Faculty Bibliography

Objective: Distinguishing post-COVID-19 residual abnormalities from interstitial lung abnormalities (ILA) on CT can be challenging if clinical information is limited. This study aimed to evaluate the diagnostic performance of radiologists in distinguishing post-COVID-19 residual abnormalities from ILA. Methods: This multi-reader, multi-case study included 60 age- and sex-matched subjects with chest CT scans. There were 40 cases of ILA (20 fibrotic and 20 non-fibrotic) and 20 cases of post-COVID-19 residual abnormalities. Fifteen radiologists from multiple nations with varying levels of experience independently rated suspicion scores on a 5-point scale to distinguish post-COVID-19 residual abnormalities from fibrotic ILA or non-fibrotic ILA. Interobserver agreement was assessed using the weighted κ value, and the scores of individual readers were compared with the consensus of all readers. Receiver operating characteristic curve analysis was conducted to evaluate the diagnostic performance of suspicion scores for distinguishing post-COVID-19 residual abnormalities from ILA and for differentiating post-COVID-19 residual abnormalities from both fibrotic and non-fibrotic ILA. Results: Radiologists"™ diagnostic performance for distinguishing post-COVID-19 residual abnormalities from ILA was good (area under the receiver operating characteristic curve (AUC) range, 0.67"“0.92; median AUC, 0.85) with moderate agreement (κ = 0.56). The diagnostic performance for distinguishing post-COVID-19 residual abnormalities from non-fibrotic ILA was lower than that from fibrotic ILA (median AUC = 0.89 vs. AUC = 0.80, p = 0.003). Conclusion: Radiologists demonstrated good diagnostic performance and moderate agreement in distinguishing post-COVID-19 residual abnormalities from ILA, but careful attention is needed to avoid misdiagnosing them as non-fibrotic ILA. Key Points: Question How good are radiologists at differentiating interstitial lung abnormalities (ILA) from changes related to COVID-19 infection? Findings Radiologists had a median AUC of 0.85 in distinguishing post-COVID-19 abnormalities from ILA with moderate agreement (κ = 0.56). Clinical relevance Radiologists showed good diagnostic performance and moderate agreement in distinguishing post-COVID-19 residual abnormalities from ILA; nonetheless, caution is needed in distinguishing residual abnormalities from non-fibrotic ILA.

Incidental pulmonary nodules are common. Although the majority are benign, most are indeterminate for malignancy when first encountered making their management challenging. CT remains the primary imaging modality to first characterize and follow-up incidental lung nodules. This document reviews available literature on various imaging modalities and summarizes management of indeterminate pulmonary nodules detected incidentally. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

PURPOSE/OBJECTIVE:The purpose of this study was to identify differences in imaging features between patients with confirmed right middle lobe (RML) torsion compared to those suspected yet without torsion. MATERIALS AND METHODS/METHODS:This retrospective study entailing a search of radiology reports from April 1, 2014, to April 15, 2021, resulted in 52 patients with suspected yet without lobar torsion and 4 with confirmed torsion, supplemented by 2 additional cases before the search period for a total of 6 confirmed cases. Four thoracic radiologists (1 an adjudicator) evaluated chest radiographs and computed tomography (CT) examinations, and Fisher exact and Mann-Whitney tests were used to identify any significant differences in imaging features (P<0.05). RESULTS:A reversed halo sign was more frequent for all readers (P=0.001) in confirmed RML torsion than patients without torsion (83.3% vs. 0% for 3 readers, one the adjudicator). The CT coronal bronchial angle between RML bronchus and bronchus intermedius was larger (P=0.035) in torsion (121.28 degrees) than nontorsion cases (98.26 degrees). Patients with torsion had a higher percentage of ground-glass opacity in the affected lobe (P=0.031). A convex fissure towards the adjacent lobe on CT (P=0.009) and increased lobe volume on CT (P=0.001) occurred more often in confirmed torsion. CONCLUSION/CONCLUSIONS:A reversed halo sign, larger CT coronal bronchial angle, greater proportion of ground-glass opacity, fissural convexity, and larger lobe volume on CT may aid in early recognition of the rare yet highly significant diagnosis of lobar torsion.

