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A Nomogram Based on Preoperative Clinical Bio-Indicators to Predict 5-year Survivals for Patients with Gastric Cancer After Radical Gastrectomy

Authors Yin QH, Liu BZ, Xu MQ, Tao L, Wang K, Li F, Zhang WJ

Received 17 December 2019

Accepted for publication 24 April 2020

Published 27 May 2020 Volume 2020:12 Pages 3995—4007


Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 2

Editor who approved publication: Dr Eileen O'Reilly

Qi Hang Yin,1,2 Bin Zheng Liu,3 Meng Qing Xu,1,4 Lin Tao,1,2 Kui Wang,5 Feng Li,1,6 Wen Jie Zhang1,2

1Department of Pathology, The First Affiliated University Hospital, Shihezi University School of Medicine, Shihezi, Xinjiang, People’s Republic of China; 2The Key Laboratories for Xinjiang Endemic and Ethnic Diseases, Shihezi University School of Medicine, Shihezi, Xinjiang, People’s Republic of China; 3Department of Pathology, The First People’s Hospital, Jiande, Zhejiang, People’s Republic of China; 4Department of Gastroenterology and Hepatology, Suzhou City Hospital, Suzhou, Anhui, People’s Republic of China; 5Department of Preventive Medicine, Shihezi University School of Medicine, Shihezi, Xinjiang, People’s Republic of China; 6Department of Pathology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, People’s Republic of China

Correspondence: Wen Jie Zhang
Department of Pathology, Shihezi University School of Medicine, 59 North 2nd Road, Shihezi, Xinjiang 832002, People’s Republic of China
Tel +86-139-8627-7576
Fax +86-993-205-7136

Purpose: This study aimed to improve the prediction of postoperative survival outcomes for patients with gastric cancer (GC) using a nomogram based on preoperative bio-indicators.
Patients and Methods: This retrospective study included 303 GC patients who had undergone radical gastrectomy from 2004 to 2013 at the First Affiliated Hospital, Shihezi University. The patients were followed up for 175 months after surgery and then divided into short-term (n=201) or long-term (n=102) survival groups. We used an expectation-maximization method to fill any missing data from the reviewed patient files. We then employed the Cox proportional hazard regression to identify biochemical markers that could predict 5-year overall survival (OS) as an endpoint among GC patients. Based on the results from the biochemical analysis, we developed a nomogram and assessed its performance and reliability.
Results: The variables significantly associated with OS in a multivariate analysis were age, body mass index (BMI), cell differentiation, high-density lipoprotein cholesterol (HDL-C), as well as serum potassium or serum magnesium. Combining all these predictors allowed us to establish a nomogram (C-index=0.701) whose accuracy of predicting survival was higher than the TNM staging system established by the 8th American Joint Committee on Cancer (C-index=0.666; p=0.016). Furthermore, decision curve of this nomogram was shown to have an ideal net clinical benefit rate.
Conclusion: We have developed an algorithm using preoperative bio-indicators and clinical features to predict prognosis for GC patients. This tool may help clinicians to strategize appropriate treatment options for GC patients prior to surgery.

Keywords: gastric cancer, 5-year overall survival, prognosis, nomogram

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