A study design for statistical learning technique to predict radiological progression with an application of idiopathic pulmonary fibrosis using chest CT images.

Background: Idiopathic pulmonary fibrosis (IPF) is a fatal interstitial lung disease characterized by an unpredictable decline in lung function. Predicting IPF progression from the early changes in lung function tests have known to be a challenge due to acute exacerbation. Although it is unpredictable, the neighboring regions of fibrotic reticulation increase during IPF's progression. With this clinical information, quantitative characteristics of high-resolution computed tomography (HRCT) and a statistical learning paradigm, the aim is to build a model to predict IPF progression.

Design: A paired set of anonymized 193 HRCT images from IPF subjects with 6-12 month intervals were collected retrospectively. The study was conducted in two parts: (1) Part A collects the ground truth in small regions of interest (ROIs) with labels of "expected to progress" or "expected to be stable" at baseline HRCT and develop a statistical learning model to classify voxels in the ROIs. (2) Part B uses the voxel-level classifier from Part A to produce whole-lung level scores of a single-scan total probability's (STP) baseline.

Methods: Using annotated ROIs from 71 subjects' HRCT scans in Part A, we applied Quantum Particle Swarm Optimization-Random Forest (QPSO-RF) to build the classifier. Then, 122 subjects' HRCT scans were used to test the prediction. Using Spearman rank correlations and survival analyses, we ascertained STP associations with 6-12 month changes in quantitative lung fibrosis and forced vital capacity.

Conclusion: This study can serve as a reference for collecting ground truth, and developing statistical learning techniques to predict progression in medical imaging.