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Evaluating Clinically Significant Prostate Cancer with Pathology-Registered Radiomics: A Multi-Reader Assessment

Omer Tarik Esengur1, Stephanie A Harmon1, David G Gelikman1

  • 1Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD (O.T.E., S.A.H., D.G.G., E.C.Y., H.Z., B.S., R.L., P.L.C., B.T.).

Academic Radiology
|April 28, 2026
PubMed
Summary
This summary is machine-generated.

A new radiomics signature aids in detecting clinically significant prostate cancer (csPCa). Combining it with prostate-specific antigen density (PSAD) improves detection accuracy, approaching PI-RADS scores.

Keywords:
Histopathology-MRI registrationMagnetic resonance imagingProstate cancerProstate-specific antigen densityRadiomics

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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging

Background:

  • Prostate cancer detection relies on MRI and PI-RADS scoring.
  • Developing accurate, pathology-derived imaging biomarkers is crucial for improved diagnostics.

Purpose of the Study:

  • To create a pathology-derived radiomics signature for detecting clinically significant prostate cancer (csPCa).
  • To assess the signature's performance using simplified, lesion diameter-based MRI segmentations.

Main Methods:

  • Retrospective analysis of 175 participants with biparametric MRI and whole-mount histopathology.
  • Development of a 10-feature radiomics signature using PyRadiomics and recursive feature elimination.
  • Evaluation of models including the signature, prostate-specific antigen density (PSAD), and PI-RADS scores using logistic regression, random forest, and XGBoost.

Main Results:

  • The radiomics signature alone achieved 0.66 AUC and 62% accuracy.
  • Combining the signature with PSAD (PSAD+Signature) yielded 0.75 AUC and 68% accuracy.
  • PI-RADS combined with PSAD (PI-RADS+PSAD) achieved the highest accuracy at 74% (0.77 AUC).

Conclusions:

  • A histopathology-trained radiomics signature shows moderate standalone performance for csPCa detection.
  • The PSAD+Signature model significantly improved diagnostic performance over the signature alone.
  • The PSAD+Signature approach offers a simplified, localized method that complements PI-RADS assessment with low implementation complexity.