Natural History of Lung Hernias.

BACKGROUND: Lung herniation has been described in case reports or series. There are scare data in the form of original research studies to systematically evaluate this condition. OBJECTIVE: Our aim was to evaluate lung hernias with a focus on their natural history. METHODS: This is a retrospective study at our institution of patients who were found to have lung herniation on imaging between September 2010 and November 2022. Electronic medical record review was performed to extract clinical information regarding patients. Computed tomographic imaging was used to evaluate hernia size and size progression over time with a median follow-up time of 3.8 years. RESULTS: Thirty-eight patients were eligible for analysis. Majority of patients were overweight or obese (31/38), smokers (31/38), had prior thoracic surgery (30/38), and were asymptomatic (33/38). Twenty of 38 patients had stability in hernia size, 12 of 38 patients had hernia size progression, and 6 of 38 patients showed hernia size regression. Younger age was found to be predictive of hernia size progression with age of 60 years being the cut-off for its prediction. CONCLUSION: Lung hernias typically either remain stable in size or show size progression. Younger age (60 years cut-off) was found to be predictive of size progression. This is the largest systematic investigation at a medical institution to the best of our knowledge of lung hernias which used computed tomographic imaging to follow up lung hernias. Further information could be given to patients with this condition and to clinicians for potential management guidance.

Imaging of lung cancer.

Lung cancer is the leading cause of cancer-related mortality globally. Imaging is essential in the screening, diagnosis, staging, response assessment, and surveillance of patients with lung cancer. Subtypes of lung cancer can have distinguishing imaging appearances. The most frequently used imaging modalities include chest radiography, computed tomography, magnetic resonance imaging, and positron emission tomography. Artificial intelligence algorithms and radiomics are emerging technologies with potential applications in lung cancer imaging.

Ferumoxytol-enhanced MR venography for diagnosis of venous thoracic outlet syndrome.

Venous thoracic outlet syndrome commonly results in arm swelling and pain as the subclavian vein is obstructed within the thoracic inlet. We report the use of ferumoxytol-enhanced contrast MRI in the diagnosis of venous thoracic outlet syndrome in a male adolescent. In this patient who presented with right upper extremity thrombosis, ferumoxytol-enhanced MRI of the chest was able to show both chronic subclavian vein thromboses and dynamic occlusion of the subclavian veins with arm abduction consistent with Paget-Schroetter syndrome.

Nonsurgical Therapy for Early-Stage Lung Cancer.

Treatment options for medically inoperable, early-stage non-small cell lung cancer (NSCLC) include stereotactic ablative radiotherapy (SABR) and percutaneous image guided thermal ablation. SABR is delivered over 1-5 sessions of highly conformal ablative radiation with excellent tumor control. Toxicity is depending on tumor location and anatomy but is typically mild. Studies evaluating SABR in operable NSCLC are ongoing. Thermal ablation can be delivered via radiofrequency, microwave, or cryoablation, with promising results and modest toxicity. We review the data and outcomes for these approaches and discuss ongoing studies.

Osimertinib in NSCLC With Atypical EGFR-Activating Mutations: A Retrospective Multicenter Study.

Introduction: EGFR mutations drive a subset of NSCLC. Patients harboring the common EGFR mutations, deletion of exon 19 and L858R, respond well to osimertinib, a third-generation tyrosine kinase inhibitor. Nevertheless, the effect of osimertinib on NSCLC with atypical EGFR mutations is not well described. This multicenter retrospective study evaluates the efficacy of osimertinib among patients with NSCLC harboring atypical EGFR mutations. Methods: Patients with metastatic NSCLC treated with osimertinib, harboring at least one atypical EGFR mutation, excluding concurrent deletion of exon 19, L858R, or T790M mutations, from six U.S. academic cancer centers were included. Baseline clinical characteristics were collected. The primary end point was the time to treatment discontinuation (TTD) of osimertinib. Objective response rate by the Response Evaluation Criteria in Solid Tumors version 1.1 was also assessed. Results: A total of 50 patients with NSCLC with uncommon EGFR mutations were identified. The most frequent EGFR mutations were L861Q (40%, n = 18), G719X (28%, n = 14), and exon 20 insertion (14%, n = 7). The median TTD of osimertinib was 9.7 months (95% confidence interval [CI]: 6.5-12.9 mo) overall and 10.7 months (95% CI: 3.2-18.1 mo) in the first-line setting (n = 20). The objective response rate was 31.7% (95% CI: 18.1%-48.1%) overall and 41.2% (95% CI: 18.4%-67.1%) in the first-line setting. The median TTD varied among patients with L861Q (17.2 mo), G719X (7.8 mo), and exon 20 insertion (1.5 mo) mutations. Conclusions: Osimertinib has activity in patients with NSCLC harboring atypical EGFR mutations. Osimertinib activity differs by the type of atypical EGFR-activating mutation.

Inter-observer variability of expert-derived morphologic risk predictors in aortic dissection.

