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Objective:
Prostate tissue composition measured noninvasively using hybrid multidimensional MRI (HM-MRI) has been validated against reference standards: expert pathologists’ measures and quantitative histology results from whole mount prostatectomy. The strong correlation between HH-MRI and histology demonstrates that this approach has the potential to improve prostate cancer (PCa) diagnosis. The purpose of this study is to evaluate the role of an automated HM-MRI based tool in prospectively identifying targets before MR-US fusion biopsy for prostate cancer biopsy in comparison with random biopsy and PIRADS-based evaluation by expert radiologists.

Materials and Methods:
In this prospective clinical trial (ClinicalTrials.gov NCT03585660), patients (n = 92, mean age = 64±8 years, mean PSA=8.1±4.9 ng/ml) with known or suspected PCa underwent 3T MRI: mpMRI (T2W, DWI and DCE) and HM-MRI (TE = 57, 75, 150, 200ms, b-values=0, 150, 750, 1500s/mm^2). Tissue composition (fractional volume of stroma, epithelium and lumen) was calculated using a three compartment model and suspected PCa regions with elevated epithelium (>40%) and reduced lumen (<20%) meeting the minimum size requirement of 25 mm on an axial slice were identified using the HM-MRI tool. Patients then underwent 12-core TRUS-guided sextant random biopsy. Additional biopsy targets were selected based on an expert radiologist's mpMRI interpretation (PI-RADS 3 and higher). Up to two additional biopsy targets per patient were selected by the HM-MRI tool, if different from the targets already selected by radiologists per standard of care and biopsied using Uronav MR-US fusion biopsy device. The biopsy sample underwent H&E staining and histological analysis (cancer diagnosis and Gleason grading) by an expert GU pathologist. Analyses based on a per-patient (PP), per-tumor (PT) and per sextant (PS) based analysis were performed, with area under the ROC curve and accuracy as the primary endpoints and sensitivity, specificity, positive and negative predictive values (PPV and NPV) as secondary endpoints.

Results:
The diagnostic accuracy (AUC) of HM-MRI for clinically significant cancers (>=Gleason 3+4), was higher than that of mpMRI on per-patient (0.67 vs 0.58, p=0.04) and sextant analysis (0.77 vs 0.69, p=0.004). HM-MRI had higher accuracy (PP 61 vs 46%, PS 86 vs 82%), specificity (PP 45 vs 15%, PS 88 vs 84%) and PPV (PP 48 vs 41%, PS 31 vs 21%) than mpMRI. HM-MRI had higher sensitivity in per-sector basis (66 vs 54%), but not on per-patient basis (89 vs 100%). HM-MRI had lower NPV on per-patient basis (87 vs 100%) but similar value on sextant basis (96 vs 97%). On a per-tumor basis, HM-MRI had higher sensitivity (78 vs 68%) and PPV (35 vs 21%) than mpMRI.

Conclusion:
This study demonstrates that HM-MRI has the potential to improve MR-US fusion biopsy results by providing more accurate results compared to PIRADS based evaluation of expert radiologists. The advantage of this tool is its ability to provide automated, quantitative image interpretation, reproducible results that can improve PCa diagnosis and potentially reduce the number of unnecessary biopsies.