Faculty Bibliography

RATIONALE AND OBJECTIVES:Radiographic findings of COVID-19 pneumonia can be used for patient risk stratification; however, radiologist reporting of disease severity is inconsistent on chest radiographs (CXRs). We aimed to see if an artificial intelligence (AI) system could help improve radiologist interrater agreement. MATERIALS AND METHODS:We performed a retrospective multi-radiologist user study to evaluate the impact of an AI system, the PXS score model, on the grading of categorical COVID-19 lung disease severity on 154 chest radiographs into four ordinal grades (normal/minimal, mild, moderate, and severe). Four radiologists (two thoracic and two emergency radiologists) independently interpreted 154 CXRs from 154 unique patients with COVID-19 hospitalized at a large academic center, before and after using the AI system (median washout time interval was 16 days). Three different thoracic radiologists assessed the same 154 CXRs using an updated version of the AI system trained on more imaging data. Radiologist interrater agreement was evaluated using Cohen and Fleiss kappa where appropriate. The lung disease severity categories were associated with clinical outcomes using a previously published outcomes dataset using Fisher's exact test and Chi-square test for trend. RESULTS:Use of the AI system improved radiologist interrater agreement (Fleiss κ = 0.40 to 0.66, before and after use of the system). The Fleiss κ for three radiologists using the updated AI system was 0.74. Severity categories were significantly associated with subsequent intubation or death within 3 days. CONCLUSION:An AI system used at the time of CXR study interpretation can improve the interrater agreement of radiologists.

Lung cancer remains the leading cause of cancer-related deaths worldwide. The treatment of non-small cell lung cancer (NSCLC), which accounts for a vast majority of lung cancers, has shifted to personalized, targeted therapy following discoveries of several targetable oncogenic mutations. Targeting of specific mutations has improved outcomes in many patients. This success has led to several target-specific agents replacing chemotherapy as first-line treatment in certain mutated NSCLC. Several researchers have reported that there may be imaging biomarkers that may be predictive of the presence of these mutations. These features, when present, have the potential in triaging patients into the most appropriate diagnostic and treatment algorithms. Distinct imaging features and patterns of metastases that have been associated with NSCLC with various targetable oncogenic mutations are presented in this review.

PURPOSE/OBJECTIVE:To develop an automated measure of COVID-19 pulmonary disease severity on chest radiographs (CXRs), for longitudinal disease tracking and outcome prediction. MATERIALS AND METHODS/METHODS:A convolutional Siamese neural network-based algorithm was trained to output a measure of pulmonary disease severity on CXRs (pulmonary x-ray severity (PXS) score), using weakly-supervised pretraining on ∼160,000 anterior-posterior images from CheXpert and transfer learning on 314 frontal CXRs from COVID-19 patients. The algorithm was evaluated on internal and external test sets from different hospitals (154 and 113 CXRs respectively). PXS scores were correlated with radiographic severity scores independently assigned by two thoracic radiologists and one in-training radiologist (Pearson r). For 92 internal test set patients with follow-up CXRs, PXS score change was compared to radiologist assessments of change (Spearman ρ). The association between PXS score and subsequent intubation or death was assessed. Bootstrap 95% confidence intervals (CI) were calculated. RESULTS:PXS scores correlated with radiographic pulmonary disease severity scores assigned to CXRs in the internal and external test sets (r=0.86 (95%CI 0.80-0.90) and r=0.86 (95%CI 0.79-0.90) respectively). The direction of change in PXS score in follow-up CXRs agreed with radiologist assessment (ρ=0.74 (95%CI 0.63-0.81)). In patients not intubated on the admission CXR, the PXS score predicted subsequent intubation or death within three days of hospital admission (area under the receiver operating characteristic curve=0.80 (95%CI 0.75-0.85)). CONCLUSION/CONCLUSIONS:A Siamese neural network-based severity score automatically measures radiographic COVID-19 pulmonary disease severity, which can be used to track disease change and predict subsequent intubation or death.

BACKGROUND:mutation and stratified by tumor-node-metastasis (TNM) staging. METHODS:edition of TNM staging. Two thoracic radiologists assessed the primary tumor imaging features such, including tumor size (maximum and minimum dimensions) and density (Hounsfield units, HU). De-identified transverse CT images (DICOM) were processed with 3D slicer (Version 4.7) for manual lesion segmentation and estimation of radiomic features. Descriptive statistics, multivariate logistic regression, and receiver operating characteristics (ROC) were performed. RESULTS:There were significant differences in the radiomic features based on cancer stages I-IV with the most significant differences between stage IV and stage I lesions [AUC 0.94 (95% CI: 0.86-0.99), P<0.04]. There were also significant differences in radiomic features between stage IV and combined stages I-III [40/113 radiomic features; AUC 0.71 (95% CI: 0.59-0.85); P<0.04-0.0001]. None of the clinical (0/6) or imaging (0/3) features were significantly different between stage IV and combined stages I-III. CONCLUSIONS:driven NSCLC significantly vary with cancer stage, independent of standard imaging and clinical features.

