Gulsum Ekin Sari

Predictive Value of the CA-125 Elimination Rate Constant K (KELIM) in Predicting Progression-Free Survival and Overall Survival in Epithelial Ovarian Cancer

Background: It is crucial to predict the response to chemotherapy and identify prognostic markers for recurrence and survival in patients with epithelial ovarian cancer (EOC), in order to effectively manage patient care. The CA-125 elimination rate constant K (KELIM) has recently been developed as a means of assessing the chemotherapy response and has been tested mainly in patients enrolled in randomized controlled trials. The objective of this study was to investigate whether the KELIM score is a prognostic marker for progression-free survival (PFS) and overall survival (OS) in EOC, utilizing its role in predicting the chemotherapy response in real-life settings. Method: Demographic, surgical, and survival data of patients with EOC operated on in Antalya Training and Research Hospital between January 2015 and December 2021 were obtained from the electronic gynecological oncology clinic database system and analyzed retrospectively. Results: A total of 102 patients with EOC were included; 30 patients (29.4%) had a KELIM score ≥ 1 and 72 (70.6%) patients had a KELIM score < 1. In the group with a KELIM score < 1, recurrence and refractory disease occurred in 49 patients, while it was 11 patients in the group with a KELIM score ≥ 1 (p = 0.004). PFS was 12 months and 32 months in the groups with KELIM scores of <1 and ≥1, respectively (p = 0.012). There was no difference between groups regarding OS (p = 0.139). In the whole group, KELIM score (<1 vs. ≥1) and type of surgery (IDS vs. PDS) were found to be independent prognostic factors for PFS (RR = 0.44; 95%CI: 0.22–0.88; p = 0.021 and RR = 2.97; 95%CI: 1.76–5.01; p < 0.001, respectively). Conclusion: We found that a favorable KELIM score was associated with better PFS in all groups of patients undergoing surgery for EOC in a real-life setting. With the increasing number of studies, the KELIM score will play an important role in providing better guidance to clinicians at the initial presentation of patients and in subsequent treatment planning.

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

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.