Zhao, Sophia

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.

INTRODUCTION: Urinary neutrophil gelatinase-associated lipocalin (NGAL) has shown promise in differentiating acute tubular necrosis (ATN) from other types of acute kidney injuries (AKIs) in cirrhosis, particularly hepatorenal syndrome (HRS). However, NGAL is not currently available in clinical practice in North America. METHODS: Urinary NGAL was measured in a prospective cohort of 213 US hospitalized patients with decompensated cirrhosis (161 with AKI and 52 reference patients without AKI). NGAL was assessed for its ability to discriminate ATN from non-ATN AKI and to predict 90-day outcomes. RESULTS: Among patients with AKI, 57 (35%) had prerenal AKI, 55 (34%) had HRS, and 49 (30%) had ATN, with a median serum creatinine of 2.0 (interquartile range 1.5, 3.0) mg/dL at enrollment. At an optimal cutpoint of 244 μg/g creatinine, NGAL distinguished ATN (344 [132, 1,429] μg/g creatinine) from prerenal AKI (45 [0, 154] μg/g) or HRS (110 [50, 393] μg/g; P < 0.001), with a C statistic of 0.762 (95% confidence interval 0.682, 0.842). By 90 days, 71 of 213 patients (33%) died. Higher median NGAL was associated with death (159 [50, 865] vs 58 [0, 191] μg/g; P < 0.001). In adjusted and unadjusted analysis, NGAL significantly predicted 90-day transplant-free survival ( P < 0.05 for all Cox models) and outperformed Model for End-Stage Liver Disease score by C statistic (0.697 vs 0.686; P = 0.04), net reclassification index (37%; P = 0.008), and integrated discrimination increment (2.7%; P = 0.02). DISCUSSION: NGAL differentiates the type of AKI in cirrhosis and may improve prediction of mortality; therefore, it holds potential to affect management of AKI in cirrhosis.