Premium
Optimization of the fractionated irradiation scheme considering physical doses to tumor and organ at risk based on dose–volume histograms
Author(s) -
Sugano Yasutaka,
Mizuta Masahiro,
Takao Seishin,
Shirato Hiroki,
Sutherland Kenneth L.,
Date Hiroyuki
Publication year - 2015
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4931969
Subject(s) - radiation therapy , fractionation , histogram , dose fractionation , nuclear medicine , dosimetry , dose volume histogram , radiation treatment planning , regimen , fraction (chemistry) , mathematics , computer science , medicine , chemistry , radiology , surgery , organic chemistry , artificial intelligence , image (mathematics)
Purpose: Radiotherapy of solid tumors has been performed with various fractionation regimens such as multi‐ and hypofractionations. However, the ability to optimize the fractionation regimen considering the physical dose distribution remains insufficient. This study aims to optimize the fractionation regimen, in which the authors propose a graphical method for selecting the optimal number of fractions ( n ) and dose per fraction ( d ) based on dose–volume histograms for tumor and normal tissues of organs around the tumor. Methods: Modified linear‐quadratic models were employed to estimate the radiation effects on the tumor and an organ at risk (OAR), where the repopulation of the tumor cells and the linearity of the dose‐response curve in the high dose range of the surviving fraction were considered. The minimization problem for the damage effect on the OAR was solved under the constraint that the radiation effect on the tumor is fixed by a graphical method. Here, the damage effect on the OAR was estimated based on the dose–volume histogram. Results: It was found that the optimization of fractionation scheme incorporating the dose–volume histogram is possible by employing appropriate cell surviving models. The graphical method considering the repopulation of tumor cells and a rectilinear response in the high dose range enables them to derive the optimal number of fractions and dose per fraction. For example, in the treatment of prostate cancer, the optimal fractionation was suggested to lie in the range of 8–32 fractions with a daily dose of 2.2–6.3 Gy. Conclusions: It is possible to optimize the number of fractions and dose per fraction based on the physical dose distribution (i.e., dose–volume histogram) by the graphical method considering the effects on tumor and OARs around the tumor. This method may stipulate a new guideline to optimize the fractionation regimen for physics‐guided fractionation.