The use of Monte Carlo simulation techniques in brachytherapy: A comprehensive literature review.

Abstract

Monte Carlo techniques have become crucial in brachytherapy since their introduction in the early 1980s, offering significant improvements in source parameter characterizations, and dose calculations. It provides precise dose distributions by modeling complex radiation interactions and can be determine doses in nonhomogeneous detailed cases. They are not affected by experimental artifacts, unlike traditional detectors, and can distinguish between primary and scatter dose components. However, MC techniques have limitations. They are susceptible to systematic errors and require thorough validation against experimental data, despite generally showing smaller standard deviations. Additionally, MC simulations can be computationally intensive and depend heavily on accurate input data and models. Recent research, including 1433 publications identified up to October 2024, highlights the ongoing development and application of MC techniques in brachytherapy. Of these, 426 articles met the inclusion criteria for relevance. This comprehensive review aims to help brachytherapy researchers to identify the appropriate MC code depending on the application in BT research. Of the forty-five MC codes used in BT, MCNP is noted as the most widely used MC code due to its robust modeling capabilities in various materials and geometries. AAPM TG-186 and TG-372 reports have recommended the use of model base dose calculation algorithms, since it can offer more accurate dose calculations over TG-43 formalism, particularly in heterogeneous tissues. Despite these recommendations, further research is needed to refine dosimetry for various isotopes, geometry and media. In essence, MC techniques have greatly enhanced the accuracy, precision and flexibility of brachytherapy techniques, though challenges such as systematic errors, heterogeneities corrections, and high computational demands remain. Continued research and development of MC codes and algorithms are essential for advancing the field and improving clinical outcomes.