Novel Subtypes of Pulmonary Emphysema Based on Spatially-Informed Lung Texture Learning

TL;DR

This study introduces a spatially-informed framework for analyzing lung textures in CT scans, enabling the discovery of new emphysema subtypes that are reproducible, spatially-aware, and linked to symptoms.

Contribution

It presents a novel method combining spatial mapping with texture analysis to identify new emphysema subtypes from CT scans.

Findings

Spatial mapping allows population-wide analysis of emphysema location.

Discovered sLTPs are reproducible and encode known subtypes.

sLTPs are associated with physiological symptoms.

Abstract

Pulmonary emphysema overlaps considerably with chronic obstructive pulmonary disease (COPD), and is traditionally subcategorized into three subtypes previously identified on autopsy. Unsupervised learning of emphysema subtypes on computed tomography (CT) opens the way to new definitions of emphysema subtypes and eliminates the need of thorough manual labeling. However, CT-based emphysema subtypes have been limited to texture-based patterns without considering spatial location. In this work, we introduce a standardized spatial mapping of the lung for quantitative study of lung texture location, and propose a novel framework for combining spatial and texture information to discover spatially-informed lung texture patterns (sLTPs) that represent novel emphysema subtypes. Exploiting two cohorts of full-lung CT scans from the MESA COPD and EMCAP studies, we first show that our spatial mapping enables population-wide study of emphysema spatial location. We then evaluate the characteristics of the sLTPs discovered on MESA COPD, and show that they are reproducible, able to encode standard emphysema subtypes, and associated with physiological symptoms.