RATIONALE AND OBJECTIVES:While affiliated imaging centers play an important role in healthcare systems, little is known of how their operations are impacted by the COVID-19 pandemic. Our goal was to investigate imaging volume trends during the pandemic at our large academic hospital compared to the affiliated imaging centers. MATERIALS AND METHODS:This was a descriptive retrospective study of imaging volume from an academic hospital (main hospital campus) and its affiliated imaging centers from January 1 through May 21, 2020. Imaging volume assessment was separated into prestate of emergency (SOE) period (before SOE in Massachusetts on March 10, 2020), "post-SOE" period (time after "nonessential" services closure on March 24, 2020), and "transition" period (between pre-SOE and post-SOE). RESULTS:Imaging volume began to decrease on March 11, 2020, after hospital policy to delay nonessential studies. The average weekly imaging volume during the post-SOE period declined by 54% at the main hospital campus and 64% at the affiliated imaging centers. The rate of imaging volume recovery was slower for affiliated imaging centers (slope = 6.95 for weekdays) compared to main hospital campus (slope = 7.18 for weekdays). CT, radiography, and ultrasound exhibited the lowest volume loss, with weekly volume decrease of 41%, 49%, and 53%, respectively, at the main hospital campus, and 43%, 61%, and 60%, respectively, at affiliated imaging centers. Mammography had the greatest volume loss of 92% at both the main hospital campus and affiliated imaging centers. CONCLUSION:Affiliated imaging center volume decreased to a greater degree than the main hospital campus and showed a slower rate of recovery. Furthermore, the trend in imaging volume and recovery were temporally related to public health announcements and COVID-19 cases.

BACKGROUND:Computed tomography (CT) imaging findings in the lungs in the setting of an acute allergic response and following bronchoalveolar lavage (BAL) are not well established. Our goals are to characterize the pulmonary CT findings of acute allergic response in both asthmatic and non-asthmatic subjects and, secondarily, to characterize the pulmonary imaging findings following BAL. METHODS:In this prospective observational (cohort) study, we identified atopic, asthmatic (AA) and atopic, non-asthmatic (ANA) subjects. CT of the chest was performed following BAL and instillation of an allergen (AL) and of an inert diluent (DL). Two radiologists analyzed the CT examinations for airway and parenchymal changes. RESULTS:. DL=0/20, 0%; P<0.01). CONCLUSIONS:Acute allergic response in the lungs can result in significant bronchial wall thickening, septal thickening, and consolidations in those with atopy, particularly those with asthma. Localized ground-glass opacities may be expected following BAL, and care should be taken so as to not misinterpret these as significant pathology.

Background Disease severity on chest radiographs has been associated with higher risk of disease progression and adverse outcomes from coronavirus disease 2019 (COVID-19). Few studies have evaluated COVID-19-related racial and/or ethnic disparities in radiology. Purpose To evaluate whether non-White minority patients hospitalized with confirmed COVID-19 infection presented with increased severity on admission chest radiographs compared with White or non-Hispanic patients. Materials and Methods This single-institution retrospective cohort study was approved by the institutional review board. Patients hospitalized with confirmed COVID-19 infection between March 17, 2020, and April 10, 2020, were identified by using the electronic medical record (n = 326; mean age, 59 years ±17 [standard deviation]; male-to-female ratio: 188:138). The primary outcome was the severity of lung disease on admission chest radiographs, measured by using the modified Radiographic Assessment of Lung Edema (mRALE) score. The secondary outcome was a composite adverse clinical outcome of intubation, intensive care unit admission, or death. The primary exposure was the racial and/or ethnic category: White or non-Hispanic versus non-White (ie, Hispanic, Black, Asian, or other). Multivariable linear regression analyses were performed to evaluate the association between mRALE scores and race and/or ethnicity. Results Non-White patients had significantly higher mRALE scores (median score, 6.1; 95% confidence interval [CI]: 5.4, 6.7) compared with White or non-Hispanic patients (median score, 4.2; 95% CI: 3.6, 4.9) (unadjusted average difference, 1.8; 95% CI: 0.9, 2.8; P < .01). For both White (adjusted hazard ratio, 1.3; 95% CI: 1.2, 1.4; P < .001) and non-White (adjusted hazard ratio, 1.2; 95% CI: 1.1, 1.3; P < .001) patients, increasing mRALE scores were associated with a higher likelihood of experiencing composite adverse outcome with no evidence of interaction (P = .16). Multivariable linear regression analyses demonstrated that non-White patients presented with higher mRALE scores at admission chest radiography compared with White or non-Hispanic patients (adjusted average difference, 1.6; 95% CI: 0.5, 2.7; P < .01). Adjustment for hypothesized mediators revealed that the association between race and/or ethnicity and mRALE scores was mediated by limited English proficiency (P < .01). Conclusion Non-White patients hospitalized with coronavirus disease 2019 infection were more likely to have a higher severity of disease on admission chest radiographs than White or non-Hispanic patients, and increased severity was associated with worse outcomes for all patients. © RSNA, 2020 Online supplemental material is available for this article.

