Indications for and complications of pelvic lymph node dissection in prostate cancer: accuracy of available nomograms for the prediction of lymph node invasion. (19th September 2020) - Oderda, Marco; Diamand, Romain; Albisinni, Simone; Calleris, Giorgio; Carbone, Antonio; Falcone, Marco; Fiard, Gaelle; Gandaglia, Giorgio; Marquis, Alessandro; Marra, Giancarlo; Parola, Cinzia; Pastore, Antonio; et al.
Abstract : Objectives: To externally validate the currently available nomograms for predicting lymph node invasion (LNI) in patients with prostate cancer (PCa) and to assess the potential risk of complications of extended pelvic lymph node dissection (ePLND) when using the recommended threshold. Methods: A total of 14 921 patients, who underwent radical prostatectomy with ePLND at eight European tertiary referral centres, were retrospectively identified. After exclusion of patients with incomplete biopsy or pathological data, 12 009 were included. Of these, 609 had undergone multiparametic magnetic resonance imaging‐targeted biopsies. Among ePLND‐related complications we included lymphocele, lymphoedema, haemorrhage, infection and sepsis. The performances of the Memorial Sloan Kettering Cancer Centre (MSKCC), Briganti 2012, Briganti 2017, Briganti 2019, Partin 2016 and Yale models were evaluated using receiver‐operating characteristic curve analysis (area under the curve [AUC]), calibration plots, and decision‐curve analysis. Results: Overall, 1158 patients (9.6%) had LNI, with a mean of 17.7 and 3.2 resected and positive nodes, respectively. No significant differences in AUCs were observed between the MSKCC (0.79), Briganti 2012 (0.79), Partin 2016 (0.78), Yale (0.80), Briganti 2017 (0.81) and Briganti 2019 (0.76) models. A direct comparison of older models showed that better discrimination was achieved with the MSKCC and Briganti 2012 nomograms. A tendency for underestimation was seen for all the older models, whereas the Briganti 2017 and 2019 nomograms tended to overestimate LNI risk. Decision‐curve analysis showed a net benefit for all models, with a lower net benefit for the Partin 2016 and Briganti 2019 models. ePLND‐related complications were experienced by 1027 patients (8.9%), and 12.6% of patients with pN1 disease. Conclusions: The currently available nomograms have similar performances and limitations in the prediction of LNI. Miscalibration was present, however, for all nomograms showing a net benefit. In patients with only systematic biopsy, the MSKCC and Briganti 2012 nomograms were superior in the prediction of LNI.