CT Radiomics Features Predict Change in Lung Density and Rate of Emphysema Progression

Pratim Saha; Sandeep Bodduluri; Arie Nakhmani; Muhammad F A Chaudhary; Praneeth R Amudala Puchakalaya; Venkata Sthanam; Raul San Jose Estepar; Joseph M Reinhardt; Chengzui Zhang; Surya P Bhatt

doi:10.1513/AnnalsATS.202401-009OC

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CT Radiomics Features Predict Change in Lung Density and Rate of Emphysema Progression

Journal article

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CT Radiomics Features Predict Change in Lung Density and Rate of Emphysema Progression

Pratim Saha, Sandeep Bodduluri, Arie Nakhmani, Muhammad F A Chaudhary, Praneeth R Amudala Puchakalaya, Venkata Sthanam, Raul San Jose Estepar, Joseph M Reinhardt, Chengzui Zhang and Surya P Bhatt

Annals of the American Thoracic Society, Vol.22(1), pp.83-92

01/2025

DOI: 10.1513/AnnalsATS.202401-009OC

PMCID: PMC11708762

PMID: 39404745

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https://pmc.ncbi.nlm.nih.gov/articles/PMC11708762/pdf/AnnalsATS.202401-009OC.pdfView

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Abstract

Rationale Emphysema progression is heterogeneous. Predicting temporal changes in lung density and detecting rapid progressors may facilitate selection of individuals for targeted therapies. Objective To test whether computed tomography (CT) radiomics can be used to predict changes in lung density and detect rapid progressors. Methods We extracted radiomics features from inspiratory chest CT in 4,575 subjects with and without airflow obstruction at enrollment, who completed a follow-up visit at approximately 5 years. We quantified emphysema using adjusted lung density (ALD) and estimated emphysema progression as the annualized change in ALD (∆ALD/year) between visits. We categorized participants into rapid progressors (>1% ∆ALD/year) and stable disease (≤1% ∆ALD/year). A gradient boosting model was used (1) to predict ALD at 5-years and (2) to identify rapid progressors. Four models using demographics (base clinical model); CT density; radiomics; and combined features (clinical, radiomics, and CT density) were evaluated and tested. Results There were 1,773 (38.8%) rapid progressors. For predicting ALD at 5-years in the 20% held-out data, the base model explained 31% of the variance (adjusted R2 = 0.31) whereas R2 was 0.74 for the CT density model, 0.66 for the radiomics-only model, and 0.77 for the combined features model. For detecting rapid progressors, the base model (AUC = 0.57, 95%CI 0.53-0.61) was outperformed by the radiomics-only model (AUC = 0.73, 95%CI 0.69-0.76, ∆ =0.0003, p < 0.001) and the combined model (AUC = 0.74, 95%CI 0.71-0.77, ∆ = 0.0003, p < 0.001). Conclusions Parenchymal and airway radiomics features derived from inspiratory scans can be used to predict temporal changes in lung density and help identify rapid progressors.

chronic obstructive pulmonary disease

emphysema

progression

radiomics

Details

Title: Subtitle: CT Radiomics Features Predict Change in Lung Density and Rate of Emphysema Progression
Creators: Pratim Saha - University of Alabama at Birmingham
Sandeep Bodduluri - University of Alabama at Birmingham
Arie Nakhmani - University of Alabama at Birmingham
Muhammad F A Chaudhary - University of Alabama at Birmingham
Praneeth R Amudala Puchakalaya - University of Alabama at Birmingham
Venkata Sthanam - University of Alabama at Birmingham
Raul San Jose Estepar - Brigham and Women's Hospital
Joseph M Reinhardt - University of Iowa
Chengzui Zhang - University of Alabama at Birmingham
Surya P Bhatt - University of Alabama at Birmingham
Resource Type: Journal article
Publication Details: Annals of the American Thoracic Society, Vol.22(1), pp.83-92
DOI: 10.1513/AnnalsATS.202401-009OC
PMID: 39404745
PMCID: PMC11708762
NLM abbreviation: Ann Am Thorac Soc
ISSN: 2329-6933
eISSN: 2325-6621
Publisher: AMER THORACIC SOC; NEW YORK
Language: English
Electronic publication date: 10/15/2024
Date published: 01/2025
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology
Record Identifier: 9984736746002771

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