Alci Aysun, Ikiz Fatih, Yalcin Necim, Gokkaya Mustafa, Sari Gulsum Ekin, Ureyen Isin, Toptas Tayfun
Department of Gynecologic Oncology, Health Sciences University Antalya Training and Research Hospital, Antalya 07100, Turkey.
Department of Emergency Medicine, Health Sciences University Beyhekim Training and Research Hospital, Konya 42060, Turkey.
Medicina (Kaunas). 2025 Apr 10;61(4):695. doi: 10.3390/medicina61040695.
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. : 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. : 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 < 0.001, indicating a high degree of accuracy. : The Bayesian network model achieved the highest accuracy compared to all other models in predicting CDC Grade ≥ III complications following epithelial ovarian cancer surgery.
卵巢癌手术需要进行多次根治性切除，并发症风险很高。这项单中心回顾性研究的目的是确定使用机器学习技术预测Clavien-Dindo≥III级并发症的最佳方法。该研究纳入了2015年1月至2020年12月在安塔利亚培训与研究医院妇科肿瘤科接受手术的179例患者。数据被随机分为训练集n = 134（75%）和测试集n = 45（25%）。我们使用49个预测因子来开发最佳算法。使用平均绝对误差、均方根误差、相关系数、马修斯相关系数和F1分数来确定性能最佳的算法。评估科恩kappa值以分析模型与实际数据的一致性。然后使用混淆矩阵总结这些预测值与实际值之间的关系。评估真阳性（TP）率、假阳性（FP）率、精确度、召回率和曲线下面积（AUC）值以证明临床实用性和分类技能。139例患者（77.65%）无发病或I-II级CDC发病，而40例患者（22.35%）有III级或更高等级的CDC发病。发现贝叶斯网络是最有效的预测模型。在贝叶斯网络重要性矩阵图中未观察到主导参数。真阳性（TP）率为76%，假阳性（FP）率为15.6%，召回率（敏感性）为76.9%，总体准确率为82.2%。进行了受试者工作特征（ROC）分析以估计CDC≥III级。AUC为0.863，具有<0.001的统计学意义，表明高度准确。与所有其他模型相比，贝叶斯网络模型在预测上皮性卵巢癌手术后CDC≥III级并发症方面具有最高的准确性。
