## 1 Introduction  
The study of the effectiveness of the proposed approach to detect HPV-positive and/oropharmacous cell carcinoma (OPV) has been carried out in the last decade. The OPV status of OPV status is a key component of the OPV status, which is the most common among the most common and most  
## 1 Introduction  
In the last two decades, a growing number of pre-clinical and clinical studies have focused on the biological and molecular mechanisms of HPV-associated orophyngeal squamous cell carcinoma (OPSCC) in order to explain its peculiar radio- and chemiocresponsiveness. These studies identified specific tumor microenvironmental factors and a lower ability of this kind of malignancies to repair DNA damage and to repopulate after radiation-induced injury [1]. The substantially better prognosis of patients with HPV-related OPSCC compared to those with HPV-negative OPSCC has been defined in the American Joint Committee on Cancer TNM8 staging guidelines, which recommend stratification by HPV status to improve staging [2; 3].  
The diagnosis, staging, and follow-up of OPSCC are highly dependent on imaging [4], including ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). In clinical practice, MRI is often the modality of choice because it is a multiparametric imaging modality that provides morphologic data with high soft-tissue contrast [5]. Furthermore, MRI can investigate the physical properties of the tumor, i.e., the microvascular and cellular architecture of tissues, by the application of additional sequences such as dynamic-contrast-enhanced (DCE-MRI) and diffusion-weighted imaging (DWI), respectively [6].  
In recent years, growing attention has also been paid to radiomics, which refers to the automated extraction of mathematically defined radiological features from two- or three-dimensional (2D or 3D) medical images. By converting medical images into high-dimensional, mineable data via high-throughput extraction of quantitative features (e.g., morphology, intensity histogram, and texture), additional information beyond the original raw images can be obtained [7].  
The recent application of radiomics in head and neck studies demonstrated promising results to obtain a better diagnosis and a more accurate prediction of tumor response to treatments and of normal tissue toxicity [8; 9; 10; 11; 12]. These novel developments in medical imaging are also promoted by a machine learning approach to high-dimensional data analysis and model building, which is continuously updated and improved [7; 8].  
Multiple studies have already developed CT-based radiomic signatures for predicting HPV status [13; 14; 15; 16], while only a few studies focused on MRI-based radiomics, taking advantage of the better soft-tissue contrast of morphological sequences than CT, and of functional information provided by diffusion- and perfusion-weighted techniques [17; 18; 19]. The clinical utility of DWI in discriminating HPV-positive from HPV-negative patients and its superiority with respect to morphological sequences, i.e., T2-weighted and/or contrast-enhanced T1-weighted imaging, has been recently suggested by the investigation of [PERSON] et al. [19] and supported by previous correlation studies between DWI and histopathology in HNSCC [20; 21]. For these reasons, we hypothesized that DWI-based radiomics could be particularly helpful to non-invasively detect HPV positivity and to better explore the peculiar tissue architecture of HPV-associated OPSCC.  
The aim of this study is to investigate, through a radiomic approach, the potential role of DWI in predicting HPV status in patients affected by OPSCC.