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ORIGINAL ARTICLE
Year : 2022  |  Volume : 27  |  Issue : 1  |  Page : 10

Determining the optimum tumor control probability model in radiotherapy of glioblastoma multiforme using magnetic resonance imaging data pre- and post- radiation therapy


1 Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
2 Department of Radiation Oncology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
3 MD Student, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Correspondence Address:
Prof. Daryoush Shahbazi-Gahrouei
Department of Medical Physics and Medical Engineering, Isfahan University of Medical Sciences, Isfahan
Iran
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jrms.JRMS_1138_20

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Background: Glioblastoma multiforme (GBM) is the most common and malignant brain tumor. The current standard of care is surgery followed by radiation therapy (RT). Radiotherapy treatment plan evaluation relies on radiobiological models for accurate estimation of tumor control probability (TCP). This study aimed to assess the impact of obtained magnetic resonance imaging (MRI) data before and 12 weeks after RT to achieve the optimum TCP model to improve dose prescriptions in radiation therapy of GBM. Materials and Methods:: In this quasi-experimental study, MR images and its relevant data from 30 patients consisting of 9 females and 21 males (mean age of 46.3 ± 15.8 years) diagnosed with GBM, whose referred for radiotherapy were selected. The data of age, gender, tumor size, volume, and signal intensity using analysis of MRI data pre- and postradiotherapy were used for calculating TCP. TCP was calculated from three common radiobiological models including Poisson, linear quadratic, and equivalent uniform dose. The impact of some radiobiological parameters on final TCP in all patients planned with three-dimensional conformal radiation therapy was obtained. Results: A statistically significant difference was found among TCP in Poisson model compared to the other two models (P < 0.001). Changes in tumor volume and size after treatment were statistically significant (P < 0.05). Different combinations of radiobiological parameters (α/β and SF2 in all models) observed were meaningful (P < 0.05). Conclusion: The results showed that among TCP radiobiological models, the optimum is the Poisson. The results also identified the importance of TCP radiobiological models in order to improve radiotherapy dose prescriptions.


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