## 4 Discussion
### 18F-Fluorodeoxyglucose Positron-Emission Tomography (18F-FDG PET)-Based Radiomic Features (Table 3)  
18F-FDG PET/CT has a high tumor detection rate and can better define a tumor's outline from the background. It also has a higher performance in detecting nodal and distant metastases, if compared to single-modality MRI or CT, and it allows direct imaging of metabolism. 18F-FDG PET/CT metabolic parameters can differ between HPV-positive and HPV-negative disease, possibly due to the different distribution of hypoxic areas within the primary tumor [49]. Radiomic models based on features extracted from 18F-FDG PET/CT scans proved useful in the determination of HPV status in many series. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
[PERSON] et al. [14] retrospectively analyzed 171 OPSCC patients who underwent pre-treatment 18F-FDG PET and who were divided into a training (\(n=90\)) and a validation (\(n=30\)) cohort. In the training session, 2160 18F-FDG-PET images were analyzed to create a diagnostic model to distinguish between HPV-positive and HPV-negative OPSCC. Confirmation of diagnostic accuracy was reached through the analysis of validation cohort data, with a sensitivity of 0.83, specificity of 0.83, positive predictive value of 0.88, negative predictive value of 0.77 and diagnostic accuracy of 0.83; the results of visual evaluation of images by two independent radiologists were 0.78, 0.5, 0.7, 0.6 and 0.67 (reader 1) and 0.56, 0.67, 0.71, 0.5 and 0.6 (reader 2), respectively. A significant difference was then outlined between deep-learning- and radiologist-based diagnostic accuracy. [PERSON] et al. [50] explored PET and non-contrast-enhanced CT radiomic features both in primary tumors and in metastatic cervical lymph nodes from OPSCC. In total, 435 primary tumors and 741 metastatic adenopathies were analyzed and divided into training and validation cohorts. Single-modality PET and CT final models had similar classification performance in independent validation; nonetheless, PET and CT combined models outperformed single-modality PET- or CT-based models, with AUC of 0.78 and 0.77 for prediction of HPV in cross-validation and independent validation, respectively. For all lymph nodes, AUC was 0.73, whereas final models achieved an AUC of 0.83 for a virtual VOI combining primary tumor and lymph nodes. Context-aware saliency-guided radiomics was also applied for prediction of survival outcomes and HPV status in a retrospective multicenter study from [PERSON] et al. [51]. Six models were constructed after feature extraction; in the OPC HPV testing cohort, the model FusedImg (fused PET/CT imaging) showed higher AUC for HPV status prediction compared with the Origin model (0.653 vs. 0.484). In the OPC testing cohort, also, radiomic score for the prediction of both survival outcomes and HPV (Rad_Ocm_HPV) performed the best for OS and DFS predictions, compared with radiomic score for outcome prediction (Rad_Ocm) or HPV detection (Rad_HPV) alone. Radiomics is time-consuming because of the need for Region-Of-Interest (ROI) mapping, complex preprocessing and feature extraction. This extensive process can represent a disadvantage for its clinical application. Therefore, a recent Korean study [52] tried to develop an HPV status classifier model based on metabolic parameters that were simple and easy to measure. Indeed, only the maximum standard uptake volume (SUVmax) and SUVmax- tumor-to-liver ratio (TLR) were required. It found that the model based on metabolic parameters and clinical data showed higher performance than models using either PET or clinical parameters alone.