Comparison of Four Validated Nomograms (Memorial Sloan Kettering Cancer Center, Briganti 2012, 2017, and 2019) Predicting Lymph Node Invasion in Patients with High-Risk Prostate Cancer Candidates for Radical Prostatectomy and Extended Pelvic Lymph Node Dissection. Clinical Experience and Review of the Literature

Simple Summary The indication for ePLND at the time of RP is based on a risk assessment of LNI through validated nomograms such as the MSKCC; Briganti 2012; Briganti 2017 and Briganti 2019. However, in daily practice, a relevant percentage of cases, including those with the high-risk disease, show no LNI at the final histopathological assay pathology (pN0) after ePLND. Furthermore, currently available evidence does not demonstrate the superiority of one nomogram over the others, and there is still lacking data to support the routine use of one predictive model over another, even in more aggressive diseases. Therefore, we evaluated the accuracy of the most used nomograms (MSKCC, Briganti 2012, Briganti 2017, and Briganti 2019) for predicting LNI and compared them in our sub-cohort of high-risk PC patients treated with ePLND. We found that the predictive performance of the four nomograms as well as their ability to avoid unnecessary ePLND, are virtually the same, even in high-risk PC patients. Background: The indication for extended pelvic lymph node dissection (ePLND) at the time of radical prostatectomy (RP) is based on nomograms predicting the risk of lymph node invasion (LNI). However, limited data are available on the comparison of these predictive models in high-risk prostate cancer (PC) patients. Therefore, we compared the accuracy of the most used nomograms (MSKCC, Briganti 2012, 2017, and 2019) in the setting of high-risk PC patients submitted to ePLND. Methods: 150 patients with high-risk PC disease treated from 2019 to 2022 were included. Before RP + ePLND, we assessed the MSKCC, Briganti 2012, 2017, and 2019 nomograms for each patient, and we compared the prediction of LNI with the final histopathological analysis of the ePLND using pathologic results as a reference. Results: LNI was found in 39 patients (26%), and 71.3% were cT2. The percentage of patients with estimated LNI risk above the cut-off was significantly higher in pN+ cases than in pN0 for all Briganti nomograms. The percentage of patients at risk of LNI, according to Briganti Nomogram (2012, 2017, and 2019), was significantly higher in pN+ cases than in pN0 (p < 0.04), while MSKCC prediction didn't vary significantly between pN0 and pN+ groups (p = 0.2). All nomograms showed high sensitivity (Se > 0.90), low specificity (Sp < 0.20), and similar AUC (range: 0.526-0.573) in predicting pN+. Particularly, 74% of cases patients with MSKCC estimated risk > 7% showed pN0 compared to 71% with Briganti 2012 > 5%, 69% with Briganti 2017 > 7%, and 70% with Briganti 2019 > 7%. Conclusions: Despite the high-risk disease, in our patients treated with ePLND emerges a still high number of pN0 cases and a similar low specificity of nomograms in predicting LNI.