Prevalence of Adenopathy at Chest Computed Tomography After Vaccination for Severe Acute Respiratory Syndrome Coronavirus 2.

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.

Deep Learning Denoising of Low-Dose Computed Tomography Chest Images: A Quantitative and Qualitative Image Analysis.

PURPOSE: 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.

Who Wants to Learn How to Teach? Perceptions of Radiology Residents and Radiology Teaching Faculty Regarding Resident as Teacher Training.

RATIONALE AND OBJECTIVES: While the ACGME requires Resident as Teacher (RAT) training, curricula in radiology remain limited. Our study was performed to examine radiology residents (RR) and teaching faculty (TF) perceptions about RAT training. MATERIALS AND METHODS: In 2021, anonymous online surveys were administered to all RR (53-item) and to all TF (24-item) of a radiology residency program. Content domains included attitudes about RAT training and learning topics. RESULTS: Response rates were 97% (38/39) for RR and 54% (58/107) for TF. Most RR desired training to become better educators to medical students (MS) (81%) and other residents (83%). Seventy-seven percent of RR reported the importance regarding how to give feedback to other learners, while 94% desired formal training on delivering case presentations. While 94% of RR reported that resident feedback was valuable, only 6% reported always giving feedback to MS. Seventy-two percent of RR did not apply at least some best-practices in their reading room teaching. Fifty-nine percent of RR wanted TF to observe their own teaching skills and provide feedback although 70% reported rarely or never receiving TF feedback. Ninety-three percent of TF reported RR should receive RAT training, while 88% reported that feedback of RR to MS was important. CONCLUSION: RR and TF strongly endorsed the need for RAT training. RR anticipate teaching to be an important part of their careers. We identified learning topics and possible gaps regarding how TF are meeting RR needs, which could inform the development of RAT curricula.

Clinical significance of lung nodules reported on abdominal CT.

OBJECTIVE: The objective of our study was to identify the significance of lung nodules reported on abdominal CT. MATERIALS AND METHODS: Abdominal CT reports from a 1-year period were reviewed for the terms "nodule," "nodular," or "mass" in reference to the lung bases. Patients with prior chest or abdominal CT examinations were excluded; the study population included patients with an initial abdominal CT study and at least one follow-up chest or abdominal CT examination. Two thoracic radiologists characterized nodules in consensus. Radiology and clinical records were reviewed for nodule growth and clinical diagnoses. RESULTS: The term "nodule," "nodular," or "mass" in reference to the lung bases was reported in 364 of 12,287 abdominal CT studies (3%). Of 125 patients with no prior CT examination, 42 had undergone follow-up chest CT, abdominal CT, or both. Common imaging indications included abdominal pain (13/42, 31%) and preexisting malignancy (n = 7, 16.7%). Regardless of the indication for imaging, 16 (38.1%) had malignancy that was known (n = 13) or newly diagnosed (n = 3) on the initial abdominal CT. Three of 42 patients (7.1%) had malignant nodules representing metastatic disease: Nodule growth was seen in one patient with preexisting colon cancer, one patient with newly diagnosed metastatic pancreatic cancer, and a third with known bladder cancer. The latter patient had suspected lung metastases that were confirmed on chest CT 1 day later. Three of the 16 patients (18.8%) with preexisting or newly diagnosed cancer had malignant nodules. No malignant nodules were identified without such history. Six patients (14.3%) had an infection. CONCLUSION: Lung nodules incidentally detected on abdominal CT were rarely malignant and were seen only in the setting of an underlying abdominal malignancy. Knowledge of such history is of critical importance to both the clinician and the radiologist. Dedicated chest CT is most useful when assessing pulmonary nodules in patients with localized malignancy.

Medical Student Engagement and Educational Value of a Remote Clinical Radiology Learning Environment: Creation of Virtual Read-Out Sessions in Response to the COVID-19 Pandemic.

RATIONALE AND OBJECTIVES: The need for social distancing has resulted in rapid restructuring of medical student education in radiology. While students traditionally spend time learning in the reading room, remote clinical learning requires material shared without direct teaching at the radiology workstation. Can remote clinical learning meet or exceed the educational value of the traditional in-person learning experience? Can student engagement be matched or exceeded in a remote learning environment? MATERIALS AND METHODS: To replace the in-person reading room experience, a small-group learning session for medical students named Virtual Read-Out (VRO) was developed using teleconferencing software. After Institutional Review Board approval, two student groups were anonymously surveyed to assess differences in student engagement and perceived value between learning environments: "Conventional" students participating in the reading room (before the pandemic) and "Remote" students participating in VRO sessions. Students reported perceived frequency of a series of five-point Likert statements. Based on number of respondents, an independent t-test was performed to determine the significance of results between two groups. RESULTS: Twenty-seven conventional and 41 remote students responded. Remote students reported modest but significantly higher frequency of active participation in reviewing radiology exams (p < 0.05). There was significantly lower frequency of reported boredom among Remote students (p < 0.05). There was no significant difference in perceived educational value between the two groups. CONCLUSION: Students report a high degree of teaching quality, clinical relevance, and educational value regardless of remote or in-person learning format. Remote clinical radiology education can be achieved with equal or greater student interaction and perceived value in fewer contact hours than conventional learning in the reading room.

Incidental Lung Nodules on Cross-sectional Imaging: Current Reporting and 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.

Management of Incidental Lung Nodules: Current Strategy and Rationale.

