Spatial mapping of the biologic effectiveness of scanned particle beams: towards biologically optimized particle therapy

TLDR

Particle therapy’s physical dose distribution is well characterized, yet its biologic effectiveness, which varies with LET along the beam path, has not been fully exploited due to time‑consuming, uncertain mapping methods and limited data. The study aims to enable biologically optimized proton therapy by selectively targeting high‑effectiveness regions and generating data for variable RBE‑based planning. The authors employed Monte Carlo simulations coupled with high‑content clonogenic survival assays to map scanned proton beam effectiveness accurately and efficiently. They found a complex, non‑unique dose–LET–cell‑kill relationship, with biologic effects markedly higher than prior reports and a non‑linear survival response even at the highest LET values.

Abstract

Abstract The physical properties of particles used in radiation therapy, such as protons, have been well characterized and their dose distributions are superior to photon-based treatments. However, proton therapy may also have inherent biologic advantages that have not been capitalized on. Unlike photon beams, the linear energy transfer (LET) and hence biologic effectiveness of particle beams varies along the beam path. Selective placement of areas of high effectiveness could enhance tumor cell kill and simultaneously spare normal tissues. However, previous methods for mapping spatial variations in biologic effectiveness are time-consuming and often yield inconsistent results with large uncertainties. Thus the data needed to accurately model relative biological effectiveness to guide novel treatment planning approaches are limited. We used Monte Carlo modeling and high-content automated clonogenic survival assays to spatially map the biologic effectiveness of scanned proton beams with high accuracy and throughput while minimizing biological uncertainties. We found that the relationship between cell kill, dose and LET, is complex and non-unique. Measured biologic effects were substantially greater than in most previous reports, and non-linear surviving fraction response was observed even for the highest LET values. Extension of this approach could generate data needed to optimize proton therapy plans incorporating variable RBE.