## 4 Discussion
This metric was used extensively across studies to confirm DHV threshold predictive power; for instance, [PERSON] et al. (2004) achieved AUC values as high as 0.89 when predicting rectal bleeding, suggesting that specific dose constraints could reliably prevent such toxicity ([PERSON] et al., 2014). While the AUC focuses on the model's discriminative power, Odds Ratios (ORs) derived from logistic regression provide an interpretable measure of the increased risk associated with specific DVH values. For instance, [PERSON] et al. (2002) reported that a rectal V50 above 63% increased the odds of late rectal bleeding significantly, making this threshold a practical guide for clinicians. ORs have the advantage of directly quantifying risk in an easily understandable format, allowing dose adjustments in clinical settings ([PERSON] et al., 2014). Calibration plots and the Hosmer-Lemeshow test offer a way to ensure that a model's probability predictions align with actual outcomes. Calibration plots visually compare predicted probabilities with observed results, while the Hosmer-Lemeshow test statistically confirms this fit. Studies like [PERSON] et al. (2021) found calibration plots useful in validating their bladder toxicity predictions, helping fine-tune the model to match real-world occurrences ([PERSON] et al., 2021). [PERSON] et al. (2021) also leveraged calibration plots to confirm that their bowel toxicity predictions aligned well with observed data, thus validating the chosen DVH constraints ([PERSON] et al., 2021). In some cases, studies employed bootstrap validation to enhance model stability confidence. By repeatedly sampling from the dataset, bootstrap validation tests the model's robustness and reduces the risk of overfitting, which is especially valuable in studies with smaller patient cohorts. For instance, [PERSON] et al. (2021) used bootstrapping to confirm the stability of their bladder toxicity model, ensuring that the AUC and other metrics remained consistent across samples ([PERSON] et al., 2021). Traditional models (Logistic Regression, Cox, GEE) offer interpretable predictions using established statistical methods, excelling with binary outcomes or time-to-event data but often faltering with complex, non-linear relationships. ML models, in contrast, handle high-dimensional, non-linear data and offer greater predictive power, but require complex data processing and stringent validation to avoid overfitting. Validation metrics like AUC, ORs, calibration plots, and bootstrap methods assess model accuracy and reliability. Overall, while traditional models are valued for interpretability, ML techniques are advancing predictive accuracy and supporting personalized approaches in RT. Studies using AI will certainly be published within the next few years in order to predict dosimetric parameters. Indeed, AI and ML are emerging powerful tools for predicting toxicity. Such models are able to analyze large amounts of dosimetric, clinical, and multiomics data to identify complex patterns that predict toxicity ([PERSON] et al., 2014; [PERSON] et al., 2014; [PERSON] et al., 2014). Some approaches include regression models, used to identify linear relationships between dosimetric parameters and toxicity outcomes; artificial neural networks, used to analyze non-linear and complex data, improving predictivity over traditional models; and random forests and Gradient Boosting, i.e., Ensemble Learning Techniques that combine multiple predictive models to improve prediction accuracy ([PERSON] et al., 2014; [PERSON] et al., 2014). In our research, we found several studies reporting the use of AI tools for the prediction of toxicity in RT treatment in patients with PCa, but they were not included as they did not provide specific predictive dosimetric parameters. For example, [PERSON] et al. (1997) investigated toxicity outcomes in PCa RT, focusing on erectile dysfunction (ED), GI, rectal, and GU toxicities.