Comparative Analysis of TALYS, EMPIRE, and Artificial Neural Networks for Proton-Induced 124I Production

The growing clinical demand for positron-emitting radionuclides has increased the need for accurate nuclear data and optimized production routes for medical applications. Iodine-124 (124I), owing to its relatively long half-life and suitability for immunoPET and theranostic applications, represents a particularly important radionuclide. In this study, proton-induced production of 124I via the 124Te(p,n)124I and 126Te(p,3n)124I reactions was investigated using conventional nuclear reaction codes (TALYS and EMPIRE) and artificial neural network (ANN). Reaction cross sections were calculated and benchmarked against available experimental data, and production yield and activity analyses were performed under medically relevant irradiation conditions. The results indicate that while TALYS and EMPIRE reproduce the general trends of the excitation functions, their predictive accuracy is limited in certain energy regions due to uncertainties in nuclear input parameters. In contrast, the ANN model achieved significantly improved agreement with experimental data, yielding lower mean squared error and root mean squared error values for both reaction channels. The calculated activity and yield values confirm the feasibility of both production routes, with higher yields obtained via the (p,3n) reaction at increased proton energies. These findings demonstrate that ANN-based modeling provides a reliable complementary tool for nuclear reaction analysis and optimization of 124I production in medical cyclotron facilities.