## 4 Discussion  
Xerostomia is a common complication in patients receiving radiation therapy as a consequence of damage to the salivary glands. Morphological and structural changes in the irradiated glands can be non-invasively evaluated with MRI [32; 40; 41]. In addition, the indirect assessment of the microarchitecture of the salivary glands with texture analysis has been recently hypothesized to be a useful tool in the identification of the severity of RT-induced xerostomia [21; 34; 37; 42]. Overall, predictive models employing texture analysis alongside imaging techniques have shown very promising results in the assessment of head and neck disease [17; 18; 34; 43; 44]. Therefore, interest towards artificial intelligence and its applications to imaging is steadily growing. In the context of the ever-growing academic importance of texture analysis [17; 28; 29; 34], the present study was an effort to investigate its role in the assessment of salivary gland alterations and, specifically, RT-induced xerostomia. While it is known that the development of RT-induced xerostomia correlates with the dose distribution to the salivary glands [45; 46], the correlation between imaging techniques and specific alterations in the gland microarchitectural structure is still unclear [34; 47]. The current study represented the first attempt to assess RT-induced xerostomia by taking advantage of different MRI techniques, including functional imaging--DCE-PWI and DWI--and texture analysis performed on CE-T1 sequences and ADC maps in both pre-and post-RT imaging. No parameter for neither morphological (T2 sequences) nor functional (DWI and DCE-PWI) MRI yielded statistically significant results. Such findings were different from previous studies conducted by [PERSON] et al. [33] on DCE-PWI and [PERSON] et al. [21; 30; 31] on DWI since they suggested a possible role for parameters obtained from these imaging techniques as biomarkers in the evaluation of RT-induced xerostomia. Possible explanations for the disagreement between our findings and those studies [21; 30; 31; 33] could be that, in  
Figure 4: Receiver operating characteristics (ROC) curve for IMC 1 in the parotid gland (pre-RT population). Diagnostic accuracy was considered acceptable when the AUC values were higher than 0.7.  
those last studies, a larger sample was enrolled and acid stimulation was used to assess the ADC values while also taking into account time-related parameters, such as time-to-peak ADC. In addition to that, the parameters obtained from DWI and DCE-PWI were correlated with the different amount of radiation dose emitted, whereas in our case, all patients enrolled received the same treatment. Texture analysis techniques on CT images were used in previous studies to investigate RT-induced xerostomia [34; 35; 36; 42], but they have not yet been employed on CE-MRI and ADC maps. In the present study, the CE-T1 MRI texture features did not show statistically significant correlation with the development of RT-induced xerostomia. On the contrary, the texture analysis carried out on the ADC maps yielded significant results. More specifically, three texture features for the submandibular glands (GLNN--First Order, IMC 2--First Order, and GLNN--Gray Level Run Length Matrix) and two texture features for the parotid glands (IMC 1 and IMC 2, both belonging to the Gray Level Run Length Matrix) showed significant differences between pre- and post-RT imaging. Among the aforementioned texture features, only IMC 1 showed acceptable levels of diagnostic accuracy (AUC = 0.727) for the development of moderate xerostomia when applied on the pre-RT population where, in fact, was found a significant decrease in its values in patients with moderate xerostomia as opposed to patients with mild xerostomia. While no feature except for IMC 1 yielded an acceptable diagnostic accuracy on its own, a more complex model taking all of them into account might better discriminate different degrees of severity of RT-induced xerostomia.