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Related Experiment Video

Updated: Feb 28, 2026

Retzius-Sparing Robot-Assisted Radical Prostatectomy
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Contemporary Evidence for Optimization of Robotic Radical Prostatectomy Outcomes Using Advanced Imaging Techniques.

Gary K Shahinyan1, David S Finley1

  • 1Department of Urology, Kaiser Permanente Los Angeles Medical Center, Los Angeles, CA 91605, USA.

Journal of Clinical Medicine
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

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Advanced imaging and AI in robotic-assisted radical prostatectomy (RARP) can improve surgical precision. Integrating MRI, PSMA PET/CT, AI modeling, and intraoperative assessment optimizes oncologic control and functional outcomes in prostate cancer treatment.

Area of Science:

  • Urology
  • Surgical Oncology
  • Medical Imaging

Background:

  • Robotic-assisted radical prostatectomy (RARP) is a standard treatment for prostate cancer.
  • Optimizing oncologic control while preserving urinary continence and erectile function remains a challenge.
  • Advances in preoperative imaging, molecular diagnostics, AI, and intraoperative assessment offer potential improvements.

Purpose of the Study:

  • To review contemporary evidence on advanced imaging and intraoperative technologies for optimizing RARP outcomes.
  • To summarize the role of MRI, PSMA PET/CT, AI, and intraoperative assessment in RARP.

Main Methods:

  • A narrative literature review of English-language studies (2015-2025) from PubMed/MEDLINE, Scopus, and Google Scholar.
  • Included studies evaluated multi-parametric/bi-parametric MRI, PSMA PET/CT, AI tumor modeling, and intraoperative histologic/molecular imaging for RARP.
Keywords:
PSMA PET/CTartificial intelligencebi-parametric MRIintraoperative imagingmultiparametric MRInerve-sparing surgeryprostate cancerrobotic-assisted radical prostatectomysurgical margins

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  • Evidence synthesis from RCTs, prospective/retrospective studies, feasibility reports, and consensus statements.

    • Main Results:

    • MRI aids staging but often underestimates tumor extent, impacting margin control.
    • AI platforms enhance tumor contouring accuracy and surgical decision-making.
    • PSMA-based imaging improves detection of extra-prostatic and nodal disease, with potential for intraoperative guidance.
    • Intraoperative margin assessment techniques show improved functional outcomes without compromising oncologic safety; digital histology offers real-time evaluation.

    Conclusions:

    • Integrating advanced imaging (MRI, PSMA PET/CT), AI, and intraoperative assessment creates a multimodal paradigm for RARP.
    • This combined approach may enable precise, individualized surgery balancing oncologic control and functional preservation.
    • Further validation is needed for routine clinical implementation.