Deep Learning Classification of Usual Interstitial Pneumonia Predicts Outcomes.

Rationale: Computed tomography (CT) enables noninvasive diagnosis of usual interstitial pneumonia (UIP), but enhanced image analyses are needed to overcome the limitations of visual assessment. Objectives: Apply multiple instance learning (MIL) to develop an explainable deep learning algorithm for prediction of UIP from CT and validate its performance in independent cohorts. Methods: We trained an MIL algorithm using a pooled dataset (n = 2,143) and tested it in three independent populations: data from a prior publication (n = 127), a single-institution clinical cohort (n = 239), and a national registry of patients with pulmonary fibrosis (n = 979). We tested UIP classification performance using receiver operating characteristic analysis, with histologic UIP as ground truth. Cox proportional hazards and linear mixed-effects models were used to examine associations between MIL predictions and survival or longitudinal FVC. Measurements and Main Results: In two cohorts with biopsy data, MIL improved accuracy for histologic UIP (area under the curve, 0.77 [n = 127] and 0.79 [n = 239]) compared with visual assessment (area under the curve, 0.65 and 0.71). In cohorts with survival data, MIL-UIP classifications were significant for mortality (n = 239, mortality to April 2021: unadjusted hazard ratio, 3.1; 95% confidence interval [CI], 1.96-4.91; P < 0.001; and n = 979, mortality to July 2022: unadjusted hazard ratio, 3.64; 95% CI, 2.66-4.97; P < 0.001). Individuals classified as UIP positive by the algorithm had a significantly greater annual decline in FVC than those classified as UIP negative (-88 ml/yr vs. -45 ml/yr; n = 979; P < 0.01), adjusting for extent of lung fibrosis. Conclusions: Computerized assessment using MIL identifies clinically significant features of UIP on CT. Such a method could improve confidence in radiologic assessment of patients with interstitial lung disease, potentially enabling earlier and more precise diagnosis.

Impact on patient attitudes towards lung cancer screening and smoking cessation with radiology consultation: Pilot survey project.

PURPOSE: We developed a novel patient-radiologist consultation for patients scheduled for lung cancer screening (LCS). We hypothesized that this intervention would improve patient attitudes towards LCS adherence and smoking cessation. METHODS: This quality improvement project enrolled 38 LCS patients (a majority were African American) and included 20 current and 18 former smokers. The intervention, a 5-10 min consultation with a radiologist who provided preliminary interpretation of pertinent imaging findings in conjunction with smoking cessation counseling, took place in the radiology reading room immediately after the low dose computed tomography (LDCT) patient scan. Pre- and post-intervention surveys assessed patient attitudes towards LCS and smoking cessation. RESULTS: All recruited patients consented to participate in this project. Regarding viewing their LCS imaging, 86.8% (33/38) expressed general interest initially, with 100.0% (38/38) being more interested afterwards. On LCS logistics, 71.1% (27/38) reported prior knowledge at baseline, while 89.5% (34/38) reported being more informed following the intervention. Among current smokers, 90.0% (18/20) were already motivated towards quitting smoking at baseline, with 100.0% (20/20) exiting the intervention being more interested in doing so. Regarding smoking cessation resources, 95.0% (19/20) were interested in accessing such resources at baseline, and 90.0% (18/20, 2 were same/neutral) were more interested afterwards. CONCLUSIONS: Patients' attitudes towards LCS and self-reported interest in quitting smoking were directionally higher after the consultation than at baseline. Incorporating LCS consultations with radiologists as part of patient-centered care provides a resource to educate patients on their own LCS imaging findings while promoting LCS adherence and smoking cessation.

A Deep Learning-Based Radiomic Classifier for Usual Interstitial Pneumonia.

