Keywords: Diagnosis/Prediction, Cancer
Motivation: Accurate identification of lymph nodes (LNs) and assessment of metastatic burden are essential for effective clinical decision-making in prostate cancer treatment.
Goal(s): To develop an LN segmentation model utilizing the V-Net on pelvic DWI and validate its performance across external datasets.
Approach: We trained the 3D V-Net model on pelvic DWI images from 1,151 patients with 32,507 annotated LNs and validated it on data from 401 patients with 7,707 LNs across four hospitals.
Results: The model achieved a sensitivity of 60.1% (95%CI, 57.6%-62.6%), a positive predictive value of 79.2% (95%CI, 76.6%-81.5%), and a false positive volume of 0.56 at the LN level.
Impact: The results confirmed the feasibility of this method, which could aid in LN staging, quantitative measurements of tumor burden, and image-guided treatment of patients with PCa.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords