammation in severe COVID-19. Whether and how adipose tissue may respond to the COVID-19 therapies is unknown. METHODS: The difference in computed tomography-measured EAT and subcutaneous (SAT) attenuation, defined as mean attenuation expressed in Hounsfield units (HU), was retrospectively analyzed in 72 patients (mean [SD] age was 59.6 [12.4] years, 50 patients [69%] were men) at the hospital admission for COVID-19 and 99 days (interquartile range = 71-129) after discharge. RESULTS: At the admission, EAT-HU was significantly correlated with blood glucose levels, interleukin 6, troponin T levels, and waist circumference. EAT-HU decreased from -87.21 (16.18) to -100.0 (11) (p < 0.001), whereas SAT-HU did not change (-110.21 [12.1] to -111.11 [27.82]; p = 0.78) after therapy. Changes in EAT-HU (expressed as ∆) significantly correlated with dexamethasone therapy (r = -0.46, p = 0.006) and when dexamethasone was combined with tocilizumab (r = -0.24, p = 0.04). CONCLUSIONS: Dexamethasone therapy was associated with significant reduction of EAT inflammation in COVID-19 patients, whereas SAT showed no changes. Anti-inflammatory therapies targeting visceral fat may be helpful in COVID-19.

Abdominal obesity phenotype is associated with COVID-19 chest X-ray severity score better than BMI-based obesity.

PURPOSE: Chest X-ray (CXR) severity score and BMI-based obesity are predictive risk factors for COVID-19 hospital admission. However, the relationship between abdominal obesity and CXR severity score has not yet been fully explored. METHODS: This retrospective cohort study analyzed the association of different adiposity indexes, including waist circumference and body mass index (BMI), with CXR severity score in 215 hospitalized patients with COVID-19. RESULTS: Patients with abdominal obesity showed significantly higher CXR severity scores and had higher rates of CXR severity scores ≥ 8 compared to those without abdominal obesity (P < 0.001; P = 0.001, respectively). By contrast, patients with normal weight, with overweight and those with BMI-based obesity showed no significant differences in either CXR severity scores or in the rates of CXR severity scores ≥ 8 (P = 0.104; P = 0.271, respectively). Waist circumference and waist-to-height ratio (WHtR) correlated more closely with CXR severity scores than BMI (r = 0.43, P < 0.001; r = 0.41, P < 0.001; r = 0.17, P = 0.012, respectively). The area under the curves (AUCs) for waist circumference and WHtR were significantly higher than that for BMI in identifying a high CXR severity score (≥ 8) (0.68 [0.60-0.75] and 0.67 [0.60-0.74] vs 0.58 [0.51-0.66], P = 0.001). A multivariate analysis indicated abdominal obesity (risk ratio: 1.75, 95% CI: 1.25-2.45, P < 0.001), bronchial asthma (risk ratio: 1.73, 95% CI: 1.07-2.81, P = 0.026) and oxygen saturation at admission (risk ratio: 0.96, 95% CI: 0.94-0.97, P < 0.001) as the only independent factors associated with high CXR severity scores. CONCLUSION: Abdominal obesity phenotype is associated with a high CXR severity score better than BMI-based obesity in hospitalized patients with COVID-19. Therefore, when visiting the patient in a hospital setting, waist circumference should be measured, and patients with abdominal obesity should be monitored closely. Level of evidence Cross-sectional descriptive study, Level V.

Artificial Intelligence Applied to Chest X-ray for Differential Diagnosis of COVID-19 Pneumonia.

We assessed the role of artificial intelligence applied to chest X-rays (CXRs) in supporting the diagnosis of COVID-19. We trained and cross-validated a model with an ensemble of 10 convolutional neural networks with CXRs of 98 COVID-19 patients, 88 community-acquired pneumonia (CAP) patients, and 98 subjects without either COVID-19 or CAP, collected in two Italian hospitals. The system was tested on two independent cohorts, namely, 148 patients (COVID-19, CAP, or negative) collected by one of the two hospitals (independent testing I) and 820 COVID-19 patients collected by a multicenter study (independent testing II). On the training and cross-validation dataset, sensitivity, specificity, and area under the curve (AUC) were 0.91, 0.87, and 0.93 for COVID-19 versus negative subjects, 0.85, 0.82, and 0.94 for COVID-19 versus CAP. On the independent testing I, sensitivity, specificity, and AUC were 0.98, 0.88, and 0.98 for COVID-19 versus negative subjects, 0.97, 0.96, and 0.98 for COVID-19 versus CAP. On the independent testing II, the system correctly diagnosed 652 COVID-19 patients versus negative subjects (0.80 sensitivity) and correctly differentiated 674 COVID-19 versus CAP patients (0.82 sensitivity). This system appears promising for the diagnosis and differential diagnosis of COVID-19, showing its potential as a second opinion tool in conditions of the variable prevalence of different types of infectious pneumonia.