BACKGROUND: Because chest CT scan has largely supplanted surgical lung biopsy for diagnosing most cases of interstitial lung disease (ILD), tools to standardize CT scan interpretation are urgently needed. RESEARCH QUESTION: Does a deep learning (DL)-based classifier for usual interstitial pneumonia (UIP) derived using CT scan features accurately discriminate radiologist-determined visual UIP? STUDY DESIGN AND METHODS: A retrospective cohort study was performed. Chest CT scans acquired in individuals with and without ILD were drawn from a variety of public and private data sources. Using radiologist-determined visual UIP as ground truth, a convolutional neural network was used to learn discrete CT scan features of UIP, with outputs used to predict the likelihood of UIP using a linear support vector machine. Test performance characteristics were assessed in an independent performance cohort and multicenter ILD clinical cohort. Transplant-free survival was compared between UIP classification approaches using the Kaplan-Meier estimator and Cox proportional hazards regression. RESULTS: A total of 2,907 chest CT scans were included in the training (n = 1,934), validation (n = 408), and performance (n = 565) data sets. The prevalence of radiologist-determined visual UIP was 12.4% and 37.1% in the performance and ILD clinical cohorts, respectively. The DL-based UIP classifier predicted visual UIP in the performance cohort with sensitivity and specificity of 93% and 86%, respectively, and in the multicenter ILD clinical cohort with 81% and 77%, respectively. DL-based and visual UIP classification similarly discriminated survival, and outcomes were consistent among cases with positive DL-based UIP classification irrespective of visual classification. INTERPRETATION: A DL-based classifier for UIP demonstrated good test performance across a wide range of UIP prevalence and similarly discriminated survival when compared with radiologist-determined UIP. This automated tool could efficiently screen for UIP in patients undergoing chest CT scan and identify a high-risk phenotype among those with known ILD.

Pictorial Review of Fibrotic Interstitial Lung Disease on High-Resolution CT Scan and Updated Classification.

TOPIC IMPORTANCE: Given the recently expanded approval of antifibrotics for various fibrotic interstitial lung diseases (ILDs), early and correct recognition of these diseases is imperative for physicians. Because high-resolution chest CT scan forms the backbone of diagnosis for ILD, this review will discuss evidence-based imaging findings of key fibrotic ILDs and an approach for differentiating these diseases. REVIEW FINDINGS: (1) Imaging findings of nonspecific interstitial pneumonia may evolve over time and become indistinguishable from usual interstitial pneumonia. Therefore, if remote imaging can be reviewed, this would increase the likelihood of an accurate imaging diagnosis, particularly if findings appear to represent a usual interstitial pneumonia pattern on the recent examination. (2) Given the difficulty and lack of objectivity in classifying patients with hypersensitivity pneumonitis into acute, subacute, and chronic categories and that prognosis depends primarily on presence or absence of fibrosis, the new set of guidelines released in 2020 categorizes patients with hypersensitivity pneumonitis as either nonfibrotic (purely inflammatory) or fibrotic (either purely fibrotic or mixed fibrotic/inflammatory) based on imaging and/or histologic findings, and the prior temporal terms are no longer used. (3) Interstitial lung abnormalities are incidental CT scan findings that may suggest early ILD in patients without clinical suspicion for ILD. Patients with high-risk features should undergo clinical evaluation for ILD and be actively monitored for disease progression. SUMMARY: Fibrotic ILD on high-resolution chest CT scan is a complex topic, but with use of an evidence-based analysis and algorithm as provided in this article, the probability of a correct imaging diagnosis increases.

Occupational Lung Diseases: Spectrum of Common Imaging Manifestations.

Occupational lung diseases (OLD) are a group of preventable conditions caused by noxious inhalation exposure in the workplace. Workers in various industries are at a higher risk of developing OLD. Despite regulations contributing to a decreased incidence, OLD remain among the most frequently diagnosed work-related conditions, contributing to significant morbidity and mortality. A multidisciplinary discussion (MDD) is necessary for a timely diagnosis. Imaging, particularly computed tomography, plays a central role in diagnosing OLD and excluding other inhalational lung diseases. OLD can be broadly classified into fibrotic and non-fibrotic forms. Imaging reflects variable degrees of inflammation and fibrosis involving the airways, parenchyma, and pleura. Common manifestations include classical pneumoconioses, chronic granulomatous diseases (CGD), and small and large airway diseases. Imaging is influenced by the type of inciting exposure. The findings of airway disease may be subtle or solely uncovered upon expiration. High-resolution chest CT, including expiratory-phase imaging, should be performed in all patients with suspected OLD. Radiologists should familiarize themselves with these imaging features to improve diagnostic accuracy.

