Development of a simplified tumor-lymph node ratio classification system for patients with resected gastric cancer: A
Cristina Díaz Del Arco1, Lourdes Estrada Muñoz2, Andrés Sánchez Pernaute3
1Complutense University of Madrid, Av. Séneca 2, 28040 Madrid, Spain; Hospital Clínico San Carlos, c/Profesor Martín Lagos s/n, 28040 Madrid, Spain.
Introduction:
Gastric cancer (GC) shows high recurrence and mortality rates. The AJCC TNM staging system is the best prognostic predictor, but lymph node assessment is a major source of controversy. Recent studies have found that lymph node ratio (LNR) may overcome TNM limitations. Our aim is to develop a simplified tumor-LNR (T-LNR) classification for predicting prognosis of resected GC.
Methods:
Retrospective study of all GC resected in a tertiary center in Spain (N = 377). Clinicopathological features were assessed, LNR was classified into N0:0%, N1:1-25%, N2:>25%, and a T-LNR classification was developed. Statistical analyses were performed.
Results:
317 patients were finally included. Most patients were male (54.6%) and mean age was 72 years. Tumors were intestinal (61%), diffuse (30.8%) or mixed (8.1%). During follow-up, 36.7% and 27.4% of patients progressed and died, respectively. T-LNR classification divided patients into five prognostic categories (S1-S5). Most cases were S1-S4 (26.2%, 19.9%, 22.6% and 23.6%, respectively). 7.6% of tumors were S5. T-LNR classification was significantly associated with tumor size, depth, macroscopical type, Laurén subtype, signet ring cells, histologic grade, lymphovascular invasion, perineural infiltration, infiltrative growth, patient progression and death. Kaplan-Meier curves for OS showed an excellent patient stratification with evenly spaced curves. As for DFS, T-LNR classification also showed good discriminatory ability with non-overlapping curves. T-LNR classification was independently related to both OS and DFS.
Conclusions:
T-LNR classifications can successfully predict prognosis of GC patients. Larger studies in other geographic regions should be performed to refine this classification and to validate its prognostic relevance.
