Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a new virus recently isolated from humans. SARS-CoV-2 was discovered to be the pathogen responsible for a cluster of pneumonia cases ...associated with severe respiratory disease that occurred in December 2019 in China. This novel pulmonary infection, formally called Coronavirus Disease 2019 (COVID-19), has spread rapidly in China and beyond. On 8 March 2020, the number of Italians with SARS-CoV-2 infection was 7375 with a 48% hospitalization rate. At present, chest-computed tomography imaging is considered the most effective method for the detection of lung abnormalities in early-stage disease and quantitative assessment of severity and progression of COVID-19 pneumonia. Although chest X-ray (CXR) is considered not sensitive for the detection of pulmonary involvement in the early stage of the disease, we believe that, in the current emergency setting, CXR can be a useful diagnostic tool for monitoring the rapid progression of lung abnormalities in infected patients, particularly in intensive care units. In this short communication, we present our experimental CXR scoring system that we are applying to hospitalized patients with COVID-19 pneumonia to quantify and monitor the severity and progression of this new infectious disease. We also present the results of our preliminary validation study on a sample of 100 hospitalized patients with SARS-CoV-2 infection for whom the final outcome (
recovery or death
) was available.
Purpose
To improve the risk stratification of patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), an experimental chest X-ray (CXR) scoring system for quantifying ...lung abnormalities was introduced in our Diagnostic Imaging Department. The purpose of this study was to retrospectively evaluate correlations between the CXR score and the age or sex of Italian patients infected with SARS-CoV-2.
Materials and methods
Between March 4, 2020, and March 18, 2020, all CXR reports containing the new scoring system were retrieved. Only hospitalized patients with SARS-CoV-2 infection were enrolled. For each patient, age, sex, and the CXR report containing the highest score were considered for the analysis. Patients were also divided into seven groups according to age. Nonparametric statistical tests were used to examine the relationship between the severity of lung disease and the age or sex.
Results
783 Italian patients (532 males and 251 females) with SARS-CoV-2 infection were enrolled. The CXR score was significantly higher in males than in females only in groups aged 50 to 79 years. A significant correlation was observed between the CXR score and age in both males and females. Males aged 50 years or older and females aged 80 years or older with coronavirus disease 2019 showed the highest CXR score (median ≥ 8).
Conclusions
Males aged 50 years or older and females aged 80 years or older showed the highest risk of developing severe lung disease. Our results may help to identify the highest-risk patients and those who require specific treatment strategies.
Objective
We aim to demonstrate that a chest X-ray (CXR) scoring system for COVID-19 patients correlates with patient outcome and has a prognostic value.
Methods
This retrospective study included ...CXRs of COVID-19 patients that reported the Brixia score, a semi-quantitative scoring system rating lung involvement from 0 to 18. The highest (H) and lowest (L) values were registered along with scores on admission (A) and end of hospitalization (E). The Brixia score was correlated with the outcome (death or discharge).
Results
A total of 953 patients met inclusion criteria. In total, 677/953 were discharged and 276/953 died during hospitalization. A total of 524/953 had one CXR and 429/953 had more than one CXR. H-score was significantly higher in deceased (median, 12; IQR 9–14) compared to that in discharged patients (median, 8; IQR 5–11) (
p
< 0.0001). In 429/953 patients with multiple CXR, A-score, L-score, and E-score were higher in deceased than in discharged patients (A-score 9 vs 8;
p
= 0.039; L-score 7 vs 5;
p
< 0.0003; E-score 12 vs 7;
p
< 0.0001). In the entire cohort, logistic regression showed a significant predictive value for age (
p
< 0.0001, OR 1.13), H-score (
p
< 0.0001, OR 1.25), and gender (
p
= 0.01, male OR 1.67). AUC was 0.863. In patients with ≥ 2 CXR, A-, L-, and E-scores correlated significantly with the outcome. Cox proportional hazards regression indicated age (
p
< 0.0001, HR 4.17), H-score (< 9, HR 0.36,
p
= 0.0012), and worsening of H-score vs A score > 3 (HR 1.57,
p
= 0.0227) as associated with worse outcome.