Tobacco smoking is associated with combined pulmonary fibrosis and emphysema and worse outcomes in interstitial lung disease.

Tobacco smoking is an established cause of pulmonary disease whose contribution to interstitial lung disease (ILD) is incompletely characterized. We hypothesized that compared with nonsmokers, subjects who smoked tobacco would differ in their clinical phenotype and have greater mortality. We performed a retrospective cohort study of tobacco smoking in ILD. We evaluated demographic and clinical characteristics, time to clinically meaningful lung function decline (LFD), and mortality in patients stratified by tobacco smoking status (ever vs. never) within a tertiary center ILD registry (2006-2021) and replicated mortality outcomes across four nontertiary medical centers. Data were analyzed by two-sided t tests, Poisson generalized linear models, and Cox proportional hazard models adjusted for age, sex, forced vital capacity (FVC), diffusion capacity of the lung for carbon monoxide (DLCO), ILD subtype, antifibrotic therapy, and hospital center. Of 1,163 study participants, 651 were tobacco smokers. Smokers were more likely to be older, male, have idiopathic pulmonary fibrosis (IPF), coronary artery disease, CT honeycombing and emphysema, higher FVC, and lower DLCO than nonsmokers (P < 0.01). Time to LFD in smokers was shorter (19.7 ± 20 mo vs. 24.8 ± 29 mo; P = 0.038) and survival time was decreased [10.75 (10.08-11.50) yr vs. 20 (18.67-21.25) yr; adjusted mortality HR = 1.50, 95%CI 1.17-1.92; P < 0.0001] compared with nonsmokers. Smokers had 12% greater odds of death for every additional 10 pack yr of smoking (P < 0.0001). Mortality outcomes remained consistent in the nontertiary cohort (HR = 1.51, 95%CI = 1.03-2.23; P = 0.036). Tobacco smokers with ILD have a distinct clinical phenotype strongly associated with the syndrome of combined PF and emphysema, shorter time to LFD, and decreased survival. Smoking prevention may improve ILD outcomes.NEW & NOTEWORTHY Smoking in ILD is associated with combined pulmonary fibrosis and emphysema and worse clinical outcomes.

ACR Appropriateness Criteria® Routine Chest Imaging.

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.

Integration and Application of Clinical Practice Guidelines for the Diagnosis of Idiopathic Pulmonary Fibrosis and Fibrotic Hypersensitivity Pneumonitis.

Recent clinical practice guidelines have addressed the diagnosis of idiopathic pulmonary fibrosis (IPF) and fibrotic hypersensitivity pneumonitis (fHP). These disease-specific guidelines were developed independently, without clear direction on how to apply their respective recommendations concurrently within a single patient, where discrimination between these two fibrotic interstitial lung diseases represents a frequent diagnostic challenge. The objective of this review, created by an international group of experts, was to suggest a pragmatic approach on how to apply existing guidelines to distinguish IPF and fHP. Key clinical, radiologic, and pathologic features described in previous guidelines are integrated in a set of diagnostic algorithms, which then are placed in the broader context of multidisciplinary discussion to guide the generation of a consensus diagnosis. Although these algorithms necessarily reflect some uncertainty wherever strong evidence is lacking, they provide insight into the current approach favored by experts in the field based on currently available knowledge. The authors further identify priorities for future research to clarify ongoing uncertainties in the diagnosis of fibrotic interstitial lung diseases.

Interstitial Lung Disease in Firefighters: An Emerging Occupational Hazard.

