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

Abstract

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.

Keywords: ANN-based model; IGABT; cervical cancer; intra-fractional dose variations.

Fig. 1

Data workflow, which summarized the study approach

Fig. 2

Flowchart describing different steps of study

Fig. 3

Flowchart demonstrating steps tested to determine optimized applicators parts for treatment plan correction

Fig. 4

An example of a regression plots for an multilayer perceptron network designed to predict intra-fractional organs at risk dose variations. The four plots represent the training, testing, validation, and all data. Dashed line of each plot represents result – outputs = targets

Fig. 5

An example of performance (mean square error – MSE) plot for an radial basis functions network designed to predict intra-fractional organs at risk dose variations