Abstract #1476

Improved differentiation of BI-RADS 4 breast lesions based on ultrafast dynamic contrast-enhanced MRI radiomics and artificial neural network

Lingsong Meng¹, Xin Zhao¹, Jinxia Guo², Lin Lu¹, Meiying Cheng¹, Qingna Xing¹, Honglei Shang¹, Yan Chen¹, Penghua Zhang¹, and Xiaoan Zhang¹

¹The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China, ²General Electric (GE) Healthcare, MR Research China, Beijing, Beijing, China

Synopsis

Keywords: Breast, RadiomicsImproving the assessment of Breast Imaging Reporting and Data System (BI-RADS) 4 lesions can avoid unnecessary biopsies. As an emerging field, radiomics has been successfully explored as a means to aid decision-making for the diagnosis and risk stratification of several kinds of cancers^1-4. In this study, we combined radiomics features extracted from ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) (using the Differential sub-sampling with cartesian ordering (DISCO) technique) with an artificial neural network (ANN) to improve diagnostic performance in assessing BI-RADS 4 lesions and evaluate the potential to avoid unnecessary biopsies.

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.