使用18F-氟脱氧葡萄糖（18F-FDG）PET衍生参数和恰当的机器学习方法，开发和测试一个机器学习模型，用于分类口咽部鳞状细胞癌（OPSCC）患者的人乳头瘤病毒（HPV）状况。该回顾性研究纳入了126名新被诊断为OPSCC的患者（118名男性；平均年龄60岁），这些患者在2012年1月至2020年2月间接受18F-FDG PET-计算机断层扫描（CT）。将患者随机分配到7:3的训练和内部验证集中。从另外两个三级医院依次招募了19名外部测试集患者（16名男性，平均年龄65.3岁）。 模型1仅使用PET参数，模型2仅使用临床特征，模型3使用PET和临床参数。研究了多个特征转换、特征选择、过采样和训练模型。外部测试集用于测试在内部验证集中表现最佳的三个模型的值。比较了接受操作特征曲线（AUC）的值之间的差异。在外部测试集中，基于ExtraTrees的模型3表现最佳，使用了两个PET衍生参数和三个临床特征，采用MinMaxScaler、互信息选择和自适应合成采样方法的组合（AUC = 0.78；95%置信区间，0.46-1）。模型3在预测HPV状态时的表现优于仅使用PET参数的模型1（AUC = 0.48，p = 0.047）和仅使用临床参数的模型2（AUC = 0.52，p = 0.142）。使用过采样和互信息选择，结合来自18F-FDG PET/CT的代谢参数和临床参数，开发了基于ExtraTree的HPV状态分类器，其表现优于仅使用PET或临床参数的模型。版权所有 © 2023年韩国放射学会。

To develop and test a machine learning model for classifying human papillomavirus (HPV) status of patients with oropharyngeal squamous cell carcinoma (OPSCC) using 18F-fluorodeoxyglucose (18F-FDG) PET-derived parameters in derived parameters and an appropriate combination of machine learning methods in patients with OPSCC.This retrospective study enrolled 126 patients (118 male; mean age, 60 years) with newly diagnosed, pathologically confirmed OPSCC, that underwent 18F-FDG PET-computed tomography (CT) between January 2012 and February 2020. Patients were randomly assigned to training and internal validation sets in a 7:3 ratio. An external test set of 19 patients (16 male; mean age, 65.3 years) was recruited sequentially from two other tertiary hospitals. Model 1 used only PET parameters, Model 2 used only clinical features, and Model 3 used both PET and clinical parameters. Multiple feature transforms, feature selection, oversampling, and training models are all investigated. The external test set was used to test the three models that performed best in the internal validation set. The values for area under the receiver operating characteristic curve (AUC) were compared between models.In the external test set, ExtraTrees-based Model 3, which uses two PET-derived parameters and three clinical features, with a combination of MinMaxScaler, mutual information selection, and adaptive synthetic sampling approach, showed the best performance (AUC = 0.78; 95% confidence interval, 0.46-1). Model 3 outperformed Model 1 using PET parameters alone (AUC = 0.48, p = 0.047) and Model 2 using clinical parameters alone (AUC = 0.52, p = 0.142) in predicting HPV status.Using oversampling and mutual information selection, an ExtraTree-based HPV status classifier was developed by combining metabolic parameters derived from 18F-FDG PET/CT and clinical parameters in OPSCC, which exhibited higher performance than the models using either PET or clinical parameters alone.Copyright © 2023 The Korean Society of Radiology.