A planning study to optimise a simultaneously integrated boost treatment of larynx cancer with seven intensity-modulated radiation therapy (IMRT) beams

Author:

Erraoudi M.,Youssoufi M. A.,Bentayeb F.,Malisan M. R.

Abstract

AbstractBackgroundIntensity-modulated radiation therapy (IMRT) is one of the most reported techniques for head and neck cancer treatment, as it allows a good coverage of the planning target volume (PTV) while sparing the surrounding organs at risk (OAR) better than conventional conformal radiotherapy. The objective of this work is to optimise an IMRT technique for the simultaneously integrated boost (SIB) treatment of larynx cancer delivering a total dose of 69·96 Gy to the boost volume and 54·45 Gy to the elective volume in 33 fractions.MethodsThree IMRT techniques, each using seven equally spaced beams, were planned for a sample of 10 patients. The first two techniques (IMRT-0 and IMRT-26) differ only for the starting angle of the seven beams, whereas the third (IMRT-CT) combines both these techniques by delivering IMRT-0 in the first half of treatment, and IMRT-26 in the second half, thus taking advantage of using 14 beams in total while using seven at a time only. The planning results were compared according to the dose coverage, homogeneity and conformity of the two PTVs, as well as to the dose to OARs, that is, spinal cord, parotids, mandible, brainstem and healthy tissue (defined as the body volume minus the sum of PTVs).ResultsBasically the PTV coverage resulted acceptable and comparable with all the three techniques. Concerning OARs, statistically better results are obtained in IMRT-CT when compared with IMRT-26 and IMRT-0.ConclusionThe IMRT-CT technique, combining two different seven-beam setups, delivered in two treatment phases, improves dose distribution without increasing delivery time.

Publisher

Cambridge University Press (CUP)

Subject

Oncology,Radiology, Nuclear Medicine and imaging

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Method for Predicting Dose Distribution of Nasopharyngeal Carcinoma Cases by Multiple Deep Neural Networks;2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR);2020-08-26

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