PROJECT SUMMARY/ABSTRACT
The goals of this proposal are to develop reproducible radiographic phenotypes of pulmonary sarcoidosis and
integrate radiographic data with clinical data, genetic variants and transcriptional signatures, redefining
sarcoidosis biomarkers. Our long-term goal is to use these integrative phenotypes and corresponding analytic
approaches to develop 1) new objective intermediate endpoints of disease progression and 2) predictive models
of disease progression to aid clinicians in clinical decision-making and researchers in trial design. Sarcoidosis is
a systemic granulomatous disease, primarily involving the lungs, which affects ~ 110 thousand individuals in the
United States, a prevalence which is likely underestimated. Usually diagnosed between 20-50 years of life, it
results in a significant decrease in quality of life and productivity. While some individuals experience spontaneous
resolution, others go on to develop severe disease. Current studies of pulmonary sarcoidosis rely on
characterization of lung abnormalities based on chest x-ray, visually using the Scadding staging system. It is
well recognized that there is misclassification of pulmonary disease based solely on Scadding stage. In addition,
this system is not useful for treatment decisions and variably predicts disease course or prognosis. Computed
tomography (CT) imaging of the lung to quantify parenchymal and other pulmonary abnormalities has offered
improved disease quantification in other lung diseases (idiopathic pulmonary fibrosis and COPD-emphysema).
We hypothesize that detailed radiomic analysis of lung CT images in sarcoidosis combined with visual
scoring metrics will identify new, more refined, phenotypes of lung disease and that combined with
clinical and transcriptomic information, will identify novel integrative disease phenotypes that can be
shown, in future longitudinal studies, to predict pulmonary disease resolution or progression. In this
project, we will 1) Develop a radiomic and comprehensive radiographic characterization of lung abnormalities in
a large cohort of sarcoidosis patients, 2) Integrate clinical, genetic, transcriptomic and radiomic characterizations
of sarcoidosis to identify predictors of radiomic features of sarcoidosis and develop new integrative phenotypes
of sarcoidosis, 3) Validate the clinical and genetic associated with radiographic features and a new integrative
phenotypes in a real-world clinical population, and 4) Characterize the longitudinal stability of radiographic
characterizations of lung abnormalities among a retrospective cohort of 75 patients. Successful completion of
this research will answer critical knowledge gaps as to how radiographic pulmonary abnormalities in sarcoidosis
relate to clinical and genetic phenotypes and how to combine radiographic assessment, clinical and
genetic/genomic data to identify distinct phenotypes of sarcoidosis. Ultimately, this proposal will establish new
standardized phenotypes for following disease longitudinally and identifying groups on which to intervene.
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