Ultrasound muscle assessment for sarcopenia detection in inflammatory bowel disease: A prospective study.

BACKGROUND: Sarcopenia is prevalent in patients with inflammatory bowel disease (IBD) and impacts surgical and therapeutic outcomes; thus, effective diagnostic tools are needed to assess muscle mass and function in this population. METHODS: 153 consecutive patients were included, 100 in the training cohort and 53 in the study cohort. Three superficial muscles (rectus femoris = RF, rectus abdominis = RA, and biceps brachii = BB) were selected for the detection of sarcopenia using muscle ultrasound (US). The training cohort consisted of consecutive patients with or without IBD and was used to evaluate the feasibility and inter- and intra-observer variability of the US measurement. The study cohort consisted of only IBD patients and served to test US diagnostic accuracy. In the latter, muscle US, bioelectrical impedance analysis (BIA), and magnetic resonance imaging (MRI) were used to measure muscle parameters. RESULTS: Sarcopenia prevalence in IBD patients was 50%. Muscle US showed excellent inter-rater and intra-rater reliability (ICC >0.95) and a good diagnostic accuracy in detecting sarcopenia compared to BIA with area under the receiver operating characteristic curve (AUROC) values of 80% and 85% for RA and BB thickness, respectively. Moreover, an Ultrasound Muscle Index (USMI) was defined as the sum of the RA, BB, and RF thickness divided by the square of the patient's height, resulting in an AUROC of 81%. Muscle thresholds for sarcopenia were detected, with RA and USMI values correlated with the highest positive (84.3%) and negative (99%) predictive values, respectively. Additionally, the agreement between the US and MRI measurements of RA was excellent (ICC 0.96). CONCLUSIONS: The findings of this study emphasize the potential of muscle US as a reliable diagnostic tool for assessing sarcopenia in IBD patients. This research has significant implications for disease management in IBD patients and underscores the need for further investigations to validate these findings in larger cohorts.

Feasibility of Low-Dose and Low-Contrast Media Volume Approach in Computed Tomography Cardiovascular Imaging Reconstructed with Model-Based Algorithm.

Aim: To evaluate the dose reduction and image quality of low-dose, low-contrast media volume in computed tomography (CT) examinations reconstructed with the model-based iterative reconstruction (MBIR) algorithm in comparison with the hybrid iterative (HIR) one. Methods: We prospectively enrolled a total of 401 patients referred for cardiovascular CT, evaluated with a 256-MDCT scan with a low kVp (80 kVp) reconstructed with an MBIR (study group) or a standard HIR protocol (100 kVp-control group) after injection of a fixed dose of contrast medium volume. Vessel contrast enhancement and image noise were measured by placing the region of interest (ROI) in the left ventricle, ascending aorta; left, right and circumflex coronary arteries; main, right and left pulmonary arteries; aortic arch; and abdominal aorta. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were computed. Subjective image quality obtained by consensus was assessed by using a 4-point Likert scale. Radiation dose exposure was recorded. Results: HU values of the proximal tract of all coronary arteries; main, right and left pulmonary arteries; and of the aorta were significantly higher in the study group than in the control group (p < 0.05), while the noise was significantly lower (p < 0.05). SNR and CNR values in all anatomic districts were significantly higher in the study group (p < 0.05). MBIR subjective image quality was significantly higher than HIR in CCTA and CTPA protocols (p < 0.05). Radiation dose was significantly lower in the study group (p < 0.05). Conclusions: The MBIR algorithm combined with low-kVp can help reduce radiation dose exposure, reduce noise, and increase objective and subjective image quality.

Artificial Intelligence Applied to Chest X-ray: A Reliable Tool to Assess the Differential Diagnosis of Lung Pneumonia in the Emergency Department.

