TY - JOUR

T1 - Adaptive fractionation therapy

T2 - II. Biological effective dose

AU - Chen, Mingli

AU - Lu, Weiguo

AU - Chen, Quan

AU - Ruchala, Kenneth

AU - Olivera, Gustavo

PY - 2008/10/7

Y1 - 2008/10/7

N2 - Radiation therapy is fractionized to differentiate the cell killing between the tumor and organ at risk (OAR). Conventionally, fractionation is done by dividing the total dose into equal fraction sizes. However, as the relative positions (configurations) between OAR and the tumor vary from fractions to fractions, intuitively, we want to use a larger fraction size when OAR and the tumor are far apart and a smaller fraction size when OAR and the tumor are close to each other. Adaptive fractionation accounts for variations of configurations between OAR and the tumor. In part I of this series, the adaptation minimizes the OAR (physical) dose and maintains the total tumor (physical) dose. In this work, instead, the adaptation is based on the biological effective dose (BED). Unlike the linear programming approach in part I, we build a fraction size lookup table using mathematical induction. The lookup table essentially describes the fraction size as a function of the remaining tumor BED, the OAR/tumor dose ratio and the remaining number of fractions. The lookup table is calculated by maximizing the expected survival of OAR and preserving the tumor cell kill. Immediately before the treatment of each fraction, the OAR-tumor configuration and thus the dose ratio can be obtained from the daily setup image, and then the fraction size can be determined by the lookup table. Extensive simulations demonstrate the effectiveness of our method compared with the conventional fractionation method.

AB - Radiation therapy is fractionized to differentiate the cell killing between the tumor and organ at risk (OAR). Conventionally, fractionation is done by dividing the total dose into equal fraction sizes. However, as the relative positions (configurations) between OAR and the tumor vary from fractions to fractions, intuitively, we want to use a larger fraction size when OAR and the tumor are far apart and a smaller fraction size when OAR and the tumor are close to each other. Adaptive fractionation accounts for variations of configurations between OAR and the tumor. In part I of this series, the adaptation minimizes the OAR (physical) dose and maintains the total tumor (physical) dose. In this work, instead, the adaptation is based on the biological effective dose (BED). Unlike the linear programming approach in part I, we build a fraction size lookup table using mathematical induction. The lookup table essentially describes the fraction size as a function of the remaining tumor BED, the OAR/tumor dose ratio and the remaining number of fractions. The lookup table is calculated by maximizing the expected survival of OAR and preserving the tumor cell kill. Immediately before the treatment of each fraction, the OAR-tumor configuration and thus the dose ratio can be obtained from the daily setup image, and then the fraction size can be determined by the lookup table. Extensive simulations demonstrate the effectiveness of our method compared with the conventional fractionation method.

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U2 - 10.1088/0031-9155/53/19/016

DO - 10.1088/0031-9155/53/19/016

M3 - Article

C2 - 18780956

AN - SCOPUS:51849086699

SN - 0031-9155

VL - 53

SP - 5513

EP - 5525

JO - Physics in Medicine and Biology

JF - Physics in Medicine and Biology

IS - 19

ER -