A CT-Based Multiparametric Imaging Biomarker for Assessment of Pediatric Interstitial Lung Disease

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PROJECT SUMMARY Childhood (diffuse) interstitial lung diseases affects infants, children, and teens and manifests as dyspnea, hypoxemia, and respiratory compromise resulting in high morbidity and mortality or life-long sequelae for survivors. Despite improvements in the understanding of and interventions for pediatric diffuse lung disease in the past 15 years, the current standard-of-care for confirmation and characterization of chILD by thin-section chest computed tomography (CT) has had limited progress through visual assessments that are inherently subjective and suffer from inter-reader variability in defining the specific characteristic findings. This can limit accurate diagnosis of early and progressive disease and translates to a more time-consuming and burdensome clinical evaluation. An automated and objective approach to quantify common radiological lung CT patterns observed in ILDs specifically in pediatrics would provide more reliable information and reduce costs by standardizing and streamlining image- based assessment. In this grant proposal, Imbio Inc., an industry leader in developing, commercializing, and achieving regulatory approval of imaging biomarker software, proposes to develop a fully-automated software application for quantifying lung CT textures. The collaboration leverages the clinical and research expertise at the Children’s Hospital Los Angeles (CHLA) which is a large, safety-net pediatric hospital. The specific aims are 1) to develop a data repository of chest CT scans in subjects with pediatric diffuse lung disease and control subjects along with expert annotations of radiologic textures and 2) develop and validate an integrated pediatric/adult deep-learning-based algorithm to quantify radiological lung textures (i.e., DeepLTA). Upon completion of the aims, the final algorithm will have broad applicability and generalizability to detect parenchymal CT textures in both adult and pediatric ILDs. This will enable a Phase II submission and integration of this software to enhance pediatric access to state-of-the-art quantitative medical imaging analysis for improved prognosis, diagnosis, and therapy response assessment in pediatric interstitial lung disease.