Artificial neural network based gynaecological image-guided adaptive brachytherapy treatment planning correction of intra-fractional organs at risk dose variation

• Main Authors: Ramin Jaberi, Zahra Siavashpour, Mahmoud Reza Aghamiri, Christian Kirisits, Reza Ghaderi
• Format: Article
• Language: English
• Published: Termedia Publishing House 2017-12-01
• Series: Journal of Contemporary Brachytherapy
• Subjects: ANN-based model; cervical cancer; IGABT; intra-fractional dose variations
• Online Access: https://www.termedia.pl/Artificial-neural-network-based-gynaecological-image-guided-adaptive-brachytherapy-treatment-planning-correction-of-intra-fractional-organs-at-risk-dose-variation,54,31433,1,1.html

Purpose : Intra-fractional organs at risk (OARs) deformations can lead to dose variation during image-guided adaptive brachytherapy (IGABT). The aim of this study was to modify the final accepted brachytherapy treatment plan to dosimetrically compensate for these intra-fractional organs-applicators position variations and, at the same time, fulfilling the dosimetric criteria. Material and methods : Thirty patients with locally advanced cervical cancer, after external beam radiotherapy (EBRT) of 45-50 Gy over five to six weeks with concomitant weekly chemotherapy, and qualified for intracavitary high-dose-rate (HDR) brachytherapy with tandem-ovoid applicators were selected for this study. Second computed tomography scan was done for each patient after finishing brachytherapy treatment with applicators in situ. Artificial neural networks (ANNs) based models were used to predict intra-fractional OARs dose-volume histogram parameters variations and propose a new final plan. Results : A model was developed to estimate the intra-fractional organs dose variations during gynaecological intracavitary brachytherapy. Also, ANNs were used to modify the final brachytherapy treatment plan to compensate dosimetrically for changes in ‘organs-applicators’, while maintaining target dose at the original level. Conclusions : There are semi-automatic and fast responding models that can be used in the routine clinical workflow to reduce individually IGABT uncertainties. These models can be more validated by more patients’ plans to be able to serve as a clinical tool.