Abstract #0832

Added Value of DCE in Machine Learning-based Tumor Probability Maps for Predicting Clinically Significant Cancer Foci in Pre-biopsy MR images

Gabriel Addio Nketiah¹, Léo Pallas², Adrian L Breto ³, Radka Stoyanova³, Mattijs Elschot ¹, and Tone F. Bathen^1,4

¹Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway, ²Department of Digital Science, CPE Lyon, Lyon, Norway, ³Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, United States, ⁴Clinic of Radiology and Nuclear Medicine, St. Olavs University Hospital, Trondheim, Norway

The utility of multiparametric (mp) versus biparametric (bp) MRI protocol in prostate cancer diagnosis has been compared in several large studies, but mainly using manual qualitative evaluation. In this study we employed machine learning models to investigate the added value of DCE (i.e. mpMRI) in predicting significant cancer foci in pre-biopsy MR images. Whereas both protocols had comparable results in the whole prostate and transition zone analyses, we found mpMRI model to be more useful in the peripheral zone, where significant differences (p < 0.05) were found for all performance measures i.e. area under the curve, accuracy, sensitivity and specificity.

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.