CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 11314 > Article
Paper
16 March 2020 Assessment of CT image reconstruction parameters on radiomic features in a lung cancer screening cohort: the PROSPR study
Babak Haghighi, Peter B. Noël, Eric Cohen, Lauren Pantalone, Anil Vachani, Katharine A. Rendle, Jocelyn Wainwright, Chelsea Saia, Eduardo Mortani Barbosa Jr., Despina Kontos
Author Affiliations +
Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113142G (2020) https://doi.org/10.1117/12.2550161
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States
Abstract
Background: Imaging biomarkers derived from quantitative computed tomography (QCT) enable to quantify lung diseases and to distinguish their phenotypes. However, variability in radiomic features can have an impact on their diagnosis and prognosis significance. We aim to assess the effect of CT image reconstruction parameters on radiomic features in the PROSPR lung cancer screening cohort (1); thereby identifying more robust imaging features across heterogeneous CT images. Methods: CT feature extraction analysis was performed using a lattice-based texture estimation for data (n = 330) collected from a single CT scanner (Siemens Healthineers, Erlangen, Germany) with two different sets of image reconstruction kernels (medium (I30f), sharp (I50f)). A total of 26 features from three major statistical approaches, graylevel histogram, co-occurrence, and run-length, were computed. Features were calculated and averaged within a range of window sizes (W) from 4mm to 20mm. Furthermore, an unsupervised hierarchal clustering was applied to the features to identify distinct phenotypic patterns for the two kernels. The difference across phenotypes by age, sex, and Lung-Rads was assessed. Results: The results showed two distinct subtypes for two kernels across different window sizes. The heat map generated by radiomic features of the sharper kernel provided more distinct patterns compared to the medium kernel. The extracted features across the two kernels and their corresponding clusters were compared based on different clinical features. Conclusions: Our results suggest a set of radiomic features across different kernels can distinguish distinct phenotypes and can also help to assess the sensitivity of texture analysis to CT variabilities; helping for a better characterization of CT heterogeneity.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Babak Haghighi, Peter B. Noël, Eric Cohen, Lauren Pantalone, Anil Vachani, Katharine A. Rendle, Jocelyn Wainwright, Chelsea Saia, Eduardo Mortani Barbosa Jr., and Despina Kontos "Assessment of CT image reconstruction parameters on radiomic features in a lung cancer screening cohort: the PROSPR study", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113142G (16 March 2020); https://doi.org/10.1117/12.2550161
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
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
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 6 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Lung
Lung cancer
Computed tomography
Feature extraction
CT reconstruction
Scanners
Cancer
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top