Grade Group assessed using Gleason combined score and tumor extent is a main determinant for risk stratification and therapeutic planning of prostate cancer.


To develop a 3-dimensional magnetic resonance imaging (MRI) model regarding Grade Group and tumor extent in collaboration with uroradiologists and uropathologists for optimal treatment planning for prostate cancer.


We studied the data from 83 patients with prostate cancer who underwent multiparametric MRI and subsequent MRI–transrectal ultrasound fusion biopsy and radical prostatectomy. A 3-dimensional MRI model was constructed by integrating topographic information of MRI-based segmented lesions, biopsy paths, and histopathologic information of biopsy specimens. The multiparametric MRI–integrated Grade Group and laterality were assessed by using the 3-dimensional MRI model and compared with the radical prostatectomy specimen.


The MRI-defined index tumor was concordant with radical prostatectomy in 94.7% (72 of 76) of cases. The multiparametric MRI–integrated Grade Group revealed the highest agreement (weighted κ, 0.545) and a significantly higher concordance rate (57.9%) than the targeted (47.8%, P = .008) and systematic (39.4%, P = .01) biopsies. The multiparametric MRI–integrated Grade Group showed significantly less downgrading rates than the combined biopsy (P = .001), without significant differences in upgrading rate (P = .06). The 3-dimensional multiparametric MRI model estimated tumor laterality in 66.2% (55 of 83) of cases, and contralateral clinically significant cancer was missed in 9.6% (8 of 83) of cases. The tumor length measured by multiparametric MRI best correlated with radical prostatectomy as compared with the biopsy-defined length.


The 3-dimensional model incorporating MRI and MRI–transrectal ultrasound fusion biopsy information easily recognized the spatial distribution of MRI-visible and MRI-nonvisible cancer and provided better Grade Group correlation with radical prostatectomy specimens but still requires validation.

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This work was supported by the National Research Foundation of Korea, a grant funded by the Korea Government (MSIT, 2019R1A2C1088246) and the Asa Institute for Life Sciences, Asan Medical Center (2019-0392).

The authors have no relevant financial interest in the products or companies described in this article.

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