Introduction: Occupational risk factors for interstitial lung disease (ILD) are a remediable aspect of this progressive pulmonary disorder. The association between firefighting and ILD is unknown. Our objective was to assess the characteristics of firefighters with ILD from a large single-center ILD registry. Methods: The University of Chicago ILD database was reviewed for patients with a history of firefighting. Clinical information was abstracted from the medical record. The prevalence rate ratio of firefighters in the database compared to the baseline prevalence of firefighting in the Chicago metropolitan area was calculated via the Poisson distribution. Results: Nineteen firefighters were identified; all were men. A variety of ILD subtypes were seen across the cohort, including four patients with a diagnosis of connective tissue disease. Patients had mild forced vital capacity (FVC) and moderate diffusing capacity for carbon monoxide (DLCO) decrements on presentation; three patients died and two received lung transplantation over an average follow-up time of 76 months. Firefighters were seen at a greater proportion in the ILD registry than in the general population with a prevalence rate ratio of 3.98. Conclusions: Firefighting was overrepresented in our cohort compared to the general population, suggesting that there may be a causative association between firefighting and the presence of ILD. The wide variety of ILD subtypes observed suggest that all ILD patients should be asked about their occupational history. Further investigation to identify occupational exposures and determine the benefit of remediation is needed.

A Fully Automated Deep Learning Pipeline for Multi-Vertebral Level Quantification and Characterization of Muscle and Adipose Tissue on Chest CT Scans.

Body composition on chest CT scans encompasses a set of important imaging biomarkers. This study developed and validated a fully automated analysis pipeline for multi-vertebral level assessment of muscle and adipose tissue on routine chest CT scans. This study retrospectively trained two convolutional neural networks on 629 chest CT scans from 629 patients (55% women; mean age, 67 years ± 10 [standard deviation]) obtained between 2014 and 2017 prior to lobectomy for primary lung cancer at three institutions. A slice-selection network was developed to identify an axial image at the level of the fifth, eighth, and 10th thoracic vertebral bodies. A segmentation network (U-Net) was trained to segment muscle and adipose tissue on an axial image. Radiologist-guided manual-level selection and segmentation generated ground truth. The authors then assessed the predictive performance of their approach for cross-sectional area (CSA) (in centimeters squared) and attenuation (in Hounsfield units) on an independent test set. For the pipeline, median absolute error and intraclass correlation coefficients for both tissues were 3.6% (interquartile range, 1.3%-7.0%) and 0.959-0.998 for the CSA and 1.0 HU (interquartile range, 0.0-2.0 HU) and 0.95-0.99 for median attenuation. This study demonstrates accurate and reliable fully automated multi-vertebral level quantification and characterization of muscle and adipose tissue on routine chest CT scans. Keywords: Skeletal Muscle, Adipose Tissue, CT, Chest, Body Composition Analysis, Convolutional Neural Network (CNN), Supervised Learning Supplemental material is available for this article. © RSNA, 2022.

Multilevel Body Composition Analysis on Chest Computed Tomography Predicts Hospital Length of Stay and Complications After Lobectomy for Lung Cancer: A Multicenter Study.

OBJECTIVE: To investigate the impact of thoracic body composition on outcomes after lobectomy for lung cancer. SUMMARY AND BACKGROUND DATA: Preoperative identification of patients at risk for adverse outcomes permits treatment modification. The impact of body composition on lung resection outcomes has not been investigated in a multicenter setting. METHODS: A total of 958 consecutive patients undergoing lobectomy for lung cancer at 3 centers from 2014 to 2017 were retrospectively analyzed. Muscle and adipose tissue cross-sectional area at the fifth, eighth, and tenth thoracic vertebral body was quantified. Prospectively collected outcomes from a national database were abstracted to characterize the association between sums of muscle and adipose tissue and hospital length of stay (LOS), number of any postoperative complications, and number of respiratory postoperative complications using multivariate regression. A priori determined covariates were forced expiratory volume in 1 second and diffusion capacity of the lungs for carbon monoxide predicted, age, sex, body mass index, race, surgical approach, smoking status, Zubrod and American Society of Anesthesiologists scores. RESULTS: Mean patient age was 67 years, body mass index 27.4 kg/m2 and 65% had stage i disease. Sixty-three percent underwent minimally invasive lobectomy. Median LOS was 4 days and 34% of patients experienced complications. Muscle (using 30 cm2 increments) was an independent predictor of LOS (adjusted coefficient 0.972; P = 0.002), any postoperative complications (odds ratio 0.897; P = 0.007) and postoperative respiratory complications (odds ratio 0.860; P = 0.010). Sarcopenic obesity was also associated with LOS and adverse outcomes. CONCLUSIONS: Body composition on preoperative chest computed tomography is an independent predictor of LOS and postoperative complications after lobectomy for lung cancer.

