Abstract #1887

Development and validation of a deep learning model trained on MRI for the prediction of hepatocellular carcinoma survival

Lidi Ma¹, Congrui Li², Haixia Li³, Kan Deng³, Cheng Zhang¹, Weijing Zhang¹, and Chuanmiao Xie¹

¹Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China, ²Department of Diagnostic Radiology, Hunan Cancer Hospital, Central South University, Changsha, China, ³Philips Healthcare, Guangzhou, China

Synopsis

Keywords: Machine Learning/Artificial Intelligence, LiverIn this study, we developed a deep learning model based on GD-DTPA-enhanced MRI data to predict the overall survival (OS) of patients with HCC. Our results showed that 3D-CNN model based on GD-DTPA-enhanced MRI can non-invasively predict the OS of patients with HCC. The combined model integrating the deep learning score and clinical factors showed a higher predictive value than the clinical and 3D-CNN models and may be more useful in guiding clinical treatment decisions to improve the prognosis of patients with HCC.

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.