PURPOSE/OBJECTIVE:To assess deep learning denoised (DLD) computed tomography (CT) chest images at various low doses by both quantitative and qualitative perceptual image analysis. METHODS:Simulated noise was inserted into sinogram data from 32 chest CTs acquired at 100 mAs, generating anatomically registered images at 40, 20, 10, and 5 mAs. A DLD model was developed, with 23 scans selected for training, 5 for validation, and 4 for test.Quantitative analysis of perceptual image quality was assessed with Structural SIMilarity Index (SSIM) and Fréchet Inception Distance (FID). Four thoracic radiologists graded overall diagnostic image quality, image artifact, visibility of small structures, and lesion conspicuity. Noise-simulated and denoised image series were evaluated in comparison with one another, and in comparison with standard 100 mAs acquisition at the 4 mAs levels. Statistical tests were conducted at the 2-sided 5% significance level, with multiple comparison correction. RESULTS:At the same mAs levels, SSIM and FID between noise-simulated and reconstructed DLD images indicated that images were closer to a perfect match with increasing mAs (closer to 1 for SSIM, and 0 for FID).In comparing noise-simulated and DLD images to standard-dose 100-mAs images, DLD improved SSIM and FID. Deep learning denoising improved SSIM of 40-, 20-, 10-, and 5-mAs simulations in comparison with standard-dose 100-mAs images, with change in SSIM from 0.91 to 0.94, 0.87 to 0.93, 0.67 to 0.87, and 0.54 to 0.84, respectively. Deep learning denoising improved FID of 40-, 20-, 10-, and 5-mAs simulations in comparison with standard-dose 100-mAs images, with change in FID from 20 to 13, 46 to 21, 104 to 41, and 148 to 69, respectively.Qualitative image analysis showed no significant difference in lesion conspicuity between DLD images at any mAs in comparison with 100-mAs images. Deep learning denoising images at 10 and 5 mAs were rated lower for overall diagnostic image quality (P < 0.001), and at 5 mAs lower for overall image artifact and visibility of small structures (P = 0.002), in comparison with 100 mAs. CONCLUSIONS:Deep learning denoising resulted in quantitative improvements in image quality. Qualitative assessment demonstrated DLD images at or less than 10 mAs to be rated inferior to standard-dose images.

OBJECTIVE:This study aimed to determine the prevalence of axillary and subpectoral (SP) lymph nodes after ipsilateral COVID-19 vaccine administration on chest computed tomography (CT). METHODS:Subjects with chest CTs between 2 and 25 days after a first or second vaccine dose, December 15, 2020, to February 12, 2021, were included. Orthogonal measures of the largest axillary and SP nodes were recorded by 2 readers blinded to vaccine administration and clinical details. A mean nodal diameter discrepancy of ≥6 mm between contralateral stations was considered positive for asymmetry. Correlation with the side of vaccination, using a Spearman rank correlation, was performed on the full cohort and after excluding patients with diseases associated with adenopathy. RESULTS:Of the 138 subjects (81 women, 57 men; mean [SD] age, 74.4 ± 11.7 years), 48 (35%) had asymmetrically enlarged axillary and/or SP lymph nodes, 42 (30%) had ipsilateral, and 6 (4%) had contralateral to vaccination ( P = 0.003). Exclusion of 29 subjects with conditions associated with adenopathy showed almost identical correlation, with asymmetric nodes in 32 of 109 (29%) ipsilateral and in 5 of 109 (5%) contralateral to vaccination ( P = 0.002). CONCLUSIONS:Axillary and/or SP lymph nodes ipsilateral to vaccine administration represents a clinical conundrum. Asymmetric nodes were detected at CT in 30% of subjects overall and 29% of subjects without conditions associated with adenopathy, approximately double the prevalence rate reported to the Centers for Disease Control and Prevention by vaccine manufacturers. When interpreting examinations correlation with vaccine administration timing and site is important for pragmatic management.

Acute pulmonary embolism (PE) is a critical, potentially life-threatening finding on contrast-enhanced cross-sectional chest imaging. Timely and accurate diagnosis of thrombus acuity and extent directly influences patient management, and outcomes. Technical and interpretive pitfalls may present challenges to the radiologist, and by extension, pose nuance in the development and integration of artificial intelligence support tools. This review delineates imaging considerations for diagnosis of acute PE, and rationale, hurdles and applications of artificial intelligence for the PE task.

