Purpose: The purpose of our study was to combine differences in radiomic features extracted from lung regions in the computed tomography (CT) scans of patients diagnosed with idiopathic pulmonary fibrosis (IPF) to identify associations with genetic variations and patient survival.
Approach: A database of CT scans and genomic data from 169 patients diagnosed with IPF was collected retrospectively. Six region-of-interest pairs (three per lung, positioned posteriorly, anteriorly, and laterally) were placed in each of three axial CT sections for each patient. Thirty-one features were used in logistic regression to classify patients’ genetic mutation status; classification performance was evaluated through the area under the receiver operating characteristic (ROC) curve [average area under the ROC curve (AUC)]. Kaplan–Meier (KM) survival curve models quantified the ability of each feature to differentiate between survival curves based on feature-specific thresholds.
Results: Nine first-order texture features and one fractal feature were correlated with TOLLIP-1 (rs4963062) mutations (AUC: 0.54 to 0.74), and five Laws’ filter features were correlated with TOLLIP-2 (rs5743905) mutations (AUC: 0.53 to 0.70). None of the features analyzed were found to be correlated with MUC5B mutations. First-order and fractal features demonstrated the greatest discrimination between KM curves.
Conclusions: A radiomics approach for the correlation of patient genetic mutations with image texture features has potential as a biomarker. These features also may serve as prognostic indicators using a survival curve modeling approach in which the combination of radiomic features and genetic mutations provides an enhanced understanding of the interaction between imaging phenotype and patient genotype on the progression and treatment of IPF.