Related Experiment Video
Updated: Sep 19, 2025
Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
Published on: June 10, 2025
Artificial Intelligence-Assisted Segmentation of Prostate Tumors and Neurovascular Bundles: Applications in Precision
Haonan Mei1,2, Rui Yang1,2, Jun Huang1,2
1Department of Urology, Renmin Hospital of Wuhan University, 238 Jiefang Road, Wuchang District, Wuhan, 430060, Hubei, China.
Background:
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).
Methods:
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.
Results:
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.
Conclusions:
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.
Insights
Artificial intelligence accurately segments prostate cancer tumors and nerves using MRI scans. This technology helps surgeons choose the best surgical approach for improved urinary control after prostatectomy.
Area of Science:
- Urology
- Oncology
- Medical Imaging
- Artificial Intelligence
Background:
- Prostate cancer treatment requires precise surgical techniques to balance tumor removal and functional preservation.
- The preservation of neurovascular bundles (NVB) is critical for postoperative urinary control.
- Differentiating surgical approaches (intrafascial vs. extrafascial) impacts NVB preservation and patient outcomes.
Purpose of the Study:
- To employ artificial intelligence (AI) for segmenting gross tumor volume (GTV) and NVB in prostate cancer.
- To guide the selection of surgical approach (intrafascial vs. extrafascial robot-assisted radical prostatectomy - RARP) based on AI segmentation.
- To evaluate the impact of surgical approach on postoperative urinary function.
Main Methods:
- Trained the nnU-Net v2 AI model on biparametric MRI (bpMRI) data from 220 patients for GTV and NVB segmentation.
- Validated the AI model on an external cohort of 209 patients.
- Used 3D reconstruction and point cloud analysis to assess GTV-NVB spatial relationships.
- Prospectively analyzed 40 RARP patients to classify surgical approach and utilized 3D printing for surgical guidance.
- Monitored short- and long-term urinary function post-surgery.
Main Results:
- Achieved high Dice scores for segmentation: GTV (0.5573), NVB (0.7679), and prostate (0.7483).
- Successfully predicted the surgical approach by analyzing the distance between GTV and NVB.
- The extrafascial surgical approach demonstrated superior postoperative urinary function compared to the intrafascial approach.
Conclusions:
- AI-driven segmentation of GTV and NVB from preoperative bpMRI is accurate and feasible.
- AI can effectively guide the choice between intrafascial and extrafascial RARP.
- Preserving NVB during intrafascial RARP may lead to improved postoperative urinary control, enabling personalized prostate cancer management.
