## 4 Discussion
Another statistical tool applied in these studies is the Generalized Estimating Equation (GEE), which is particularly helpful for tracking repeated measures over time, such as patient-reported outcomes (PROs) on urinary or bowel function. The longitudinal perspective of GEEs allows for insights into how symptoms might change after treatment, providing a dynamic view of toxicity progression. For example, [PERSON] et al. (2016) used GEEs to analyze how continence scores evolved over time, showing that higher bladder V70 volumes significantly correlated with deteriorating urinary function ([PERSON] et al., 2014). Only one study went beyond traditional statistical methods, applying ML techniques for enhanced predictive accuracy. Techniques like stacking ensembles, which integrate multiple models such as support vector machines and random forests, allow for a more complex analysis of toxicity risk by incorporating a broader array of clinical, dosimetric, and even radiomic data. [PERSON] et al. (2019), indeed, applied a stacking ensemble to predict urinary toxicity with high precision, underscoring the potential of ML to refine toxicity prediction in cases where conventional models may fall short ([PERSON] et al., 2014). The process began with three different ML algorithms, or \"base models\", with each analyzing radiomic data derived from pre-treatment CT scans. A random forest model was used to capture complex, non-linear patterns, taking advantage of its ability to manage intricate datasets and detect subtle variations in tissue characteristics. Alongside it, a support vector machine (SVM) contributed its strength to handling high-dimensional data--a necessary skill given the detailed, feature-rich radiomic inputs. Lastly, a neural network was included to detect finer, subtle patterns within the tissue structure, which was especially useful for understanding the small-scale changes caused by radiation. After each base model analyzed the data and made its own predictions, a final layer, known as the meta-learner, brought these insights together. The researchers chose elastic net penalized logistic regression for this role, as it is particularly effective at selecting the most relevant features and balancing them for optimal performance. This choice allowed the meta-learner to focus on the most meaningful radiomic, clinical, and dosimetric features, filtering out any less important details to avoid overfitting and improve the model's generalizability. To ensure the model's reliability, the team implemented cross-validation, dividing the data into multiple parts to test and refine the model across different data splits. This method strengthened the model's consistency, ensuring it would perform reliably even when applied to new patients. The results were encouraging: by combining the radiomic, clinical, and dosimetric data, the stacking model reached an impressive accuracy level, with an AUC of 0.77 for urinary toxicity prediction. This performance was notably higher than models using only clinical or dosimetric information, underscoring the additional predictive power that radiomic features bring. The study concluded that integrating machine learning into toxicity prediction models offers a significant step toward personalized RT, allowing clinicians to identify patients at higher risk of treatment side effects and potentially adjust treatment plans accordingly. In essence, [PERSON] et al. 's study highlights how a sophisticated machine learning approach can offer deeper insights into patient-specific responses to RT, paving the way for more individualized, safe, and effective cancer treatments. To assess the reliability of these models, various performance metrics were employed, each with its own strengths and limitations. One of the most commonly used metrics is the AUC from ROC analysis, which measures a model's ability to distinguish between patients who will and will not experience toxicity. An AUC close to 1 signals a high level of accuracy in the model's predictions.