CT-derived Chest Muscle Metrics for Outcome Prediction in Patients with COVID-19.

Background Lower muscle mass is a known predictor of unfavorable outcomes, but its prognostic impact on patients with COVID-19 is unknown. Purpose To investigate the contribution of CT-derived muscle status in predicting clinical outcomes in patients with COVID-19. Materials and Methods Clinical or laboratory data and outcomes (intensive care unit [ICU] admission and death) were retrospectively retrieved for patients with reverse transcriptase polymerase chain reaction-confirmed SARS-CoV-2 infection, who underwent chest CT on admission in four hospitals in Northern Italy from February 21 to April 30, 2020. The extent and type of pulmonary involvement, mediastinal lymphadenopathy, and pleural effusion were assessed. Cross-sectional areas and attenuation by paravertebral muscles were measured on axial CT images at the T5 and T12 vertebral level. Multivariable linear and binary logistic regression, including calculation of odds ratios (ORs) with 95% CIs, were used to build four models to predict ICU admission and death, which were tested and compared by using receiver operating characteristic curve analysis. Results A total of 552 patients (364 men and 188 women; median age, 65 years [interquartile range, 54-75 years]) were included. In a CT-based model, lower-than-median T5 paravertebral muscle areas showed the highest ORs for ICU admission (OR, 4.8; 95% CI: 2.7, 8.5; P < .001) and death (OR, 2.3; 95% CI: 1.0, 2.9; P = .03). When clinical variables were included in the model, lower-than-median T5 paravertebral muscle areas still showed the highest ORs for both ICU admission (OR, 4.3; 95%: CI: 2.5, 7.7; P < .001) and death (OR, 2.3; 95% CI: 1.3, 3.7; P = .001). At receiver operating characteristic analysis, the CT-based model and the model including clinical variables showed the same area under the receiver operating characteristic curve (AUC) for ICU admission prediction (AUC, 0.83; P = .38) and were not different in terms of predicting death (AUC, 0.86 vs AUC, 0.87, respectively; P = .28). Conclusion In hospitalized patients with COVID-19, lower muscle mass on CT images was independently associated with intensive care unit admission and in-hospital mortality. © RSNA, 2021 Online supplemental material is available for this article.

Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy.

BACKGROUND: We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy. METHODS: We used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard. RESULTS: At 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74-0.81), 0.82 specificity (95% CI 0.78-0.85), and 0.89 area under the curve (AUC) (95% CI 0.86-0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72-0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73-0.87), and 0.81 AUC (95% CI 0.73-0.87). Radiologists' reading obtained 0.63 sensitivity (95% CI 0.52-0.74) and 0.78 specificity (95% CI 0.61-0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52-0.74) and 0.86 specificity (95% CI 0.71-0.95) in Centre 2. CONCLUSIONS: This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.

Pulmonary thromboembolism in coronavirus disease 2019 patients undergoing thromboprophylaxis.

ABSTRACT: We aimed to investigate the prevalence of pulmonary thromboembolism (PTE) and its association with clinical variables in a cohort of hospitalized coronavirus disease 2019 (COVID-19) patients receiving low-molecular-weight heparin (LMWH) at prophylactic dosage.In this retrospective observational study we included COVID-19 patients receiving prophylactic LMWH from admission but still referred for lower-limbs venous Doppler ultrasound (LL-US) and computed tomography pulmonary angiography (CTPA) for clinical PTE suspicion. A dedicated radiologist reviewed CTPA images to assess PTE presence/extension.From March 1 to April 30, 2020, 45 patients were included (34 men, median age 67 years, interquartile range [IQR] 60-76). Twenty-seven (60%) had PTE signs at CTPA, 17/27 (63%) with bilateral involvement, none with main branch PTE. In 33/45 patients (73%) patients LL-US was performed before CTPA, with 3 patients having superficial vein thrombosis (9%, none with CTPA-confirmed PTE) and 1 patient having deep vein thrombosis (3%, with CTPA-confirmed PTE). Thirty-three patients (73%) had at least one comorbidity, mainly hypertension (23/45, 51%) and cardiovascular disease (15/45, 33%). Before CTPA, 5 patients had high D-dimer (11.21 µg/mL, IQR 9.10-13.02), 19 high fibrinogen (550 mg/dL, IQR 476-590), 26 high interleukin-6 (79 pg/mL, IQR 31-282), and 11 high C-reactive protein (9.60 mg/dL, IQR 6.75-10.65), C-reactive protein being the only laboratory parameter significantly differing between patients with and without PTE (P = .002)High PTE incidence (60%) in COVID-19 hospitalized patients under prophylactic LMWH could substantiate further tailoring of anticoagulation therapy.

