A novel predictive model of prostate cancer (PCa) on multiparametric MRI was developed that takes into account the spatial distribution of PCa within the prostate and the spatially-autocorrelated nature of mpMRI data. The performance of the proposed model was compared to the LASSO-based model we previously described on 34 PCa cases using both voxel-wise metrics (AUC) and slice-wise metrics ($$$s_s$$$) we recently developed. The proposed model achieved superior predictive performance both in terms of AUC (0.81 vs 0.77) and $$$s_s$$$ (0.45 vs. 0.35) over the 34 cases, with significant improvements for the majority of cases.
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