Machine-learning-assisted prediction of surgical outcomes in patients undergoing gastrectomy.

Sheng Lu,Zhenggang Zhu,Chen Li,Chao Yan,Wencong Lu,Min Yan

doi:10.21147/j.issn.1000-9604.2019.05.09

Abstract

ObjectivePostoperative complications adversely affected the prognosis in patients with gastric cancer. This study intends to investigate the feasibility of using machine-learning model to predict surgical outcomes in patients undergoing gastrectomy.MethodsIn this study, cancer patients who underwent gastrectomy at Shanghai Rui Jin Hospital in 2017 were randomly assigned to a development or validation cohort in a 9:1 ratio. A support vector classification (SVC) model to predict surgical outcomes in patients undergoing gastrectomy was developed and further validated.ResultsA total of 321 patients with 32 features were collected. The positive and negative outcomes of postoperative complication after gastrectomy appeared in 100 (31.2%) and 221 (68.8%) patients, respectively. The SVC model was constructed to predict surgical outcomes in patients undergoing gastrectomy. The accuracy of 10-fold cross validation and external verification was 78.17% and 78.12%, respectively. Further, an online web server has been developed to share the SVC model for machine-learning-assisted prediction of surgical outcomes in patients undergoing gastrectomy in the future procedures, which is accessible at the web address: http://47.100.47.97:5005/r_model_prediction.ConclusionsThe SVC model was a useful predictor for measuring the risk of postoperative complications after gastrectomy, which may help stratify patients with different overall status for choice of surgical procedure or other treatments. It can be expected that machine-learning models in cancer informatics research are possibly shareable and accessible via web address all over the world.

Highlights

Gastric cancer is one of the most common malignancies and the second leading cause of cancer death in the world
The workflow of modelling mainly consists of procedures for basic statistics after collection of original data, data pretreatment such as deletion of corelated variables and resampling of data set, reduction of features via principal component analysis, model selection based on machine-learning approaches, model optimization via adjusting hyper-parameters, model validation, model accessibility, and model application
Our support vector classification (SVC) model demonstrated that chronologic age was the most important variable concerning on postoperative complications after gastrectomy, followed by tumor size, number of comorbidities, etc. (Figure 5)

Summary

Introduction

Gastric cancer is one of the most common malignancies and the second leading cause of cancer death in the world. In China, more than 679,100 new diagnoses are made every year. An estimated 498,000 patients died from gastric cancer in 2015 [1]. Surgery is the only possible curative treatment, and results of gastrectomy have improved throughout the years with respect to survival, morbidity and postoperative mortality [2]. Concerning the risk of postoperative complications, researchers would generally perform a Student’s t test or Chi square test to discover the risk factors. Other methods include prognostic nutritional index (PNI) [3], modified Glasgow prognostic score (mGPS) [4], the Estimation of Physiological Ability and Surgical Stress (E-PASS) scoring system [5], etc. The reliability and practicability of the previous criteria were indeterminate, and the www.cjcrcn.org

Methods

Results

Conclusion