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External Validation and Addition of Prostate-specific Membrane Antigen Positron Emission Tomography to the Most Frequently Used Nomograms for the Prediction of Pelvic Lymph-node Metastases: an International Multicenter Study

  • Dennie Meijer,
  • Pim J. van Leeuwen,
  • Matthew J. Roberts,
  • Amila R. Siriwardana,
  • Andrew Morton,
  • John W. Yaxley,
  • Hemamali Samaratunga,
  • Louise Emmett,
  • Peter M. van de Ven,
  • Henk G. van der Poel,
  • Maarten L. Donswijk,
  • Thierry N. Boellaard,
  • Ivo G. Schoots,
  • Daniela E. Oprea-Lager,
  • Geoffrey D. Coughlin,
  • AndrĂ© N. Vis

Background

Different nomograms exist for the preoperative prediction of pelvic lymph-node metastatic disease in individual patients with prostate cancer (PCa). These nomograms do not incorporate modern imaging techniques such as prostate-specific membrane antigen (PSMA) positron emission tomography (PET).

Objective

To determine the predictive performance of the Briganti 2017, Memorial Sloan Kettering Cancer Center (MSKCC), and Briganti 2019 nomograms with the addition of PSMA-PET in an international, multicenter, present-day cohort of patients undergoing robot-assisted radical prostatectomy (RARP) and extended pelvic lymph-node dissection (ePLND) for localized PCa.

Design, setting, and participants

All 757 eligible patients who underwent a PSMA-PET prior to RARP and ePLND in three reference centers for PCa surgery between January 2016 and November 2020 were included.

Outcome measurements and statistical analysis

Performance of the three nomograms was assessed using the receiver operating characteristic curve–derived area under the curve (AUC), calibration plots, and decision curve analyses. Subsequently, recalibration and addition of PSMA-PET to the nomograms were performed.

Results and limitations

Overall, 186/757 patients (25%) had pelvic lymph-node metastatic (pN1) disease on histopathological examination. AUCs of the Briganti 2017, MSKCC, and Briganti 2019 nomograms were 0.70 (95% confidence interval [95% CI]: 0.64–0.77), 0.71 (95% CI: 0.65–0.77), and 0.76 (95% CI: 0.71–0.82), respectively. PSMA-PET findings showed a significant association with pN1 disease when added to the nomograms (p < 0.001). Addition of PSMA-PET substantially improved the discriminative ability of the models yielding cross-validated AUCs of 0.76 (95% CI: 0.70–0.82), 0.77 (95% CI: 0.72–0.83), and 0.82 (95% CI: 0.76–0.87), respectively. In decision curve analyses, the addition of PSMA-PET to the three nomograms resulted in increased net benefits.

Conclusions

The addition of PSMA-PET to the previously developed nomograms showed substantially improved predictive performance, which suggests that PSMA-PET is a likely future candidate for a modern predictive nomogram.