Chest x-ray severity score in COVID-19 patients on emergency department admission: a two-centre study.

BACKGROUND: Integration of imaging and clinical parameters could improve the stratification of COVID-19 patients on emergency department (ED) admission. We aimed to assess the extent of COVID-19 pulmonary abnormalities on chest x-ray (CXR) using a semiquantitative severity score, correlating it with clinical data and testing its interobserver agreement. METHODS: From February 22 to April 8, 2020, 926 consecutive patients referring to ED of two institutions in Northern Italy for suspected SARS-CoV-2 infection were reviewed. Patients with reverse transcriptase-polymerase chain reaction positive for SARS-CoV-2 and CXR images on ED admission were included (295 patients, median age 69 years, 199 males). Five readers independently and blindly reviewed all CXRs, rating pulmonary parenchymal involvement using a 0-3 semiquantitative score in 1-point increments on 6 lung zones (range 0-18). Interobserver agreement was assessed with weighted Cohen's κ, correlations between median CXR score and clinical data with Spearman's ρ, and the Mann-Whitney U test. RESULTS: Median score showed negative correlation with SpO2 (ρ = -0.242, p < 0.001), positive correlation with white cell count (ρ = 0.277, p < 0.001), lactate dehydrogenase (ρ = 0.308, p < 0.001), and C-reactive protein (ρ = 0.367, p < 0.001), being significantly higher in subsequently dead patients (p = 0.003). Considering overall scores, readers' pairings yielded moderate (κ = 0.449, p < 0.001) to almost perfect interobserver agreement (κ = 0.872, p < 0.001), with better interobserver agreement between readers of centre 2 (up to κ = 0.872, p < 0.001) than centre 1 (κ = 0.764, p < 0.001). CONCLUSIONS: Proposed CXR pulmonary severity score in COVID-19 showed moderate to almost perfect interobserver agreement and significant but weak correlations with clinical parameters, potentially furthering CXR integration in patients' stratification.

Visceral adipose tissue area predicts intensive care unit admission in COVID-19 patients.

We retrospectively investigated, in 62 consecutive hospitalised COVID-19 patients (aged 70 ± 14 years, 40 males), the prognostic value of CT-derived subcutaneous adipose tissue and visceral adipose tissue (VAT) metrics, testing them in four predictive models for admission to intensive care unit (ICU), with and without pre-existing comorbidities. Multivariate logistic regression identified VAT score as the best ICU admission predictor (odds ratios 4.307-12.842). A non-relevant contribution of comorbidities at receiver operating characteristic analysis (area under the curve 0.821 for the CT-based model, 0.834 for the one including comorbidities) highlights the potential one-stop-shop prognostic role of CT-derived lung and adipose tissue metrics.

Chest x-ray in the COVID-19 pandemic: Radiologists' real-world reader performance.

PURPOSE: To report real-world diagnostic performance of chest x-ray (CXR) readings during the COVID-19 pandemic. METHODS: In this retrospective observational study we enrolled all patients presenting to the emergency department of a Milan-based university hospital from February 24th to April 8th 2020 who underwent nasopharyngeal swab for reverse transcriptase-polymerase chain reaction (RT-PCR) and anteroposterior bedside CXR within 12 h. A composite reference standard combining RT-PCR results with phone-call-based anamnesis was obtained. Radiologists were grouped by CXR reading experience (Group-1, >10 years; Group-2, <10 years), diagnostic performance indexes were calculated for each radiologist and for the two groups. RESULTS: Group-1 read 435 CXRs (77.0 % disease prevalence): sensitivity was 89.0 %, specificity 66.0 %, accuracy 83.7 %. Group-2 read 100 CXRs (73.0 % prevalence): sensitivity was 89.0 %, specificity 40.7 %, accuracy 76.0 %. During the first half of the outbreak (195 CXRs, 66.7 % disease prevalence), overall sensitivity was 80.8 %, specificity 67.7 %, accuracy 76.4 %, Group-1 sensitivity being similar to Group-2 (80.6 % versus 81.5 %, respectively) but higher specificity (74.0 % versus 46.7 %) and accuracy (78.4 % versus 69.0 %). During the second half (340 CXRs, 81.8 % prevalence), overall sensitivity increased to 92.8 %, specificity dropped to 53.2 %, accuracy increased to 85.6 %, this pattern mirrored in both groups, with decreased specificity (Group-1, 58.0 %; Group-2, 33.3 %) but increased sensitivity (92.7 % and 93.5 %) and accuracy (86.5 % and 81.0 %, respectively). CONCLUSIONS: Real-world CXR diagnostic performance during the COVID-19 pandemic showed overall high sensitivity with higher specificity for more experienced radiologists. The increase in accuracy over time strengthens CXR role as a first line examination in suspected COVID-19 patients.