Faculty Bibliography

INTRODUCTION/BACKGROUND:Interpretation of high-resolution CT images plays an important role in the diagnosis and management of interstitial lung diseases. However, interreader variation may exist due to varying levels of training and expertise. This study aims to evaluate interreader variation and the role of thoracic radiology training in classifying interstitial lung disease (ILD). METHODS:This is a retrospective study where seven physicians (radiologists, thoracic radiologists, and a pulmonologist) classified the subtypes of ILD of 128 patients from a tertiary referral center, all selected from the Interstitial Lung Disease Registry which consists of patients from November 2014 to January 2021. Each patient was diagnosed with a subtype of interstitial lung disease by a consensus diagnosis from pathology, radiology, and pulmonology. Each reader was provided with only clinical history, only CT images, or both. Reader sensitivity and specificity and interreader agreements using Cohen's κ were calculated. RESULTS:Interreader agreement based only on clinical history, only on radiologic information, or combination of both was most consistent amongst readers with thoracic radiology training, ranging from fair (Cohen's κ: 0.2-0.46), moderate to almost perfect (Cohen's κ: 0.55-0.92), and moderate to almost perfect (Cohen's κ: 0.53-0.91) respectively. Radiologists with any thoracic training showed both increased sensitivity and specificity for NSIP as compared to other radiologists and the pulmonologist when using only clinical history, only CT information, or combination of both (p < 0.05). CONCLUSIONS:Readers with thoracic radiology training showed the least interreader variation and were more sensitive and specific at classifying certain subtypes of ILD. SUMMARY SENTENCE/UNASSIGNED:Thoracic radiology training may improve sensitivity and specificity in classifying ILD based on HRCT images and clinical history.

INTRODUCTION/BACKGROUND:Interpretation of high-resolution CT images plays an important role in the diagnosis and management of interstitial lung diseases. However, interreader variation may exist due to varying levels of training and expertise. This study aims to evaluate interreader variation and the role of thoracic radiology training in classifying interstitial lung disease (ILD). METHODS:This is a retrospective study where seven physicians (radiologists, thoracic radiologists, and a pulmonologist) classified the subtypes of ILD of 128 patients from a tertiary referral center, all selected from the Interstitial Lung Disease Registry which consists of patients from November 2014 to January 2021. Each patient was diagnosed with a subtype of interstitial lung disease by a consensus diagnosis from pathology, radiology, and pulmonology. Each reader was provided with only clinical history, only CT images, or both. Reader sensitivity and specificity and interreader agreements using Cohen's κ were calculated. RESULTS:Interreader agreement based only on clinical history, only on radiologic information, or combination of both was most consistent amongst readers with thoracic radiology training, ranging from fair (Cohen's κ: 0.2-0.46), moderate to almost perfect (Cohen's κ: 0.55-0.92), and moderate to almost perfect (Cohen's κ: 0.53-0.91) respectively. Radiologists with any thoracic training showed both increased sensitivity and specificity for NSIP as compared to other radiologists and the pulmonologist when using only clinical history, only CT information, or combination of both (p < 0.05). CONCLUSIONS:Readers with thoracic radiology training showed the least interreader variation and were more sensitive and specific at classifying certain subtypes of ILD. SUMMARY SENTENCE/UNASSIGNED:Thoracic radiology training may improve sensitivity and specificity in classifying ILD based on HRCT images and clinical history.

For accurate diagnosis of interstitial lung disease (ILD), a consensus of radiologic, pathological, and clinical findings is vital. Management of ILD also requires thorough follow-up with computed tomography (CT) studies and lung function tests to assess disease progression, severity, and response to treatment. However, accurate classification of ILD subtypes can be challenging, especially for those not accustomed to reading chest CTs regularly. Dynamic models to predict patient survival rates based on longitudinal data are challenging to create due to disease complexity, variation, and irregular visit intervals. Here, we utilize RadImageNet pretrained models to diagnose five types of ILD with multimodal data and a transformer model to determine a patient's 3-year survival rate. When clinical history and associated CT scans are available, the proposed deep learning system can help clinicians diagnose and classify ILD patients and, importantly, dynamically predict disease progression and prognosis.

Patients recovered from COVID-19 may develop long-COVID symptoms in the lung. For this patient population (post-COVID patients), they may benefit from longitudinal, radiation-free lung MRI exams for monitoring lung lesion development and progression. The purpose of this study was to investigate the performance of a spiral ultrashort echo time MRI sequence (Spiral-VIBE-UTE) in a cohort of post-COVID patients in comparison with CT and to compare image quality obtained using different spiral MRI acquisition protocols. Lung MRI was performed in 36 post-COVID patients with different acquisition protocols, including different spiral sampling reordering schemes (line in partition or partition in line) and different breath-hold positions (inspiration or expiration). Three experienced chest radiologists independently scored all the MR images for different pulmonary structures. Lung MR images from spiral acquisition protocol that received the highest image quality scores were also compared against corresponding CT images in 27 patients for evaluating diagnostic image quality and lesion identification. Spiral-VIBE-UTE MRI acquired with the line in partition reordering scheme in an inspiratory breath-holding position achieved the highest image quality scores (score range = 2.17-3.69) compared to others (score range = 1.7-3.29). Compared to corresponding chest CT images, three readers found that 81.5% (22 out of 27), 81.5% (22 out of 27) and 37% (10 out of 27) of the MR images were useful, respectively. Meanwhile, they all agreed that MRI could identify significant lesions in the lungs. The Spiral-VIBE-UTE sequence allows for fast imaging of the lung in a single breath hold. It could be a valuable tool for lung imaging without radiation and could provide great value for managing different lung diseases including assessment of post-COVID lesions.

OBJECTIVES/OBJECTIVE:To investigate the incidence, risk factors, and outcomes of barotrauma (pneumomediastinum and subcutaneous emphysema) in mechanically ventilated COVID-19 patients. To describe the chest radiography patterns of barotrauma and understand the development in relation to mechanical ventilation and patient mortality. METHODS:We performed a retrospective study of 363 patients with COVID-19 from March 1 to April 8, 2020. Primary outcomes were pneumomediastinum or subcutaneous emphysema with or without pneumothorax, pneumoperitoneum, or pneumoretroperitoneum. The secondary outcomes were length of intubation and death. In patients with pneumomediastinum and/or subcutaneous emphysema, we conducted an imaging review to determine the timeline of barotrauma development. RESULTS:Forty three out of 363 (12%) patients developed barotrauma radiographically. The median time to development of either pneumomediastinum or subcutaneous emphysema was 2 days (IQR 1.0-4.5) after intubation and the median time to pneumothorax was 7 days (IQR 2.0-10.0). The overall incidence of pneumothorax was 28/363 (8%) with an incidence of 17/43 (40%) in the barotrauma cohort and 11/320 (3%) in those without barotrauma (p ≤ 0.001). In total, 257/363 (71%) patients died with an increase in mortality in those with barotrauma 33/43 (77%) vs. 224/320 (70%). When adjusting for covariates, barotrauma was associated with increased odds of death (OR 2.99, 95% CI 1.25-7.17). CONCLUSION/CONCLUSIONS:Barotrauma is a frequent complication of mechanically ventilated COVID-19 patients. In comparison to intubated COVID-19 patients without barotrauma, there is a higher rate of pneumothorax and an increased risk of death.