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( Ju Hyun Oh ),( Grace Hyun J. Kim ),( David W Dai ),( S Sam Weigt ),( Jonathan G Goldin ),( Lila Pourzand ),( Jooae Choe ),( Fereidoun Abtin ),( Matthew S. Brown ),( Pangyu Teng ),( Jin Woo Song ) 대한결핵 및 호흡기학회 2021 대한결핵 및 호흡기학회 추계학술대회 초록집 Vol.129 No.-
Background Interstitial lung disease (ILD) includes a heterogeneous group of disease entities. Idiopathic pulmonary fibrosis (IPF) is ultimately fatal, and accurate diagnosis of IPF is critical to clinical decision making. Visual interpretation of chest high resolution CT (HRCT) is subjective and has limited reproducibility, especially with early disease. So we have previously developed attention-gated deep learning algorithm to diagnosis IPF and machine learning to predict IPF progression. The overall aim of IS-IPF is to collect the data from two centers of excellence and evaluate the robustness of the algorithm. We present the preliminary data of the patients studied following disease classification by the multidisciplinary review committees (MDCs) at UCLA and Asan Medical Center (AMC). Methods The IS-IPF study plans to include 234 IPF and 266 non-IPF cases from two large ILD centers (UCLA and AMC). Eligible patients were evaluated in ILD MDC, were >18 years old, had a HRCT, pulmonary function testing, and a committee diagnosis of IPF or non-IPF. Relevant demographic information was collected from the medical record. Results Total 185 IPF and 266 non-IPF patients’ HRCT images have been collected in the IS-IPF study. By center, 51 IPF and 133 non- IPF patients’ HRCT were collected from UCLA, and 134 IPF and 133 non-IPF patients’ HRCT were collected from AMC. On MDC diagnosis, non-IPF cohorts consisted of 33% hypersensitivity pneumonitis, and 67% other connective tissue disease-ILD. Mean age was 61 years (63 IPF and 58 non-IPF), and 63% were male (82% IPF and 57 % non-IPF). Up to date, the predicted FVC was 74.7% and the predicted DLco was 61.6 % in the IPF cohort. Data collection is on-going. Conclusions These well-characterized cohorts will be used to evaluate HRCT image signatures for distinguishing IPF from other ILD, and predicting patient-specific IPF progression within 2 years of diagnosis.
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