Surgical management of periampullary adenocarcinoma: defining an optimal prognostic lymph node stratification schema

Background: Lymph node (LN) metastasis is a strong predictor of unfavorable prognosis for patients with periampullary adenocarcinoma after surgical resection. We sought to assess the prognostic performance of several LN staging systems, including American Joint Committee on Cancer (AJCC)/ International Union Against Cancer (7th edition) N stage, the total number of LN (TLN), the number of metastatic LN (MLN), the lymph node ratio (LNR) and the log odds of MLNs (LODDS), in patients with periampullary adenocarcinoma after surgical resection and identify the optional LN staging system to accurately stratify patients with different prognoses. Methods: We retrospectively analyzed 205 patients with periampullary adenocarcinoma after surgical resection. The predictive effects of several LN staging systems on overall survival (OS) and progression free survival (PFS) for all included patients and patients with more than 12 TLNs examined were evaluated and compared using the time-dependent receive operating characteristic (ROC) curve and decision curve analysis (DCA), respectively. Results: Eighty-nine patients (43.4%) had LN metastasis and their survival was not significantly decreased compared with patients without LN metastasis. LODDS and LNR were able to stratify patients into various subgroups with significant differences of both OS and PFS. When assessed using ROC curve and DCA, LODDS outperformed LNR and other LN staging systems in predicting OS and PFS. In addition, when analyzed in patients with more than 12 TLNs examined, LODDS had a higher value of area under ROC curve (AUC) and showed better performance of DCA. Conclusion: LODDS performs better than other LN staging systems in predicting OS and PFS for patients with periampullary adenocarcinoma after surgical resection. Adequate LN dissection is necessary for curative surgery, as well as to achieve a more accurate staging of the disease and a more precise prediction of survival for these patients.