Faculty Bibliography

Imaging plays a major role in the care of the intensive care unit (ICU) patients. An understanding of the monitoring devices is essential for the interpretation of imaging studies. An awareness of their expected locations aids in identifying complications in a timely manner. This review describes the imaging of ICU monitoring and support catheters, tubes, and pulmonary and cardiac devices, some more commonly encountered and others that have been introduced into clinical patient care more recently. Special focus will be placed on chest radiography and potential pitfalls encountered.

[This retracts the article DOI: 10.21037/jtd.2019.08.33.].

OBJECTIVE:To evaluate the association between census-tract level measures of social vulnerability and residential segregation and IPN follow up. METHODS:This retrospective cohort study included patients with IPN ≥6 mm in size or multiple subsolid/ground-glass IPNs <6 mm (with non-optional follow-up recommendations) diagnosed between January 1, 2018 and December 30, 2019 at a large urban tertiary center and followed ≥two years. Geographic sociodemographic context was characterized by 2018 U.S. Centers for Disease Control and Prevention Social Vulnerability Index (SVI) and the Index of Concentration at the Extreme (ICE), categorized in quartiles. Multivariable binomial regression models were utilized with a primary outcome of inappropriate IPN follow up (late or no follow up). Models were also stratified by nodule risk. RESULTS:The study consisted of 2,492 patients (mean age 65.6 years +/- 12.6 years; 1,361 women). Top-quartile SVI patients were more likely to have inappropriate follow up (Risk Ratio [RR]: 1.24, 95% Confidence Interval [95% CI], 1.12-1.36]), compared with the bottom quartile; risk was also elevated in top-quartile SVI subcategories of Socioeconomic Status (RR: 1.23, 95% CI, 1.13-1.34), Minority Status and Language (RR: 1.24, 95% CI, 1.03-1.48), Housing and Transportation (RR: 1.13, 95% CI, 1.02-1.26), and ICE (RR: 1.20, 95% CI, 1.11-1.30). Further, top-quartile ICE was associated with greater risk of inappropriate follow up among high-risk vs. lower-risk IPN (1.33 [1.18-1.50] vs. 1.13 [1.02-1.25]), respectively, P for interaction= 0.017). DISCUSSION/CONCLUSIONS:Local social vulnerability and residential segregation are associated with inappropriate IPN follow up and may inform policy or interventions tailored for neighborhoods.

PURPOSE:To characterize the effectiveness, safety, and length of stay (LOS) associated with pulmonary cryoablation for management of primary lung malignancies in patients aged ≥80 years. MATERIALS AND METHODS:A retrospective single-center database was compiled of all consecutive patients aged ≥80 years who underwent percutaneous computed tomography-guided cryoablation using modified triple-freeze protocol (1-3 ablation probes) for Stage IA-IIB primary lung malignancies between March 2017 and March 2020 (n = 19; 53% women; mean age, 85 years ± 3.5; range, 80-94 years). Follow-up imaging was assessed for local recurrence. Adverse events and LOS were recorded from chart review. Kaplan-Meier analysis was performed to assess both overall and local recurrence-free survival. RESULTS:Mean patient follow-up period was 21.6 months ± 10.8, and mean imaging follow-up period was 19.2 months ± 9.6. Overall survival at 3 years was 94% (95% CI, 81%-100%). Local recurrence-free survival was 100% throughout the imaging follow-up period. Intraprocedural pneumothorax occurred in 37% (7 of 19) of patients; pneumothorax risk was significantly associated with increased tumor distance from pleura (odds ratio, 1.2; P = .018). Sixty-three percent (12 of 19) of patients were discharged on the day of the procedure, with a mean LOS of 7.7 hours ± 1.6, whereas 37% of patients required overnight observation (2 of 19) or admission (5 of 19), with a mean LOS of 48.1 hours ± 19.4. Overall LOS for all patients was 22.6 hours ± 22.9. CONCLUSIONS:Percutaneous cryoablation of primary pulmonary malignancies can be performed in select octogenarians and nonagenarians with high 3-year overall and recurrence-free survival. Despite nonnegligible risk of pneumothorax, most patients are discharged on the day of the procedure.

RATIONALE AND OBJECTIVES/OBJECTIVE:Finding comparison to relevant prior studies is a requisite component of the radiology workflow. The purpose of this study was to evaluate the impact of a deep learning tool simplifying this time-consuming task by automatically identifying and displaying the finding in relevant prior studies. MATERIALS AND METHODS/METHODS:The algorithm pipeline used in this retrospective study, TimeLens (TL), is based on natural language processing and descriptor-based image-matching algorithms. The dataset used for testing comprised 3872 series of 246 radiology examinations from 75 patients (189 CTs, 95 MRIs). To ensure a comprehensive testing, five finding types frequently encountered in radiology practice were included: aortic aneurysm, intracranial aneurysm, kidney lesion, meningioma, and pulmonary nodule. After a standardized training session, nine radiologists from three university hospitals performed two reading sessions on a cloud-based evaluation platform resembling a standard RIS/PACS. The task was to measure the diameter of the finding-of-interest on two or more exams (a most recent and at least one prior exam): first without use of TL, and a second session at an interval of at least 21 days with the use of TL. All user actions were logged for each round, including time needed to measure the finding at all timepoints, number of mouse clicks, and mouse distance traveled. The effect of TL was evaluated in total, per finding type, per reader, per experience (resident vs. board-certified radiologist), and per modality. Mouse movement patterns were analyzed with heatmaps. To assess the effect of habituation to the cases, a third round of readings was performed without TL. RESULTS:Across scenarios, TL reduced the average time needed to assess a finding at all timepoints by 40.1% (107 vs. 65 seconds; p < 0.001). Largest accelerations were demonstrated for assessment of pulmonary nodules (-47.0%; p < 0.001). Less mouse clicks (-17.2%) were needed for finding evaluation with TL, and mouse distance traveled was reduced by 38.0%. Time needed to assess the findings increased from round 2 to round 3 (+27.6%; p < 0.001). Readers were able to measure a given finding in 94.4% of cases on the series initially proposed by TL as most relevant series for comparison. The heatmaps showed consistently simplified mouse movement patterns with TL. CONCLUSION/CONCLUSIONS:A deep learning tool significantly reduced both the amount of user interactions with the radiology image viewer and the time needed to assess findings of interest on cross-sectional imaging with relevant prior exams.

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.

Routine chest imaging has been used to identify unknown or subclinical cardiothoracic abnormalities in the absence of symptoms. Various imaging modalities have been suggested for routine chest imaging. We review the evidence for or against the use of routine chest imaging in different clinical scenarios. This document aims to determine guidelines for the use of routine chest imaging as initial imaging for hospital admission, initial imaging prior to noncardiothoracic surgery, and surveillance imaging for chronic cardiopulmonary disease. 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: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.