BACKGROUND: Considering the large number of patients with pulmonary symptoms admitted to the emergency department daily, it is essential to diagnose them correctly. It is necessary to quickly solve the differential diagnosis between COVID-19 and typical bacterial pneumonia to address them with the best management possible. In this setting, an artificial intelligence (AI) system can help radiologists detect pneumonia more quickly. METHODS: We aimed to test the diagnostic performance of an AI system in detecting COVID-19 pneumonia and typical bacterial pneumonia in patients who underwent a chest X-ray (CXR) and were admitted to the emergency department. The final dataset was composed of three sub-datasets: the first included all patients positive for COVID-19 pneumonia (n = 1140, namely "COVID-19+"), the second one included all patients with typical bacterial pneumonia (n = 500, "pneumonia+"), and the third one was composed of healthy subjects (n = 1000). Two radiologists were blinded to demographic, clinical, and laboratory data. The developed AI system was used to evaluate all CXRs randomly and was asked to classify them into three classes. Cohen's κ was used for interrater reliability analysis. The AI system's diagnostic accuracy was evaluated using a confusion matrix, and 95%CIs were reported as appropriate. RESULTS: The interrater reliability analysis between the most experienced radiologist and the AI system reported an almost perfect agreement for COVID-19+ (κ = 0.822) and pneumonia+ (κ = 0.913). We found 96% sensitivity (95% CIs = 94.9-96.9) and 79.8% specificity (76.4-82.9) for the radiologist and 94.7% sensitivity (93.4-95.8) and 80.2% specificity (76.9-83.2) for the AI system in the detection of COVID-19+. Moreover, we found 97.9% sensitivity (98-99.3) and 88% specificity (83.5-91.7) for the radiologist and 97.5% sensitivity (96.5-98.3) and 83.9% specificity (79-87.9) for the AI system in the detection of pneumonia+ patients. Finally, the AI system reached an accuracy of 93.8%, with a misclassification rate of 6.2% and weighted-F1 of 93.8% in detecting COVID+, pneumonia+, and healthy subjects. CONCLUSIONS: The AI system demonstrated excellent diagnostic performance in identifying COVID-19 and typical bacterial pneumonia in CXRs acquired in the emergency setting.

Association of chronic statin use, myopenia, myosteatosis and major morbidity in surgical patients with upper gastrointestinal cancer.

Derangements of body composition affect surgical outcomes. Chronic statin use may induce muscle wasting and impair muscle tissue quality. Aim of this study was to evaluate the association of chronic statin use, skeletal muscle area (SMA), myosteatosis and major postoperative morbidity. Between 2011 and 2021, patients undergoing pancreatoduodenectomy or total gastrectomy for cancer, and using statins since at least 1 year, were retrospective studied. SMA and myosteatosis were measured at CT scan. The cut-off for SMA and myosteatosis were determined using ROC curve and considering severe complications as the binary outcome. The presence of myopenia was defined when SMA was lower that the cut-off. A multivariable logistic regression was applied to assess the association between several factors and severe complications. After a matching procedure (1:1) for key baseline risk factors (ASA; age; Charlson comorbidity index; tumor site; intraoperative blood loss), a final sample of 104 patients, of which 52 treated and 52 not treated with statins, was obtained. The median age was 75 years, with an ASA score ≥ 3 in 63% of the cases. SMA (OR 5.119, 95% CI 1.053-24.865) and myosteatosis (OR 4.234, 95% CI 1.511-11.866) below the cut-off values were significantly associated with major morbidity. Statin use was predictive of major complication only in patients with preoperative myopenia (OR 5.449, 95% CI 1.054-28.158). Myopenia and myosteatosis were independently associated with an increased risk of severe complications. Statin use was associated with a higher risk of having major morbidity only in the subgroup of patients with myopenia.

Ultrasound Versus Computed Tomography for Diaphragmatic Thickness and Skeletal Muscle Index during Mechanical Ventilation.

Background: Diaphragmatic alterations occurring during mechanical ventilation (MV) can be monitored using ultrasound (US). The performance of computed tomography (CT) to evaluate diaphragmatic thickness is limited. Further, the association between muscle mass and outcome is increasingly recognized. However, no data are available on its correlation with diaphragmatic thickness. We aimed to determine correlation and agreement of diaphragmatic thickness between CT and US; and its association with muscle mass and MV parameters. Methods: Prospective observational study. US measurements of the diaphragmatic thickness were collected in patients undergoing MV within 12 h before or after performing a CT scan of the thorax and/or upper abdomen. Data on skeletal muscle index (SMI), baseline, and ventilatory data were recorded and correlated with US and CT measures of diaphragmatic thickness. Agreement was explored between US and CT data. Results: Twenty-nine patients were enrolled and the diaphragm measured by CT resulted overall thicker than US-based measurement of the right hemidiaphragm. The US thickness showed the strongest correlation with the left posterior pillar at CT (r = 0.49, p = 0.008). The duration of the controlled MV was negatively correlated with US thickness (r = -0.45, p = 0.017), the thickness of the right anterior pillar (r = -0.41, p = 0.029), and splenic dome by CT (r = -0.43, p = 0.023). SMI was positively correlated with US diaphragmatic thickness (r = 0.50, p = 0.007) and inversely correlated with the duration of MV before enrollment (r = -0.426, p = 0.027). Conclusions: CT scan of the left posterior pillar can estimate diaphragmatic thickness and is moderately correlated with US measurements. Both techniques show that diaphragm thickness decreases with MV duration. The diaphragmatic thickness by US showed a good correlation with SMI.

