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Prognostic role of the number of resected and negative lymph nodes in Spanish patients with gastric cancer

Publicado en:Annals Of Diagnostic Pathology. 67 152209- - 2023-12-01 67(), DOI: 10.1016/j.anndiagpath.2023.152209

Autores: del Arco, CD; Medina, LO; Muñoz, LE; Roldán, EM; de las Heras, SGG; Aceñero, MJF

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Introduction: Lymph node (LN) involvement is one of the most critical prognostic factors in resected gastric cancer (GC). Some analyses, mainly conducted in Asian populations, have found that patients with a higher number of total lymph nodes (NTLN) and/or negative lymph nodes (NNLN) have a better prognosis, although other authors have failed to confirm these results. Materials and methods: Retrospective study including all patients with GC resected in a tertiary hospital in Spain between 2001 and 2019 (n = 315). Clinicopathological features were collected and patients were categorized according to the NTLN and the NNLN. Statistical analyses were performed. Results: Mean NNLN was 17. The NNLN was significantly related to multiple clinicopathological variables, including recurrence and tumor-related death. The classification based on the NNLN (N1: ?16, N2: 8–15, N3: ?7) effectively stratified the entire cohort into three distinct prognostic groups and maintained its prognostic value within both the pN0 and pN+ patient subsets. Furthermore, it was an independent prognostic indicator for both overall and disease-free survival. Conversely, the mean NTLN was 21.9. Patients with ?16 LN retrieved exhibited distinct clinicopathological features compared to those with >16 LN, but no significant differences were observed in terms of recurrence or disease-associated death. The application of alternative cut-off points for NTLN (10, 20, 25, 30, and 40) showed no prognostic significance. Conclusions: In Spanish patients with resected GC the NNLN hold prognostic significance, while the NTLN does not appear to be prognostically significant. Incorporating the NNLN into GC staging may enhance the accuracy of the TNM system. © 2023 Elsevier Inc.

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