ACR Appropriateness Criteria® Nontraumatic Chest Wall Pain.

Chest pain is a common reason that patients may present for evaluation in both ambulatory and emergency department settings, and is often of musculoskeletal origin in the former. Chest wall syndrome collectively describes the various entities that can contribute to chest wall pain of musculoskeletal origin and may affect any chest wall structure. Various imaging modalities may be employed for the diagnosis of nontraumatic chest wall conditions, each with variable utility depending on the clinical scenario. We review the evidence for or against use of various imaging modalities for the diagnosis of nontraumatic chest wall pain. 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 include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

High-titer rheumatoid factor seropositivity predicts mediastinal lymphadenopathy and mortality in rheumatoid arthritis-related interstitial lung disease.

Rheumatoid arthritis-related interstitial lung disease (RA-ILD) is a common connective tissue disease-related ILD (CTD-ILD) associated with high morbidity and mortality. Although rheumatoid factor (RF) seropositivity is a risk factor for developing RA-ILD, the relationship between RF seropositivity, mediastinal lymph node (MLN) features, and disease progression is unknown. We aimed to determine if high-titer RF seropositivity predicted MLN features, lung function impairment, and mortality in RA-ILD. In this retrospective cohort study, we identified patients in the University of Chicago ILD registry with RA-ILD. We compared demographic characteristics, serologic data, MLN size, count and location, and pulmonary function over 36 months among patients who had high-titer RF seropositivity (≥ 60 IU/ml) and those who did not. Survival analysis was performed using Cox regression modeling. Amongst 294 patients with CTD-ILD, available chest computed tomography (CT) imaging and serologic data, we identified 70 patients with RA-ILD. Compared to RA-ILD patients with low-titer RF, RA-ILD patients with high-titer RF had lower baseline forced vital capacity (71% vs. 63%; P = 0.045), elevated anti-cyclic citrullinated peptide titer (122 vs. 201; P = 0.001), CT honeycombing (50% vs. 80%; P = 0.008), and higher number of MLN ≥ 10 mm (36% vs. 76%; P = 0.005). Lung function decline over 36 months did not differ between groups. Primary outcomes of death or lung transplant occurred more frequently in the high-titer RF group (HR 2.8; 95% CI 1.1-6.8; P = 0.028). High-titer RF seropositivity was associated with MLN enlargement, CT honeycombing, and decreased transplant-free survival. RF titer may be a useful prognostic marker for stratifying patients by pulmonary disease activity and mortality risk.

Computed Tomography Findings Suggestive of Connective Tissue Disease in the Setting of Usual Interstitial Pneumonia.

PURPOSE: A usual interstitial pneumonia (UIP) pattern is common in idiopathic pulmonary fibrosis (IPF) and connective tissue disease-related interstitial lung disease (CTD-ILD). The purpose of the study was to validate imaging findings differentiating CTD-ILD from IPF in UIP. METHODS: Patients with a multidisciplinary diagnosis of CTD-ILD or IPF and a UIP pattern on computed tomography and/or pathology were included in this study. Prevalence of 3 computed tomography findings shown to be associated with CTD-ILD (the straight edge sign [SES], the exuberant honeycombing sign, and the anterior upper lobe sign [AULS]) were tabulated in CTD-ILD and IPF subjects. The ability of each of these signs to discriminate between CTD-ILD and IPF was evaluated. Survival analysis was also performed using log-rank analysis. RESULTS: The study cohort included 50 CTD-ILD and 100 IPF subjects with UIP. The SES and the AULS were more common in CTD-ILD than IPF (prevalence, 36.0% and 34.9% in CTD-ILD vs 8.3% and 17.2% in IPF, respectively [P = 0.0105 - <0.001]). The highest specificity (95.7%) of CTD-ILD diagnosis was seen with bilateral SES. Moreover, the SES was associated with improved survival (P = 0.0383), which appeared to be largely because of improvement in survival in IPF subjects. The presence of AULS was associated with pulmonary functional abnormalities. CONCLUSIONS: A radiographic UIP pattern with evidence of SES or the AULS should raise suspicion for CTD-ILD rather than IPF. Patients with IPF and SES have an attenuated disease course and might represent a different phenotype than those without the SES.

