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SyMRI histogram analysis for diagnosing clinically significant prostate cancer.

Hao Cheng1,2, Bowen Yang1,3, Yadong Cui1,4

  • 1Department of Radiology, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China.

Acta Radiologica (Stockholm, Sweden : 1987)
|June 18, 2025
PubMed
Summary

Histogram analysis of synthetic magnetic resonance imaging (SyMRI) relaxation maps effectively differentiates clinically significant prostate cancer (csPCa) from clinically insignificant disease. Combining SyMRI with diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) further improves diagnostic accuracy.

Keywords:
Prostate cancerhistogram analysismagnetic resonance imagingquantitative MRI

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Area of Science:

  • Radiology and Imaging Science
  • Oncology Diagnostics
  • Medical Physics

Background:

  • Quantitative parameters from synthetic magnetic resonance imaging (SyMRI) show promise for diagnosing clinically significant prostate cancer (csPCa).
  • Histogram analysis, by evaluating spatial heterogeneity, can enhance diagnostic accuracy for csPCa.

Purpose of the Study:

  • To evaluate the diagnostic performance of histogram analysis models using SyMRI relaxation maps for csPCa detection.
  • To compare histogram analysis models with mean-value-based models for csPCa differentiation.

Main Methods:

  • Prospective enrollment of 124 men with suspected csPCa.
  • Analysis of 224 ROIs including csPCa, insignificant PCa, peripheral zone lesions, and benign prostatic hyperplasia.
  • Construction of histogram analysis models using SyMRI, diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and combined modalities; comparison with mean-value models.

Main Results:

  • Histogram analysis models significantly outperformed mean-value models in both training and validation groups.
  • SyMRI-based histogram models showed diagnostic effectiveness comparable to DWI and ADC models.
  • The combined SyMRI, DWI, and ADC histogram model achieved the highest AUC values in the peripheral zone (0.898) and transition zone (0.944), significantly outperforming PI-RADS in the TZ.

Conclusions:

  • Histogram analysis of SyMRI relaxation maps is a valuable tool for differentiating csPCa from clinically insignificant disease (CIS).
  • Combining SyMRI with DWI and ADC in histogram analysis models substantially improves diagnostic accuracy for csPCa.