Hariri, Lida P.

Abstract Background Despite advances in diagnosis and management, the interstitial lung disease (ILD) lexicon is plagued by ambiguous and inconsistent terminology that complicates communication and impedes knowledge generation. The objective of this Fleischner Society Consensus Statement was to produce standardized terminology for ILD multidisciplinary diagnoses and major phenotypes. Methods Interviews with 10 experts were used to identify ILD clinical diagnoses and major phenotypes. The preferred terms for each entity and potential alternatives were identified, alongside the rationale for the preferred term. Entities with more than one potential term were the subject of an online modified Delphi survey posed to the 29 committee members, aiming to achieve consensus. Committee members rated their agreement with the initially preferred term—5 (strongly agree), 4 (agree), 3 (neutral/unsure), 2 (disagree), and 1 (strongly disagree)—with the option to provide additional comments. Median score ≥4 and interquartile range ≤1 were considered consensus agreement. Terms not reaching agreement were discussed by video conference, followed by an additional survey that incorporated feedback. Results From the 60 initial terms, there were two root terms that required upfront consensus before survey initiation (ILD and interstitial pneumonia) and another eight terms that had no alternative suggested by the committee or in the literature. Agreement was met by 47/50 terms (94%) in Round 1 of the survey. The three terms (6%) that did not reach agreement met agreement in Round 2. Conclusions This document provides standardized recommended terms for ILD multidisciplinary diagnoses and major phenotypes that will facilitate communication among clinicians, researchers, patients, and other stakeholders.

Abstract Introduction: Endobronchial optical coherence tomography (EB-OCT) imaging has unique potential to diagnose and quantify early, microscopic disease changes and assess therapeutic responsivity in interstitial lung disease (ILD). However, training and time requirements for interpretation and quantitative evaluation of large volumetric EB-OCT datasets need to be optimized to facilitate clinical adoption. We develop and validate a computationally-efficient, rapid EB-OCT quantitative image analysis framework using artificial intelligence (AI) with deep learning architecture for 2D feature segmentation, 3D feature burden and spatial distribution mapping, and computer-aided diagnosis in early ILD subjects. Methods: Volumetric EB-OCT datasets, consisting of 2D cross-sectional images, were acquired from multiple locations in the bilateral lungs of ILD and healthy control subjects. EB-OCT features were manually segmented from cross-sectional images by an expert EB-OCT reader, including fibrosis, microscopic honeycombing, traction bronchiolectasis, normal parenchyma, and emphysema. EB-OCT images with corresponding feature segmentations were used to train and validate a multiclass convolutional neural network. The 2D AI-based segmentation model was subsequently evaluated against manual ‘ground truth’ segmentations for each feature in a de novo ‘test’ dataset of independent subjects using dice similarity score, precision, recall/sensitivity, specificity, and balanced accuracy. To generate 3D AI-based quantitative feature burden and spatial distribution maps, AI-based feature segmentation was performed on each EB-OCT 2D cross-sectional image within a volumetric imaging site. Three external pathologist readers independently interpreted 3D AI-based features maps for each EB-OCT imaging site acquired for a subject (without access to EB-OCT images) and provided a single diagnosis of usual interstitial pneumonia (UIP) or non-UIP ILD. Performance of computer-aided ILD diagnosis was compared against independently evaluated histopathology, clinical diagnosis, and prior EB-OCT image interpretation. Results: EB-OCT was acquired in 51 subjects: 30 in vivo early ILD, 5 in vivo non-ILD controls, and 16 ex vivo end-stage ILD. Overall pixel similarity between 2D manual and AI-based segmentation demonstrated high accuracy for each ILD feature (mean balanced accuracy 0.9). 3D quantitative AI-based feature maps demonstrated strong agreement (Spearman ρ0.87, p<0.001) between manual and AI-based segmentation. AI-based computer-aided diagnosis demonstrated 100% sensitivity/specificity for histologic UIP and clinical IPF for all pathologist readers, with improved diagnostic accuracy and significantly reduced training (45-minute training, 75% reduction) and interpretation time (<1 minute per subject for computer-aided diagnosis, 88% reduction) compared to EB-OCT image interpretation. Conclusion: The AI-based EB-OCT image analysis framework could expedite computer-aided diagnosis of early ILD and robust, quantitative assessment of microscopic disease burden, progression, and therapeutic responsivity.

Abstract Accumulating evidence suggests dysregulated immune checkpoint (IC) signaling can exacerbate COVID-19 severity, but the role of these molecules in the pathogenesis of fatal COVID-19-related diffuse alveolar damage (DAD) remains elusive. Understanding how IC proteins influence acute lung injury due to COVID-19 can provide insights into potential therapeutic strategies to modulate immune responses and improve patient outcomes. Here, in a single-center autopsy cohort, we determined the cellular localization of ICs in lung tissue from cases of fatal COVID-19, DAD-comparators, and non-fibrotic controls by using immunohistochemistry, and investigated their association with clinical outcomes. We expanded our findings by performing analyses of publicly available single-cell RNA sequencing datasets from patients with fatal COVID-19 and non-fibrotic controls. We demonstrated the presence of protein-protein interaction networks of ICs in the lung cellular niche by performing transcriptomic profiling of lung tissue-derived RNA counts from patients with fatal COVID-19. Further, we leveraged data from the largest international, multi-center COVID-19 autopsy cohort and validated that, among patients with fatal COVID-19, those with higher PD-L1/CD274 expression in lung endothelial cells had more rapid clinical deterioration. Lastly, in a cohort of individuals with early COVID-19, IC plasma protein levels were elevated in those with persistent SARS-CoV-2 RNAemia and adverse clinical outcomes. Collectively, our data provide unique pathological insights into the role of IC dysregulation and differential disease severity in COVID-19.

