## 4 Discussion  
In the present investigation, we explored the potential of DWI-based radiomics of both primary tumors and metastatic nodes, in conjunction with patient-related factors, to determine the patient HPV status in a homogeneous cohort of OPSCCs. The role of DWI in differentiating HPV-positive from HPV-negative patients was concordantly reported in previous studies, which found a marked reduction in ADC values and a more leptokurtic and skewed right ADC histogram in HPV-positive tumors, compared to HPV-negative tumors [18; 34; 35; 36; 37]. This was attributed to the peculiar tissue architecture of HPV-positive tumors, characterized by more densely packed cells, a smaller tumor-stroma component, and higher levels of tumor-infiltrating lymphocytes. Although more than 200 features extracted from both PTs and LNs were significantly related to HPV status in our training set, only two of them were selected as the best predictors from the MRMR algorithm: the 50 th percentile of D\({}_{\text{PT}}\) and the inverse difference moment of ADC\({}_{\text{LN}}\) (which represents an index of homogeneity from GLCM family), together with smoke habits and tumor subsite. The selection of the 50 th percentile of DPT in place of the 50 th percentile of ADC\({}_{\text{PT}}\) (even though strongly correlated with HPV status as well) seemed to confirm that the perfusion-free diffusion coefficient, D, may better reveal the specific HPV-positive tumor characteristics related to cell density and tissue microstructure, compared to ADC, as also supported by other investigators [36; 38]. The increase in inverse difference moment of ADC in HPV-positive patients indicated a higher homogeneity of metastatic lymph nodes, in accordance with a number of previous studies based on both CT and PET imaging, which reported an increase in homogeneity of CT density and in FDG uptakes [13; 14; 15; 39]. This can be explained considering the different mechanisms of carcinogenesis between HPV-positive and HPV-negative HNSCC, the former being more homogeneously triggered by the HPV infection, the latter being the consequence of more intricate genetic alterations attributable to the toxic effect of alcohol drinking and tobacco smoking [12; 33]. The important role of both smoking habits and alcohol consumption consistently emerged also from our findings, indicating that patients with HPV-negative OPSCC smoked and drank more than patients with HPV-related OPSCC [33; 34; 35; 36]. The quite large patient population included in the present study allowed us to identify the potential of the tumor subsite in distinguishing HPV-positive from -negative subjects, which was scarcely documented in earlier studies. In accordance with [PERSON] et al. [33], OPSCCs located in the tonsil were found to have an increased risk of HPV positivity with respect to those in the base of the tongue. The entire patient cohort of 144 subjects was split into a training set and a validation set to test the possibility of generalizing and transporting the proposed models to a completely separate patient group, according to the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis) statements [40]. As several ML algorithms can be used to guide the model building, each of which offers specific advantages and disadvantages in terms of interpretability, assumptions about the data, and computational performance, we trained different families of ML algorithms, identifying the Naive Bayes method as the best classifier in terms of diagnostic accuracy. The main benefits of Naive Bayes algorithms are the simplicity and robustness to missing and noisy data, and the ability to work well also on small sample sizes [7]. To better explore the role of imaging and clinical predictors, alone or in combination, we proposed six possible models, including 50 th Percentile of \(\mathrm{D_{PT}}\), Inverse Difference Moment of \(\mathrm{ADC_{LN}}\), Smoke, Subsite, Gender, and Energy of \(\mathrm{ADC_{PT}}\).