Predicting the Recurrence of Ovarian Cancer Based on Machine Learning

Abstract

Lining Zhou,1,* Hong Hong,2,* Fuying Chu,1 Xiang Chen,1 Chenlu Wang1 1Department of Clinical Laboratory, The Second Affiliated Hospital of Nantong University and Nantong City No.1 People’s Hospital, Nantong, People’s Republic of China; 2Department of Clinical Laboratory, Nantong Traditional Chinese Medicine Hospital, Nantong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Chenlu Wang; Xiang Chen, Email [email protected]; [email protected]: Recurrence is the main factor for poor prognosis in ovarian cancer, but few prognostic biomarkers were reported. In this study, we used machine learning methods based on multiple biomarkers to develop a specific prediction model for the recurrence of ovarian cancer.Methods: A total of 277 ovarian cancer patients were enrolled in this study and randomly classified into training and testing cohorts. The prediction information was obtained through 47 clinical parameters using six supervised clustering machine learning algorithms, including K-Nearest Neighbor (K-NN), Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost).Results: In predicting the recurrence of ovarian cancer, machine learning algorithm was superior to conventional logistic regression analysis. In this study, XGBoost showed the best performance in predicting the recurrence of ovarian cancer, with an accuracy of 0.95. In addition, neoadjuvant chemotherapy, Monocyte ratio (MONO%), Hematocrit (HCT), Prealbumin (PAB), Aspartate aminotransferase (AST), and carbohydrate antigen 125 (CA125) are the most important biomarkers to predict the recurrence of ovarian cancer.Conclusion: The machine learning techniques can achieve a more accurate assessment of the recurrence of ovarian cancer, which can help clinicians make decisions, and develop personalized treatment strategies.Keywords: ovarian cancer, recurrence, machine learning, biomarkers, predictive modeling

Keywords

• ovarian cancer
• recurrence
• machine learning
• biomarkers
• predictive modeling