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.
•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.
The purpose of this study was to compare the prognostic value of chest X-ray (CXR) and chest computed tomography (CT) in a group of hospitalized patients with COVID-19. For this study, we ...retrospectively selected a cohort of 106 hospitalized patients with COVID-19 who underwent both CXR and chest CT at admission. For each patient, the pulmonary involvement was ranked by applying the
Brixia
score for CXR and the percentage of well-aerated lung (WAL) for CT. The
Brixia
score was assigned at admission (A-
Brixia
score) and during hospitalization. During hospitalization, only the highest score (H-
Brixia
score) was considered. At admission, the percentage of WAL (A-CT%WAL) was quantified using a dedicated software. On logistic regression analyses, H-
Brixia
score was the most effective radiological marker for predicting in-hospital mortality and invasive mechanical ventilation. Additionally, A-CT%WAL did not provide substantial advantages in the risk stratification of hospitalized patients with COVID-19 compared to A-
Brixia
score.
In the British Thoracic Society guidelines for incidental pulmonary nodules, volumetric analysis has become the recommended method for growth assessment in solid indeterminate pulmonary nodules ...(SIPNs) <300 mm3. In these guidelines, two different volume doubling time (VDT) cut-offs, 400 and 600 days, were proposed to differentiate benign from malignant nodules. The present study aims to evaluate the performance of these VDT cut-offs in a group of SIPNs <300 mm3 which were incidentally detected in a routine clinical setting. During a 7-year period, we retrospectively selected 60 patients with a single SIPN <300 mm3. For each SIPN, the volume and VDT were calculated using semiautomatic software throughout the follow-up period, and the performance of the 400- and 600-day VDT cut-offs was compared. In the selected sample, there were 38 benign and 22 malignant nodules. In this group of nodules, the sensitivity, negative predictive value and accuracy of the 600-day VDT cut-off were higher than those of the 400-day VDT cut-off. Therefore, in the management of SIPNs <300 mm3 which were incidentally detected in a clinical setting, the 600-day VDT cut-off was better at differentiating benign from malignant nodules than the 400-day VDT cut-off, by reducing the number of false negatives.
Pulmonary subsolid nodules (SSNs) are observed not infrequently on thin-section chest computed tomography (CT) images. SSNs persisting after a follow-up period of three to six months have a high ...likelihood of being pre-malignant or malignant lesions. Malignant SSNs usually represent the histologic spectrum of pulmonary adenocarcinomas, and pulmonary adenocarcinomas presenting as SSNs exhibit quite heterogeneous behavior. In fact, while most lesions show an indolent course and may grow very slowly or remain stable for many years, others may exhibit significant growth in a relatively short time. Therefore, it is not yet clear which persistent SSNs should be surgically removed and for how many years stable SSNs should be monitored. In order to solve these two open issues, the use of quantitative analysis has been proposed to define the "tailored" management of persistent SSNs. The main purpose of this review was to summarize recent results about quantitative CT analysis as a diagnostic tool for predicting the behavior of persistent SSNs. Thus, a literature search was conducted in PubMed/MEDLINE, Scopus, and Web of Science databases to find original articles published from January 2014 to October 2019. The results of the selected studies are presented and compared in a narrative way.
Persistent part-solid nodules (PSNs) with a solid component <6 mm usually represent minimally invasive adenocarcinomas and are significantly less aggressive than PSNs with a solid component ≥6 mm. ...However, not all PSNs with a small solid component behave in the same way: some nodules exhibit an indolent course, whereas others exhibit more aggressive behavior. Thus, predicting the future behavior of this subtype of PSN remains a complex and fascinating diagnostic challenge. The main purpose of this study was to apply open-source software to investigate which quantitative computed tomography (CT) features may be useful for predicting the behavior of a select group of PSNs. We retrospectively selected 50 patients with a single PSN with a solid component <6 mm and diameter <15 mm. Computerized analysis was performed using ImageJ software for each PSN and various quantitative features were calculated from the baseline CT images. The area, perimeter, mean Feret diameter, linear mass density, circularity and solidity were significantly related to nodule growth (p ≤ 0.031). Therefore, quantitative CT analysis was helpful for predicting the future behavior of a select group of PSNs with a solid component <6 mm and diameter <15 mm.
The much-heralded second wave of coronavirus disease (COVID-19) has arrived in Italy. Right now, one of the main questions about COVID-19 is whether the second wave is less severe and deadly than the ...first wave. In order to answer this challenging question, we decided to evaluate the chest X-ray (CXR) severity of COVID-19 pneumonia, the mechanical ventilation (MV) use, the patient outcome, and certain clinical/laboratory data during the second wave and compare them with those of the first wave.
During the two COVID-19 waves two independent groups of hospitalised patients were selected. The first group consisted of the first 100 COVID-19 patients admitted to our hospital during the first wave. The second group consisted of another 100 consecutive COVID-19 patients admitted to our hospital during the second wave. We enlisted only Caucasian male patients over the age of fifty for whom the final outcome was available. For each patient, the CXR severity of COVID-19 pneumonia, the MV use, the patient outcome, comorbidities, corticosteroid use, and C-reactive protein (CRP) levels were considered. Nonparametric statistical tests were used to compare the data obtained from the two waves.
The CXR severity of COVID-19 pneumonia, the in-hospital mortality, and CRP levels were significantly higher in the first wave than in the second wave (
≤ .041). Although not statistically significant, the frequency of MV use was higher in the first wave.