Quantitative radiomic and iodine imaging features have been explored for diagnosis and characterization of tumors. In this work, we invistigate combined whole-lesion radiomic and iodine analysis for the differentiation of pulmonary tumors on contrast-enhanced dual-energy CT (DECT) chest images. 100 biopsy-proven solid lung lesions on contrast-enhanced DECT chest exams within 3 months of histopathologic sampling were identified. Lesions were volumetrically segmented using open-source software. Lesion segmentations and iodine density volumes were loaded into a radiomics prototype for quantitative analysis. Univariate analysis was performed to determine differences in volumetric iodine concentration (mean, median, maximum, minimum, 10th percentile, 90th percentile) and first and higher order radiomic features (n = 1212) between pulmonary tumors. Analyses were performed using a 2-sample t test, and filtered for false discoveries using Benjamini-Hochberg method. 100 individuals (mean age 65 ± 13 years; 59 women) with 64 primary and 36 metastatic lung lesions were included. Only one iodine concentration parameter, absolute minimum iodine, significantly differed between primary and metastatic pulmonary tumors (FDR-adjusted p = 0.015, AUC 0.69). 310 (FDR-adjusted p = 0.0008 to p = 0.0491) radiomic features differed between primary and metastatic lung tumors. Of these, 21 features achieved AUC ≥ 0.75. In subset analyses of lesions imaged by non-CTPA protocol (n = 72), 191 features significantly differed between primary and metastatic tumors, 19 of which achieved AUC ≥ 0.75. In subset analysis of tumors without history of prior treatment (n = 59), 40 features significantly differed between primary and metastatic tumors, 11 of which achieved AUC ≥ 0.75. Volumetric radiomic analysis provides differentiating capability beyond iodine quantification. While a high number of radiomic features differentiated primary versus metastatic pulmonary tumors, fewer features demonstrated good individual discriminatory utility.

OBJECTIVES/OBJECTIVE:Patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may develop end-stage lung disease requiring lung transplantation. We report the clinical course, pulmonary pathology with radiographic correlation, and outcomes after lung transplantation in three patients who developed chronic respiratory failure due to postacute sequelae of SARS-CoV-2 infection. METHODS:A retrospective histologic evaluation of explanted lungs due to coronavirus disease 2019 was performed. RESULTS:None of the patients had known prior pulmonary disease. The major pathologic findings in the lung explants were proliferative and fibrotic phases of diffuse alveolar damage, interstitial capillary neoangiogenesis, and mononuclear inflammation, specifically macrophages, with varying numbers of T and B lymphocytes. The fibrosis varied from early collagen deposition to more pronounced interstitial collagen deposition; however, pulmonary remodeling with honeycomb change was not present. Other findings included peribronchiolar metaplasia, microvascular thrombosis, recanalized thrombi in muscular arteries, and pleural adhesions. No patients had either recurrence of SARS-CoV-2 infection or allograft rejection following transplant at this time. CONCLUSIONS:The major pathologic findings in the lung explants of patients with SARS-CoV-2 infection suggest ongoing fibrosis, prominent macrophage infiltration, neoangiogenesis, and microvascular thrombosis. Characterization of pathologic findings could help develop novel management strategies.

Lung nodules are frequently encountered while interpreting chest CTs and are challenging to detect, characterize, and manage given they can represent both benign or malignant etiologies. An understanding of features associated with malignancy and causes of interpretive pitfalls is helpful to avoid misdiagnoses. This review addresses pertinent topics related to the etiologies for missed lung nodules on radiography and CT. Additionally, CT imaging technical pitfalls and challenges in addition to issues in the evaluation of nodule morphology, attenuation, and size will be discussed. Nodule management guidelines will be addressed as well as recent investigations that further our understanding of lung nodules.

Imaging of the thoracic aorta is a common request in both the acute and outpatient settings, playing a crucial role in diagnosis and treatment planning of aortic disease. The findings of aortic pathology may be obvious or occult on imaging. Recognizing subtle changes is essential and may lead to early detection and prevention of serious morbidity and mortality. Knowledge of the anatomy and understanding the pathophysiology of aortic disease, as well as selecting the appropriate imaging modality and protocol will enable prompt diagnosis and early intervention of aortic pathology. Currently, computed tomography angiography and magnetic resonance angiography of the aorta are the most commonly used imaging modalities to evaluate the aorta. This review focuses on a spectrum of aortic pathology manifestations on computed tomography and magnetic resonance, including atherosclerosis and acute aortic syndromes, highlighting diagnostic challenges and approaches to aid in image interpretation.