Optimization of the fractionated irradiation scheme considering physical doses to tumor and organ at risk based on dose–volume histograms. Issue 11 (8th October 2015)

• Record Type: Journal Article
• Authors: Sugano, Yasutaka; Mizuta, Masahiro; Takao, Seishin; Shirato, Hiroki; Sutherland, Kenneth L.; Date, Hiroyuki
• Is Part Of: Medical physics. Volume 42:Issue 11(2015)
• Page Start: 6203
• Page End: 6210
• Publication Date: 2015-10-08
• Subjects: cellular biophysics -- dosimetry -- radiation therapy -- tumours; Dose‐volume analysis -- Subcellular structure and processes; Radiation therapy -- Scintigraphy; fractionated radiotherapy -- linear‐quadratic (LQ) model -- universal survival curve (USC) -- repopulation of tumor cells -- dose–volume histogram (DVH); Dosimetry -- Cancer -- Graphical methods -- Biomedical modeling -- Intensity modulated radiation therapy -- Optimization -- Physical radiation effects -- Anatomy -- Probability density functions; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4931969
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : 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.