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

ABSTRACT:

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 tru

SUBMITTER: Kim GHJ 

PROVIDER: S-EPMC8204677 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature