## 4 Discussion  
An accurate understanding of dosimetric parameters and their correlation with toxicity is crucial for optimizing RT plans. Improving toxicity prediction can lead to more effective treatment tailoring, minimizing adverse effects and improving patients' QoL. Furthermore, the identification of key dosimetric parameters can guide future research and development of advanced RT techniques [135]. We are aware of the main dosimetric parameters, such as the DVH, Dmax and Dmean, conformity, and Gamma Index [136; 137]. The DVH is a fundamental tool that shows the relationship between the received dose and the volume of irradiated tissue. Two of the main parameters extracted from the DVH are Vx and Dx (dose received in x% of tissue volume). The Dmean and Dmax are used to assess the radiation load on tissue [138]. Figure 2 illustrates a DVH sample in an RT of PCa to highlight the relationship between dosimetric parameters like the Dmean, Dmax, and Vdose with toxicity outcomes. * Dmean: A higher Dmean in organs at risk may correlate with increased toxicity; thus, it is crucial to minimize this value. * Dmax: Identifying the Dmax helps in understanding potential hotspots that could lead to adverse effects. * Vdose: Higher Vdose values (e.g., V50 > 50% of the rectum receiving more than 50 Gy) often correlate with increased risk of toxicity. Haemorrhagic cyystitis can occur as a result of high doses given to the bladder in the context of the prostate. In contrast, a high maximum dose to the rectum may increase proctitis risk [136]. The conformity index (CI) measures how well the irradiated dose matches the tumour target shape while minimizing exposure to surrounding healthy tissue. A high CI indicates effective compliance, reducing the risk of damage to non-target tissues [139]. The Gamma Index is used to assess the quality of the treatment plan by comparing the planned dose with the delivered dose. Significant discrepancies may indicate an increased risk of toxicity, as tissue may receive unplanned doses [140]. This review provides a comprehensive overview of the dosimetric parameters used to predict toxicity in PCa RT, highlighting key correlations and the most effective methodologies. Almost all studies included in this review use logistic regression analysis as a well-established method, which helps determine the probability of toxicity events. Logistic regression models are particularly effective for straightforward yes-or-no outcomes, making them ideal for assessing whether specific thresholds of radiation exposure (e.g., rectum V50 or bladder V70) increase the odds of developing a certain grade of toxicity. For instance,  
Figure 2: RT of PCa using a DVH sample. the study by [PERSON] et al. (2017) showed that increased rectal volume exposed to 60 Gy was a strong predictor of Grade 2 acute toxicity ([PERSON] et al., 2014). Meanwhile, [PERSON] et al. (2016) demonstrated that bone marrow exposure levels could accurately predict hematologic toxicity with high AUC values, reinforcing the model's accuracy ([PERSON] et al., 2014). In cases where the timing of toxicity onset is important, such as when assessing the risk of late GU toxicity over several months or years, studies used the Cox proportional hazard model. This approach allowed researchers to analyze not just the likelihood of toxicity but also when it was likely to develop after treatment. For example, [PERSON] et al. (2014) used Cox regression to explore bladder and rectal dose levels in hypoffractionated treatments, finding that certain dose-volume constraints could help reduce the risk of delayed toxicity ([PERSON] et al., 2014). Similarly, [PERSON] et al. (2008) employed Cox regression to investigate rectal dose thresholds, confirming that limiting rectum V70 to lower levels could mitigate the long-term risk of severe rectal bleeding ([PERSON] et al., 2014).