Contemporary Evidence for Optimization of Robotic Radical Prostatectomy Outcomes Using Advanced Imaging Techniques

TL;DR

This review explores how advanced imaging and AI can improve robotic prostate cancer surgery outcomes by enhancing precision and preserving function.

Contribution

The paper synthesizes recent evidence on multimodal imaging and AI integration for optimizing robotic prostatectomy.

Findings

MRI underestimates tumor extent, affecting margin control in prostatectomy.

AI-assisted platforms improve tumor contouring accuracy and surgical decision-making.

PSMA-based imaging improves detection of extra-prostatic disease and shows promise for intraoperative guidance.

Abstract

Background/Objectives: Robotic-assisted radical prostatectomy (RARP) is a standard treatment for localized and locally advanced prostate cancer; however, optimizing oncologic control while preserving urinary continence and erectile function remains challenging. Advances in preoperative imaging, molecular diagnostics, artificial intelligence (AI), and intraoperative assessment have the potential to refine surgical planning and execution. This review summarizes contemporary evidence on advanced imaging and intraoperative technologies used to optimize RARP outcomes. Methods: A narrative literature review was conducted of English-language studies published between 2015 and 2025 using PubMed/MEDLINE, Scopus, and Google Scholar. Studies evaluating multi-parametric and bi-parametric MRI, prostate-specific membrane antigen-based positron emission tomography/computed tomography (PSMA PET/CT), AI-assisted tumor modeling, and intraoperative histologic or molecular imaging techniques in the context of robotic-assisted radical prostatectomy were included. Evidence from randomized controlled trials, prospective and retrospective studies, technical feasibility reports, and expert consensus statements was reviewed. Results: MRI remains central to anatomic mapping and local staging but consistently underestimates true tumor extent, with implications for margin control. AI-assisted platforms improve tumor contouring accuracy and may meaningfully influence surgical decision-making. PSMA-based imaging enhances detection of extra-prostatic extension and nodal disease and shows early promise for ex vivo and intraoperative guidance. Intraoperative margin assessment techniques are supported by randomized evidence demonstrating improved functional outcomes without compromising short-term oncologic safety and emerging digital histologic technologies offer scalable alternatives for real-time margin evaluation. Conclusions: Integration of advanced anatomic, molecular, and intraoperative imaging technologies represents an evolving multimodal paradigm in RARP. Combined use of MRI, PSMA-based imaging, AI-assisted modeling, and rapid histologic assessment may enable more precise, individualized surgery that balances oncologic control with functional preservation. Further validation is required to define optimal implementation in routine clinical practice.

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