Li Yingjia, Zhao Xingping, Zhou Yanhua, Gong Lina, Peng Enuo
The Third Xiangya Hospital of Central South University, Changsha, 410013, China.
BMC Med Inform Decis Mak. 2025 Feb 20;25(1):94. doi: 10.1186/s12911-025-02934-8.
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.
卵巢癌是威胁女性健康的严重恶性肿瘤。卵巢癌的早期诊断和有效治疗仍存在不足，约70%的卵巢癌在发现时已处于晚期。本研究旨在利用人工智能机器学习的决策树方法构建预测卵巢癌患者良恶性程度的模型。
本研究共纳入758例患者。这些患者通过B超、CT或MR进行诊断。记录并分析其临床病理特征和循环血细胞指标。采用CART决策树构建卵巢肿瘤良恶性预测模型，并绘制受试者工作特征（ROC）曲线评估决策树模型的预测价值。
发现显著的预测变量包括年龄、病程、患者一般状况和绝经状态、腹水、肿瘤大小、HE4、CA125、ROMA指数以及血常规相关指标（嗜碱性粒细胞计数百分比和绝对值除外）。在构建的决策树模型中，ROMA_after是信息增益最大的根节点。ROMA_after、肿块大小（MR/CT）、HE4、CA125、血小板数量、淋巴细胞比例、白细胞计数、绝经后、血细胞比容和平均血小板体积是决策树模型中的重要指标。该模型预测卵巢癌良恶性的受试者工作特征曲线下面积为0.86。
基于临床指标和术前循环血细胞成功构建了决策树模型，在预测卵巢癌良恶性方面比单纯的影像学指标或生物标志物在我们的数据中表现出更好的结果，这意味着我们的模型能够更准确地预测卵巢癌的良恶性。
