Single Time Point Prediction as Earlier Diagnosis of Progressive Pulmonary Fibrosis

Overview

This study is a prospective observational study for subjects with idiopathic pulmonary fibrosis (IPF) or non-IPF interstitial lung diseases (ILD). The purpose of this study is to compare whether imaging patterns from high-resolution computed tomography (HRCT) at baseline can predict worsening. Single Time point Prediction (STP) is a score derived from an artificial intelligenc/ machine learning (AI/ML) using the radiomic features from a HRCT scan that quantifies the imaging patterns of short-term predictive worsening.

Primary objective is to predict early for progression in both IPF and non-IPF ILD population using an artificial intelligence (AI)/Machine Learning (ML) algorithm of STP score. The primary interest is to validate STP score in identifying a cohort early for the candidate of anti-fibrotic treatment. The study plans to collect clinical information such as pulmonary function tests (PFT), symptom scores, 6-minute walk tests (6MWT), and radiologic information from HRCT. This study does not intervene with patient's standard medical care. This proposal is a prospective study that will enroll patients from the UCLA ILD Center. STP scores of subjects' baseline HRCT images will be grouped to one of 2 arms based on the baseline HRCT. * Arm A: STP\>=30% in whole lung * Arm B: STP \< 30% in whole lung A subject's allocation will be determined by the baseline HRCT scan. STP score will be derived from the baseline HRCT to compare the early prediction of progression in ILD, STP of 30% threshold is expected to be close to the mean of overall population. In addition, a multi-scale guided attention (MSGA) is an imaging marker from deep learning model with two attention models to classify an IPF-likeliness using HRCT. Primary endpoint of progression-free survival (PFS) is uniformly defined in IPF and non-IPD ILD subjects by the reduction of 10% or more by FVC in volume or 15% or more by DLCO or death from any cause, whichever came first. Key secondary endpoint of this study are: In IPF, progression-free survival (PFS) is defined by the reduction of 10% or more by FVC in volume or 15% or more by DLCO or death from any cause, whichever came first. In non-IPF ILD, PFS is defined by two worsening outcomes out of three elements of PFT worsening, radiological worsening or symptom or disease-related death alone. * Worsening in PFT is defined by 5% or more absolute decreases in the percent predicted FVC or 10% or more absolute decrease in the percent predicted DLCO. * Radiological evidence of disease progression is defined by visual worsening (one or more of the following) from a radiological report or quantitative lung fibrosis (QLF) changes \>=2% in whole lung * Symptomatic worsening can be measure by King's Brief Interstitial Lung Disease (K-BILD) or Leicester Cough Questionnaire (LCD). Secondary outcomes of this study are: * To compare additional PFS criteria between two arms of STP * To compare overall survival between the two arms of STP * To compare the changes in 6-minute walk tests between the two arms of STP * To compare PFS between two groups of MSGA marker positive and negative * To compare overall survival between two groups of MSGA marker positive and negative With a chronic ILD or IPF, lung function may be stable for a few years or continue to deteriorate slowly or rapidly develop more scar tissues over time. While it is known that age, biological sex, and lung function are factors that can impact risk of worsening lung function, there is a great need for better methods to predict which patients are at risk of worsening lung function. Having better methods to predict disease progression could allow more timely treatment with anti-fibrotic treatment to prevent the disease progression. In both IPF and non-IPF ILD, HRCT scan is required for diagnosis. Imaging patterns derived from HRCT, called STP is designed to predict the areas in lung that may be likely to progress in the next 6 to 12 months. High STP scores are associated with poor prognosis and worsening the pulmonary function. The goal of this study is to test whether an AI-algorithm, the STP score from a single CT study, can predict disease progression in subjects with IPF and non IPF-ILD in a prospective study. This AI-algorithm was developed under NIH-sponsored study. The purpose of prospective observational cohort study from UCLA is to test for the early sign of progressive fibrosis using baseline HRCT. This study, Imaging Signature of Progressive Pulmonary Fibrosis (IS-PPF) Research is a prospective study that will collect information regarding HRCT images, pulmonary function test, 6-minute walk, symptomatic score, and patients' clinical information to set up AI-driven imaging signature for evaluating the STP in predicting progression in IPF and non-IPF ILD. This is an observational study; only minimally invasive procedures will be performed with study subjects (blood draws and nasal swabs). These biological samples will support future research studies. The study subject's will participation in the study for up to 3 years, the length of participation may vary. All subjects will continue to receive their usual care and treatment. In summary, this research will create an opportunity to test and validate the imaging score and early prediction for IPF and non-IPF ILD that can impact current and future care practices.