Prognostic Model Based on Preoperative FAR and SII Versus TNM Staging System in Evaluating Prognosis of Patients with Pancreatic Cancer After Radical Resection
10.3971/j.issn.1000-8578.2023.22.1090
- VernacularTitle:基于术前FAR和SII的预后模型与TNM分期系统在评估胰腺癌根治术患者预后中的比较
- Author:
Xudong LIU
1
;
Bin ZHAO
;
Peng DU
;
Guoqiang ZHANG
;
Qiang ZHENG
;
Jiamin LAI
;
Zhibin CHENG
Author Information
1. Department of General Surgery, Lanzhou University Second Hospital, Lanzhou 730000, China
- Publication Type:Research Article
- Keywords:
Pancreatic ductal adenocarcinoma;
Fibrinogen/albumin ratio;
Systemic immune inflammation index;
Nomogram;
Prognostic model
- From:
Cancer Research on Prevention and Treatment
2023;50(3):264-270
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the predictive value of preoperative fibrinogen/albumin ratio (FAR) and systemic immune inflammation index (SII) on the postoperative prognosis of patients with pancreatic ductal adenocarcinoma. Methods An ROC curve was used in determining the best cutoff values of FAR and SII and then grouped. The Cox proportional hazards model was used in analyzing the prognostic factors of radical pancreatic cancer surgery, and then a Nomogram prognostic model was established. C-index, AUC, and calibration curve were used in evaluating the discrimination and calibration ability of the Nomogram. DCA curves were used in assessing the clinical validity of the Nomograms. Results The optimal cutoff values for preoperative FAR and SII were 0.095 and 532.945, respectively. FAR≥ 0.095, SII≥ 532.945, CA199≥ 450.9 U/ml, maximum tumor diameter≥ 4 cm, and the absence of postoperative chemotherapy were independent risk factors for the poor prognosis of pancreatic cancer (P<0.05). The discrimination ability, calibration ability, and clinical effectiveness of Nomogram prognostic model were better than those of the TNM staging system. Conclusion The constructed Nomogram prognostic model has higher accuracy and level of discrimination and more clinical benefits than the TNM staging prognostic model.
