Decision tree model for predicting ovarian tumor malignancy based on clinical markers and preoperative circulating blood cells
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Ovarian cancer is a serious malignant tumor threatening women's health. The early diagnosis and effective treatments of ovarian cancer remain inadequate, and about 70% of ovarian cancers are in advanced stages when discovered. This study aimed to use the decision tree method of artificial intelligence machine learning to build a model for predicting the benign and malignant degree of ovarian cancer patients. A total of 758 patients were included in the study. These patients were diagnosed by B-ultrasound, CT or MR. The clinicopathological features and circulating blood cell indexes were recorded and analyzed. The prediction model of benign and malignant ovarian tumors was constructed by CART decision tree, and the receiver operating characteristic (ROC) curve was drawn to evaluate the predictive value of the decision tree model. It was found that significant predictor variables included age, disease duration, patient general condition and menopausal status, ascites, tumor size, HE4, CA125, ROMA index, and blood routine related indicators (except for basophil count percentage and absolute value). In the constructed decision tree model, ROMA_after was the root node with the maximum information gain. ROMA_after, Mass size (MR/CT), HE4, CA125, platelet number, lymphocyte ratio, white blood cell count, post-menopause, hematocrit and mean platelet volume were important indicators in the decision tree model. The area under the receiver operating characteristic curve of this model for predicting benign and malignant ovarian cancer was 0.86. The decision tree model was successfully constructed based on clinical indicators and preoperative circulating blood cells, and showed better results in predicting benign and malignant ovarian cancer than alone imaging indicators or biomarkers among our data, which means that our model can more accurately predict benign and malignant ovarian cancer.