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  • Title: Validation and head-to-head comparison of three nomograms predicting probability of lymph node invasion of prostate cancer in patients undergoing extended and/or sentinel lymph node dissection.
    Author: Grivas N, Wit E, Tillier C, van Muilekom E, Pos F, Winter A, van der Poel H.
    Journal: Eur J Nucl Med Mol Imaging; 2017 Dec; 44(13):2213-2226. PubMed ID: 28780722.
    Abstract:
    PURPOSE: The updated Winter nomogram is the only nomogram predicting lymph node invasion (LNI) in prostate cancer (PCa) patients based on sentinel node (SN) dissection (sLND). The aim of the study was to externally validate the Winter nomogram and examine its performance in patients undergoing extended pelvic lymph node dissection (ePLND), ePLND combined with SN biopsy (SNB) and sLND only. The results were compared with the Memorial Sloan Kettering Cancer Center (MSKCC) and updated Briganti nomograms. METHODS: This retrospective study included 1183 patients with localized PCa undergoing robot-assisted laparoscopic radical prostatectomy (RARP) combined with pelvic lymphadenectomy and 224 patients treated with sLND and external beam radiotherapy (EBRT), aiming to offer pelvic radiotherapy only in case of histologically positive SNs. In the RARP population, ePLND was applied in 956 (80.8%) patients,while 227 (19.2%) patients were offered ePLND combined with additional SNB. RESULTS: The median numbers of removed nodes were 10 (interquartile range, IQR = 6-14), 15 (IQR = 10-20) and 7 (IQR = 4-10) in the ePLND, ePLND + SNB, and sLND groups, respectively. Corresponding LNI rates were 16.6%, 25.5% and 42%. Based on the AUC, the performance of the Briganti nomogram (0.756) in the ePLND group was superior to both the MSKCC (0.744) and Winter nomogram (0.746). The Winter nomogram, however, was the best predictor of LNI in both the ePLND + SNB (0.735) and sLND (0.709) populations. In the calibration analysis, all nomograms showed better accuracy in the low/intermediate risk patients, while in the high-risk population, an overestimation of the risk for LNI was observed. CONCLUSION: The SN-based updated nomogram showed better prediction in the SN population. The results were also comparable, relative to predictive tools developed with (e)PLND, suggesting a difference in sampling accuracy between SNB and non-SNB. Patients who benefit most from the nomogram would be those with a low/intermediate risk of LN metastasis.
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