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Radiomics approach for survival prediction in chronic obstructive pulmonary disease.

Young Hoon Cho1,2, Joon Beom Seo3, Sang Min Lee2

  • 1Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, 148, Gurodong-ro, Guro-gu, Seoul, 08308, South Korea.

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Radiomics analysis of chest CT scans can predict survival in patients with chronic obstructive pulmonary disease (COPD). A radiomics signature (RS) effectively identifies high-risk COPD patients, aiding in mortality risk stratification.

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Computer-assisted radiographic image interpretationPulmonary disease, chronic obstructiveSurvival analysisThoraxTomography, X-ray computed

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Area of Science:

  • Pulmonary Medicine
  • Radiology
  • Data Science

Background:

  • Chronic obstructive pulmonary disease (COPD) poses a significant global health challenge.
  • Accurate prediction of overall survival and risk stratification are crucial for managing COPD patients.
  • Current prognostic models may not fully capture the complexity of COPD progression.

Purpose of the Study:

  • To develop and validate a radiomics signature (RS) for predicting overall survival in COPD patients.
  • To evaluate the performance of the RS in identifying patients at high risk of mortality.
  • To explore the utility of radiomics in risk stratification for COPD.

Main Methods:

  • A cohort of 344 COPD patients (Korean Obstructive Lung Disease cohort) was used for discovery, with external validation on 112 patients.
  • 525 radiomics features were extracted from chest CT scans.
  • A radiomics signature (RS) was generated using five features selected by LASSO Cox regression, and its performance was assessed using Kaplan-Meier analysis and Cox regression.

Main Results:

  • The RS comprised five key features: %LAA-950, AWT_Pi10_6th, AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA18mm.
  • The RS achieved a C-index of 0.774 in the discovery group and 0.805 in the validation group.
  • High-risk patients (RS > 1.053) exhibited significantly worse overall survival (HR > 5.2) in both cohorts, independent of age and BMI.

Conclusions:

  • Radiomics analysis is a feasible approach for predicting survival and stratifying risk in COPD patients.
  • The developed RS demonstrates acceptable performance and effectively identifies COPD patients at increased risk of mortality.
  • CT-based radiomics offers a promising tool for enhancing prognostic assessment in COPD management.