A scoring model for predicting early recurrence of gastric cancer with normal preoperative tumor markers: A multicenter study.
Eur J Surg Oncol
; 49(11): 107094, 2023 11.
Article
em En
| MEDLINE
| ID: mdl-37797381
ABSTRACT
INTRODUCTION:
Prognostic factors for postoperative early recurrence (ER) of gastric cancer (GC) in patients with normal or abnormal preoperative tumor markers (pre-TMs) remain unclear. MATERIALS ANDMETHODS:
2875 consecutive patients with GC who underwent radical gastrectomy (RG) between January 2010 and December 2016 were enrolled and randomly divided into training and internal validation groups. ER was defined as recurrence within two years of gastrectomy. Normal pre-TMs were defined as CEA≤5 ng/mL and CA199 ≤ 37 U/mL. Least absolute shrinkage selection operator (LASSO) Cox regression analysis was used to screen ER predictors. The scoring model was validated using 546 patients from another hospital.RESULTS:
A total of 3421 patients were included. Multivariate Cox analysis showed that pre-TMs was an independent prognostic factor for ER. Survival after ER was equally poor in the normal and abnormal pre-TMs groups (P = 0.160). Based on LASSO Cox regression, the ER of patients with abnormal pre-TMs was only associated with the pT and pN stages; however, in patients with normal pre-TMs, it was also associated with tumor size, perineural invasion, and prognostic nutritional index. Scoring model constructed for patients with normal pre-TMs had better predictive performance than TNM staging (concordance-index0.826 vs. 0.807, P < 0.001) and good reproducibility in both validation sets. Moreover, through risk stratification, the scoring model could not only identify the risk of ER but also distinguish ER patterns and adjuvant chemotherapy benefit subgroups.CONCLUSION:
pre-TMs is an independent prognostic factor for ER in GC after RG. The established scoring model demonstrates excellent predictive performance and clinical utility.Palavras-chave
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Base de dados:
MEDLINE
Assunto principal:
Neoplasias Gástricas
/
Biomarcadores Tumorais
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article