Conclusions
The Brixia score correlates strongly with disease severity and outcome; it may support the clinical decision-making, particularly in patients with moderate-to-severe signs and symptoms. The Brixia score should be incorporated in a prognostic model, which would be desirable, particularly in resource-constraint scenarios.
Key Points
• To demonstrate the importance of the Brixia score in assessing and monitoring COVID-19 lung involvement.
• The Brixia score strongly correlates with patient outcome and can be easily implemented in the routine reporting of CXR.
•Brixia score is a new chest X-ray scoring system designed for COVID-19 pneumonia.•Brixia score, patient age and immunosuppressive conditions predict fatal outcome.•High Brixiascore and at least one ...other predictor confer the highest risk of death.
This study aimed to assess the usefulness of a new chest X-ray scoring system — the Brixia score — to predict the risk of in-hospital mortality in hospitalized patients with coronavirus disease 2019 (COVID-19).
Between March 4, 2020 and March 24, 2020, all CXR reports including the Brixia score were retrieved. We enrolled only hospitalized Caucasian patients with COVID-19 for whom the final outcome was available. For each patient, age, sex, underlying comorbidities, immunosuppressive therapies, and the CXR report containing the highest score were considered for analysis. These independent variables were analyzed using a multivariable logistic regression model to extract the predictive factors for in-hospital mortality.
302 Caucasian patients who were hospitalized for COVID-19 were enrolled. In the multivariable logistic regression model, only Brixia score, patient age, and conditions that induced immunosuppression were the significant predictive factors for in-hospital mortality. According to receiver operating characteristic curve analyses, the optimal cutoff values for Brixia score and patient age were 8 points and 71 years, respectively. Three different models that included the Brixia score showed excellent predictive power.
Patients with a high Brixia score and at least one other predictive factor had the highest risk of in-hospital death.
Abstract
Aims
To compare demographic characteristics, clinical presentation, and outcomes of patients with and without concomitant cardiac disease, hospitalized for COVID-19 in Brescia, Lombardy, ...Italy.
Methods and results
The study population includes 99 consecutive patients with COVID-19 pneumonia admitted to our hospital between 4 March and 25 March 2020. Fifty-three patients with a history of cardiac disease were compared with 46 without cardiac disease. Among cardiac patients, 40% had a history of heart failure, 36% had atrial fibrillation, and 30% had coronary artery disease. Mean age was 67 ± 12 years, and 80 (81%) patients were males. No differences were found between cardiac and non-cardiac patients except for higher values of serum creatinine, N-terminal probrain natriuretic peptide, and high sensitivity troponin T in cardiac patients. During hospitalization, 26% patients died, 15% developed thrombo-embolic events, 19% had acute respiratory distress syndrome, and 6% had septic shock. Mortality was higher in patients with cardiac disease compared with the others (36% vs. 15%, log-rank P = 0.019; relative risk 2.35; 95% confidence interval 1.08–5.09). The rate of thrombo-embolic events and septic shock during the hospitalization was also higher in cardiac patients (23% vs. 6% and 11% vs. 0%, respectively).
Conclusions
Hospitalized patients with concomitant cardiac disease and COVID-19 have an extremely poor prognosis compared with subjects without a history of cardiac disease, with higher mortality, thrombo-embolic events, and septic shock rates.
