Introduction:

Utilization of nomograms and predictive tools to determine lymph node involvement (LNI) in patients with prostate cancer (PCa) undergoing radical prostatectomy (RP) has significantly reduced the incidence of unnecessary extended pelvic lymph node dissection (ePLND), thereby reducing operative time and postoperative complications. Only a few studies have incorporated information from modern-day imaging techniques such as magnetic resonance imaging (MRI) into their predictive algorithms; the relatively small sample size and few events have limited the generalizability of these models. We aim to develop and validate a predictive tool in men undergoing RP with suspicious MRI lesions.

Methods:

Retrospective analysis of n = 2172 men who had suspicious MRI (PI-RADS ≥ 3) and underwent MRI-US fusion targeted biopsy combined with 12-core systematic biopsy. This cohort underwent RP at a single institution with a single surgeon, and final pathology data was collected. ePLND was defined as removal of obturator, internal iliac, external iliac and distal part of common iliac lymph nodes (LN). Univariate and multivariate logistic regression analysis performed to create nomogram for predicting LNI. Receiver operative characteristics (ROC) and decision curve analysis (DCA) were performed to evaluate net benefit.

Results:

Overall, 138 patients (6.4%) had LNI (Table 1A). Multivariable/univariable analysis showed that preoperative PSA, biopsy GGG, number of positive biopsy cores, MRI lesion size, MRI T stage and MRI LNI were significant predictors of LNI on final pathology (Table 1A). ROC for predicting LNI was 0.92 (Figure 1C). Nomogram derived cutoffs of 4% and 6%, 1466 (67.5%) and 1626 (74.9%) ePLND procedures would be avoided; LNI would have been missed in only 18 (1.2%) and 22 (1.4%) patients, respectively. DCA showed a clinical benefit for the model detection of LN metastases for the cohort (Figure 1B).

Conclusion:

Available models predicting LNI are historically based on clinical parameters and only recently on data from suspicious MRI. Utilization of this nomogram will help avoid ePLND in high percentage of patients at the cost of missing of low percentage of LNI.

Funding: N/A