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Abstract：
The histological grade of oral squamous cell carcinoma affects the prognosis. In the present study, we performed a radiomics analysis to extract features from 18F-FDG PET image data, created machine learning models from the features, and verified the accuracy of the prediction of the histological grade of oral squamous cell carcinoma. The subjects were 191 patients in whom an 18F-FDG-PET examination was performed preoperatively and a histopathological grade was confirmed after surgery, and their tumor sizes were sufficient for a radiomics analysis. These patients were split in a 70%/30% ratio for use as training data and testing data, respectively. We extracted 2993 radiomics features from the PET images of each patient. Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) machine learning models were created. The areas under the curve obtained from receiver operating characteristic curves for the prediction of the histological grade of oral squamous cell carcinoma were 0.72, 0.71, 0.84, 0.74, and 0.73 for LR, SVM, RF, NB, and KNN, respectively. We confirmed that a PET radiomics analysis is useful for the preoperative prediction of the histological grade of oral squamous cell carcinoma.
摘要：
口腔鳞状细胞癌的组织学分级影响预后。在本研究中,我们进行了影像组学分析，从18F-FDGPET图像数据中提取特征，从功能创建机器学习模型，并验证了口腔鳞状细胞癌组织学分级预测的准确性。研究对象为191例患者，术前进行18F-FDG-PET检查，术后确认组织病理学分级，它们的肿瘤大小足以进行影像组学分析。这些患者被分成70%/30%的比例，用作训练数据和测试数据，分别。我们从每位患者的PET图像中提取了2993个影像组学特征。逻辑回归(LR)，支持向量机(SVM)随机森林(RF),朴素贝叶斯(NB)，并创建了K最近邻(KNN)机器学习模型。从受试者工作特征曲线获得的预测口腔鳞状细胞癌组织学分级的曲线下面积分别为LR的0.72、0.71、0.84、0.74和0.73，SVM,射频,NB,和KNN,分别。我们证实，PET影像组学分析可用于术前预测口腔鳞状细胞癌的组织学分级。
