Radiomics-based texture analysis of idiopathic pulmonary fibrosis for genetic and survival predictions

This study aims to combine differences in radiomic features between internal and peripheral portions of lungs diagnosed with idiopathic pulmonary fibrosis (IPF) and with TOLLIP and MUC5B genetic mutations to predict patient prognosis. A database of computed tomography (CT) scans from 169 IPF patients was selected from the INSPIRE study along with the corresponding genomic and demographic datasets. Three CT sections per patient were chosen to represent the superior, middle, and inferior portions of the lungs. Twelve regions of interest (ROIs) were placed in central and peripheral portions at each level of the lungs, and 142 radiomics features were calculated within each ROI. Based on feature reproducibility, 30 features were used with logistic regression and receiver operating characteristic (ROC) analysis to classify patients with various genetic mutations. Kaplan-Meier survival curve models quantified the ability of each feature to differentiate between survival curves based on a feature-specific threshold. Nine first-order features and one fractal feature were found to be predictive of TOLLIP-1 (rs4963062) mutation (AUC 0.54-0.74). Five Laws’ filter features were predictive of TOLLIP-2 (rs5743905) mutation (AUC 0.53-0.70), while no feature was found to be predictive for MUC5B mutations. First-order and fractal features reflected the greatest discrimination between Kaplan-Meier curves. A radiogenomic approach for predicting patient genetic mutations based on radiomics features extracted from thoracic CT images of patients with IPF has potential as a biomarker. These same features can also serve as predictors of patient prognosis using a survival curve modeling approach.