Zhou Lining, Hong Hong, Chu Fuying, Chen Xiang, Wang Chenlu
Department of Clinical Laboratory, The Second Affiliated Hospital of Nantong University and Nantong City No.1 People's Hospital, Nantong, People's Republic of China.
Department of Clinical Laboratory, Nantong Traditional Chinese Medicine Hospital, Nantong, People's Republic of China.
Cancer Manag Res. 2024 Oct 9;16:1375-1387. doi: 10.2147/CMAR.S482837. eCollection 2024.
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.
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).
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.
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.
复发是卵巢癌预后不良的主要因素，但报道的预后生物标志物较少。在本研究中，我们使用基于多种生物标志物的机器学习方法来开发一种卵巢癌复发的特异性预测模型。
本研究共纳入277例卵巢癌患者，并随机分为训练组和测试组。使用六种有监督聚类机器学习算法，通过47个临床参数获取预测信息，这些算法包括K近邻（K-NN）、决策树（DT）、随机森林（RF）、自适应提升（AdaBoost）、梯度提升机（GBM）和极端梯度提升（XGBoost）。
在预测卵巢癌复发方面，机器学习算法优于传统逻辑回归分析。在本研究中，XGBoost在预测卵巢癌复发方面表现最佳，准确率为0.95。此外，新辅助化疗、单核细胞比例（MONO%）、血细胞比容（HCT）、前白蛋白（PAB）、天冬氨酸转氨酶（AST）和糖类抗原125（CA125）是预测卵巢癌复发最重要的生物标志物。
机器学习技术能够更准确地评估卵巢癌的复发情况，这有助于临床医生做出决策，并制定个性化的治疗策略。
