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A New Technique for Treating Low-risk Prostate Cancer—Super Active Surveillance
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Contemporary Preoperative Detection of Extraprostatic Extension in Prostate Cancer.

Jan Stępka1,2,3, Tomasz Milecki3, Jędrzej Ksepka4

  • 1Doctoral School, Poznań University of Medical Sciences, 60-812 Poznań, Poland.

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|February 13, 2026
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Summary

Predicting extraprostatic extension (EPE) in prostate cancer is crucial. Advanced AI models using radiomics and deep learning show promise, potentially outperforming traditional methods for improved surgical planning.

Keywords:
deep learningexplainable artificial intelligenceextraprostatic extensionmachine learningmultiparametric MRInerve sparing surgeryprostate cancerradiomicsside specific predictionsurgical planning

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Area of Science:

  • Urology
  • Radiology
  • Oncology
  • Artificial Intelligence in Medicine

Background:

  • Extraprostatic extension (EPE) is a key prognostic factor in prostate cancer, impacting surgical decisions like nerve-sparing radical prostatectomy.
  • Multiparametric MRI (mpMRI) is standard for local staging but has limitations in EPE detection sensitivity and interpretation variability.
  • Accurate preoperative EPE detection is vital for optimizing treatment strategies and patient outcomes.

Purpose of the Study:

  • To provide a comprehensive overview of current strategies for preoperative extraprostatic extension detection in prostate cancer.
  • To evaluate the diagnostic performance, validation, and clinical applicability of various EPE prediction methods.
  • To identify emerging technologies and future directions for improving EPE staging.

Main Methods:

  • A narrative review of literature from PubMed, Embase, Web of Science, and Google Scholar (2015-2025).
  • Inclusion of studies evaluating clinical parameters, mpMRI features, nomograms, radiomics, machine learning (ML), and deep learning (DL) for EPE prediction.
  • Comparative analysis of diagnostic performance, validation, and clinical utility of reviewed methods.

Main Results:

  • Clinical parameters and nomograms offer moderate EPE detection accuracy.
  • mpMRI enhances staging, with tumor-capsule contact length being a significant imaging marker.
  • Radiomics, ML, and DL models achieve AUCs of 0.75-0.85, sometimes exceeding expert radiologist performance, though DL models often lack external validation.
  • Side-specific and graded EPE assessment is more clinically relevant than binary classification.

Conclusions:

  • Radiomics and AI-driven models show significant potential for improving preoperative EPE detection in prostate cancer.
  • External validation and multimodal approaches are necessary for clinical translation of these advanced tools.
  • Future research should focus on developing robust, externally validated, and explainable AI frameworks for routine clinical use.