## 1 Introduction
The latter can be split into two steps [34]:
* Personalization of the model: This step involves identifying the model parameters that correspond to the individual patient, transforming the population model into a personalized one. * Personalization of the treatment: This step involves determining the optimal chemotherapy dosing protocol tailored to the patient based on the personalized model. Several studies have explored the integration of machine learning (ML) into oncological treatment, particularly in pharmacogenomics and chemotherapy personalization or by integrating medical imagining [35]. The studies [36; 37] demonstrated the potential of ML in analyzing large-scale genomic, proteomic, and other omics datasets to identify genetic markers associated with drug responses. However, a drawback of these studies is that the methods used remain relatively simple, relying on heuristic or data-driven models without a strong mechanistic foundation. The studies [38; 39] focus on the widely used Gompertz tumor growth model, employing methods such as least squares minimization, maximum likelihood estimation, and the Extended Kalman Filter (EKF). While these methods offer robust estimation techniques, they require precise knowledge of process noise and measurement noise parameters to ensure optimal results. This presents a major limitation, as these noise parameters are often unknown in practical applications. The studies [40; 41] employ nonlinear function optimization techniques to estimate the parameters of a fourth-order tumor growth model. These methods rely on cost functions based on the least-squares criterion, where parameter values are chosen to minimize the sum of squared differences between simulated and measured tumor volumes. However, the issue is that fitting the model response to the data does not ensure that parameters are estimated accurately. The studies [42; 43] explore the combination of state and parameter estimation techniques, comparing the Extended Kalman Filter (EKF) with Moving Horizon Estimation (MHE). These approaches augment the state vector with the parameter vector and use optimization techniques to minimize the root-mean-square error between predicted and measured tumor volumes. Both methods require accurate noise modeling, which remains an open problem in practical applications. The study in [44] applies Monte Carlo Approximate Bayesian Computation (ABC) to simultaneously perform model selection and parameter estimation for five different tumor growth models. This approach provides a probabilistic framework for quantifying uncertainty in both model choice and parameter values. However, a key drawback is the high computational cost, which may limit scalability. Finally, the most relevant studies [45; 46; 47] employ artificial neural networks to estimate tumor model parameters using sparse measurement data and prior information about parameter intervals. These approaches leverage in silico-generated training data spanning large parameter intervals and incorporate randomized parameters to improve generalization. Two different neural networks were trained, to estimate the pharmacodynamic and the pharmacokinetic parameters. The most significant limitation is that the inputs of the neural networks are directly the measurements of the tumor volume, which hinders scalability and necessitates the use of a four-layered neural network structure. Besides that, these studies did not develop any personalized treatment strategies. Therefore, to reflect the current gaps in the state of the art, the strategy proposed in this paper is to obtain personalized model parameters by evaluating the patient's response to an initial dose of chemotherapy with a multilayer perceptron artificial neural network (MLP NN) with only one hidden layer. The proposed approach does not utilize an MLP NN as a replacement for the original biological dynamic model or as a digital twin [48].