An early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED) was developed and validated using a machine-learning model. In total, 2782 ...patients were enrolled between March 2020 and December 2020, including 2106 patients (first wave) and 676 patients (second wave) in the COVID-19 outbreak in Italy. The first-wave patients were divided into two groups with 1474 patients used to train the model, and 632 to validate it. The 676 patients in the second wave were used to test the model. Age, 17 blood analytes, and Brescia chest X-ray score were the variables processed using a random forests classification algorithm to build and validate the model. Receiver operating characteristic (ROC) analysis was used to assess the model performances. A web-based death-risk calculator was implemented and integrated within the Laboratory Information System of the hospital. The final score was constructed by age (the most powerful predictor), blood analytes (the strongest predictors were lactate dehydrogenase, D-dimer, neutrophil/lymphocyte ratio, C-reactive protein, lymphocyte %, ferritin std, and monocyte %), and Brescia chest X-ray score (https://bdbiomed.shinyapps.io/covid19score/). The areas under the ROC curve obtained for the three groups (training, validating, and testing) were 0.98, 0.83, and 0.78, respectively. The model predicts in-hospital mortality on the basis of data that can be obtained in a short time, directly at the ED on admission. It functions as a web-based calculator, providing a risk score which is easy to interpret. It can be used in the triage process to support the decision on patient allocation.
Objectives
To assess whether tumour heterogeneity, quantified by texture analysis (TA) on contrast-enhanced computed tomography (CECT), can predict response to chemotherapy in advanced non-small cell ...lung cancer (NSCLC).
Methods
Fifty-three CECT studies of patients with advanced NSCLC who had undergone first-line chemotherapy were retrospectively reviewed. Response to chemotherapy was evaluated according to RECIST1.1. Tumour uniformity was assessed by a TA method based on Laplacian of Gaussian filtering. The resulting parameters were correlated with treatment response and overall survival by multivariate analysis.
Results
Thirty-one out of 53 patients were non-responders and 22 were responders. Average overall survival was 13 months (4–35), minimum follow-up was 12 months. In the adenocarcinoma group (
n
= 31), the product of tumour uniformity and grey level (GL*U) was the unique independent variable correlating with treatment response. Dividing the GL*U (range 8.5-46.6) into tertiles, lesions belonging to the second and the third tertiles had an 8.3-fold higher probability of treatment response compared with those in the first tertile. No association between texture features and response to treatment was observed in the non-adenocarcinoma group (
n
= 22). GL*U did not correlate with overall survival.
Conclusions
TA on CECT images in advanced lung adenocarcinoma provides an independent predictive indicator of response to first-line chemotherapy.
Key Points
•
Contrast enhanced computed tomography is currently used to stage lung cancer.
•
Texture analysis allows tumour heterogeneity to be quantified on CT images.
•
Texture parameters seem to predict chemotherapy response in advanced NSCLC.
Background
Olfactory (OD) and gustatory (GD) dysfunction have been proven to be a typical symptom of severe acute respiratory syndrome‐coronavirus‐2 (SARS‐CoV‐2) infection. However, their prevalence ...in different patient populations still needs to be clarified.
Methods
A cross‐sectional study was performed from March 27 to April 1, 2020, in Northern Italy. Physicians administered a survey‐based questionnaire to SARS‐CoV‐2–positive patients with the aim of assessing symptoms, focusing on OD and GD. Two groups were studied: group A, patients hospitalized at Azianda Socio Sanitaria Territoriale (ASST) Spedali Civili University Hospital of Brescia; and group B, home‐quarantined subjects.
Results
A total of 508 patients were enrolled: 295 in group A and 213 in group B. Mean age ± standard deviation (SD) was 55 ± 15 years; 56% were men. Overall, OD and GD were present in 56% (95% confidence interval CI, 51% to 60%) and 63% (95% CI, 59% to 67%) of cases, respectively. In group A, the prevalence of OD and GD was 44% (95% CI, 38% to 50%) and 52% (95% CI, 46% to 58%), respectively. In group B, the prevalence of OD and GD was 72% (95% CI, 65% to 79%) and 79% (95% CI, 73% to 84%), respectively. In the entire cohort, total loss of olfaction and taste was reported in 64% and 60% of cases, respectively. OD and GD occurred as the first symptom in 10% and 11% of cases, respectively; in the remaining cases, they occurred after a mean of 4 ± 3 days following the first symptom. At the time of the questionnaire, complete resolution of OD and GD was reported in 52% and 55% of cases, respectively (mean duration, 9 ± 5 days in both).
