Akazawa Munetoshi, Hashimoto Kazunori, Noda Katsuhiko, Yoshida Kaname
Department of Obstetrics and Gynecology, Tokyo Women's Medical University Medical Center East, Tokyo, Japan.
SIOS Technology Inc., Tokyo, Japan.
Obstet Gynecol Sci. 2021 May;64(3):266-273. doi: 10.5468/ogs.20248. Epub 2020 Dec 28.
Most women with early stage endometrial cancer have a favorable prognosis. However, there is a subset of patients who develop recurrence. In addition to the pathological stage, clinical and therapeutic factors affect the probability of recurrence. Machine learning is a subtype of artificial intelligence that is considered effective for predictive tasks. We tried to predict recurrence in early stage endometrial cancer using machine learning methods based on clinical data.
We enrolled 75 patients with early stage endometrial cancer (International Federation of Gynecology and Obstetrics stage I or II) who had received surgical treatment at our institute. A total of 5 machine learning classifiers were used, including support vector machine (SVM), random forest (RF), decision tree (DT), logistic regression (LR), and boosted tree, to predict the recurrence based on 16 parameters (age, body mass index, gravity/parity, hypertension/diabetic, stage, histological type, grade, surgical content and adjuvant chemotherapy). We analyzed the classification accuracy and the area under the curve (AUC).
The highest accuracy was 0.82 for SVM, followed by 0.77 for RF, 0.74 for LR, 0.66 for DT, and 0.66 for boosted trees. The highest AUC was 0.53 for LR, followed by 0.52 for boosted trees, 0.48 for DT, and 0.47 for RF. Therefore, the best predictive model for this analysis was LR.
The performance of the machine learning classifiers was not optimal owing to the small size of the dataset. The use of a machine learning model made it possible to predict recurrence in early stage endometrial cancer.
大多数早期子宫内膜癌女性患者预后良好。然而，有一部分患者会出现复发。除了病理分期外，临床和治疗因素也会影响复发概率。机器学习是人工智能的一个子类型，被认为对预测任务有效。我们尝试使用基于临床数据的机器学习方法来预测早期子宫内膜癌的复发情况。
我们纳入了75例在我院接受手术治疗的早期子宫内膜癌患者（国际妇产科联盟分期为I期或II期）。共使用了5种机器学习分类器，包括支持向量机（SVM）、随机森林（RF）、决策树（DT）、逻辑回归（LR）和增强树，基于16个参数（年龄、体重指数、产次、高血压/糖尿病、分期、组织学类型、分级、手术内容和辅助化疗）来预测复发情况。我们分析了分类准确率和曲线下面积（AUC）。
SVM的最高准确率为0.82，其次是RF的0.77、LR的0.74、DT的0.66和增强树的0.66。LR的最高AUC为0.53，其次是增强树的0.52、DT的0.48和RF的0.47。因此，该分析的最佳预测模型是LR。
由于数据集规模较小，机器学习分类器的性能并非最佳。使用机器学习模型能够预测早期子宫内膜癌的复发情况。
