Mei Haonan, Yang Rui, Huang Jun, Jiao Panpan, Liu Xiuheng, Chen Zhiyuan, Chen Hui, Zheng Qingyuan
Department of Urology, Renmin Hospital of Wuhan University, 238 Jiefang Road, Wuchang District, Wuhan, 430060, Hubei, China.
Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan, China.
Ann Surg Oncol. 2025 Jun 18. doi: 10.1245/s10434-025-17659-1.
The aim of this study was to guide prostatectomy by employing artificial intelligence for the segmentation of tumor gross tumor volume (GTV) and neurovascular bundles (NVB).
The preservation and dissection of NVB differ between intrafascial and extrafascial robot-assisted radical prostatectomy (RARP), impacting postoperative urinary control. We trained the nnU-Net v2 neural network using data from 220 patients in the PI-CAI cohort for the segmentation of prostate GTV and NVB in biparametric magnetic resonance imaging (bpMRI). The model was then validated in an external cohort of 209 patients from Renmin Hospital of Wuhan University (RHWU). Utilizing three-dimensional reconstruction and point cloud analysis, we explored the spatial distribution of GTV and NVB in relation to intrafascial and extrafascial approaches. We also prospectively included 40 patients undergoing intrafascial and extrafascial RARP, applying the aforementioned procedure to classify the surgical approach. Additionally, 3D printing was employed to guide surgery, and follow-ups on short- and long-term urinary function in patients were conducted.
The nnU-Net v2 neural network demonstrated precise segmentation of GTV, NVB, and prostate, achieving Dice scores of 0.5573 ± 0.0428, 0.7679 ± 0.0178, and 0.7483 ± 0.0290, respectively. By establishing the distance from GTV to NVB, we successfully predicted the surgical approach. Urinary control analysis revealed that the extrafascial approach yielded better postoperative urinary function, facilitating more refined management of patients with prostate cancer and personalized medical care.
Artificial intelligence technology can accurately identify GTV and NVB in preoperative bpMRI of patients with prostate cancer and guide the choice between intrafascial and extrafascial RARP. Patients undergoing intrafascial RARP with preserved NVB demonstrate improved postoperative urinary control.
本研究的目的是利用人工智能对肿瘤大体肿瘤体积（GTV）和神经血管束（NVB）进行分割，以指导前列腺切除术。
筋膜内和筋膜外机器人辅助根治性前列腺切除术（RARP）中NVB的保留和解剖有所不同，这会影响术后尿控。我们使用PI-CAI队列中220例患者的数据训练nnU-Net v2神经网络，用于在双参数磁共振成像（bpMRI）中分割前列腺GTV和NVB。然后在武汉大学人民医院（RHWU）的209例患者的外部队列中对该模型进行验证。利用三维重建和点云分析，我们探讨了GTV和NVB相对于筋膜内和筋膜外入路的空间分布。我们还前瞻性纳入了40例行筋膜内和筋膜外RARP的患者，应用上述程序对手术入路进行分类。此外，采用3D打印指导手术，并对患者的短期和长期尿功能进行随访。
nnU-Net v2神经网络对GTV、NVB和前列腺进行了精确分割，Dice分数分别为0.5573±0.0428、0.7679±0.0178和0.7483±0.0290。通过建立GTV到NVB的距离，我们成功预测了手术入路。尿控分析显示，筋膜外入路术后尿功能更好，有助于对前列腺癌患者进行更精细的管理和个性化医疗。
人工智能技术可以在前列腺癌患者术前bpMRI中准确识别GTV和NVB，并指导筋膜内和筋膜外RARP之间的选择。保留NVB的筋膜内RARP患者术后尿控改善。
