Prediction of Clavien Dindo Classification ≥ Grade III Complications After Epithelial Ovarian Cancer Surgery Using Machine Learning Methods

Abstract

Background and Objectives: Ovarian cancer surgery requires multiple radical resections with a high risk of complications. The aim of this single-centre, retrospective study was to determine the best method for predicting Clavien-Dindo grade ≥ III complications using machine learning techniques. Material and Methods: The study included 179 patients who underwent surgery at the gynaecological oncology department of Antalya Training and Research Hospital between January 2015 and December 2020. The data were randomly split into training set n = 134 (75%) and test set n = 45 (25%). We used 49 predictors to develop the best algorithm. Mean absolute error, root mean squared error, correlation coefficients, Mathew's correlation coefficient, and F1 score were used to determine the best performing algorithm. Cohens' kappa value was evaluated to analyse the consistency of the model with real data. The relationship between these predicted values and the actual values were then summarised using a confusion matrix. True positive (TP) rate, False positive (FP) rate, precision, recall, and Area under the curve (AUC) values were evaluated to demonstrate clinical usability and classification skills. Results: 139 patients (77.65%) had no morbidity or grade I-II CDC morbidity, while 40 patients (22.35%) had grade III or higher CDC morbidity. BayesNet was found to be the most effective prediction model. No dominant parameter was observed in the Bayesian net importance matrix plot. The true positive (TP) rate was 76%, false positive (FP) rate was 15.6%, recall rate (sensitivity) was 76.9%, and overall accuracy was 82.2% A receiver operating characteristic (ROC) analysis was performed to estimate CDC grade ≥ III. AUC was 0.863 with a statistical significance of p < 0.001, indicating a high degree of accuracy. Conclusions: The Bayesian network model achieved the highest accuracy compared to all other models in predicting CDC Grade ≥ III complications following epithelial ovarian cancer surgery.

Keywords: Bayesian network model; Clavien–Dindo classification; artificial intelligence; diagnosis; machine learning models; ovarian cancer surgery.

Figure 1

ROC analysis of BayesNet algorithm in predicting CDC grade 3-4-5; (p value < 0.001, AUC = 0.863, TP rate = 0.769). Yellow line represents lower thresholds where the classifier tends to classify more instances as positive, leading to a higher True Positive Rate (TPR) but also a higher False Positive Rate (FPR). On the other hand the blue line represents higher thresholds where the classifier becomes more selective in predicting positive instances, leading to a lower FPR. The relevant coloring of the lines was done to emphasize the basic principle of ROC analysis.