## 4 Discussion
The maximum number of predictors was limited to six to reduce overfitting in consideration of the size of the training set and the dramatic reduction of the predictor ranks already after the first four features. In the training set, all models provided fair to good accuracies (ranging between 82% and 92%), with high sensitivities (ranging between 79% and 91%), and specificities (ranging between 76% and 94%). A substantial decrease in diagnostic performances was found after testing the models on the validation cohort: the decrease in accuracy (ranging between 67% and 80%) can be mainly attributable to a marked reduction in specificity (ranging between 50% and 63%), while the sensitivity remained still high or was further improved (73-94%). The models tested on the validation cohort also suggested superiority of the clinical model (Model 6) in terms of sensitivity, while a slight superiority of combined or image-based models in terms of specificity, although no significant differences emerged. It can also be noted that simpler models, i.e., those including a smaller number of predictors, gave a better performance with respect to more complex ones. This may be explained by a reduced overfitting effect of simpler models, which positively impacted the model's transportability on new data. Although HPV-specific testing is recommended, imaging-based predictors of HPV status could be used to non-invasively detect HPV positivity in cases in which biopsy is not feasible because of a hardly accessible tumor location, or when the HPV test results are conflicting. Radiomics signatures for HPV status would also help in OPSCC for an easier diagnosis in unknown primary head and neck tumors, contributing to reducing costs and invasiveness of the current gold-standard modality for disease staging. Lastly, it would be of interest to explore the role of image-based signatures for HPV status classification as prognostic biomarkers, in order to better identify distinct risk classes of OPSCC patients, as suggested by [PERSON] et al. [14]. This study had some limitations. A larger dataset is needed to further investigate the generalization of our results in an independent cohort. We applied a correction for the class imbalance, HPV-negative patients representing the minority class according to the current prevalence of HPV positivity in OPSCC [41]; however, random oversampling may have also increased the overfitting and decreased the classifier performance on the validation dataset as a final result. It should also be underlined that the proposed models were based on ADC and D quantifications from both PTs and LNs; thus patients with only one representative volume, PT or LN, may have a higher probability of being wrongly classified. In future investigations, we could propose predictive models from DWI-based radiomics of PTs and LNs separately, considered as distinct tumor entities, also in view of their different responses to radio-chemotherapy. It should be noted that, after the process of feature selection, the MRMR algorithm assigned a much higher importance score to the smoke compared to alcohol, although they were both significantly related to HPV status. This may be explained considering the strong association between these factors, thus the algorithm preferred one of them to reduce data redundancy. This result cannot be generalized and may be dependent on the smoking and drinking habits of our patient population. Finally, we could not investigate the role of HPV vaccination as a predictor of HPV status because both the training and the validation cohorts of patients were unvaccinated against HPV.