Inter-observer agreement and image quality of model-based algorithm applied to the Coronary Artery Disease-Reporting and Data System score.

PURPOSE: To evaluate the inter-observer agreement of the CAD-RADS reporting system and compare image quality between model-based iterative reconstruction algorithm (MBIR) and standard iterative reconstruction algorithm (IR) of low-dose cardiac computed tomography angiography (CCTA). METHODS: One-hundred-sixty patients undergone a 256-slice MDCT scanner using low-dose CCTA combined with prospective ECG-gated techniques were enrolled. CCTA protocols were reconstructed with both MBIR and IR. Each study was evaluated by two readers using the CAD-RADS lexicon. Vessels enhancement, image noise, signal-to-noise (SNR), and contrast-to-noise (CNR) were computed in the axial native images, and inter-observer agreement was assessed. Radiation dose exposure as dose-length product (DLP) and effective dose were finally reported. RESULTS: The reliability analysis between the two readers was almost perfect for all CAD-RADS standard categories. Moreover, a significantly higher value of subjective qualitative analysis, SNR, and CNR in MBIR images compared to IR were found, due to a lower noise level (all p < 0.05). The mean DLP measured was 63.9 mGy*cm, and the mean effective dose was 0.9 mSv. CONCLUSION: Inter-observer agreement of CAD-RADS was excellent confirming the importance, the feasibility, and the reproducibility of the CAD-RADS scoring system for CCTA. Moreover, lower noise and higher image quality with MBIR compared to IR were found. IMPLICATIONS FOR PRACTICE: MBIR, by reducing noise and improving image quality, can help a better assessment of CAD-RADS, in comparison with standard IR algorithm.

Estimating Fatty Pancreas-A Preoperative Bedside Assessment by Bioelectric Impedance Analysis: Implications for Pancreatic Surgery.

OBJECTIVE: The aim of the study was to evaluate whether fatty pancreas could be estimated by fat mass measurement by preoperative bioelectric impedance analysis. Preoperative computed tomography scan and pathologic evaluation were used as validation methods. Moreover, the 3 methodologies were tested for their ability in predicting postoperative pancreatic fistula. METHODS: Seventy-five patients who underwent pancreatic resection were analyzed. Preoperative computed tomography attenuation in Hounsfield unit (CT-HU) was used to assess fatty pancreas. Bioelectric impedance analysis was performed the day before surgery and fat mass index (FMI) was calculated. Pancreatic steatosis was assessed by pathologists at the line of surgical transection. The ability of the methods in predicting postoperative pancreatic fistula was evaluated by the area under the receiver operating characteristics curves. RESULTS: There was a strong correlation between CT-HU values and grade of pancreatic steatosis evaluated at histology ( r = -0.852, P < 0.001) and a moderate correlation between FMI and histologic pancreatic steatosis ( r = 0.612, P < 0.001) and between CT-HU value and FMI ( r = -0.659, P < 0.001) values. The area under the curve (95% confidence interval) was 0.942 (0.879-1) for histology, 0.924 (0.844-1) for CT-HU, and 0.884 (0.778-0.990) for FMI. CONCLUSIONS: Bioelectric impedance analysis represents a valid alternative to assess pancreatic steatosis.

Compressive ultrasound can predict early pulmonary embolism onset in COVID patients.

