Dr. Ewa Zabrocka Profile

Dr. Ewa Zabrocka

at Stony Brook Medicine

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 4 April 2022 Presentation + Paper

Pre-treatment radiomics from radiotherapy dose regions predict distant brain metastases in stereotactic radiosurgery

Joseph Bae, Renee Cattell, Ewa Zabrocka, John Roberson, David Payne, Kartik Mani, Prateek Prasanna

Proceedings Volume 12031, 120311O (2022) https://doi.org/10.1117/12.2612088

KEYWORDS: Machine learning, Brain cancer, Magnetic resonance imaging, Brain, Radiotherapy, Tumors, Feature extraction, Neuroimaging, Radiation oncology, Statistical analysis

Read Abstract +

Stereotactic radiosurgery (SRS) is frequently employed to treat brain metastases. However, <50% of patients treated with this method develop distant brain metastases (DBMs). As a result, these patients are followed using Magnetic Resonance Imaging (MRI) to identify DBM development. There is no current pre-treatment risk metric to identify which patients might be likely to develop DBMs. In this study, pre-treatment MRIs and radiotherapy planning data including structure sets and radiation dose maps were obtained for 81 SRS brain metastases treatment courses. Clinical variables including performance status, age, number of tumors, and primary tumor type were also collected. Pre-treatment MRIs were skull-stripped and normalized. 3D radiomic features from grey-intensity, Laws Energy, Gabor, Haralick, and CoLlAGe feature families were extracted from T1, T1 contrast-enhanced (T1w), T2, and FLAIR pre-treatment MRI sequences in brain regions receiving 0-25%, 25-50%, 50-75%, and 75-100% of prescribed radiation dose. A baseline classification model for DBM was created using clinical variables. Ablation studies were performed to determine which dose region and MRI sequence contained radiomic features most predictive for DBM development using machine learning (ML) classifiers. An ML classifier trained on 3D radiomic features from the 50-75% dose region of pre-treatment T1w MRI (AUC: 0.71, 95% CI: 0.68-0.74) outperformed the baseline model (AUC: 0.50, 95% CI: 0.47-0.53) for DBM prediction. In conclusion, we leverage radiotherapy dose regions to identify subcompartments for radiomic feature extraction from multi-parametric pre-treatment MRI data. We demonstrate that radiomic features from these dose regions can be used to predict DBM for SRS-treated brain metastases.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years