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Spatial Mapping of High Gleason Grade Prostate Cancer Using Quantitative Multi-Parametric MRI for Improving Targeted Biopsies

G Ghobadi1*, P van Houdt2 , J de Jong3 , I Walraven4 , C Dinh5 , H van der Poel6 , S Heijmink7 , F Pos8 , S Isebaert9 , R Oyen10 , S Rylander11 , L Bentzen12 , S Hoyer13 , E Klawer14 , C Dinis Fernandes15 , K Tanderup16 , K Haustermans17 , U van der Heide18 , (1) (2) The Netherlands Cancer Institute, Amsterdam, NOORD-HOLLAND, (3) The Netherlands Cancer Institute, Amsterdam, ,(4) The Netherlands Cancer Institute, Amsterdam, ,(5) The Netherlands Cancer Institute, Amsterdam, NOORD-HOLLAND, (6) The Netherlands Cancer Institute, Amsterdam, NOORD-HOLLAND, (7) The Netherlands Cancer Institute, Amsterdam, NOORD-HOLLAND, (8) The Netherlands Cancer Institute, Amsterdam, NOORD-HOLLAND, (9) University Hospitals Leuven, Leuven, ,(10) University Hospitals Leuven, Leuven, ,(11) Aarhus University Hospital, Aarhus, ,(12) Aarhus University Hospital, Aarhus, ,(13) Aarhus University Hospital, Aarhus, ,(14) The Netherlands Cancer Institute, Amsterdam, ,(15) The Netherlands Cancer Institute, Amsterdam, ,(16) Aarhus University Hospital, Aarhus, ,(17) University Hospitals Leuven, Leuven, ,(18) The Netherlands Cancer Institute, Amsterdam,

Presentations

(Sunday, 7/29/2018) 1:00 PM - 1:55 PM

Room: Karl Dean Ballroom B1

Purpose: Random biopsies may miss the presence of high Gleason grade prostate cancer. We developed a quantitative MRI (qMRI)-based model for spatial mapping of the high-grade Gleason Pattern (GP) at a voxel-level to improve the accuracy of targeted biopsies to detect high-grade GP.

Methods: At three centers, qMRI consisting of T2 mapping, diffusion-weighted, and dynamic contrast-enhanced MRI was acquired for 30 patients prior to prostatectomy. We used generalized linear mixed-effect modelling for spatial mapping of high-grade GP (GP≥ 4). To ensure that the model is trained on voxels with a homogeneous Gleason component, we used segmentations of GP 3, 4 and 5 at a resolution of 0.5 µm/pixel on whole-mount histology sections to map to the qMRI. We averaged the voxel-level probabilities of high-grade GP per patient and used it as a measure for the probability of high-grade prostate cancer (GS≥ 4+3). We assessed the accuracy of the model with the area under the curve (AUC) from leave-one-institute-out validation, training the model on two of the institutes and calculating its performance on the third.

Results: A combination of all qMRI parameters was most predictive for localizing high-grade GP. Based on the mapping of high-grade GP inside the lesions per patient, we accurately derived the Gleason Score (GS): probabilities in the range of 0-36% for GS≤ 3+4 and 67-100% for GS≥ 4+3. The model distinguished high-grade GP, on voxel-level with AUC of 0.67 and high-grade prostate cancer, GS≥ 4+3, on patient-level with AUC of 0.70.

Conclusion: Based on qMRI-mapping of high-grade GP at voxel-level we could accurately derive the GS per patient and distinguish high-grade prostate cancer on a patient level. Use of this method is expected to improve the accuracy of qMRI-based targeted biopsies and benefit monitoring of disease progression during active surveillance.

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