Feasibility and clinical usefulness of modelling glioblastoma migration in adjuvant radiotherapy.

ABSTRACT

Glioblastoma (GBM) is one of the most common primary brain tumours in adults, with a dismal prognosis despite aggressive multimodality treatment by a combination of surgery and adjuvant radiochemotherapy. A detailed knowledge of the spreading of glioma cells in the brain might allow for more targeted escalated radiotherapy, aiming to reduce locoregional relapse. Recent years have seen the development of a large variety of mathematical modelling approaches to predict glioma migration. The aim of this study is hence to evaluate the clinical applicability of a detailed micro- and meso-scale mathematical model in radiotherapy. First and foremost, a clinical workflow is established, in which the tumour is automatically segmented as input data and then followed in time mathematically based on the diffusion tensor imaging data. The influence of several free model parameters is individually evaluated, then the full model is retrospectively validated for a collective of 3 GBM patients treated at our institution by varying the most important model parameters to achieve optimum agreement with the tumour development during follow-up. Agreement of the model predictions with the real tumour growth as defined by manual contouring based on the follow-up MRI images is analyzed using the dice coefficient. The tumour evolution over 103-212 days follow-up could be predicted by the model with a dice coefficient better than 60% for all three patients. In all cases, the final tumour volume was overestimated by the model by a factor between 1.05 and 1.47. To evaluate the quality of the agreement between the model predictions and the ground truth, we must keep in mind that our gold standard relies on a single observer's (CB) manually-delineated tumour contours. We therefore decided to add a short validation of the stability and reliability of these contours by an inter-observer analysis including three other experienced radiation oncologists from our department. In total, a dice coefficient between 63% and 89% is achieved between the four different observers. Compared with this value, the model predictions (62-66%) perform reasonably well, given the fact that these tumour volumes were created based on the pre-operative segmentation and DTI.