## 5 Major Limitations of Radiomic Analysis in Clinical Practice and New Future Perspectives  
Our search is limited by the small number of retrospective evidence available regarding only the relationship between radiomic features and the assessment of HPV infection status in selected oropharyngeal squamous cell carcinoma patients. Radiomics' impact on survival outcomes or on response prediction to treatment has not been assessed in the present analysis.  
Radiomics has shown promising results when used to assess HPV status in OPSCC patients; however, its practical application in daily routine still presents some critical issues, mainly represented by lack of validation. As a matter of fact, lack of image and feature extraction standardization, data sharing-related problems and clinicians' lack of confidence in the radiomic setting are the major current pitfalls. Furthermore, tumor segmentation and features extraction raise concerns regarding the reproducibility and the repeatability of this procedure [20]. Also, the \"black-box\" nature of deep-learning may raise concerns. Generated models are indeed not (or barely) interpretable and this is currently one of the major ethical challenges of the application of artificial intelligence (AI) in medical image analysis [57].  
Further cooperation is then required from radiomics and other disciplines; in this regard, radio-genomics, which relates to imaging features and gene signatures, has recently achieved good results [19].  
Finally, an increasing number of prospective, multicenter, large-sample studies with external validation should be encouraged. Most reported studies are indeed limited by their retrospective nature, which inevitably lead to biases, and by monocentric design.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{**Year**} & **Imaging** & **No. of** & **HPV** & **Enrollment** & **Prospective** & **Multicenter** & **Predictive** & **Main Findings** \\  & & **Modality** & **Patients** & **Status** & **Period** & **Procedure** & **Performances** & **A**n HPV status classifier was developed by combining metabolic parameters derived from \\  & & & & & January & & & & \\ \multirow{2}{*}{[PERSON] et al. [52]} & \multirow{2}{*}{2023} & 18F-FDG & \multirow{2}{*}{126} & \multirow{2}{*}{HPV+- 103} & \multirow{2}{*}{2012–} & \multirow{2}{*}{No} & \multirow{2}{*}{Yes} & \multirow{2}{*}{AUC (PET+ clinical)} & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } \\  & & & & HPV- : 23 & & & & 0.78 & 18F-FDG PET/CT and clinical parameters in \\  & & & & & & & & clinical parameters in \\  & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._[PERSON] et al. conducted interesting research focusing on characteristics and trends in the emerging field of radiomics. They found that studies related to lung, breast and prostate cancers are more developed than studies on head and neck disease, which showed lower inter-class and intra-class correlation and a consequent need of integration and fusion of methods and topics from other fields [58]. Head and neck tumors represent a real challenge for both clinicians and radiologists due to their complex anatomy, often small size, microenvironment variability and changes after treatment. Feature-based and deep-learning-based radiomics may overcome current pitfalls in imaging in head and neck cancers, and further efforts must be directed to standardize, refine and finally implement software in current clinical practice [59].