ACR Appropriateness Criteria® Imaging of Mediastinal Masses.

Mediastinal masses can present with symptoms, signs, and syndromes or incidentally. Selecting the appropriate diagnostic imaging study for mediastinal mass evaluation requires awareness of the strengths and weaknesses of the various imaging modalities with regard to tissue characterization, soft tissue contrast, and surveillance. This publication expounds on the differences between chest radiography, CT, PET/CT, ultrasound, and MRI in terms of their ability to decipher and surveil mediastinal masses. Making the optimal imaging choice can yield diagnostic specificity, avert unnecessary biopsy and surgery, guide the interventionist when necessary, and serve as a means of surveillance for probably benign, but indeterminate mediastinal masses. 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 include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

CT Findings and Patterns of e-Cigarette or Vaping Product Use-Associated Lung Injury: A Multicenter Cohort of 160 Cases.

BACKGROUND: e-Cigarette or vaping-induced lung injury (EVALI) causes a spectrum of CT lung injury patterns. Relative frequencies and associations with vaping behavior are unknown. RESEARCH QUESTION: What are the frequencies of imaging findings and CT patterns in EVALI and what is the relationship to vaping behavior? STUDY DESIGN AND METHODS: CT scans of 160 subjects with EVALI from 15 institutions were retrospectively reviewed. CT findings and patterns were defined and agreed on via consensus. The parenchymal organizing pneumonia (OP) pattern was defined as regional or diffuse ground-glass opacity (GGO) ± consolidation without centrilobular nodules (CNs). An airway-centered OP pattern was defined as diffuse CNs with little or no GGO, whereas a mixed OP pattern was a combination of the two. Other patterns included diffuse alveolar damage (DAD), acute eosinophilic-like pneumonia, and pulmonary hemorrhage. Cases were classified as atypical if they did not fit into a pattern. Imaging findings, pattern frequencies, and injury severity were correlated with substance vaped (marijuana derives [tetrahydrocannabinol] [THC] only, nicotine derivates only, and both), vaping frequency, regional geography, and state recreational THC legality. One-way analysis of variance, χ2 test, and multivariable analyses were used for statistical analysis. RESULTS: A total of 160 patients (79.4% men) with a mean age of 28.2 years (range, 15-68 years) with EVALI underwent CT scan. Seventy-seven (48.1%), 15 (9.4%), and 68 (42.5%) patients admitted to vaping THC, nicotine, or both, respectively. Common findings included diffuse or lower lobe GGO with subpleural (78.1%), lobular (59.4%), or peribronchovascular (PBV) sparing (40%). Septal thickening (50.6%), lymphadenopathy (63.1%), and CNs (36.3%) were common. PBV sparing was associated with younger age (P = .02). Of 160 subjects, 156 (97.5%) had one of six defined patterns. Parenchymal, airway-centered, and mixed OP patterns were seen in 89 (55.6%), 14 (8.8%), and 32 (20%) patients, respectively. Acute eosinophilic-like pneumonia (six of 160, 3.8%), DAD (nine of 160, 5.6%), pulmonary hemorrhage (six of 160, 3.8%), and atypical (four of 160, 2.5%) patterns were less common. Increased vaping frequency was associated with more severe injury (P = .008). Multivariable analysis showed a negative association between vaping for > 6 months and DAD pattern (P = .03). Two subjects (1.25%) with DAD pattern died. There was no relation between pattern and injury severity, geographic location, and state legality of recreational use of THC. INTERPRETATION: EVALI typically causes an OP pattern but exists on a spectrum of acute lung injury. Vaping habits do not correlate with CT patterns except for negative correlation between vaping > 6 months and DAD pattern. PBV sparing, not previously described in acute lung injury, is a common finding.

