Khan Nawal, Prezzi Davide, Raison Nicholas, Shepherd Andrew, Antonelli Michela, Byrne Nick, Heath Maia, Bunton Christopher, Seneci Carlo, Hyde Eoin, Diaz-Pinto Andres, Macaskill Findlay, Challacombe Benjamin, Noel Jonathan, Brown Christian, Jaffer Ata, Cathcart Paul, Ciabattini Margherita, Stabile Armando, Briganti Alberto, Gandaglia Giorgio, Montorsi Francesco, Ourselin Sebastien, Dasgupta Prokar, Granados Alejandro
School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Department of Urology, Guy's Hospital, London, UK.
BJU Int. 2025 Sep;136(3):420-428. doi: 10.1111/bju.16850. Epub 2025 Jul 13.
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.
机器人辅助根治性前列腺切除术（RARP）是治疗前列腺癌的标准手术方法。RARP需要在进行更广泛的切除以降低手术切缘阳性（PSM）风险与尽量减少对决定功能结局（如尿失禁和性功能）的神经束进行切除之间进行权衡，而这些功能结局会影响患者的生活质量。为了取得良好的治疗效果，需要精确了解前列腺、神经束和肿瘤病变的三维（3D）解剖结构。
这是一项单中心可行性研究方案，包括一个前瞻性双臂干预组（一个3D虚拟前列腺模型和一个3D打印前列腺模型）以及一个前瞻性对照组。
主要终点将是PSM状态，次要终点将是功能结局，包括尿失禁和性功能。
该研究总共将纳入270名患者：干预组（3D虚拟模型组、3D打印模型组）各纳入54名患者，回顾性对照组纳入54名患者，前瞻性对照组纳入108名患者。将使用“AutoProstate”和“AutoLesion”深度学习方法在多参数磁共振成像（mpMRI）上对前列腺和病变进行自动分割，同时由放射科医生在mpMRI上对神经血管束、尿道和外括约肌进行手动标注。这将生成掩码，对其进行后处理以生成3D打印/虚拟模型。患者将被分配到任一干预组，在RARP手术开始时，外科医生将获得一个3D打印模型或一个3D虚拟模型。在术后6周的随访中，外科医生将与患者会面，告知PSM状态，并通过问卷收集患者的功能结局。我们将收集这些指标作为分析的终点。这些问卷将在术后3个月、6个月和12个月重新发放。
