Feng Hai-Huan, Zhang Wei-Han, Liu Kai, Chen Xiao-Long, Zhao Lin-Yong, Chen Xin-Zu, Yang Kun, Hu Jian-Kun
Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu 610041, China.
Gastric Cancer Center, Laboratory of Gastric Cancer, Department of General Surgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China.
J Clin Med. 2025 Jun 7;14(12):4043. doi: 10.3390/jcm14124043.
: Surgery remains the cornerstone of diagnosis and treatment for gastric cancer. This study aims to develop and validate a serology-based clinical scoring system to predict and evaluate the prognosis of gastric cancer patients. : Clinicopathological data of primary gastric cancer patients who underwent surgical treatment from 2009 to 2018 were collected and divided into training and validation cohorts. Preoperative serological indicators were screened, and a serum risk score (SerScore) was developed using LASSO-Cox analysis. Prognosis prediction models incorporating the SerScore were established and validated. : A total of 5493 patients were screened, and 43 serological indicators were assessed. Twelve serological indicators were selected to construct the SerScore. Patients with a SerScore below the cut-off value of -1.73 had significantly better survival rates compared to those with higher scores. Multivariate Cox analysis identified SerScore, age, tumor location, T stage, and N stage as independent prognostic factors for overall survival in the training cohort. A multivariate nomogram was developed, achieving a C-index of 0.745 in the training cohort and 0.750 in the validation cohort. The nomogram demonstrated superior predictive accuracy compared to the SerScore alone, with AUC values of 0.783 versus 0.639 in the training cohort and 0.805 versus 0.657 in the validation cohort. Calibration curves closely aligned with ideal predictions in both cohorts. : The SerScore model provides an effective tool for prognostic assessment in primary gastric cancer patients. This model not only enhances prognostic evaluation but also establishes a foundation for developing advanced prediction tools for gastric cancer.
手术仍然是胃癌诊断和治疗的基石。本研究旨在开发并验证一种基于血清学的临床评分系统，以预测和评估胃癌患者的预后。
收集2009年至2018年接受手术治疗的原发性胃癌患者的临床病理数据，并将其分为训练队列和验证队列。筛选术前血清学指标，采用LASSO-Cox分析建立血清风险评分（SerScore）。建立并验证了包含SerScore的预后预测模型。
共筛选5493例患者，评估43项血清学指标。选择12项血清学指标构建SerScore。SerScore低于-1.73临界值的患者生存率明显高于得分较高的患者。多因素Cox分析确定SerScore、年龄、肿瘤位置、T分期和N分期为训练队列中总生存的独立预后因素。开发了多因素列线图，训练队列中的C指数为0.745，验证队列中的C指数为0.750。与单独的SerScore相比，列线图显示出更高的预测准确性，训练队列中的AUC值分别为0.783和0.639，验证队列中的AUC值分别为0.805和0.657。两个队列的校准曲线均与理想预测紧密吻合。
SerScore模型为原发性胃癌患者的预后评估提供了一种有效工具。该模型不仅增强了预后评估，还为开发先进的胃癌预测工具奠定了基础。
