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find Keyword "locally advanced gastric cancer" 1 results
  • Prognostic value of lymphocyte monocyte ratio changes in patients with locally advanced gastric cancer: a single center retrospective cohort study

    ObjectiveTo evaluate prognostic value of change of immune status in locally advanced gastric cancer (LAGC) patients. Methods We retrospective collected 210 LAGC patients who underwent treatment in our department from January 2013 to December 2018, then we collected lymphocyte-to-monocyte ratio (LMR) and cLMR (change of lymphocyte-to-monocyte ratio, cLMR) before operation and after three cycles of adjuvant chemotherapy. We had developed a new immune state change score (ICS) based on preoperative LMR (pLMR) and cLMR, and explored its prognostic value. The definition of ICS in this study was: ICS=1, pLMR≤4.53 and cLMR≤1; ICS=2, pLMR≤4.53 and cLMR>1, or pLMR>4.53 and cLMR≤1; ICS=3, pLMR>4.53 and cLMR>1. Results The results of multivariate Cox proportional hazard regression model showed that ICS was an influencing factor for overall survival [ICS=2, RR=0.397, 95%CI (0.260, 0.608), P<0.001; ICS=3, RR=0.080, 95%CI (0.040, 0.162), P<0.001), patients with ICS scores of 2 and 3 had better overall survival. In addition, the prognostic accuracy of ICS was superior to pLMR and Clmr, and the C-index of ICS [0.806, 95%CI (0.746, 0.865)] was higher than that of pLMR [0.717, 95%CI (0.635, 0.799), P=0.003)] and cLMR [0.723, 95%CI (0.641, 0.806), P=0.005)]. Based on this, a Nomogram model included ICS, CEA, and pTNM staging was constructed to predict the 3-year and 5-year survival rates of patients. The calibration curve and C-index [0.821, 95%CI (0.783, 0.859)] showed high discrimination and accuracy of Nomogram, and decision curve analysis confirmed that the model had good clinical application value. Conclusions The dynamic changes in the patient’s immune status before and after adjuvant therapy are related to the overall survival of LAGC patients. As an evaluating system which combined the cLMR and pLMR, ICS can better predict the prognosis of LAGC patients.

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