Faculty Bibliography

Tracheobronchial neoplasms are much less common than lung parenchymal neoplasms but can be associated with significant morbidity and mortality. They include a broad differential of both malignant and benign entities, extending far beyond more commonly known pathologic conditions such as squamous cell carcinoma and carcinoid tumor. Airway lesions may be incidental findings at imaging or manifest with symptoms related to airway narrowing or mucosal irritation, invasion of adjacent structures, or distant metastatic disease. While there is considerable overlap in clinical manifestation, imaging features, and bronchoscopic appearances, an awareness of potential distinguishing factors may help narrow the differential diagnosis. The authors review the epidemiology, imaging characteristics, typical anatomic distributions, bronchoscopic appearances, and histopathologic findings of a wide range of neoplastic entities involving the tracheobronchial tree. Malignant neoplasms discussed include squamous cell carcinoma, malignant salivary gland tumors (adenoid cystic carcinoma and mucoepidermoid carcinoma), carcinoid tumor, sarcomas, primary tracheobronchial lymphoma, and inflammatory myofibroblastic tumor. Benign neoplasms discussed include hamartoma, chondroma, lipoma, papilloma, amyloidoma, leiomyoma, neurogenic lesions, and benign salivary gland tumors (pleomorphic adenoma and mucous gland adenoma). Familiarity with the range of potential entities and any distinguishing features should prove valuable to thoracic radiologists, pulmonologists, and cardiothoracic surgeons when encountering the myriad of tracheobronchial neoplasms in clinical practice. Attention is paid to any features that may help render a more specific diagnosis before pathologic confirmation. ^©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

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.

Pulmonary nodules are the most common incidental finding in the chest, particularly on computed tomographs that include a portion or all of the chest, and may be encountered more frequently with increasing utilization of cross-sectional imaging. Established guidelines address the reporting and management of incidental pulmonary nodules, both solid and subsolid, synthesizing nodule and patient features to distinguish benign nodules from those of potential clinical consequence. Standard nodule assessment is essential for the accurate reporting of nodule size, attenuation, and morphology, all features with varying risk implications and thus management recommendations.

OBJECTIVE. The purpose of this study is to report unanticipated lung base findings on abdominal CT in 23 patients concerning for coronavirus disease (COVID-19). In these patients, who were not previously suspected of having COVID-19, abdominal pain was the most common indication for CT (n = 19), and 11 patients had no extrapulmonary findings. Seventeen patients underwent polymerase chain reaction testing, which returned positive results for all 17. CONCLUSION. Unsuspected coronavirus disease may be strongly suggested on the basis of lung findings on abdominopelvic CT.

RATIONALE AND OBJECTIVES/OBJECTIVE:This study aims to determine the optimal photon energy for image quality of the pulmonary arteries (PAs) on dual-energy computed tomography (CT) pulmonary angiography (CTPA) utilizing low volumes of iodinated contrast. MATERIALS AND METHODS/METHODS:The study received institutional review board exemption and was Health Insurance Portability and Accountability Act compliant. Adults (n = 56) who underwent dual-energy CTPA with 50-60 cc of iodinated contrast on a third-generation dual-source multidetector CT were retrospectively and consecutively identified. Twelve virtual monoenergetic kiloelectron volt (keV) image data sets (40-150 keV, 10-keV increments) were generated with a second-generation noise-reducing algorithm. Standard regions of interest were placed on main, right, left, and right interlobar pulmonary arteries; pectoralis muscle; and extrathoracic air. Attenuation [mean CT number (Hounsfield unit, HU)], noise [standard deviation (HU)], signal to noise (SNR), and contrast to noise ratio were evaluated. Three blinded chest radiologists rated (from 1 to 5, with 5 being the best) randomized monoenergetic and weighted-average images for attenuation and noise. P <.05 was considered significant. RESULTS:Region of interest mean CT number increased as keV decreased, with 40 keV having the highest value (P < .001). Mean SNR was highest for 40-60 keV (P <.05) (14.5-14.7) and was higher (P <.05) than all remaining energies (90-150 keV) for all vessel regions combined. Contrast to noise ratio was highest for 40 keV (P <.001) and decreased as keV increased. SNR was highest at 60 and 70 keV, only slightly higher than 40-50 keV (P <.05). Reader scores for 40-50 keV were greater than other energies and weighted-average images (P <.05). CONCLUSIONS:Kiloelectron volt images of 40-50 keV from the second-generation algorithm optimize attenuation on dual-energy CTPA and can potentially aid in interpretation and avoiding nondiagnostic examinations.

OBJECTIVE:Radiologic technologists may repeat images within a radiographic examination because of perceived suboptimal image quality, excluding these original images from submission to a PACS. This study assesses the appropriateness of technologists' decisions to repeat musculoskeletal and chest radiographs as well as the utility of repeat radiographs in addressing examinations' clinical indication. MATERIALS AND METHODS/METHODS:We included 95 musculoskeletal and 87 chest radiographic examinations in which the technologist repeated one or more images because of perceived image quality issues, rejecting original images from PACS submission. Rejected images were retrieved from the radiograph unit and uploaded for viewing on a dedicated server. Musculoskeletal and chest radiologists reviewed rejected and repeat images in their timed sequence, in addition to the studies' remaining images. Radiologists answered questions regarding the added value of repeat images. RESULTS:The reviewing radiologist agreed with the reason for rejection for 64.2% of musculoskeletal and 60.9% of chest radiographs. For 77.9% and 93.1% of rejected radiographs, the clinical inquiry could have been satisfied without repeating the image. For 75.8% and 64.4%, the repeated images showed improved image quality. Only 28.4% and 3.4% of repeated images were considered to provide additional information that was helpful in addressing the clinical question. CONCLUSION/CONCLUSIONS:Most repeated radiographs (chest more so than musculoskeletal radiographs) did not add significant clinical information or alter diagnosis, although they did increase radiation exposure. The decision to repeat images should be made after viewing the questionable image in context with all images in a study and might best be made by a radiologist rather than the performing technologist.

OBJECTIVE: : To describe pathologic findings in symptomatic World Trade Center-exposed local workers, residents, and cleanup workers enrolled in a treatment program. METHODS: : Twelve patients underwent surgical lung biopsy for suspected interstitial lung disease (group 1, n = 6) or abnormal pulmonary function tests (group 2, n = 6). High-resolution computed axial tomography and pathologic findings were coded. Scanning electron microscopy with energy-dispersive x-ray spectroscopy was performed. RESULTS: : High-resolution computed axial tomography showed reticular findings (group 1) or normal or airway-related findings (group 2). Pulmonary function tests were predominantly restrictive. Interstitial fibrosis, emphysematous change, and small airway abnormalities were seen. All cases had opaque and birefringent particles within macrophages, and examined particles contained silica, aluminum silicates, titanium dioxide, talc, and metals. CONCLUSIONS: : In symptomatic World Trade Center-exposed individuals, pathologic findings suggest a common exposure resulting in alveolar loss and a diverse response to injury