Incidentally detected lung nodules are increasingly common in routine diagnostic computed tomography (CT) imaging. Formal management recommendations for incidental nodules, such as those outlined by the Fleischner Society, must therefore reflect a balance of malignancy risk and the clinical context in which nodules are discovered. Nodule size, attenuation, morphology, and location all influence the likelihood of malignancy and, thus, the necessity and timing of follow-up according to current Fleischner recommendations. As technological advancements in CT imaging continue, there may be greater reliance on advanced computerized analysis of lung nodule features to help determine the risk of clinically significant disease.

Congenital Coronary Artery Anomalies and Implications.

This pictorial essay presents cases of congenital coronary artery anomalies, including congenital anomalies of origin, course, and termination. Familiarity with atypical coronary anatomy and clinical presentation may facilitate appropriate diagnosis and management, particularly as cardiac and thoracic computed tomographic utilization increases.

Image Quality on Dual-energy CTPA Virtual Monoenergetic Images: Quantitative and Qualitative Assessment.

RATIONALE AND OBJECTIVES: 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: 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.

Lepidic Predominant Pulmonary Lesions (LPL): CT-based Distinction From More Invasive Adenocarcinomas Using 3D Volumetric Density and First-order CT Texture Analysis.

RATIONALE AND OBJECTIVES: This study aimed to differentiate pathologically defined lepidic predominant lesions (LPL) from more invasive adenocarcinomas (INV) using three-dimensional (3D) volumetric density and first-order texture histogram analysis of surgically excised stage 1 lung adenocarcinomas. MATERIALS AND METHODS: This retrospective study was institutional review board approved and Health Insurance Portability and Accountability Act compliant. Sixty-four cases of pathologically proven stage 1 lung adenocarcinoma surgically resected between September 2006 and October 2015, including LPL (n = 43) and INV (n = 21), were evaluated using high-resolution computed tomography. Quantitative measurements included nodule volume, percent solid volume (% solid), and first-order texture histogram analysis including skewness, kurtosis, entropy, and mean nodule attenuation within each histogram quartile. Binomial logistic regression models were used to identify the best set of parameters distinguishing LPL from INV. RESULTS: Univariate analysis of 3D volumetric density and histogram features was statistically significant between LPL and INV groups (P < .05). Accuracy of a binomial logistic model to discriminate LPL from INV based on size and % solid was 85.9%. With optimized probability cutoff, the model achieves 81% sensitivity, 76.7% specificity, and area under the receiver operating characteristic curve of 0.897 (95% confidence interval, 0.821-0.973). An additional model based on size and mean nodule attenuation of the third quartile (Hu_Q3) of the histogram achieved similar accuracy of 81.3% and area under the receiver operating characteristic curve of 0.877 (95% confidence interval, 0.790-0.964). CONCLUSIONS: Both 3D volumetric density and first-order texture analysis of stage 1 lung adenocarcinoma allow differentiation of LPL from more invasive adenocarcinoma with overall accuracy of 85.9%-81.3%, based on multivariate analyses of either size and % solid or size and Hu_Q3, respectively.

CT pulmonary angiography of adult pulmonary vascular diseases: Technical considerations and interpretive pitfalls.

Computed tomography pulmonary angiography (CTPA) has become the primary imaging modality for evaluating the pulmonary arteries. Although pulmonary embolism is the primary indication for CTPA, various pulmonary vascular abnormalities can be detected in adults. Knowledge of these disease entities and understanding technical pitfalls that can occur when performing CTPA are essential to enable accurate diagnosis and allow timely management. This review will cover a spectrum of acquired abnormalities including pulmonary embolism due to thrombus and foreign bodies, primary and metastatic tumor involving the pulmonary arteries, pulmonary hypertension, as well as pulmonary artery aneurysms and stenoses. Additionally, methods to overcome technical pitfalls and interventional treatment options will be addressed.

Imaging the solitary pulmonary nodule.

The development of widespread lung cancer screening programs has the potential to dramatically increase the number of thoracic computed tomography (CT) examinations performed annually in the United States, resulting in a greater number of newly detected, indeterminate solitary pulmonary nodules (SPNs). Additional imaging studies, such as fluorodeoxyglucose F 18 (FDG)-positron emission tomography (PET), have been shown to provide valuable information in the assessment of indeterminate SPNs. Newer technologies, such as contrast-enhanced dual-energy chest CT and FDG-PET/CT, also have the potential to facilitate diagnosis of potentially malignant SPNs.

Imaging the post-thoracotomy patient: anatomic changes and postoperative complications.

Thoracotomy with lung resection produces postoperative changes that can be challenging for the radiologist. Complications related to anatomic and physiologic changes, infection, and breakdown of surgical anastomoses can significantly increase morbidity and mortality. Prompt and accurate diagnosis of serious postoperative complications is essential.

Imaging of incidental findings on thoracic computed tomography.

With continued improvement of high-resolution multidetector computed tomography imaging, there is an increasing number of unsuspected thoracic findings. Although many of these findings are of little clinical significance, other findings such as small incidental lung nodules require additional imaging to exclude more worrisome causes, often resulting in greater exposure to ionizing radiation, increased cost, and patient anxiety. Although greater uniformity among radiologists regarding likely benign findings may help reduce unnecessary imaging studies, the lack of clear follow-up guidelines for many findings suggests that further investigation is needed in some areas.