Quantitative Analysis of Lung Shape in Idiopathic Pulmonary Fibrosis: Insights Into Disease- and Age-Associated Patterns

Joyce John; Alys R Clark; Haribalan Kumar; Kelly S Burrowes; Alain C Vandal; Margaret L Wilsher; David G Milne; Brian J Bartholmai; David L Levin; Merryn H Tawhai

doi:10.1016/j.acra.2024.04.026

Quantitative Analysis of Lung Shape in Idiopathic Pulmonary Fibrosis: Insights Into Disease- and Age-Associated Patterns

Acad Radiol. 2024 Apr 27:S1076-6332(24)00235-6. doi: 10.1016/j.acra.2024.04.026. Online ahead of print.

Authors

Affiliations

¹ Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
² Department of Statistics, University of Auckland, Auckland, New Zealand.
³ Respiratory Services, Auckland City Hospital, Auckland, New Zealand.
⁴ Radiology, Auckland City Hospital, Auckland, New Zealand.
⁵ Radiology, Mayo Clinic, Rochester, Minnesota, USA.
⁶ Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand. Electronic address: m.tawhai@auckland.ac.nz.

PMID: 38679527
DOI: 10.1016/j.acra.2024.04.026

Abstract

Rationale and objectives: Fibrotic scarring in idiopathic pulmonary fibrosis (IPF) typically develops first in the posterior-basal lung tissue before advancing to involve more of the lung. The complexity of lung shape in the costo-diaphragmatic region has been proposed as a potential factor in this regional development. Intrinsic and disease-related shape could therefore be important for understanding IPF risk and its staging. We hypothesized that lung and lobe shape in IPF would have important differences from controls.

Materials and methods: A principal component (PC) analysis was used to derive a statistical shape model (SSM) of the lung for a control cohort aged > 50 years (N = 39), using segmented lung and fissure surface data from CT imaging. Individual patient shape models derived for baseline (N = 18) and follow-up (N = 16) CT scans in patients with IPF were projected to the SSM to describe shape as the sum of the SSM average and weighted PC modes. Associations between the first four PC shape modes, lung function, percentage of fibrosis (fibrosis%) and pulmonary vessel-related structures (PVRS%), and other tissue metrics were assessed and compared between the two cohorts.

Results: Shape was different between IPF and controls (P < 0.05 for all shape modes), with IPF shape forming a distinct shape cluster. Shape had a negative relationship with age in controls (P = 0.013), but a positive relationship with age in IPF (P = 0.026). Some features of shape changed on follow-up. Shape in IPF was associated with fibrosis% (P < 0.05) and PVRS% (P < 0.05).

Conclusion: Quantitative comparison of lung and lobe shape in IPF with controls of a similar age reveals shape differences that are strongly associated with age and percent fibrosis. The clustering of IPF cohort shape suggests that it could be an important feature to describe disease.

Keywords: Idiopathic pulmonary fibrosis; Lung shape; Principal component analysis; Quantitative CT; Statistical shape model.