## 4 Discussion
Nevertheless, the Kaplan-Meier curve for the entire study cohort shows that no relapse occurs after 20 months from the end of treatment (radiotherapy or radiotherapy plus surgery), which suggests that within the group of patients who respond satisfactorily, the vast majority achieve a sustained response, with low rates of relapse. Most patients in our database are smokers (92.31%), which hinders finding differences between smokers and non-smokers in terms of survival, as well as differences between HPV-positive and HPV-negative smokers. Nevertheless, various studies have evidenced that HPV-positive patients with a smoking history have worse treatment outcomes and an increased risk of death. The specific impact of tobacco remains unknown, but it could have an effect by inducing additional genetic alterations or, indeed, provoking other disorders such as cardiac or respiratory diseases [38; 39]. As well as HPV, other clinical features are evaluated for survival analysis. Tumor stage and localization are important features to characterize the tumor obtaining high HR for every analysis, but no statistically significant relationship to survival rates was observed. On the other hand, greater values of some quantitative features, such as SUV\({}_{\text{max}}\), TLG, and MTV, are in all cases related to a poorer prognosis [2; 3]. Nonetheless, volumetric features (MTV and TLG) turn out to be better predictors than SUV measures. The prognostic value of TLG can be highlighted, with higher values being related to lower survival rates in Kaplan-Meier (\(p\) = 0.029) and Cox regression analyses (\(p\) = 0.001) for OS. TLG demonstrated a prognostic value in RFS analysis as well, being related to higher relapse rates in Kaplan-Meier (\(p\) = 0.010) and Cox regression analyses (\(p\) = 0.026). It is worth mentioning that Surface resulted in a great prognostic feature in the OS Cox regression analysis (\(p\) = 0.001), being, along with TLG, the unique feature that remained statistically significant after multiple-testing correction. This shape feature also was a statistically significant predictor for RFS Kaplan-Meier (\(p\) = 0.004) and Cox regression (\(p\) = 0.028) analyses, with higher values related to poorer prognosis. Although several studies have demonstrated the utility of SUV metrics as well as metabolic features such as TLG and MTV to predict clinical outcomes in head and neck cancer [3; 7; 10], they do not reflect information of intratumoral heterogeneity. Spatial distribution of metabolic activity in the tumor can be evaluated through textural image features describing spatial relationships between pixel intensities [8; 14; 15]. Recent studies have shown the potential of texture analysis to reveal intratumoral heterogeneity, which can lead to poor prognosis in head and neck cancer [8; 17; 18]. Our analyses suggest heterogeneity patterns with respect to gray-level non-uniformity matrixes (GLRLM and GLZLM). Cox regression analyses for 2-year OS showed that gray-level texture indices (GLRLM_RLNU, GLZLM_GLNU and GLZLM_ZLNU) result in poorer prognosis for higher values of these indices. This fact is reflected by means of the HR for these image features (GLRLM_RLNU, HR = 1.002 (1.000-1.003), \(p\) = 0.043; GLZLM_GLNU, HR = 1.069 (1.005-1.137), \(p\) = 0.033; GLZLM_ZLNU, HR = 1.017 (1.005-1.030), \(p\) = 0.007). Additionally, RFS Cox regression analysis revealed higher relapse rates for higher values of GLZLM_ZLNU (HR = 1.011 (1.001-1.022), \(p\) = 0.031). As mentioned, recent studies have supported the assumption of higher intratumoral heterogeneity being related to poorer outcomes [8; 17; 18]. Specifically, these three textural features have also been found to be prognostic factors in head and neck cancer by previous studies [13; 19; 20]. This premise is reinforced in univariate RFS analysis.