OBJECTIVES: Establishing the reproducibility of expert-derived measurements on CTA exams of aortic dissection is clinically important and paramount for ground-truth determination for machine learning. METHODS: Four independent observers retrospectively evaluated CTA exams of 72 patients with uncomplicated Stanford type B aortic dissection and assessed the reproducibility of a recently proposed combination of four morphologic risk predictors (maximum aortic diameter, false lumen circumferential angle, false lumen outflow, and intercostal arteries). For the first inter-observer variability assessment, 47 CTA scans from one aortic center were evaluated by expert-observer 1 in an unconstrained clinical assessment without a standardized workflow and compared to a composite of three expert-observers (observers 2-4) using a standardized workflow. A second inter-observer variability assessment on 30 out of the 47 CTA scans compared observers 3 and 4 with a constrained, standardized workflow. A third inter-observer variability assessment was done after specialized training and tested between observers 3 and 4 in an external population of 25 CTA scans. Inter-observer agreement was assessed with intraclass correlation coefficients (ICCs) and Bland-Altman plots. RESULTS: Pre-training ICCs of the four morphologic features ranged from 0.04 (-0.05 to 0.13) to 0.68 (0.49-0.81) between observer 1 and observers 2-4 and from 0.50 (0.32-0.69) to 0.89 (0.78-0.95) between observers 3 and 4. ICCs improved after training ranging from 0.69 (0.52-0.87) to 0.97 (0.94-0.99), and Bland-Altman analysis showed decreased bias and limits of agreement. CONCLUSIONS: Manual morphologic feature measurements on CTA images can be optimized resulting in improved inter-observer reliability. This is essential for robust ground-truth determination for machine learning models. KEY POINTS: • Clinical fashion manual measurements of aortic CTA imaging features showed poor inter-observer reproducibility. • A standardized workflow with standardized training resulted in substantial improvements with excellent inter-observer reproducibility. • Robust ground truth labels obtained manually with excellent inter-observer reproducibility are key to develop reliable machine learning models.

Pericardial computed tomography imaging findings in the setting of coccidioidomycosis.

BACKGROUND: Pericardial disease can be a manifestation of infection and imaging can have a role in its diagnosis. coccidioidomycosis endemic fungal infection has been more frequently reported over the past few decades. Other than case reports or series, there has been no systemic study evaluating pericardial imaging findings in patients with coccidioidomycosis to the best of our knowledge. The purpose of this study was to evaluate intrathoracic computed tomographic (CT) imaging abnormalities in patients with coccidioidal infection with specific emphasis on the pericardium. METHODS: Retrospective review of radiology reports and clinical chart review was performed to identify patients with coccidioidomycosis between January 2000 and September 2021 at our medical center. Diagnosis of infection was confirmed predominately with serology. Patients were excluded if a CT was not performed within 3 months of confirmed diagnosis date and if there was concomitant additional granulomatous or fungal infection. Chest CT was reviewed for pericardial and additional intrathoracic findings. RESULTS: The final retrospective cohort consisted of 37 patients. Imaging findings included lung nodules (N = 33/37), consolidation (N = 25/37), mediastinal or hilar lymphadenopathy (N = 20/37) and pleural effusions (N = 13/37). Eleven of 37 patients (30%) had either trace pericardial fluid (N = 3/37) or small pericardial effusions (N = 8/37). One patient had pericardial enhancement/thickening and history of pericardial tamponade. No other patient had clinical pericarditis or pericardial tamponade. Pericardial calcifications were not seen in any patient. Pericardial effusion was statistically associated with presence of pleural effusion as 9/13 patients with pleural effusion had pericardial effusion versus 2/26 patients without pleural effusion had pericardial effusion (p < 0.001). Otherwise patients with and without pericardial imaging findings were similar in terms of demographics, comorbidities and other imaging findings. CONCLUSION: Pulmonary parenchymal pathology is a common manifestation of coccidioidal infection. Most patients with coccidioidomycosis do not have pericardial imaging abnormalities on CT.

Limited Aortic Intimal Tears: CT Imaging Features and Clinical Characteristics.

Limited aortic intimal tear is an uncommon lesion of the dissection spectrum. The lesion has several imaging features that are not well known, including asymmetric aortic contour abnormalities, filling defects, and various morphologic patterns, such as linear, L-shaped, T-shaped, and stellate configurations. Hemorrhage of the aortic wall may also be present in patients with this rare entity. This imaging essay reviews the CT imaging findings and clinical characteristics of patients with limited intimal tears. Keywords: Aorta, CT © RSNA, 2022.

Aorto-iliac/right leg arterial thrombosis necessitating limb amputation, pulmonary arterial, intracardiac, and ilio-caval venous thrombosis in a 40-year-old with COVID-19.

We describe a 40-year-old man with severe COVID-19 requiring mechanical ventilation who developed aorto-bi-iliac arterial, right lower extremity arterial, intracardiac, pulmonary arterial and ilio-caval venous thromboses and required right lower extremity amputation for acute limb ischemia. This unique case illustrates COVID-19-associated thrombotic complications occurring at multiple, different sites in the cardiovascular system of a single infected patient.

Machine learning for endoleak detection after endovascular aortic repair.

Diagnosis of endoleak following endovascular aortic repair (EVAR) relies on manual review of multi-slice CT angiography (CTA) by physicians which is a tedious and time-consuming process that is susceptible to error. We evaluate the use of a deep neural network for the detection of endoleak on CTA for post-EVAR patients using a novel data efficient training approach. 50 CTAs and 20 CTAs with and without endoleak respectively were identified based on gold standard interpretation by a cardiovascular subspecialty radiologist. The Endoleak Augmentor, a custom designed augmentation method, provided robust training for the machine learning (ML) model. Predicted segmentation maps underwent post-processing to determine the presence of endoleak. The model was tested against 3 blinded general radiologists and 1 blinded subspecialist using a held-out subset (10 positive endoleak CTAs, 10 control CTAs). Model accuracy, precision and recall for endoleak diagnosis were 95%, 90% and 100% relative to reference subspecialist interpretation (AUC = 0.99). Accuracy, precision and recall was 70/70/70% for generalist1, 50/50/90% for generalist2, and 90/83/100% for generalist3. The blinded subspecialist had concordant interpretations for all test cases compared with the reference. In conclusion, our ML-based approach has similar performance for endoleak diagnosis relative to subspecialists and superior performance compared with generalists.