Meeting Banner
Abstract #0549

Prediction of breast cancer recurrence based on automatically extracted quantitative MR features

Junghwa Kang1, Dayeon Bak1, Yoonho Nam1, Ga Eun Park2, and Sung Hun Kim2
1Department of Biomedical engineering, Hankuk university of Foreign Studies, Yongin-si, Korea, Republic of, 2Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea, Republic of

Synopsis

Keywords: Breast, Breast, Breast cancer, background parenchymal enhancement, fibroglandular tissue, Recurrence, Radiomics

Motivation: There has been growing interest in predicting breast cancer outcomes using MR imaging features related to cancer or background parenchymal enhancement.

Goal(s): To evaluate the potential for recurrence prediction by extracting features from both breasts using a fully automated quantitative method.

Approach: Quantitative imaging features were extracted by applying automatically segmented masks to the subtraction images.Using these features, a recurrence prediction model was trained and evaluated.

Results: Incorporating features from the contralateral FGT mask slightly improved prediction accuracy compared to using cancer mask features alone.

Impact: We suggested a cancer recurrence prediction model using breast MRIs from over 1,600 subjects, employing an automated feature extraction process to investigate its feasibility. Additionally, incorporating features from both the ipsilateral and contralateral sides demonstrated an improvement in predictions.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Keywords