Ms. Jing Zhang Profile

Jing Zhang

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 Poster + Presentation + Paper

Identifying sinus invasion in meningioma patients before surgery with deep learning

Qi Qiu, Kai Sun, Jing Zhang, Panpan Liu, Liang Wang, Junting Zhang, Junlin Zhou, Zhenyu Liu, Jie Tian

Proceedings Volume 12033, 120332B (2022) https://doi.org/10.1117/12.2611054

KEYWORDS: Surgery, Data modeling, Tumors, Magnetic resonance imaging, Visualization, Structural imaging, Performance modeling, Medicine, Information fusion, Image segmentation

Read Abstract +

Meningioma is the most common intracranial non-malignant tumor but is usually closely associated with the major venous sinuses. It has been recognized by neurosurgeons that meningioma should be treated with different surgical options depending on the status of sinus invasion. Therefore, it is necessary to accurately identify the venous sinus invasion status of meningioma patients before surgery; however, appropriate techniques are still lacking. Our study aimed to construct a deep learning model for accurate determination of sinus invasion before surgery. In this study, we collected multi-modal imaging data and clinical information for a total of 1048 meningioma patients from two hospitals. ResNet-50 with a dual attention mechanism was used on the preprocessed T1c and T2WI data respectively, and the final model was generated by combining the two unimodal models. The classification performance was evaluated by the area under receiver operating characteristic (ROC) curve (AUC). The results implied that the multimodal fusion classification model showed good performance in predicting meningioma sinus invasion. Further analysis on the patients with different WHO gradings indicated that our model has the best classification ability under WHO grading 1 in an independent validation cohort (0.84 AUC). This study shows that deep learning is a reliable method for predicting sinus invasion in patients with meningioma before surgery.

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