Benchmarking radiomic features using CT simulated liver lesions

Shrey S. Sukhadia; Abdibaset A. Bare; Marthony L. Robins

doi:10.1117/12.3007038

2 April 2024 Benchmarking radiomic features using CT simulated liver lesions

Shrey S. Sukhadia, Abdibaset A. Bare, Marthony L. Robins

Proceedings Volume 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications; 129310W (2024) https://doi.org/10.1117/12.3007038
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Radiomics, a burgeoning field within medical imaging, has gained momentum for its potential to provide nuanced insights into lesion characteristics. This study introduces a pioneering approach to benchmarking radiomic features, delving into their correlations with ground truth measurements and subsequent clustering patterns. By analyzing 59 simulated lesions mined from the publicly available QIDW Liver II Hybrid Dataset, a vast array of radiomic features were extracted and evaluated using Pyradiomics. A total of 2060 features are correlated with ground truth volume and contrast measurements, revealing intricate relationships. Novel co-clustering patterns emerge, underscoring the versatility and complexity of radiomic features. These findings not only contribute to lesion characterization precision but also advanced our understanding of radiomic intricacies. The presented research holds potential to refine radiomics-driven medical insights, paving the way for more informed clinical decision-making and improved patient care.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shrey S. Sukhadia, Abdibaset A. Bare, and Marthony L. Robins "Benchmarking radiomic features using CT simulated liver lesions", Proc. SPIE 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications, 129310W (2 April 2024); https://doi.org/10.1117/12.3007038

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available