Background: Lymph node ratio (LNR; positive/harvested lymph nodes) was identified as overall survival predictor in several cancers, including pancreatic adenocarcinoma. It remains unclear if LNR is... Show moreBackground: Lymph node ratio (LNR; positive/harvested lymph nodes) was identified as overall survival predictor in several cancers, including pancreatic adenocarcinoma. It remains unclear if LNR is predictive of overall survival in pancreatic adenocarcinoma patients staged pN2. This study assessed the prognostic overall survival role of LNR in pancreatic adenocarcinoma patients in relation with lymph node involvement.Methods: A retrospective international study in six different centers (Europe and United States) was performed. Pancreatic adenocarcinoma patients who underwent pancreatoduodenectomy from 2000 to 2017 were included. Patients with neoadjuvant treatment, metastases, R2 resections, or missing data regarding nodal status were excluded. Survival curves were calculated using Kaplan-Meier method and compared using log-rank test. Multivariable Cox regressions were performed to find independent overall survival predictors adjusted for potential confounders.Results: A total of 1,327 patients were included. Lymph node involvement (pN+) was found in 1,026 patients (77%), 561 pN1 (55%) and 465 pN2 (45%). Median LNR in pN+ patients was 0.214 (IQR: 0.105-0.364). On multivariable analysis, LNR was the strongest overall survival predictor in the entire cohort (HR 5.5, 95% CI: 3.1-9.9, P<0.001) and pN+ patients (HR 3.8, 95% CI: 2.2-6.6, P<0.001). Median overall survival was better in patients with LNR <0.225 compared to patients with LNR >= 0.225 in the entire cohort and pN+ patients. Similar results were found in pN2 patients (worse overall survival when LNR >= 0.225).Conclusions: LNR appeared as an important prognostic factor in patients undergoing surgery for pancreatic adenocarcinoma and permitted to stratify overall survival in pN2 patients. LNR should be routinely used in complement to TNM stage to better predict patient prognosis. Show less