Context.— The pathologic diagnosis of usual interstitial pneumonia (UIP) remains a challenging area, and application of histologic UIP guidelines has proved difficult. Objective.— To understand current practice approaches by pulmonary pathologists for the histologic diagnosis of UIP and other fibrotic interstitial lung diseases (ILDs). Design.— The Pulmonary Pathology Society (PPS) ILD Working Group developed and sent a 5-part survey on fibrotic ILD electronically to the PPS membership. Results.— One hundred sixty-one completed surveys were analyzed. Of the respondents, 89% reported using published histologic features in clinical guidelines for idiopathic pulmonary fibrosis (IPF) in their pathologic diagnosis; however, there was variability in reporting terminology, quantity and quality of histologic features, and the use of guideline categorization. Respondents were very likely to have access to pulmonary pathology colleagues (79%), pulmonologists (98%), and radiologists (94%) to discuss cases. Half of respondents reported they may alter their pathologic diagnosis based on additional clinical and radiologic history if it is pertinent. Airway-centered fibrosis, granulomas, and types of inflammatory infiltrates were considered important, but there was poor agreement on how these features are defined. Conclusions.— There is significant consensus among the PPS membership on the importance of histologic guidelines/features of UIP. There are unmet needs for (1) consensus and standardization of diagnostic terminology and incorporation of recommended histopathologic categories from the clinical IPF guidelines into pathology reports, (2) agreement on how to incorporate into the report relevant clinical and radiographic information, and (3) defining the quantity and quality of features needed to suggest alternative diagnoses.

Abstract Histological and lineage immunofluorescence examination revealed that healthy conducting airways of humans and animals harbor sporadic poorly differentiated epithelial patches mostly in the dorsal noncartilage regions that remarkably manifest squamous differentiation. In vitro analysis demonstrated that this squamous phenotype is not due to intrinsic functional change in underlying airway basal cells. Rather, it is a reversible physiological response to persistent Wnt signaling stimulation during de novo differentiation. Squamous epithelial cells have elevated gene signatures of glucose uptake and cellular glycolysis. Inhibition of glycolysis or a decrease in glucose availability suppresses Wnt-induced squamous epithelial differentiation. Compared with pseudostratified airway epithelial cells, a cascade of mucosal protective functions is impaired in squamous epithelial cells, featuring increased epithelial permeability, spontaneous epithelial unjamming, and enhanced inflammatory responses. Our study raises the possibility that the squamous differentiation naturally occurring in healthy airways identified herein may represent “vulnerable spots” within the airway mucosa that are sensitive to damage and inflammation when confronted by infection or injury. Squamous metaplasia and hyperplasia are hallmarks of many airway diseases, thereby expanding these areas of vulnerability with potential pathological consequences. Thus, investigation of physiological and reversible squamous differentiation from healthy airway basal cells may provide critical knowledge to understand pathogenic squamous remodeling, which is often nonreversible, progressive, and hyperinflammatory.

Abstract Rationale The leading cause of death in coronavirus disease 2019 (COVID-19) is severe pneumonia, with many patients developing acute respiratory distress syndrome (ARDS) and diffuse alveolar damage (DAD). Whether DAD in fatal COVID-19 is distinct from other causes of DAD remains unknown. Objective To compare lung parenchymal and vascular alterations between patients with fatal COVID-19 pneumonia and other DAD-causing etiologies using a multidimensional approach. Methods This autopsy cohort consisted of consecutive patients with COVID-19 pneumonia (n = 20) and with respiratory failure and histologic DAD (n = 21; non–COVID-19 viral and nonviral etiologies). Premortem chest computed tomography (CT) scans were evaluated for vascular changes. Postmortem lung tissues were compared using histopathological and computational analyses. Machine-learning-derived morphometric analysis of the microvasculature was performed, with a random forest classifier quantifying vascular congestion (CVasc) in different microscopic compartments. Respiratory mechanics and gas-exchange parameters were evaluated longitudinally in patients with ARDS. Measurements and Main Results In premortem CT, patients with COVID-19 showed more dilated vasculature when all lung segments were evaluated (P = 0.001) compared with controls with DAD. Histopathology revealed vasculopathic changes, including hemangiomatosis-like changes (P = 0.043), thromboemboli (P = 0.0038), pulmonary infarcts (P = 0.047), and perivascular inflammation (P < 0.001). Generalized estimating equations revealed significant regional differences in the lung microarchitecture among all DAD-causing entities. COVID-19 showed a larger overall CVasc range (P = 0.002). Alveolar-septal congestion was associated with a significantly shorter time to death from symptom onset (P = 0.03), length of hospital stay (P = 0.02), and increased ventilatory ratio [an estimate for pulmonary dead space fraction (Vd); p = 0.043] in all cases of ARDS. Conclusions Severe COVID-19 pneumonia is characterized by significant vasculopathy and aberrant alveolar-septal congestion. Our findings also highlight the role that vascular alterations may play in Vd and clinical outcomes in ARDS in general.

Abstract 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.