## 4 Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Balanced**} \\  & \multicolumn{2}{c}{**C-Index**} \\ \cline{2-3}
**Model** & \multicolumn{2}{c}{**Without**} & \multicolumn{2}{c}{**With**} \\  & \multicolumn{2}{c}{**Clinical Variables**} & \multicolumn{2}{c}{**Clinical Variables**} \\ \hline CT-\(R\) & 0.684 & 0.650 \\ CT-\(L\) & 0.690 & 0.627 \\ DD-\(L\) & 0.656 & 0.521 \\ DD-\(T\) & 0.752 & 0.751 \\ DD-\(RT\) & 0.739 & 0.728 \\ DD-\(LT\) & 0.718 & 0.683 \\ CD-\(RLT\) & 0.739 & 0.541 \\ \hline \hline \end{tabular}  
* CT: computed tomography, DD: dose distribution, CD: combined CT with DD.  
\end{table} 
Table 4: Comparison of performance with and without clinical variables in the treatment failure prediction models. This study had some limitations. First, inter-observer variability in tumor delineation was not considered. This may affect the robustness and generalizability of the radiomics-based prediction models. [PERSON] et al. reported that the stability rate of radiomic features with respect to inter-observer delineation variability was 59% in HNSCC [43]. This could be due to the inclusion of several tumor sites. Therefore, it is necessary to limit the number of tumor sites used in the analysis as much as possible. Moreover, employing consensus-based approaches, such as obtaining agreement among multiple observers or expert consensus, may help mitigate inter-observer delineation variability. In addition, automated or semiautomated delineation techniques may address the problem of inter-observer variability. Second, treatment planning can vary by facility, planner (i.e., oncologists and medical physicists), years of experience, and dose-calculation algorithms, leading to differences in the calculated dose distributions. This variability can affect the reliability and reproducibility of the dosiomics analyses. [PERSON] et al. calculated the target dose for nasopharyngeal carcinomas with the two dose-calculation algorithms [44]. As a result, the difference in tumor control probability reached 6.8%. Therefore, it is necessary to investigate the reliability and reproducibility of the dosiomics analysis of the prediction models in future studies. Third, although the number of selected features was optimized for the construction of treatment failure prediction models, there is scope for further study on the optimization of the number of selected features by using other adaptive selection strategies. The optimization for DL model construction is also needed. Finally, the treatment failure prediction model developed in this study is based on a limited number of patients with HNSCC (\(n=224\)). Therefore, it may be crucial to ensure an appropriate number of patients in order to improve the performance of treatment failure prediction models. Thus, a longitudinal study with a larger sample size is required to assess the reliability of these results. Another limitation of this study is that, although it was designed to address HNSCC in general, the majority of the analyzed cases were oropharyngeal cancers. Therefore, the findings may primarily reflect the characteristics of oropharyngeal cancer. This should be considered when interpreting the results, and further studies involving more diverse tumor sites should be conducted in the future.