This preliminary investigation seems to confirm that the COVID-19 second wave is less severe and deadly than the first wave.
Background and objective
COVID‐19 remains a major cause of respiratory failure, and means to identify future deterioration is needed. We recently developed a prediction score based on breath‐holding ...manoeuvres (desaturation and maximal duration) to predict incident adverse COVID‐19 outcomes. Here we prospectively validated our breath‐holding prediction score in COVID‐19 patients, and assessed associations with radiological scores of pulmonary involvement.
Methods
Hospitalized COVID‐19 patients (N = 110, three recruitment centres) performed breath‐holds at admission to provide a prediction score (Messineo et al.) based on mean desaturation (20‐s breath‐holds) and maximal breath‐hold duration, plus baseline saturation, body mass index and cardiovascular disease. Odds ratios for incident adverse outcomes (composite of bi‐level ventilatory support, ICU admission and death) were described for patients with versus without elevated scores (>0). Regression examined associations with chest x‐ray (Brixia score) and computed tomography (CT; 3D‐software quantification). Additional comparisons were made with the previously‐validated ‘4C‐score’.
Results
Elevated prediction score was associated with adverse COVID‐19 outcomes (N = 12/110), OR95%CI = 4.541.17–17.83, p = 0.030 (positive predictive value = 9/48, negative predictive value = 59/62). Results were diminished with removal of mean desaturation from the prediction score (OR = 3.300.93–11.72). The prediction score rose linearly with Brixia score (β95%CI = 0.130.02–0.23, p = 0.026, N = 103) and CT‐based quantification (β = 1.020.39–1.65, p = 0.002, N = 45). Mean desaturation was also associated with both radiological assessment. Elevated 4C‐scores (≥high‐risk category) had a weaker association with adverse outcomes (OR = 2.440.62–9.56).
Conclusion
An elevated breath‐holding prediction score is associated with almost five‐fold increased adverse COVID‐19 outcome risk, and with pulmonary deficits observed in chest imaging. Breath‐holding may identify COVID‐19 patients at risk of future respiratory failure.
An elevated breath‐holding‐based prediction score was associated with increased COVID‐19 incident adverse outcome risk in a validation cohort of 110 hospitalized COVID‐19 patients. The prediction score was also positively associated with increasing radiological severity, per chest x‐ray and computed tomography (CT) assessment. Our prediction score performed better than the previously‐validated, biomarker‐based 4C‐score.
Objectives
While chest radiograph (CXR) is the first-line imaging investigation in patients with respiratory symptoms, differentiating COVID-19 from other respiratory infections on CXR remains ...challenging. We developed and validated an AI system for COVID-19 detection on presenting CXR.
Methods
A deep learning model (RadGenX), trained on 168,850 CXRs, was validated on a large international test set of presenting CXRs of symptomatic patients from 9 study sites (US, Italy, and Hong Kong SAR) and 2 public datasets from the US and Europe. Performance was measured by area under the receiver operator characteristic curve (AUC). Bootstrapped simulations were performed to assess performance across a range of potential COVID-19 disease prevalence values (3.33 to 33.3%). Comparison against international radiologists was performed on an independent test set of 852 cases.
Results
RadGenX achieved an AUC of 0.89 on 4-fold cross-validation and an AUC of 0.79 (95%CI 0.78–0.80) on an independent test cohort of 5,894 patients. Delong’s test showed statistical differences in model performance across patients from different regions (
p
< 0.01), disease severity (
p
< 0.001), gender (
p
< 0.001), and age (
p
= 0.03). Prevalence simulations showed the negative predictive value increases from 86.1% at 33.3% prevalence, to greater than 98.5% at any prevalence below 4.5%. Compared with radiologists, McNemar’s test showed the model has higher sensitivity (
p
< 0.001) but lower specificity (
p
< 0.001).
Conclusion
An AI model that predicts COVID-19 infection on CXR in symptomatic patients was validated on a large international cohort providing valuable context on testing and performance expectations for AI systems that perform COVID-19 prediction on CXR.
Key Points
• An AI model developed using CXRs to detect COVID-19 was validated in a large multi-center cohort of 5,894 patients from 9 prospectively recruited sites and 2 public datasets.
• Differences in AI model performance were seen across region, disease severity, gender, and age.
• Prevalence simulations on the international test set demonstrate the model’s NPV is greater than 98.5% at any prevalence below 4.5%.
In the British Thoracic Society guidelines for incidental pulmonary nodules, volumetric analysis has become the recommended method for growth assessment in solid indeterminate pulmonary nodules ...(SIPNs) <300 mm
. In these guidelines, two different volume doubling time (VDT) cut-offs, 400 and 600 days, were proposed to differentiate benign from malignant nodules. The present study aims to evaluate the performance of these VDT cut-offs in a group of SIPNs <300 mm
which were incidentally detected in a routine clinical setting. During a 7-year period, we retrospectively selected 60 patients with a single SIPN <300 mm
. For each SIPN, the volume and VDT were calculated using semiautomatic software throughout the follow-up period, and the performance of the 400- and 600-day VDT cut-offs was compared. In the selected sample, there were 38 benign and 22 malignant nodules. In this group of nodules, the sensitivity, negative predictive value and accuracy of the 600-day VDT cut-off were higher than those of the 400-day VDT cut-off. Therefore, in the management of SIPNs <300 mm
which were incidentally detected in a clinical setting, the 600-day VDT cut-off was better at differentiating benign from malignant nodules than the 400-day VDT cut-off, by reducing the number of false negatives.
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