Abstract #2651

Machine Learning-Based Radiomics Predicting the IDH1 Genotype of Diffuse Gliomas

Qirui Zhao¹, Zongfang Li¹, Yi Lu¹, Han Bao¹, Zujun Hou², Liuyang Chen³, Wei Xie¹, Qing Wang¹, Wei Zhao¹, Tong-San Koh⁴, and Lisha Nie⁵

¹Department of Radiology, The First Affiliated Hospital, Kunming Medical University, Kunming, China, ²Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China, ³Fisca Healthcare Ltd, Kunming, China, ⁴Department of Oncologic Imaging, National Cancer Center, Singapore, Singapore, ⁵GE Healthcare, MR Research, Beijing, China

Synopsis

The current study aims to evaluate the value of susceptibility weighted imaging (SWI) and contrast-enhanced T1-weighted imaging (CE-T1WI) radiomics features in predicting isocitrate dehydrogenase1 (IDH1) genotype of diffuse gliomas and build prediction models. It was concluded that SWI and CE-T1WI radiomics features can effectively predict the IDH1 genotype of diffuse gliomas, and CE-T1WI performed better. By combining SWI with CE-T1WI radiomics features, the prediction performance can be improved.

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.