Conclusion
OD and GD are more prevalent in home‐quarantined subjects, and they are independently associated with younger age and female gender.
Purpose
To develop a CT texture-based model able to predict epidermal growth factor receptor (EGFR)-mutated, anaplastic lymphoma kinase (ALK)-rearranged lung adenocarcinomas and distinguish them from ...wild-type tumors on pre-treatment CT scans.
Materials and methods
Texture analysis was performed using proprietary software TexRAD (TexRAD Ltd, Cambridge, UK) on pre-treatment contrast-enhanced CT scans of 84 patients with metastatic primary lung adenocarcinoma. Textural features were quantified using the filtration-histogram approach with different spatial scale filters on a single 5-mm-thick central slice considered representative of the whole tumor. In order to deal with class imbalance regarding mutational status percentages in our population, the dataset was optimized using the synthetic minority over-sampling technique (SMOTE) and correlations with textural features were investigated using a generalized boosted regression model (GBM) with a nested cross-validation approach (performance averaged over 1000 resampling episodes).
Results
ALK rearrangements, EGFR mutations and wild-type tumors were observed in 19, 28 and 37 patients, respectively, in the original dataset. The balanced dataset was composed of 171 observations. Among the 29 original texture variables, 17 were employed for model building. Skewness on unfiltered images and on fine texture was the most important features. EGFR-mutated tumors showed the highest skewness while ALK-rearranged tumors had the lowest values with wild-type tumors showing intermediate values. The average accuracy of the model calculated on the independent nested validation set was 81.76% (95% CI 81.45–82.06).
Conclusion
Texture analysis, in particular skewness values, could be promising for noninvasive characterization of lung adenocarcinoma with respect to EGFR and ALK mutations.
The latest (4th) edition of the World Health Organization (WHO) Classification of Head and Neck Tumours, published in January 2017, has reclassified keratocystic odontogenic tumour as odontogenic ...keratocyst. Therefore, odontogenic keratocysts (OKCs) are now considered benign cysts of odontogenic origin that account for about 10% of all odontogenic cysts. OKCs arise from the dental lamina and are characterised by a cystic space containing desquamated keratin with a uniform lining of parakeratinised squamous epithelium. The reported age distribution of OKCs is considerably wide, with a peak of incidence in the third decade of life and a slight male predominance. OKCs originate in tooth-bearing regions and the mandible is more often affected than the maxilla. In the mandible, the most common location is the posterior sextant, the angle or the ramus. Conversely, the anterior sextant and the third molar region are the most common sites of origin in the maxilla. OKCs are characterised by an aggressive behaviour with a relatively high recurrence rate, particularly when OKCs are associated with syndromes. Multiple OKCs are typically associated with the nevoid basal cell carcinoma syndrome (NBCCS), an autosomal dominant multisystemic disease. Radiological imaging, mainly computed tomography (CT) and, in selected cases, magnetic resonance imaging (MRI), plays an important role in the diagnosis and management of OKCs. Therefore, the main purpose of this pictorial review is to present the imaging appearance of OKCs underlining the specific findings of different imaging modalities and to provide key radiologic features helping the differential diagnoses from other cystic and neoplastic lesions of odontogenic origin.
Key Points
•
Panoramic radiography is helpful in the preliminary assessment of OKCs
.
•
CT is considered the tool of choice in the evaluation of OKCs
.
•
MRI with DWI or DKI can help differentiate OKCs from other odontogenic lesions
.
•
Ameloblastoma, dentigerous and radicular cysts should be considered in the differential diagnosis
.
•
The presence of multiple OKCs is one of the major criteria for the diagnosis of NBCCS
.
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