Prognostic Nomograms Based on Three Lymph Node Classification Systems for Resected Gastric Adenocarcinoma: A Large
Keying Che1, Yue Wang1, Nandie Wu1
1The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School & Clinical Cancer Institute of Nanjing University, Nanjing, China.
Backgrounds:
The optimal lymph node classification system for prognostic assessment in gastric adenocarcinoma (GAC) patients who undergo lymph node dissection remains unclear. Therefore, this study aimed to compare prognostic nomograms based on AJCC N stage, lymph node ratio (LNR), and log odds of metastatic lymph nodes (LODDS) to evaluate the prognosis and differentiate risk subgroups of patients with resected GAC.
Patients And Methods:
We collected 4633 patients with resected stage I-III GAC receiving chemotherapy from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. Independent prognostic factors were selected by Cox regression analyses, based on which nomograms were constructed. External validation was performed in 228 cases from Nanjing Drum Tower Hospital. Kaplan-Meier survival analysis was used to evaluate the effect of postoperative radiotherapy (PORT) for different lymph node classifications.
Results:
Multivariate analysis indicated that age, grade, primary site, T stage, N stage, LNR, LODDS, and radiotherapy were independent predictors. Good discrimination power and high consistency of calibration plots were obtained from the LODDS system nomogram. The LODDS classification could more precisely differentiate risk subgroups and improve the discrimination of the resected GAC prognosis. A user-friendly webserver of LODDS system was built based on the nomogram for convenient clinical application.
Conclusions:
The LODDS seems to be the most reliable lymph node classification in predicting the prognosis of patients with resected GAC and should be recommended in clinical prognostic assessment. Incorporating LODDS into the staging system will enable clinicians to more accurately predict prognosis and guide radiotherapy regimen decisions.
