Keywords: Uterus, Cancer, Endometrial Cancer; Tumor Grade
Random forest models were constructed to predict tumor grade (grade 1-2 vs. grade 3) of endometrial cancer based on radiomics features extracted from quantitative T1, T2, proton density maps generated by synthetic MRI and apparent diffusion coefficient maps generated by diffusion-weighted imaging. The classification model based on features extracted from all the quantitative maps achieved the highest area under the curve of 0.804 compared to models constructed based on single quantitative map.
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.
Keywords