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.
For computed tomography (CT) imaging, it is important that the imaging protocols be optimized so that the scan is performed at the lowest dose that yields diagnostic images in order to minimize patients’ exposure to ionizing radiation. To accomplish this, it is important to verify that image quality of the acquired scan is sufficient for the diagnostic task at hand. Since the image quality strongly depends on both the characteristics of the patient as well as the imager, both of which are highly variable, using simplistic parameters like noise to determine the quality threshold is challenging. In this work, we apply deep learning using convolutional neural network (CNN) to predict whether CT scans meet the minimal image quality threshold for diagnosis. The dataset consists of 74 cases of high resolution axial CT scans acquired for the diagnosis of interstitial lung disease. The quality of the images is rated by a radiologist. While the number of cases is relatively small for deep learning tasks, each case consists of more than 200 slices, comprising a total of 21,257 images. The deep learning involves fine-tuning of a pre-trained VGG19 network, which results in an accuracy of 0.76 (95% CI: 0.748 – 0.773) and an AUC of 0.78 (SE: 0.01). While the number of total images is relatively large, the result is still significantly limited by the small number of cases. Despite the limitation, this work demonstrates the potential for using deep learning to characterize the diagnostic quality of CT scans.