Ms. Alexia Zagouras Profile

Alexia Zagouras

at Cleveland Clinic

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 | 13 March 2019 Presentation + Paper

Quantitative vessel tortuosity radiomics on baseline non-contrast lung CT predict response to immunotherapy and are prognostic of overall survival

Mehdi Alilou, Pranjal Vaidya, Mohammadhadi Khorrami, Alexia Zagouras, Pradnya Patil, Kaustav Bera, Pingfu Fu, Vamsidhar Velcheti, Anant Madabhushi

Proceedings Volume 10950, 109501F (2019) https://doi.org/10.1117/12.2513648

KEYWORDS: Computed tomography, Tumors, Image segmentation, Lung cancer, Cancer, Lung, Feature extraction, Medical research

Read Abstract +

Recently immune-checkpoint inhibitors have demonstrated promising clinical efficacy in patients with advanced non-small cell lung cancer (NSCLC). However, the response rates to immune checkpoint blockade drugs remain modest (45% in the front line setting and 20% in the second line setting). Consequently, there is an unmet need to develop accurate, validated biomarkers to predict which NSCLC patients will benefit from immunotherapy. While there has been recent interest in evaluating the role of texture and shape patterns of the nodule on CT scans to predict response to checkpoint inhibitors for NSCLC, our group has shown that nodule vessel morphology might also play a role in determining tumor aggressiveness and behavior. In this work we present a new approach using quantitative vessel tortuosity (QVT) radiomics, to predict response to checkpoint inhibitors and overall survival for patients with NSCLC treated with Nivolumab (a PD1 inhibitor) on a retrospective data set of 111 patients (D1) including 56 responders and 45 non-responders. Patients who did not receive Nivolumab after 2 cycles due to a lack of response or progression as per Response Evaluation Criteria in Solid Tumors (RECIST) were classified as non-responders, patients who had radiological response or stable disease as per RECIST were classified as responders. On D₁, in conjunction with a linear discriminant analysis (LDA) classifier the QVT features were able to predict response to immunotherapy with an AUC of 0.73_0.04. Kaplan Meier analysis showed significant difference of overall survival between patients with low risk and high risk defined by the radiomics classifier (p-value = 0.004, HR= 2.29, 95% CI= 1.35 - 3.87).

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