人乳头瘤病毒-（HPV）阳性的咽喉鳞状细胞癌（OPSCC）在生物学和临床上与HPV阴性的OPSCC有所不同，并有更好的预后。本研究旨在分析基于磁共振成像（MRI）的放射组学在预测OPSCC中的HPV状态方面的价值，并旨在开发包括HPV状态和基于MRI的放射组学在内的OPSCC预后模型。对249个原发性OPSCCs（91个HPV阳性和159个HPV阴性）的预处理原生T1加权MRI进行手动描绘，每个描绘提取498个放射组学特征。使用单变量特征选择开发了逻辑回归（LR）和随机森林（RF）模型。此外，进行因子分析，并将所得因素与临床数据结合，以评估预测HPV状态的性能。此外，在多变量生存回归分析中，将因素与临床参数相结合。基于特征的LR和RF模型在预测HPV状态中的AUC值为0.79。二十个最显著特征之一的14个在两个模型中相似，主要涉及肿瘤球度、强度变化、紧凑度和肿瘤直径。结合临床数据和放射组学因素的模型（AUC = 0.89）在预测OPSCC HPV状态方面胜过了仅使用放射组学的模型。将临床特征与MRI放射组学相结合可以更准确地预测患者的总体生存（C指数= 0.72）。基于MR放射组学特征的预测模型能够以足够的性能预测HPV状态，支持MRI放射组学作为潜在成像生物标志物的作用。通过结合临床特征和MRI放射组学，预后预测得到改善。版权所有 © 2023 Elsevier Ltd. 发布。

Human papillomavirus- (HPV) positive oropharyngeal squamous cell carcinoma (OPSCC) differs biologically and clinically from HPV-negative OPSCC and has a better prognosis. This study aims to analyze the value of magnetic resonance imaging (MRI)-based radiomics in predicting HPV status in OPSCC and aims to develop a prognostic model in OPSCC including HPV status and MRI-based radiomics.Manual delineation of 249 primary OPSCCs (91 HPV-positive and 159 HPV-negative) on pretreatment native T1-weighted MRIs was performed and used to extract 498 radiomic features per delineation. A logistic regression (LR) and random forest (RF) model were developed using univariate feature selection. Additionally, factor analysis was performed, and the derived factors were combined with clinical data in a predictive model to assess the performance on predicting HPV status. Additionally, factors were combined with clinical parameters in a multivariable survival regression analysis.Both feature-based LR and RF models performed with an AUC of 0.79 in prediction of HPV status. Fourteen of the twenty most significant features were similar in both models, mainly concerning tumor sphericity, intensity variation, compactness, and tumor diameter. The model combining clinical data and radiomic factors (AUC = 0.89) outperformed the radiomics-only model in predicting OPSCC HPV status. Overall survival prediction was most accurate using the combination of clinical parameters and radiomic factors (C-index = 0.72).Predictive models based on MR-radiomic features were able to predict HPV status with sufficient performance, supporting the role of MRI-based radiomics as potential imaging biomarker. Survival prediction improved by combining clinical features with MRI-based radiomics.Copyright © 2023. Published by Elsevier Ltd.