Radiomics approach for survival prediction in chronic obstructive pulmonary disease

The idea of quantification of disease severity of chronic obstructive pulmonary disease (COPD) with CT has been introduced as early as the late 1980s with the so-called ‘density mask’ method for emphysema quantification. Since then, many novel methods of quantification, including the assessment of airway wall thickening, air trapping, vascular change and so on, have been introduced, and many studies showed the potential clinical benefit of using quantitative image analysis. Unfortunately, the use of quantification for COPD in daily practice is not ready because of several technical hurdles and the necessity of further validation of clinical benefit.

On the other hand, the concept of radiomics has recently been introduced in the field of cancer imaging. Using several imaging analysis methods, such as texture analysis, extraction of multiple objective features representing the characteristics of the tumor became possible, comprising the ‘-omics’, which originated from the system biology. Studies showed that radiomics can be particularly useful in predicting prognosis and genomic mutation. Recent studies introduced so-called ‘deep radiomics’, in which features are extracted from the convolutional neural network (CNN) of a deep learning system, replacing the traditional feature extraction methods.

In this study, we wanted to adopt the concept of ‘radiomics’ from cancer imaging to use every quantified result from COPD CT analysis together to predict mortality. Instead of imaging features used for tumor analysis, more than five hundred measured from emphysema, air trapping, airway wall change, and vascular assessment representing local and global characteristics of disease elements from two different cohorts from South Korea and Malaysia were extracted. Using conventional analysis for radiomics, we selected the five most important features and generated radiomics signals. Our study showed that the concept of ‘radiomics analysis’ is useful not only for cancer imaging but also for other disease imaging analysis.

We believed that this study is one of the important steps in opening the ‘imaging big data platform’ for future radiology practice. With the help of AI technology such as full automation of imaging segmentation and quantification, imaging conversion to minimize measurement variation, deep reconstruction to reduce radiation dose and imagine acquisition time, the majority of images will be sent to the ‘automatic quantification server’ so that many imaging features representing diseases will be quantified. All of the quantified data will be sent to the ‘quantified data center’ and they will be analyzed and assessed in comparison to all of the data stored; an analyzed datasheet will be sent to the radiology reading room to be used by radiologists.

In conclusion, the concept of radiomics will be applied in many imaging analyses and will be one of the important components of ‘Data-Driven Radiology’ in the future.

Key points

  • A total of 525 chest CT-based radiomics features were extracted and the five radiomics features of %LAA −950, AWT_Pi10_6th, AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA 18mm were selected to generate a radiomics model.
  • A radiomics model for predicting survival of COPD patients demonstrated reliable performance with a C-index of 0.774 in the discovery group and 0.805 in the validation group.
  • Radiomics approach was able to effectively identify COPD patients with an increased risk of mortality, and patients assigned to the high-risk group demonstrated worse overall survival in both the discovery and validation groups.

Article: Radiomics approach for survival prediction in chronic obstructive pulmonary disease

Authors: Young Hoon Cho, Joon Beom Seo, Sang Min Lee, Namkug Kim, Jihye Yun, Jeong Eun Hwang, Jae Seung Lee, Yeon-Mok Oh, Sang Do Lee, Li-Cher Loh & Choo-Khoom Ong

WRITTEN BY

  • Joon Beom Seo

    Professor of Radiology, University of Ulsan College of Medicine; Division of Cardiothoracic Radiology, Department of Radiology, Asan Medical Center

Latest posts

Become A Member Today!

You will have access to a wide range of benefits that can help you advance your career and stay up-to-date with the latest developments in the field of radiology. These benefits include access to educational resources, networking opportunities with other professionals in the field, opportunities to participate in research projects and clinical trials, and access to the latest technologies and techniques. 

Check out our different membership options.

If you don’t find a fitting membership send us an email here.

Membership

for radiologists, radiology residents, professionals of allied sciences (including radiographers/radiological technologists, nuclear medicine physicians, medical physicists, and data scientists) & professionals of allied sciences in training residing within the boundaries of Europe

  • Reduced registration fees for ECR 1
  • Reduced fees for the European School of Radiology (ESOR) 2
  • Exclusive option to participate in the European Diploma. 3
  • Free electronic access to the journal European Radiology 4
  • Content e-mails for all ESR journals
  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

€ 11 /year

Yes! That is less than €1 per month.

Free membership

for radiologists, radiology residents or professionals of allied sciences engaged in practice, teaching or research residing outside Europe as well as individual qualified professionals with an interest in radiology and medical imaging who do not fulfil individual or all requirements for any other ESR membership category & former full members who have retired from all clinical practice
  • Reduced registration fees for ECR 1
  • Free electronic access to the journal European Radiology
  • Content e-mails for all 3 ESR journals 4
  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

€ 0

The best things in life are free.

ESR Friends

For students, company representatives or hospital managers etc.

  • Content e-mails for all 3 ESR journals 4
  • Updates on offers & events through our newsletters

€ 0

Friendship doesn’t cost a thing.

The membership type best fitting for you will be selected automatically during the application process.

Footnotes:

01

Reduced registration fees for ECR 2024:
Provided that ESR 2023 membership is activated and approved by August 31, 2023.

Reduced registration fees for ECR 2025:
Provided that ESR 2024 membership is activated and approved by August 31, 2024.

02
Not all activities included
03
Examination based on the ESR European Training Curriculum (radiologists or radiology residents).
04
European Radiology, Insights into Imaging, European Radiology Experimental.