Dr. Jaap Zindler Profile

Dr. Jaap Zindler

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

Effects of feature type and multiple scanners on brain metastasis stereotactic radiosurgery outcome prediction

David DeVries, Frank Lagerwaard, Jaap Zindler, Timothy Yeung, George Rodrigues, George Hajdok, Aaron Ward

Proceedings Volume 12033, 1203313 (2022) https://doi.org/10.1117/12.2611147

KEYWORDS: Radiotherapy, Scanners, Magnetic resonance imaging, Data modeling, Cancer, Brain, Neuroimaging, Machine learning, Feature extraction, Statistical analysis

Read Abstract +

The prediction of brain metastasis (BM) response to stereotactic radiosurgery could assist clinicians when choosing BM treatments. This study investigates the prediction of in-field progression, out-of-field progression, and 1-year overall survival (OS) endpoints using a machine learning classifier. It also investigates the effect of feature type and magnetic resonance imaging (MRI) scanner variability on classifier performance. The study data set consisted of n = 110 BMs across 91 patients for which endpoints, seven clinical features, and MRI scans were available. 635 radiomic features were extracted from the MRI for the BM region-of-interest (ROI) and a 5mm BM ROI dilation. A 1000-iteration bootstrap experimental design was used with a random forest classifier to provide area under the receiver operating characteristic curve (AUC) estimates. This experimental design was used for multiple endpoints, groups of features, and data partitioning by scanner model. In-field progression, out-of-field progression, and 1-year OS were predicted with respective AUC estimates of 0.70, 0.57 and 0.66. For all endpoints, clinical and/or radiomic features from the BM ROI provided optimal performance. MR scanner variability was found to decrease classifier AUC in general, though pre-processing methods were found to counteract this effect for some scanner models. This study shows that in-field progression, out-of-field progression, and 1-year OS can all be predicted to some degree, with in-field progression being predicted most accurately. The effects of scanner variability indicate that more diverse data sets and robust methods to account for scanner variability are required before clinical translation.

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