PURPOSE: To evaluate the usefulness of compressive ultrasound (CUS) for the diagnosis of deep vein thrombosis (DVT) in patients with SARS-CoV-2-related infection. METHODS: 112 hospitalized patients with confirmed SARS-CoV-2 infection were retrospectively enrolled. CUS was performed within 2 days of admission and consisted in the assessment of the proximal and distal deep venous systems. Lack of compressibility, or direct identification of an endoluminal thrombus, were the criteria used for the diagnosis of DVT. Pulmonary embolism (PE) events were investigated at computed tomography pulmonary angiography (CTPA) within 5 days of follow-up. Logistic binary regression was computed to determine which clinical and radiological parameters were independently associated with PE onset. RESULTS: Overall, the incidence of DVT in our cohort was about 43%. The most common district involved was the left lower limb (68.7%) in comparison with the right one (58.3%) while the upper limbs were less frequently involved (4.2% the right one and 2.1% the left one, respectively). On both sides, the distal tract of the popliteal vein was the most common involved (50% right side and 45.8% left side). The presence of DVT in the distal tract of the right popliteal vein (OR = 2.444 95%CIs 1.084-16.624, p = 0.038), in the distal tract of the left popliteal vein (OR = 4.201 95%CIs 1.484-11.885, p = 0.007), and D-dimer values (OR = 2.122 95%CIs 1.030-5.495, p = 0.003) were independently associated with the onset on PE within 5 days. CONCLUSIONS: CUS should be considered a useful tool to discriminate which category of patients can develop PE within 5 days from admission.

Application of low-dose CT combined with model-based iterative reconstruction algorithm in oncologic patients during follow-up: dose reduction and image quality.

OBJECTIVES: To compare image quality and radiation dose of CT images reconstructed with model-based iterative reconstruction (MBIR) and hybrid-iterative (HIR) algorithm in oncologic patients. METHODS: 125 oncologic patients underwent both contrast-enhanced low- (100 kV), and standard (120 kV) dose CT, were enrolled. Image quality was assessed by using a 4-point Likert scale. CT attenuation values, expressed in Hounsfield unit (HU), were recorded within a regions of interest (ROI) of liver, spleen, paraspinal muscle, aortic lumen, and subcutaneous fat tissue. Image noise, expressed as standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated. Radiation dose were analyzed. Paired Student's t-test was used to compare all continuous variables. RESULTS: The overall median score assessed as image quality for CT images with the MBIR algorithm was significantly higher in comparison with HIR [4 (range 3-4) vs 3 (3-4), p = 0.017].CT attenuation values and SD were significantly higher and lower, respectively, in all anatomic districts in images reconstructed with MBIR in comparison with HIR ones (all p < 0.001). SNR and CNR values were higher in CT images reconstructed with MBIR, reaching a significant difference in all districts (all p < 0.001). Radiation dose were significantly lower in the MBIR group compared with the HIR group (p < 0.001). CONCLUSIONS: MBIR combined with low-kV setting allows an important dose reduction in whole-body CT imaging, reaching a better image quality both qualitatively and quantitatively. ADVANCES IN KNOWLEDGE: MBIR with low-dose approach allows a reduction of dose exposure, maintaining high image quality, especially in patients which deserve a longlasting follow-up.

Diagnostic Value of Whole-Body MRI Short Protocols in Bone Lesion Detection in Multiple Myeloma Patients.

The aim of the study is to evaluate the effectiveness of short whole-body magnetic resonance imaging (WBMRI) protocols for the overall assessment of bone marrow involvement in patients with multiple myeloma (MM), in comparison with standard whole-body MRI protocol. Patients with biopsy-proven MM, who underwent a WBMRI with full-body coverage (from vertex to feet) were retrospectively enrolled. WBMRI images were independently evaluated by two expert radiologists, in terms of infiltration patterns (normal, focal, diffuse, and combined), according to location (the whole skeleton was divided into six anatomic districts: skull, spine, sternum and ribs, upper limbs, pelvis and proximal two-thirds of the femur, remaining parts of lower limbs) and lytic lesions number (<5, 5-20, and >20). The majority of patients showed focal and combined infiltration patterns with bone lesions predominantly distributed in the spine and pelvis. As skull and lower limbs are less frequently involved by focal bone lesions, excluding them from the standard MRI protocol allows to obtain a shorter protocol, maintaining a good diagnostic value.

Whole Body Low Dose Computed Tomography (WBLDCT) Can Be Comparable to Whole-Body Magnetic Resonance Imaging (WBMRI) in the Assessment of Multiple Myeloma.

