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

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.

Single fraction multimodal image guided focal salvage high-dose-rate brachytherapy for recurrent prostate cancer.

PURPOSE: We present a novel method for treatment of locally recurrent prostate cancer (PCa) following radiation therapy: focal, multimodal image guided high-dose-rate (HDR) brachytherapy. MATERIAL AND METHODS: We treated two patients with recurrent PCa after primary (#1) or adjuvant (#2) external beam radiation therapy. Multiparametric magnetic resonance imaging (mpMRI), choline, positron emission tomography combined with computed tomography (PET/CT), or prostate-specific membrane antigen (PSMA)-PET combined with CT identified a single intraprostatic lesion. Positron emission tomography or magnetic resonance imaging - transrectal ultrasound (MRI-TRUS) fusion guided transperineal biopsy confirmed PCa within each target lesion. We defined a PET and mpMRI based gross tumor volume (GTV). A 5 mm isotropic margin was applied additionally to each lesion to generate a planning target volume (PTV), which accounts for technical fusion inaccuracies. A D90 of 18 Gy was intended in one fraction to each PTV using ultrasound guided HDR brachytherapy. RESULTS: Six month follow-up showed adequate prostate specific antygen (PSA) decline in both patients (ΔPSA 83% in patient 1 and ΔPSA 59.3% in patient 2). Follow-up 3-tesla MRI revealed regressive disease in both patients and PSMA-PET/CT showed no evidence of active disease in patient #1. No acute or late toxicities occurred. CONCLUSIONS: Single fraction, focal, multimodal image guided salvage HDR brachytherapy for recurrent prostate cancer is a feasible therapy for selected patients with single lesions. This approach has to be evaluated in larger clinical trials.