## 4 Conclusions and Perspectives  
In this study, we showed that the LEM-II model, the Monte Carlo-based LEM model and a semi-empirical method based on microdosimetric spectra, can adequately reproduce experimentally derived survival curves from in vitro cell irradiations performed at the mid-SOBP of the CATANA 62 MeV clinical proton beam. For both cell lines, an RBE greater than 1.1 was measured and reproduced applying all of the investigated approaches. This points out that the biological damage of both healthy and tumorigenic cells is underestimated if a fixed RBE of 1.1 is assumed. Microdosimetric spectra were measured at the entrance and mid-SOBP position with both a mini-TEPC, simulating a 1 \(\upmu\)m water site size, and the MicroPlus microdosimeter (17 \(\upmu\)m water equivalent thickness). Despite the markedly different microdosimetric spectra obtained with these two detectors, when the Loncol's weighting function is applied to the dose distributions both lead to an assessment of the RBE10 that is in good agreement with radiobiological data for ARPE-19 cells obtained in the same beamline. The higher discrepancy obtained with the 92.1 cell line and the solid-state detector could be due to a lower detector sensitivity related to the higher SV volume. The remarkable obtained result showed the potentiality of using a microdosimetric spectrum to predict not only the RBE but also the full survival curve, at least at the mid-position of the 62 MeV proton SOBP. Currently, different commercial treatment planning softwares for proton therapy incorporate tools for calculating LET distributions and RBE prediction. However, while the dose prescriptions calculated by the treatment planning are routinely verified with certified ionization chambers, there is no commercial equipment currently available to perform routine verification of LET or RBE distributions calculated by the TPS. The Local Effect Model is the computational radiobiological approach most widely used in hadrontherapy. The LEM model was fully integrated into the analytical treatment planning system TRiP98 and successfully tested for clinical application. In this work, the good agreement between the Monte-Carlo-based LEM model and LEM-II model with the experimental data clearly evidence how a TPS based on an RBE and LET prediction could improve the estimation of the radiobiological response of the treated neoplasia as well as the surrounding healthy tissues. Moreover, the pretty good accordance between the semi-empirical methods based on microdosimetric spectra with the experimental data shows that the LET estimation procedure should be successfully inserted in the clinical routine to optimize the RBE distribution. The current study identifies a clinical and critical aspect of the uveal melanoma protontherapy treatment. The correct evaluation of the damage induced by ionizing radiation during RT treatment, also thanks to Monte Carlo-based simulations, represents a valuable and potent tool to foresees the impact of ionizing radiation on cancer tissues. Moreover, studying both healthy and tumorigenic cells allows obtaining fundamental information about the biological effects induced by an erroneous dose distribution during a proton therapy ocular treatment plan. The biological response is therefore of paramount importance to provide an estimate of the possible adverse effects deriving by beam forward straggling. A correct prediction of the damage is a key point for a more personalized TPS. [PERSON], [PERSON], V.B., L.M. ([PERSON]) carried out cell survival measurements at INFN-LNS. [PERSON], I.P., [PERSON], O.K. performed the cell survival measurements with gamma rays. [PERSON]. ([PERSON]), [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] provided overall conceptual contribution. [PERSON] and [PERSON] supervised beam dosimetry at INFN-LNS. [PERSON], [PERSON], [PERSON] performed microdosimetric measurements. [PERSON] estimated the biological damage starting from microdosimetric spectra. [PERSON] performed the Monte Carlo simulations.