Aim of the study is to compare the agreement between whole-body low-dose computed tomography (WBLDCT) and magnetic resonance imaging (WBMRI) in the evaluation of bone marrow involvement in patients with multiple myeloma (MM). Patients with biopsy-proven MM, who underwent both WBLDCT and WBMRI were retrospectively enrolled. After identifying the presence of focal bone involvement (focal infiltration pattern), the whole skeleton was divided into five anatomic districts (skull, spine, sternum and ribs, pelvis, and limbs). Patients were grouped according to the number and location of the lytic lesions (<5, 5-20, and >20) and Durie and Salmon staging system. The agreement between CT and MRI regarding focal pattern, staging, lesion number, and distribution was assessed using the Cohen Kappa statistics. The majority of patients showed focal involvement. According to the distribution of the focal lesions and Durie Salmon staging, the agreement between CT and MRI was substantial or almost perfect (all κ > 0.60). The agreement increased proportionally with the number of lesions in the pelvis and spine (κ = 0.373 to κ = 0.564, and κ = 0.469-0.624), while for the skull the agreement proportionally decreased without reaching a statistically significant difference (p > 0.05). In conclusion, WBLDCT showed an almost perfect agreement in the evaluation of focal involvement, staging, lesion number, and distribution of bone involvement in comparison with WBMRI.

Immune response evaluation criteria in solid tumors for assessment of atypical responses after immunotherapy.

In 2017, immune response evaluation criteria in solid tumors (iRECIST) were introduced to validate radiologic and clinical interpretations and to better analyze tumor's response to immunotherapy, considering the different time of following and response, between this new therapy compared to the standard one. However, even if the iRECIST are worldwide accepted, to date, different aspects should be better underlined and well reported, especially in clinical practice. Clinical experience has demonstrated that in a non-negligible percentage of patients, it is challenging to determine the correct category of response (stable disease, progression disease, partial or complete response), and consequently, to define which is the best management for those patients. Approaching radiological response in patients who underwent immunotherapy, a new uncommon kind of target lesions behavior was found. This phenomenon is mainly due to the different mechanisms of action of immunotherapeutic drug. Therefore, new groups of response have been described in clinical practice, defined as "atypical responses," and categorized into three new groups: pseudoprogression, hyperprogression, and dissociated response. This review summarizes and reports these patterns, helping clinicians and radiologists get used to atypical responses, in order to identify patients that respond best to treatment.

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.

Whole-body low-dose computed tomography (WBLDCT) in staging and re-staging of multiple myeloma.

The objective of this study is to evaluate the prognostic features of multiple myeloma (MM) using whole-body low-dose computed tomography (WBLDCT). One hundred three patients with biopsy-proven MM who underwent WBLDCT were retrospectively enrolled. The evolution of osteolytic lesions overtime was performed by measuring the maximum axial diameter at the baseline (T0) and the end of follow-up (Te), by using a cut-off value of 10 mm. The location and dimension of up to three lesions were registered. The time-to-fracture (TTF) was recorded. Sixty-three percent of patients presented a focal pattern, 22% a diffuse pattern, and 15% a combined one. Seventy-two percent of patients with lesions ≤ 10 mm presented stability, 27% a dimensional increase, and 1% a decrease. Patients with lesions >10 mm showed a statistically significant difference regarding the mean difference of axial diameter between T0 and Te (p = 0.015). Patients with lesions >10 mm showed an odds ratio (OR) of 29.8 (95%CIs 3.8-230.5) to develop at least one fracture. Mean TTF was significantly lower in patients with lesions >10 mm in comparison with lesions ≤ 10 mm (9 ± 3 vs 23 ± 7 months, respectively, p = 0.011). WBLDCT represents a reliable imaging-based tool for proper management of MM patients, showing that diffuse form or small lytic lesions may deserve a less frequent follow-up.

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.

Acute pulmonary embolism in hospitalized patients with SARS-CoV-2-related pneumonia: multicentric experience from Italian endemic area.

