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PURPOSE: To better estimate relative biological effectiveness (RBE) in therapeutic proton beams by using a modeled approach, in order to improve their clinical safety and effectiveness. INTRODUCTION: Concerns exist about the 1.1 RBE used in proton therapy, since it may lead to unintentional over- and under-dosage in patients and so lead to unexpected clinical outcomes. Late reacting normal tissues (with low α/β values), might be overdosed if RBE >1.1; very radiosensitive tumors (with high α/β), might be under-dosed if RBE <1.1. Some physicists recommend ignoring RBE in favor of a LET × dose product to predict effects. MATERIAL AND METHODS: Extensive linear-quadratic based modeling is scaled between a standard hospital megavoltage photon reference radiation (low LET of 0.22 keV μm-1) α and β values and their values at higher LETs, representative of the middle and end of the SOBPs. A previously published energy-efficiency model provide RBE estimates for different α/β (2-27 Gy). The concept of using a LET × dose product is assessed by comparing it with surviving fraction and the equivalent dose in 2 Gy fractions (EQD-2). RESULTS: Low α/β value biosystems have the widest RBE ranges with dose per fraction changes and increasing LET, often above 1.1 even within the SOBP LET range, with lower values at higher dose per fraction. Highly radiosensitive tumors (α/β 10-27 Gy) have the lowest RBEs, often below 1.1, and are not fraction-sensitive. RBE's generally increase with LET, so curtailment of LET in normal tissues is important. The LET × dose product is insufficiently discriminating when compared with surviving fraction and biological effective dose (BED) or EQD-2. CONCLUSIONS: An overall research framework is suggested. Proton therapy advantages will only be fully realized if reasonably correct RBE values are used.

Original publication




Journal article


Acta Oncol

Publication Date





1374 - 1378


Dose-Response Relationship, Radiation, Humans, Linear Energy Transfer, Linear Models, Neoplasms, Proton Therapy, Radiobiology, Radiotherapy Planning, Computer-Assisted, Relative Biological Effectiveness