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Prospective Validation of an Automated Hybrid Multidimensional MRI Tool for Prostate Cancer Detection Using Targeted

Aritrick Chatterjee1, Ambereen N Yousuf1, Roger Engelmann1

  • 1From the Department of Radiology (A.C., A.N.Y., R.E., C.H., G.L., M.M., E.B.J., A.L.C., B.G., G.S.K., A.O.), Sanford J. Grossman Center of Excellence in Prostate Imaging and Image Guided Therapy (A.C., A.N.Y., M.M., A.L.C., B.G.), Department of Surgery, Section of Urology (G.G., L.F.R., P.K.M., S.E.), Department of Pathology (T.A.), and Department of Public Health Sciences (M.G.), University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637.

Radiology. Imaging Cancer
|January 21, 2025
PubMed
Summary

Hybrid multidimensional MRI (HM-MRI) shows improved accuracy and specificity for prostate cancer detection compared to standard multiparametric MRI (mpMRI). This automated tool enhances MRI/US fusion biopsy, offering complementary data for better diagnosis.

Keywords:
Hybrid Multidimensional MRIMultiparametric MRIPI-RADSProstate Cancer

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Area of Science:

  • Radiology and Imaging Science
  • Oncology
  • Medical Diagnostics

Background:

  • Prostate cancer diagnosis relies on imaging and biopsy, with MRI/US fusion biopsy improving accuracy.
  • Multiparametric MRI (mpMRI) with PI-RADS scoring is standard, but its performance can be enhanced.
  • Automated tools for MRI analysis may offer objective and improved cancer detection.

Purpose of the Study:

  • To evaluate an automated hybrid multidimensional MRI (HM-MRI) tool for identifying prostate cancer targets.
  • To compare HM-MRI's performance against PI-RADS-based mpMRI for MRI/US fusion biopsy.
  • To assess HM-MRI's ability to prospectively identify biopsy targets before the procedure.

Main Methods:

  • Prospective clinical trial (NCT03585660) involving 91 participants with suspected prostate cancer.
  • Participants underwent both conventional mpMRI and HM-MRI at 3-T, followed by MRI/US fusion biopsy.
  • HM-MRI used a three-compartment model to calculate tissue composition and identify suspicious regions; targets were automatically selected.

Main Results:

  • HM-MRI demonstrated higher accuracy (55% vs 44%) and specificity (36% vs 14%) per participant compared to mpMRI.
  • On a per-lesion basis, HM-MRI showed significantly higher accuracy (58% vs 39%) and PPV (31% vs 22%).
  • HM-MRI significantly outperformed mpMRI on a per-sextant basis across all metrics, including AUC (0.76 vs 0.65).

Conclusions:

  • Automated HM-MRI shows potential to improve prostate cancer detection during MRI/US fusion biopsy.
  • HM-MRI provides complementary information to PI-RADS-based mpMRI evaluations by expert radiologists.
  • The HM-MRI tool offers enhanced diagnostic performance, particularly in specificity and accuracy for prostate cancer identification.