PURPOSE: To analyze pulmonary embolism (PE) on chest computed tomography pulmonary angiography (CTPA) in hospitalized patients affected by SARS-CoV-2, according to the severity of lung disease based both on temporal CT features changes and on CT-severity lung involvement (CT-severity score), along with the support of clinical and laboratory findings. METHODS: We retrospectively enrolled a total of 170 patients with confirmed SARS-CoV-2 infection who underwent CTPA examination for PE suspicion. Pulmonary arteries diameters, right ventricle/left ventricle (RV/LV) ratio, presence, absence, and distribution of PE, pulmonary artery obstructive index (PAO index), and lobe involvement were recorded. All CT scans were reviewed to assess temporal CT changes and the COVID CT-severity score. RESULTS: A total of 76 out of 170 patients (44.7%) developed PE without having any major risk factors for venous thromboembolism. The most severe pulmonary arteries involvement, expressed in terms of PAO Index, occurred in those patients with markedly elevated D-dimer and C-reactive protein (CRP) values and those patients with an advanced temporal stage of lung disease. The majority PE-positive patients were hospitalized in non-intensive wards. PE-positive patients showed a slightly higher hospitalization time in comparison with PE-negative ones. In the three months of study, overall 85.9% of patients were discharged while 14.1% died, of whom 13 PE-positive (54.2%). CONCLUSIONS: Patients hospitalized for SARS-CoV-2 infection present a higher cumulative incidence of PE compared to the general population of hospitalized patients, regardless of the severity of lung inflammation or the temporal stage of the disease.

Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia.

OBJECTIVES: To evaluate a semi-automated segmentation and ventilated lung quantification on chest computed tomography (CT) to assess lung involvement in patients affected by SARS-CoV-2. Results were compared with clinical and functional parameters and outcomes. METHODS: All images underwent quantitative analyses with a dedicated workstation using a semi-automatic lung segmentation software to compute ventilated lung volume (VLV), Ground-glass opacity (GGO) volume (GGO-V), and consolidation volume (CONS-V) as absolute volume and as a percentage of total lung volume (TLV). The ratio between CONS-V, GGO-V, and VLV (CONS-V/VLV and GGO-V/VLV, respectively), TLV (CONS-V/TLV, GGO-V/TLV, and GGO-V + CONS-V/TLV respectively), and the ratio between VLV and TLV (VLV/TLV) were calculated. RESULTS: A total of 108 patients were enrolled. GGO-V/TLV significantly correlated with WBC (r = 0.369), neutrophils (r = 0.446), platelets (r = 0.182), CRP (r = 0.190), PaCO2 (r = 0.176), HCO3- (r = 0.284), and PaO2/FiO2 (P/F) values (r = - 0.344). CONS-V/TLV significantly correlated with WBC (r = 0.294), neutrophils (r = 0.300), lymphocytes (r = -0.225), CRP (r = 0.306), PaCO2 (r = 0.227), pH (r = 0.162), HCO3- (r = 0.394), and P/F (r = - 0.419) values. Statistically significant differences between CONS-V, GGO-V, GGO-V/TLV, CONS-V/TLV, GGO-V/VLV, CONS-V/VLV, GGO-V + CONS-V/TLV, VLV/TLV, CT score, and invasive ventilation by ET were found (all p < 0.05). CONCLUSION: The use of quantitative semi-automated algorithm for lung CT elaboration effectively correlates the severity of SARS-CoV-2-related pneumonia with laboratory parameters and the need for invasive ventilation. KEY POINTS: • Pathological lung volumes, expressed both as GGO-V and as CONS-V, can be considered a useful tool in SARS-CoV-2-related pneumonia. • All lung volumes, expressed themselves and as ratio with TLV and VLV, correlate with laboratory data, in particular C-reactive protein and white blood cell count. • All lung volumes correlate with patient's outcome, in particular concerning invasive ventilation.

Chest CT in the emergency department for suspected COVID-19 pneumonia.

PURPOSE: In overwhelmed emergency departments (EDs) facing COVID-19 outbreak, a swift diagnosis is imperative. CT role was widely debated for its limited specificity. Here we report the diagnostic role of CT in two EDs in Lombardy, epicenter of Italian outbreak. MATERIAL AND METHODS: Admitting chest CT from 142 consecutive patients with suspected COVID-19 were retrospectively analyzed. CT scans were classified in "highly likely," "likely," and "unlikely" COVID-19 pneumonia according to the presence of typical, indeterminate, and atypical findings, or "negative" in the absence of findings, or "alternative diagnosis" when a different diagnosis was found. Nasopharyngeal swab results, turnaround time, and time to positive results were collected. CT diagnostic performances were assessed considering RT-PCR as reference standard. RESULTS: Most of cases (96/142, 68%) were classified as "highly likely" COVID-19 pneumonia. Ten (7%) and seven (5%) patients were classified as "likely" and "unlikely" COVID-19 pneumonia, respectively. In 21 (15%) patients a differential diagnosis was provided, including typical pneumonia, pulmonary edema, neoplasia, and pulmonary embolism. CT was negative in 8/142 (6%) patients. Mean turnaround time for the first COVID-19 RT-PCR was 30 ± 13 h. CT diagnostic accuracy in respect of the first test swab was 79% and increased to 91.5% after repeated swabs and/or BAL, for 18 false-negative first swab. CT performance was good with 76% specificity, 99% sensitivity, 90% positive predictive value and 97% negative predictive value. CONCLUSION: Chest CT was useful to streamline patients' triage while waiting for RT-PCR in the ED, supporting the clinical suspicion of COVID-19 or providing alternative diagnosis.

