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Erschienen in:
11.08.2021 | Original Research
Prognostic Score System Using Preoperative Inflammatory, Nutritional and Tumor Markers to Predict Prognosis for Gastric Cancer: A Two-Center Cohort Study
Erschienen in: Advances in Therapy | Ausgabe 9/2021
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Introduction
Gastric cancer (GC) is the fourth leading cause of cancer-related death worldwide. Our study aimed to investigate the prognostic value of preoperative inflammatory, nutritional and tumor markers and develop an effective prognostic score system to predict the prognosis of GC patients.
Methods
We retrospectively analyzed 1587 consecutive GC patients who received curative gastrectomy from two medical centers. A novel prognostic score system was proposed based on independently preoperative markers associated with overall survival (OS) of GC patients. A nomogram based on prognostic score system was further established and validated internally and externally.
Results
Based on multivariate analysis in the training set, a novel BLC (body mass index-lymphocyte-carbohydrate antigen 19-9) score system was proposed, which showed an effective predictability of OS in GC patients (log-rank P < 0.001). Moreover, receiver-operating characteristic (ROC) analysis showed that BLC had better performance in predicting OS than the traditional prognostic markers. The C-index of the BLC based-nomogram was 0.710 (95% CI 0.686–0.734), and the areas under ROC curves for predicting 3- and 5-year OS were 0.781 (95% CI 0.750–0.813) and 0.755 (95% CI 0.723–0.786), respectively, which were higher than those of tumor node metastasis (TNM) staging system alone. The calibration curve for probability of 3- and 5-year OS rate showed a good fitting effect between prediction by nomogram and actual observation. Verification in the internal and external validation sets showed results consistent with those in the training set.
Conclusions
The BLC combining inflammatory, nutritional and tumor markers was an independent prognostic predictor for GC patients, and the nomogram based on BLC could accurately predict the personalized survival of patients with GC.