BACKGROUND:Esophago-airway fistula (EAF) is an abnormal connection between the esophagus and the trachea or a major bronchus. While contrast esophagography remains the primary radiographic tool for the diagnosis of EAF, computed tomography (CT) is often employed in its evaluation. A systematic analysis of CT findings of EAF in adults has not been previously published. The goal of our study is to determine the direct and indirect CT findings of EAF in adults. METHODS:We identified patients with EAF detected on CT at our institution between January 1, 2001 and December 31, 2019, with endoscopic or surgical confirmation. We collected patient clinicopathologic characteristics and assessed CTs for direct and indirect imaging features of EAF in these patients. RESULTS:Twenty-six patients (median age: 56 years; range, 25-79 years; F=13, 50% and M=13, 50%) with confirmed EAF were identified. Half of the patients had an underlying malignancy. On CT, a direct connection between the esophagus and the airway was identified in most cases (22/26; 85%). Common indirect CT findings of EAF included esophageal wall thickening (21/26, 81%), mediastinal fatty stranding (21/26, 81%), airway wall thickening (20/26, 77%), fluid or debris within the airways (17/26, 65%), and focal or diffuse esophageal dilation with air (17/26, 65%). Mediastinal fluid collections were infrequently seen (4/26, 15%), but findings of aspiration or other pneumonia were common (19/26, 73%). CONCLUSIONS:CT plays an essential role in both the primary and secondary evaluation of adult EAF resulting from both malignant and benign etiologies. CT may be the first diagnostic exam to suggest and detect the presence of EAF and may precede clinical suspicion, and it can detect a subset of fistulas not demonstrated on esophagography. There are several direct and indirect imaging findings on CT that can help in the detection of EAF.

PURPOSE/OBJECTIVE:The purpose of this article was to report the utility of computed tomography (CT) for detecting unsuspected cases of Coronavirus disease 2019 (COVID-19) and the utility of the Radiological Society of North America (RSNA)/Society of Thoracic Radiology (STR)/American College of Radiology (ACR) consensus guidelines for COVID-19 reporting. MATERIALS AND METHODS/METHODS:A total of 22 patients of the 156 reverse transcriptase polymerase chain reaction confirmed COVID-19 patients who were hospitalized between March 27, 2020 and March 31, 2020 at our quaternary care academic medical center and who underwent CT imaging within 1 week of admission were included in this retrospective study. Demographics and clinical data were extracted from the electronic medical record system. Two thoracic radiologists independently categorized each CT study on the basis of RSNA/STR/ACR consensus guidelines. Disagreement in categorization was resolved by consensus discussion with a third thoracic radiologist. RESULTS:At the time of imaging, 16 patients (73%) were suspected of COVID-19, and 6 patients (27%) were not. Common symptoms at presentation were fever (73%), cough (77%), and gastrointestinal symptoms (59%). An overall 63% of suspected COVID-19 patients exhibited shortness of breath, whereas 0 unsuspected COVID-19 patients did (P=0.02). On the basis of the RSNA consensus guidelines, 68%, 18%, 9%, and 5% of studies were categorized as "typical appearance," "indeterminate appearance," "atypical appearance," and "negative for pneumonia," respectively. There was no difference of category distribution between suspected and unsuspected COVID-19 patients (P=0.20), with "typical appearance" being the most prevalent in both (69% vs. 67%, respectively). CONCLUSIONS:It is important to recognize imaging features of COVID-19 pneumonia even in unsuspected patients. Implementation of the RSNA/STR/ACR consensus guidelines may increase consistency of reporting and convey the level of suspicion for COVID-19 to other health care providers, with "typical appearance" especially warranting further attention.