Diagnostic impact of bedside chest X-ray features of 2019 novel coronavirus in the routine admission at the emergency department: case series from Lombardy region.

PURPOSE: To evaluate the diagnostic accuracy and the imaging features of routine admission chest X-ray in patients suspected for novel Coronavirus 2019 (SARS-CoV-2) infection. METHOD: We retrospectively evaluated clinical and X-ray features in all patients referred to the emergency department for suspected SARS-CoV-2 infection between March 1st and March 13th. A single radiologist with more than 15 years of experience in chest-imaging evaluated the presence and extent of alveolar opacities, reticulations, and/or pleural effusion. The percentage of lung involvement (range <25 % to 75-100 %) was also calculated. We stratified patients in groups according to the time interval between symptoms onset and X-ray imaging (≤ 5 and > 5 days) and according to age (≤ 50 and > 50 years old). RESULTS: A total of 518 patients were enrolled. Overall 314 patients had negative and 204 had positive RT-PCR results. Lung lesions in patients with SARS-Cov2 pneumonia primarily manifested as alveolar and interstitial opacities and were mainly bilateral (60.8 %). Lung abnormalities were more frequent and more severe by symptom duration and by increasing age. The sensitivity and specificity of chest X-ray at admission in the overall cohort were 57 % (95 % CI = 47-67) and 89 % (83-94), respectively. Sensitivity was higher for patients with symptom onset > 5 days compared to ≤ 5 days (76 % [62-87] vs 37 % [24-52]) and in patients > 50 years old compared to ≤ 50 years (59 % [48-69] vs 47 % [23-72]), at the expense of a slightly lower specificity (68 % [45-86] and 82 % [73-89], respectively). CONCLUSIONS: Overall chest X-ray sensitivity for SARS-CoV-2 pneumonia was 57 %. Sensitivity was higher when symptoms had started more than 5 days before, at the expense of lesser specificity, while slightly higher in older patients in comparison to younger ones.

Chest X-ray features of SARS-CoV-2 in the emergency department: a multicenter experience from northern Italian hospitals.

OBJECTIVES: To evaluate the imaging features of routine admission chest X-ray in patients referred for novel Coronavirus 2019 infection. METHODS: All patients referred to the emergency departments, RT-PCR positive for SARS-CoV-2 infection were evaluated. Demographic and clinical data were recorded. Two radiologists (8 and 15 years of experience) reviewed all the X-ray images and evaluated the following findings: interstitial opacities, alveolar opacities (AO), AO associated with consolidation, consolidation and/or pleural effusion. We stratified patients in groups according to the time interval between symptoms onset (cut-off 5 days) and X-ray imaging and according to age (cut-off 60 years old). Computed tomography was performed in case of a discrepancy between clinical symptoms, laboratory and X-ray findings, and/or suspicion of complications. RESULTS: A total of 468 patients were tested positive for SARS-CoV-2. Lung lesions primarily manifested as interstitial opacities (71.7%) and AO opacities (60.5%), more frequently bilateral (64.5%) and with a peripheral predominance (62.5%). Patients admitted to the emergency radiology department after 5 days from symptoms onset, more frequently had interstitial and AO opacities, in comparison to those admitted within 5 days, and lung lesions were more frequently bilateral and peripheral. Older patients more frequently presented interstitial and AO opacities in comparison to younger ones. Sixty-eight patients underwent CT that principally showed the presence of ground-glass opacities and consolidations. CONCLUSIONS: The most common X-ray pattern is multifocal and peripheral, associated with interstitial and alveolar opacities. Chest X-ray, compared to CT, can be considered a reliable diagnostic tool, especially in the Emergency setting.