Background: Oesophagectomy is an operation with a high risk of postoperative complications. The aim of this single-centre retrospective study was to apply machine-learning methods to predict complications (Clavien-Dindo grade IIIa or higher) and specific adverse events.
Methods: Patients with resectable adenocarcinoma or squamous cell carcinoma of the oesophagus and gastro-oesophageal junction who underwent Ivor Lewis oesophagectomy between 2016 and 2021 were included. The tested algorithms were logistic regression after recursive feature elimination, random forest, k-nearest neighbour, support vector machine, and neural network. The algorithms were also compared with a current risk score (the Cologne risk score).
Results: 457 patients had Clavien-Dindo grade IIIa or higher complications (52.9 per cent) versus 407 patients with Clavien-Dindo grade 0, I, or II complications (47.1 per cent). After 3-fold imputation and 3-fold cross-validation, the overall accuracies were: logistic regression after recursive feature elimination, 0.528; random forest, 0.535; k-nearest neighbour, 0.491; support vector machine, 0.511; neural network, 0.688; and Cologne risk score, 0.510. For medical complications, the results were: logistic regression after recursive feature elimination, 0.688; random forest, 0.664; k-nearest neighbour, 0.673; support vector machine, 0.681; neural network, 0.692; and Cologne risk score, 0.650. For surgical complications, the results were: logistic regression after recursive feature elimination, 0.621; random forest, 0.617; k-nearest neighbour, 0.620; support vector machine, 0.634; neural network, 0.667; and Cologne risk score, 0.624. The calculated area under the curve of the neural network was 0.672 for Clavien-Dindo grade IIIa or higher, 0.695 for medical complications, and 0.653 for surgical complications.
Conclusion: The neural network scored the highest accuracies compared with all of the other models for the prediction of postoperative complications after oesophagectomy.
The human gullet or stomach can develop tumours. Surgery can help to cure patients with these tumours. But the operation is risky because sometimes adverse events can happen afterwards. So far, there is no reliable prediction model. It may help to predict the risk of adverse events accurately. For example, patients with a high risk could be observed more thoroughly. Patients with a low risk may not need unnecessary procedures. The information of all patients with an operation at a specialized hospital was collected. Machine learning is a complex mathematical method and was used in this study. It is able to analyse big data sets of information. One machine-learning method called neural network was best in predicting adverse events. Right now, the performance may not be strong enough to fully rely on the prediction. However, refinement of the prediction and more data could improve the neural network in the future.
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