title: Prognostic Model Based on Systemic Inflammatory Response and Clinicopathological Factors to Predict Outcome of Patients with Node-Negative Gastric Cancer.
abstract: Prognostic models are generally used to predict gastric cancer outcomes. However, no model combining patient-, tumor- and host-related factors has been established to predict outcomes after radical gastrectomy, especially outcomes of patients without nodal involvement. The aim of this study was to develop a prognostic model based on the systemic inflammatory response and clinicopathological factors of resectable gastric cancer and determine whether the model can improve prognostic accuracy in node-negative patients. We reviewed the clinical, laboratory, histopathological and survival data of 1397 patients who underwent radical gastrectomy between 2007 and 2013. Patients were split into development and validation sets of 1123 and 274 patients, respectively. Among all 1397 patients, 545 had node-negative gastric cancer; 440 were included in the development set, 105 were included in the validation set. A prognostic model was constructed from the development set. The scoring system was based on hazard ratios in a Cox proportional hazard model. In the multivariate analysis, age, tumor size, Lauren type, depth of invasion, lymph node metastasis, and the neutrophil--lymphocyte ratio were independent prognostic indicators of overall survival. A prognostic model was then established based on the significant factors. Patients were categorized into five groups according to their scores. The 3-year survival rates for the low- to high-risk groups were 98.9%, 92.8%, 82.4%, 58.4%, and 36.9%, respectively (P < 0.001). The prognostic model clearly discriminated patients with stage pT1-4N0M0 tumor into four risk groups with significant differences in the 3-year survival rates (P < 0.001). Compared with the pathological T stage, the model improved the predictive accuracy of the 3-year survival rate by 5% for node-negative patients. The prognostic scores also stratified the patients with stage pT4aN0M0 tumor into significantly different risk groups (P = 0.004). Furthermore, the predictive value of this model was validated in an independent set of 274 patients. This model, which included the systemic inflammatory markers and clinicopathological factors, is more effective in predicting the prognosis of node-negative gastric cancer than traditional staging systems. Patients in the high-risk group might be good candidates for adjuvant chemotherapy.
authors.affiliation: Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning, China.
authors.forename: Jing-lei
authors.initials: JL
authors.lastname: Qu
authors.affiliation: Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning, China.
authors.forename: Xiu-juan
authors.initials: XJ
authors.lastname: Qu
authors.affiliation: Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning, China.
authors.forename: Zhi
authors.initials: Z
authors.lastname: Li
authors.affiliation: Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning, China.
authors.forename: Jing-dong
authors.initials: JD
authors.lastname: Zhang
authors.affiliation: Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Liu
authors.affiliation: Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning, China.
authors.forename: Yue-e
authors.initials: YE
authors.lastname: Teng
authors.affiliation: Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning, China.
authors.forename: Bo
authors.initials: B
authors.lastname: Jin
authors.affiliation: Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning, China.
authors.forename: Ming-fang
authors.initials: MF
authors.lastname: Zhao
authors.affiliation: Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning, China.
authors.forename: Ping
authors.initials: P
authors.lastname: Yu
authors.affiliation: Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Shi
authors.affiliation: Department of Clinical Epidemiology and Evidence-based Medicine, The First Hospital of China Medical University, Shenyang, Liaoning, China.
authors.forename: Ling-Yu
authors.initials: LY
authors.lastname: Fu
authors.affiliation: Department of Surgical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning, China.
authors.forename: Zhen-ning
authors.initials: ZN
authors.lastname: Wang
authors.affiliation: Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning, China.
authors.forename: Yun-peng
authors.initials: YP
authors.lastname: Liu
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015415:Biomarkers; D005260:Female; D005500:Follow-Up Studies; D005743:Gastrectomy; D006801:Humans; D053208:Kaplan-Meier Estimate; D008207:Lymphatic Metastasis; D008297:Male; D008875:Middle Aged; D060787:Neoplasm Grading; D009367:Neoplasm Staging; D017063:Outcome Assessment, Health Care; D011379:Prognosis; D012372:ROC Curve; D015203:Reproducibility of Results; D012189:Retrospective Studies; D013274:Stomach Neoplasms / Q000453:epidemiology* / Q000401:mortality / Q000473:pathology* / Q000601:surgery; D018746:Systemic Inflammatory Response Syndrome / Q000453:epidemiology / Q000401:mortality; D047368:Tumor Burden
chemical_list: D015415:Biomarkers