## 4 Discussion
### CT-Based Radionic Features (Table 2)  
Radiomic features primarily associated with the spatial arrangement and morphological appearance of the tumor on Contrast-Enhanced (CE) diagnostic CT datasets may be potentially used for prediction of HPV status, beyond what is simply apparent to the trained human eye [15; 16; 41]. Much evidence has shown that pre-treatment CT-based radiomics could potentially also aid prognostication for patients with OPSCC. [PERSON] et al. [34] retrospectively evaluated 86 OPSCC patients and provided preliminary evidence that CT-based radiomics could aid in HPV status assessment (with an AUC of 0.834 in the external validation cohort) and in survival prediction (with shape feature being the most significant). Data of [PERSON] et al. [35] provided the same correlation between radiomic signature and survival with a better prognostic performance when p16 status was combined with radiomic features. The main differences are represented by the fact that [PERSON] et al. analyzed a treated population of locally advanced head and neck cancer patients, whereas [PERSON] et al. 's target population consisted of pre-treated OPSCC patients. Peritumoral features were also evaluated in CT scans for the same prognostications with good performance results. [PERSON] et al. [42] firstly applied CT-based radiomics to the area around the tumor, which proved able to suggest discriminable differences between HPV+ and HPV\(-\) disease. The former is indeed characterized by less overall stroma, smoother borders to the nests and leading edges and by more homogeneous cellularity usually without keratin production, which suggests less intensity disorder and microscale heterogeneity in gradient orientation outside the tumor. In 2020, [PERSON] [43] analyzed 187 OPSCC patients and 172 radiomic features extracted from pre-treatment CE-CT of the Gross Tumor Volume (GTV) with the result that HPV+ disease correlates with smaller lesion size, higher intensities, higher heterogeneity and greater sphericity/roundness. In 2022, the same author [44] investigated both clinical (T status, smoking habit, age) and radiomic (tumor morphology and intensity contrast) features for the prediction of HPV status. This pilot study on 128 OPSCC patients showed encouraging results in the characterization of HPV-positive disease, with an AUC of 0.895 for combined clinical and radiomic models. In 2017, CT radiomic features were evaluated after radical chemoradiation in HNSCC patients for the assessment of HPV status, along with Local Control (LC). Extracted features were based on intensity, shape, texture and wavelet transform; the results show that more heterogeneous tumor density was associated with better LC and HPV prediction (AUC of 0.85 in the training set and AUC of 0.78 in the validation set) [45]. In 2018, [PERSON] et al. [46] investigated a radiomic approach for HPV status prediction in 778 OPSCC patients randomly assigned to the training (\(n=628\)) and validation (\(n=150\)) sets. In total, 902 radiomic features were extracted on CT scans from the GTV and used to build a multivariable logistic regression model. The AUC was between 0.70 and 0.80 for all training sets and for a subset of artefact-free training data and did not differ significantly for the validation datasets. In this regard, radiomics could prove to be a cost-effective and complementary method for HPV screening, as well as in other non-oropharyngeal squamous cell carcinomas. A radiomic biomarker could also play an important role in retrospective HPV analyses when no tissue samples are available or in countries where it is not routinely performed. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Study** & **Year** & **Imaging** & **No. of** & **HPV** & **Enrollment** & **Prospective** & **Multicenter** & **Predictive** & **Main Findings** \\ \hline _[PERSON] et al._ [15] & 2015 & CT & 40 & HPV+; 29 & 2009– & & & & Statistically significant differences & Texture analysis may be considered for the evaluation of HPV \\ \hline \hline \end{tabular}
\end{table}