This observational study involves obtaining 2 chest CT scans; a historical baseline CT within ±1 year of enrollment into PRECISIONS, and a follow-up CT (either historical or prospective) 12 months ± 180 days after the baseline CT. Many IPF patients will have a CT scan every 12 months for disease monitoring and cancer screening. Participants will have the option to share historical CTs only or they can choose to have a research CT done for the follow-up scan, if a scan for clinical purposes is not available.
Idiopathic pulmonary fibrosis (IPF) remains deadly despite two FDA-approved therapies. Forced vital capacity (FVC), a one-dimensional assessment of lung function that requires three effort-dependent and error-prone maneuvers, is the standard for evaluating disease severity and monitoring progression. FVC indirectly measures disease activity and is thus insensitive to subtle change. These limitations hamper therapeutic trials. The Gender, Age, and Physiology (GAP) score improves on FVC alone and is the most used scoring model for prognostication, but gender and age aren't influenced by treatment. Modifiable intermediate molecular markers and other metrics for assessing disease severity and progression remain unmet needs for aiding drug development and clinical decision-making. Computed tomography (CT) captures morphologic patterns and the extent of fibrosis noninvasively. Advances in quantitative CT enable objective detection and quantitation of anatomy, and highly dimensional image features, often termed radiomic, can identify sub-visual characteristics. The investigators seek to evaluate radiomic features alone and in conjunction with other disease dimensions for prognostication and response to treatment in IPF. The investigators overall objectives are to identify and validate radiologic features, such as total extent of lung fibrosis, for disease activity and intermediate response to therapy, and understand where to position these powerful markers. The investigators hypothesize that DTA scores will contribute to prediction of disease progression and that molecular markers will enhance that performance. Aim 1: The investigators will validate quantitative CT and radiomic markers for disease progression by independent replication in separate cohorts. The investigators hypothesize that quantitative CT markers will predict disease progression in UVA/Chicago cohorts. Baseline and subsequent CT scans have been voluntarily collected in many PFF-PR cases. The investigators propose collection of 1-year HRCTs in UVA/Chicago participants to evaluate: a) the prognostic value of baseline quantitative CT and radiomic markers (i.e. DTA) in predicting time to progression defined as either 10% relative decline in FVC, lung transplant, or death from any cause, b) associations between changes in CT biomarkers on sequential CT and changes in 1-year FVC and DLCO, and c) change in CT associated with drug treatment. This aim will establish the relative and synergistic value of CT to established physiologic markers. Aim 2: The investigators will determine if candidate genetic variants for IPF susceptibility and survival are associated with the DTA score and improve predictive performance for survival. The investigators hypothesize that variants in MUC5B, TOLLIP, and Telomere lengths (TL) will enhance DTA fibrosis score associations with progression-free survival in IPF. The investigators will perform a cross-sectional analysis of PFF-PR cases comparing quantitative CT and radiomic markers at baseline with and without "at risk" genotypes for association with severity and progression (decline in FVC over time). This will ascertain what markers improve performance of the DTA fibrosis extent scores using Cox regression analysis and accuracy metrics from Aim 1. Findings will be replicated in UVA/Chicago cohort and in the prospective PRECISIONS cohort. This aim will establish the additive value of genetic markers. Aim 3: The investigators will assess whether DTA and radiomic markers are additive/synergistic with plasma protein and blood transcriptome markers for disease progression. The investigators hypothesize that selected protein and transcriptomic markers will prove additive to DTA fibrosis extent for prediction of progression-free survival whereas other markers correlated with DTA will not. The investigators have chosen published markers from a 4-protein panel signature, along with CCL18, as examples, given their current level of replication and promise. The investigators will also include a 25-gene FVC predictor for disease progression. Similar analyses, as outlined in Aims 1 and 2, will determine their additive information value.
Study Type
OBSERVATIONAL
Enrollment
160
High resolution computed tomography (HRCT) scan is a medical imaging technique used to obtain detailed internal images of the body. HRCT images will be obtained at 0 and 12 months.
During blood draw, someone uses a needle to take blood from a vein, usually in your arm.
University of Virginia
Charlottesville, Virginia, United States
RECRUITINGDerivation of DTA in IPF only cases from the PFF-PR and its associations with disease severity and outcomes.
Driven texture analysis (DTA) is a machine learning method capable of automatic detection and quantification of lung fibrosis on HRCT. It is trained to discriminate fibrosis using radiologist-identified image regions demonstrating normal lung parenchyma and usual interstitial pneumonia patterns. Changes in Forced Vital Capacity (FVC) measured in liters, reflect increased elastic recoil caused by fibrosis. We will use linear-mixed effects models with random intercept to examine associations of repeated DTA-fibrosis scores with repeated percent predicted FVC measurements over time (12 months minimum). This approach will provide a more precise estimate, power, and account for baseline FVC at an individual level which has implications of how rapid a decline we anticipate. This is the most common approach to examine longitudinal changes of FVC in IPF studies. FVC decline greater than 10% has been shown to be prognostic of worse survival and is a common endpoint in IPF clinical trials.
Time frame: 12 months
Determine whether known IPF-risk genetic variants are associated with DTA score.
This is a cross-sectional analysis to determine whether genetic variants that confer higher risk of disease and progression are associated with higher DTA scores from CT.
Time frame: 12 months
Identify novel genetic variants that associate with DTA score progression.
Determine novel genetic variants that indicate higher risk of disease progression and are associated with higher DTA scores.
Time frame: 12 months
Determine if DTA or any constituent radiomic features correlate with select plasma proteins.
MMP-7, CA-125, YKL, OPN, CCL18 are plasma proteins that have been shown to be associated with risk and prognosis in IPF.
Time frame: 12 months
Determine if DTA or any of constituent radiomic features correlate with transcriptomic
We have previously published a transcriptomic classifier that is predictive of FVC decline in IPF.
Time frame: 12 months
Determine the best combination of markers (DTA, proteins and transcriptome) for machine learning algorithms for AUC evaluation of ROCs on all 3 cohorts.
12-month FVC decline is a validated marker of disease progression in IPF as it's predictive of worse mortality. Receiver operating characteristic curve (ROC) is an analytical method, represented as a graph, that is used to evaluate the performance of a binary diagnostic classification method. The diagnostic test results need to be classified into one of the clearly defined dichotomous categories, such as the presence or absence of a disease. Area under the ROC curve (AUC) measures the entire two-dimensional area underneath the entire ROC curve.
Time frame: 12 months
Determine associations of changes in DTA scores with 12-month changes in FVC and DLCO.
FVC (L) and diffusing capacity of the lungs for carbon monoxide (DLCO) have been shown to be validated markers of disease progression in IPF. DLCO is a measurement to assess the lungs' ability to transfer gas from inspired air to the bloodstream. The normal range for DLCO: 80-120% of its predicted value for men. 76-120% of its predicted value for women.
Time frame: 12 months
Determine associations of changes in DTA scores with drug treatment (i.e., antifibrotics)
Changes in DTA scores have been shown correlate strongly with changes in lung function. By establishing that changes in DTA scores occur in response to patients with IPF who start antifibrotic therapy, will provide supportive evidence of DTA as a potential tool to track treatment responses.
Time frame: 12 months
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