Key result
A Support Vector Classification (SVC) machine-learning model predicted postoperative complications after gastrectomy with an accuracy of 78.12% in an external validation cohort.
Why the study?
Postoperative complications adversely affect prognosis in patients with gastric cancer, prompting investigation into the feasibility of a machine-learning model to predict surgical outcomes after gastrectomy.
Can a support vector classification (SVC) model predict postoperative complications in patients undergoing gastrectomy?
Observational (n=321)
No
Can a support vector classification (SVC) model predict postoperative complications in patients undergoing gastrectomy?
A support vector classification model can predict postoperative complications after gastrectomy with approximately 78% accuracy.
ML models may stratify post-gastrectomy risk; prospective trials needed before any practice change.
OBJECTIVE: Postoperative 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. METHODS: In 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. RESULTS: A 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. CONCLUSIONS: The 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.
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Lu et al. (2019) conducted an observational in Gastric cancer (n=321). Support Vector Classification (SVC) model was evaluated on Accuracy of predicting postoperative complications (Clavien-Dindo Grade ≥II). A Support Vector Classification (SVC) machine-learning model predicted postoperative complications after gastrectomy with an accuracy of 78.12% in an external validation cohort.
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