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.

"Endobronchial Optical Coherence Tomography as a Novel Method for In Vivo Microscopic Assessment of Interstitial Lung Abnormalities ." American Journal of Respiratory and Critical Care Medicine, 0(ja), pp.

Rationale: Idiopathic pulmonary fibrosis (IPF) affects the subpleural lung but is considered to spare small airways. Micro-computed tomography (micro-CT) studies demonstrated small airway reduction in end-stage IPF explanted lungs, raising questions about small airway involvement in early-stage disease. Endobronchial optical coherence tomography (EB-OCT) is a volumetric imaging modality that detects microscopic features from subpleural to proximal airways. Objectives: In this study, EB-OCT was used to evaluate small airways in early IPF and control subjects in vivo. Methods: EB-OCT was performed in 12 subjects with IPF and 5 control subjects (matched by age, sex, smoking history, height, and body mass index). Subjects with IPF had early disease with mild restriction (FVC: 83.5% predicted), which was diagnosed per current guidelines and confirmed by surgical biopsy. EB-OCT volumetric imaging was acquired bronchoscopically in multiple, distinct, bilateral lung locations (total: 97 sites). IPF imaging sites were classified by severity into affected (all criteria for usual interstitial pneumonia present) and less affected (some but not all criteria for usual interstitial pneumonia present). Bronchiole count and small airway stereology metrics were measured for each EB-OCT imaging site. Measurements and Main Results: Compared with the number of bronchioles in control subjects (mean = 11.2/cm3; SD = 6.2), there was significant bronchiole reduction in subjects with IPF (42% loss; mean = 6.5/cm3; SD = 3.4; P = 0.0039), including in IPF affected (48% loss; mean: 5.8/cm3; SD: 2.8; P < 0.00001) and IPF less affected (33% loss; mean: 7.5/cm3; SD: 4.1; P = 0.024) sites. Stereology metrics showed that IPF-affected small airways were significantly larger, more distorted, and more irregular than in IPF-less affected sites and control subjects. IPF less affected and control airways were statistically indistinguishable for all stereology parameters (P = 0.36-1.0). Conclusions: EB-OCT demonstrated marked bronchiolar loss in early IPF (between 30% and 50%), even in areas minimally affected by disease, compared with matched control subjects. These findings support small airway disease as a feature of early IPF, providing novel insight into pathogenesis and potential therapeutic targets.

The ability to monitor disease progression over time is critical to inform patient care and prognosis, especially in usual interstitial pneumonia (UIP), the histopathological pattern seen in idiopathic pulmonary fibrosis (IPF). HRCT is limited in resolution to detect disease changes on a microscopic level, and surgical lung biopsy (SLB) has high risk of morbidity and mortality precluding its use to assess progression. Endobronchial optical coherence tomography (EB-OCT) is a bronchoscopic, minimally-invasive, high-resolution imaging method that accurately detects microscopic ILD features and is repeatable. Here, we evaluate the utility of repeat EB-OCT for monitoring microscopic disease progression in UIP/IPF.

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Proning awake patients with COVID-19 is associated with lower mortality and intubation rates. However, these studies also demonstrate low participation rates and tolerance of awake proning. In this study, we attempt to understand barriers to proning. Medical and dental students surveyed nonintubated patients to understand factors affecting adherence to a proning protocol. Only patients who discussed proning with their medical team attempted the practice. Eight of nine patients who were informed about benefits of proning attempted the maneuver. Discomfort was the primary reason patients stopped proning. Addressing discomfort and implementing systematic patient education may increase adherence to proning.

Rationale: Early, accurate diagnosis of interstitial lung disease (ILD) informs prognosis and therapy, especially in idiopathic pulmonary fibrosis (IPF). Current diagnostic methods are imperfect. High-resolution computed tomography has limited resolution, and surgical lung biopsy (SLB) carries risks of morbidity and mortality. Endobronchial optical coherence tomography (EB-OCT) is a low-risk, bronchoscope-compatible modality that images large lung volumes in vivo with microscopic resolution, including subpleural lung, and has the potential to improve the diagnostic accuracy of bronchoscopy for ILD diagnosis. Objectives: We performed a prospective diagnostic accuracy study of EB-OCT in patients with ILD with a low-confidence diagnosis undergoing SLB. The primary endpoints were EB-OCT sensitivity/specificity for diagnosis of the histopathologic pattern of usual interstitial pneumonia (UIP) and clinical IPF. The secondary endpoint was agreement between EB-OCT and SLB for diagnosis of the ILD fibrosis pattern. Methods: EB-OCT was performed immediately before SLB. The resulting EB-OCT images and histopathology were interpreted by blinded, independent pathologists. Clinical diagnosis was obtained from the treating pulmonologists after SLB, blinded to EB-OCT. Measurements and Main Results: We enrolled 31 patients, and 4 were excluded because of inconclusive histopathology or lack of EB-OCT data. Twenty-seven patients were included in the analysis (16 men, average age: 65.0 yr): 12 were diagnosed with UIP and 15 with non-UIP ILD. Average FVC and DlCO were 75.3% (SD, 18.5) and 53.5% (SD, 16.4), respectively. Sensitivity and specificity of EB-OCT was 100% (95% confidence interval, 75.8-100.0%) and 100% (79.6-100%), respectively, for both histopathologic UIP and clinical diagnosis of IPF. There was high agreement between EB-OCT and histopathology for diagnosis of ILD fibrosis pattern (weighted κ: 0.87 [0.72-1.0]). Conclusions: EB-OCT is a safe, accurate method for microscopic ILD diagnosis, as a complement to high-resolution computed tomography and an alternative to SLB.

Adequate tumor yield in core-needle biopsy (CNB) specimens is essential in lung cancer for accurate histological diagnosis, molecular testing for therapeutic decision-making, and tumor biobanking for research. Insufficient tumor sampling in CNB is common, primarily due to inadvertent sampling of tumor-associated fibrosis or atelectatic lung, leading to repeat procedures and delayed diagnosis. Currently, there is no method for rapid, non-destructive intraprocedural assessment of CNBs. Polarization-sensitive optical coherence tomography (PS-OCT) is a high-resolution, volumetric imaging technique that has the potential to meet this clinical need. PS-OCT detects endogenous tissue properties, including birefringence from collagen, and degree of polarization uniformity (DOPU) indicative of tissue depolarization. Here, PS-OCT birefringence and DOPU measurements were used to quantify the amount of tumor, fibrosis, and normal lung parenchyma in 42 fresh, intact lung CNB specimens. PS-OCT results were compared to and validated against matched histology in a blinded assessment. Linear regression analysis showed strong correlations between PS-OCT and matched histology for quantification of tumors, fibrosis, and normal lung parenchyma in CNBs. PS-OCT distinguished CNBs with low tumor content from those with higher tumor content with high sensitivity and specificity. This study demonstrates the potential of PS-OCT as a method for rapid, non-destructive, label-free intra-procedural tumor yield assessment.