The Role of Surgical Lung Biopsy in the Diagnosis of Fibrotic Interstitial Lung Disease: Perspective from the Pulmonary Fibrosis Foundation.

Diagnosis of interstitial lung disease (ILD) requires a multidisciplinary discussion approach that includes clinicians, radiologists, and pathologists. Surgical lung biopsy (SLB) is currently the recommended standard in obtaining pathologic specimens for patients with ILD requiring a tissue diagnosis. The increased diagnostic confidence and accuracy provided by microscopic pathology assessment of SLB specimens must be balanced with the associated risks in patients with ILD. This document was developed by the SLB Working Group of the Pulmonary Fibrosis Foundation, composed of a multidisciplinary group of ILD physicians, including pulmonologists, radiologists, pathologists, and thoracic surgeons. In this document, we present an up-to-date literature review of the indications, contraindications, risks, and alternatives to SLB in the diagnosis of fibrotic ILD; outline an integrated approach to the decision-making around SLB in the diagnosis of fibrotic ILD; and provide practical information to maximize the yield and safety of SLB.

Vessel-related structures predict UIP pathology in those with a non-IPF pattern on CT.

OBJECTIVES: To determine if a quantitative imaging variable (vessel-related structures [VRS]) could identify subjects with a non-IPF diagnosis CT pattern who were highly likely to have UIP histologically. METHODS: Subjects with a multidisciplinary diagnosis of interstitial lung disease including surgical lung biopsy and chest CT within 1 year of each other were included in the study. Non-contrast CT scans were analyzed using the Computer-Aided Lung Informatics for Pathology Evaluation and Rating (CALIPER) program, which quantifies the amount of various abnormal CT patterns on chest CT. Quantitative data were analyzed relative to pathological diagnosis as well as the qualitative CT pattern. RESULTS: CALIPER-derived volumes of reticulation (p = 0.012), honeycombing (p = 0.017), and VRS (p < 0.001) were associated with a UIP pattern on pathology on univariate analysis but only VRS was associated with a UIP pathology on multivariable analysis (p = 0.013). Using a VRS cut-off of 173 cm3, the sensitivity and specificity for pathological UIP were similar to those for standard qualitative CT assessment (55.9% and 80.4% compared to 60.6% and 80.4%, respectively). VRS differentiated pathological UIP cases in those with a non-IPF diagnosis CT category (p < 0.001) but not in other qualitative CT patterns (typical UIP, probable UIP, and indeterminate for UIP). The rate of pathological UIP in those with VRS greater than 173 cm3 (84.2%) was nearly identical to those who had a qualitative CT pattern of probable UIP (88.9%). CONCLUSIONS: VRS may be an adjunct to CT in predicting pathology in patients with interstitial lung disease. KEY POINTS: • Volume of vessel-related structures (VRS) was associated with usual interstitial pneumonia (UIP) on pathology. • This differentiation arose from those with CT scans with a non-IPF diagnosis imaging pattern. • Higher VRS has similar diagnostic ramifications for UIP as probable UIP, transitively suggesting in patients with high VRS, pathology may be obviated.

Practical Imaging Interpretation in Patients Suspected of Having Idiopathic Pulmonary Fibrosis: Official Recommendations from the Radiology Working Group of the Pulmonary Fibrosis Foundation.

Imaging serves a key role in the diagnosis of patients suspected of having idiopathic pulmonary fibrosis (IPF). Accurate pattern classification at thin-section chest CT is a key step in multidisciplinary discussions, guiding the need for surgical lung biopsy and determining available pharmacologic therapies. The recent approval of new treatments for fibrosing lung disease has made it more critical than ever for radiologists to facilitate accurate and early diagnosis of IPF. This document was developed by the Radiology Working Group of the Pulmonary Fibrosis Foundation with the goal of providing a practical guide for radiologists. In this review, the critical imaging patterns of IPF, pitfalls in imaging classifications, confounding imaging findings with other fibrotic lung diseases, and reporting standards for cases of lung fibrosis will be discussed. Published under a CC BY 4.0 license. See also the commentary by White and Galvin in this issue.