# Impact of three‐dimensional prostate models during robot‐assisted radical prostatectomy on surgical margins and functional outcomes

**Authors:** Nawal Khan, Davide Prezzi, Nicholas Raison, Andrew Shepherd, Michela Antonelli, Nick Byrne, Maia Heath, Christopher Bunton, Carlo Seneci, Eoin Hyde, Andres Diaz‐Pinto, Findlay Macaskill, Benjamin Challacombe, Jonathan Noel, Christian Brown, Ata Jaffer, Paul Cathcart, Margherita Ciabattini, Armando Stabile, Alberto Briganti, Giorgio Gandaglia, Francesco Montorsi, Sebastien Ourselin, Prokar Dasgupta, Alejandro Granados

PMC · DOI: 10.1111/bju.16850 · 2025-07-13

## TL;DR

This study explores how using 3D prostate models during surgery can improve cancer removal and patient recovery outcomes.

## Contribution

The study introduces a feasibility protocol using 3D virtual and printed prostate models during robot-assisted prostatectomy.

## Key findings

- The study will assess the impact of 3D models on reducing positive surgical margins.
- Functional outcomes like incontinence and sexual function will be evaluated over 12 months.
- Deep learning methods will automate prostate and lesion segmentation for model creation.

## Abstract

Robot‐assisted radical prostatectomy (RARP) is the standard surgical procedure for the treatment of prostate cancer. RARP requires a trade‐off between performing a wider resection in order to reduce the risk of positive surgical margins (PSMs) and performing minimal resection of the nerve bundles that determine functional outcomes, such as incontinence and potency, which affect patients’ quality of life. In order to achieve favourable outcomes, a precise understanding of the three‐dimensional (3D) anatomy of the prostate, nerve bundles and tumour lesion is needed.

This is the protocol for a single‐centre feasibility study including a prospective two‐arm interventional group (a 3D virtual and a 3D printed prostate model), and a prospective control group.

The primary endpoint will be PSM status and the secondary endpoint will be functional outcomes, including incontinence and sexual function.

The study will consist of a total of 270 patients: 54 patients will be included in each of the interventional groups (3D virtual, 3D printed models), 54 in the retrospective control group and 108 in the prospective control group. Automated segmentation of prostate gland and lesions will be conducted on multiparametric magnetic resonance imaging (mpMRI) using ‘AutoProstate’ and ‘AutoLesion’ deep learning approaches, while manual annotation of the neurovascular bundles, urethra and external sphincter will be conducted on mpMRI by a radiologist. This will result in masks that will be post‐processed to generate 3D printed/virtual models. Patients will be allocated to either interventional arm and the surgeon will be given either a 3D printed or a 3D virtual model at the start of the RARP procedure. At the 6‐week follow‐up, the surgeon will meet with the patient to present PSM status and capture functional outcomes from the patient via questionnaires. We will capture these measures as endpoints for analysis. These questionnaires will be re‐administered at 3, 6 and 12 months postoperatively.

## Linked entities

- **Diseases:** prostate cancer (MONDO:0005159)

## Full-text entities

- **Diseases:** tumour lesion (MESH:D009369), prostate cancer (MESH:D011471), incontinence (MESH:D014549)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Figures

3 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12343981/full.md

---
Source: https://tomesphere.com/paper/PMC12343981