Nodal staging of primary prostate cancer: comparative evaluation of the accuracy of NODE-RADS score 1.0, Briganti 2019 nomogram and PET CT PSMA scan
Abstract
Background and Objectives: The Reporting and Data System (RADS) have been used in the attempts to standardize the results of oncological scans in different scenarios, such as lymph nodes, adding configuration criteria to size determination. In this stydy is analyzed the predictive value of preoperative Node-RADS determination at imaging for pelvic lymph node (PLN) involvement in cases of prostate cancer (PC) considered for radical prostatectomy (RP) with extended lymph node dissection (eLND) and compared with validate predictive nomogram as Briganti 2019 and PSMA PET-CT total body scan. Methods: 267 patients with a histological diagnosis of PC (high risk or intermediate with an estimated risk for pN+ higher than 7% using the Briganti nomogram) submitted for RP with an ePLND from 2022 and 2025 were prospectivley examined. Node-RADS determination was performed in all cases using the preoperative magnetic resonance (MR), performed by a radiologist blinded for pathologic results and compared with the Briganti 2019 and PSMA PET- CT scan. Results: Node-RADS outperformed PSMA PET/CT in overall accuracy (AUC 0.637 vs. 0.526), while Briganti 2019 nomogram achieved the highest AUC (0.711). Combining Node-RADS and Briganti 2019 improved performance further (AUC 0.719). Node-RADS showed high specificity and NPV, useful to confirm nodal involvement, while Briganti 2019 demonstrated high sensitivity and excellent NPV, useful to exclude nodal disease. PET/CT exhibited limited sensitivity and anatomical correspondence. Conclusions: Node-RADS, especially when combined with Briganti 2019, provides a reliable, structured radiologic tool for preoperative nodal staging, outperforming PSMA PET/CT in diagnostic accuracy and correspondence with final pathology.
Fields of Study & Subjects
Prostate Cancer Diagnosis and Treatment
Medical Imaging Techniques and Applications
Radiomics and Machine Learning in Medical Imaging
Publication details
- Journal / source
- IRIS Research product catalog (Sapienza University of Rome)
- Publication date
- 19 February 2026
- Language
- EN
- Document type
- Thesis