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Related Experiment Video

Updated: Feb 1, 2026

Pre-clinical Orthotopic Murine Model of Human Prostate Cancer
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Development of a Prognostic Prediction Model for Clinically Significant Prostate Cancer Based on Lesion Zone and

Rani Ashouri1, Penny S Reynolds2, Shay Rajavel3

  • 1University of Florida, UF Health, Department of Urology, Gainesville, FL.

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|January 30, 2026
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Summary

A new model integrating prostate cancer imaging features improves prediction of clinically significant prostate cancer (csPCa). This tool aids biopsy decisions for men with PI-RADS 3-5 scores, but doesn't eliminate biopsies.

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Area of Science:

  • Radiology and Imaging Science
  • Oncology
  • Biostatistics

Background:

  • Accurate prediction of clinically significant prostate cancer (csPCa) is crucial for biopsy decisions.
  • Multiparametric magnetic resonance imaging (mpMRI) with PI-RADS scoring aids in detecting prostate cancer.
  • The predictive value of PI-RADS v2.1 can be enhanced by incorporating quantitative imaging features and lesion characteristics.

Purpose of the Study:

  • To develop a predictive model for csPCa in men with PI-RADS 3-5 scores.
  • To assess the impact of prostate lesion zone on predictive accuracy.
  • To establish an apparent diffusion coefficient (ADC) threshold for csPCa and create a logistic regression model incorporating zone and ADC.

Main Methods:

  • Retrospective analysis of 546 lesions from 429 men undergoing prostate mpMRI.
  • Lesion-level analysis by fellowship-trained genitourinary radiologists and pathologists.
  • Development of a logistic regression model using clinical data, lesion zone, and quantified ADC values.

Main Results:

  • Prostate zone (PZ vs. TZ) was a significant predictor of csPCa (OR 2.58, p<0.0005).
  • A combined model including zone and prostate-specific antigen density achieved an AUC of 0.78 at an ADC cutoff of 947 µm²/s.
  • Simulated scenarios showed probabilities ranging from 16% to 82%, generally above biopsy omission thresholds.

Conclusions:

  • Prostate zone and quantified ADC values independently enhance csPCa predictability.
  • The developed model shows promise but did not yield biopsy-omission thresholds in simulated cases with MRI-visible lesions.