Multi-centre evaluation of variation in cumulative dose assessment in reirradiation scenarios.

BACKGROUND AND PURPOSE: Safe reirradiation relies on assessment of cumulative doses to organs at risk (OARs) across multiple treatments. Different clinical pathways can result in inconsistent estimates. Here, we quantified the consistency of cumulative dose to OARs across multi-centre clinical pathways. MATERIAL AND METHODS: We provided DICOM planning CT, structures and doses for two reirradiation cases: head & neck (HN) and lung. Participants followed their standard pathway to assess the cumulative physical and EQD2 doses (with provided α/ß values), and submitted DVH metrics and a description of their pathways. Participants could also submit physical dose distributions from Course 1 mapped onto the CT of Course 2 using their best available tools. To assess isolated impact of image registrations, a single observer accumulated each submitted spatially mapped physical dose for every participating centre. RESULTS: Cumulative dose assessment was performed by 24 participants. Pathways included rigid (n = 15), or deformable (n = 5) image registration-based 3D dose summation, visual inspection of isodose line contours (n = 1), or summation of dose metrics extracted from each course (n = 3). Largest variations were observed in near-maximum cumulative doses (25.4 - 41.8 Gy for HN, 2.4 - 33.8 Gy for lung OARs), with lower variations in volume/dose metrics to large organs. A standardised process involving spatial mapping of the first course dose to the second course CT followed by summation improved consistency for most near-maximum dose metrics in both cases. CONCLUSION: Large variations highlight the uncertainty in reporting cumulative doses in reirradiation scenarios, with implications for outcome analysis and understanding of published doses. Using a standardised workflow potentially including spatially mapped doses improves consistency in determination of accumulated dose in reirradiation scenarios.

Assessment of a model based optimization engine for volumetric modulated arc therapy for patients with advanced hepatocellular cancer.

BACKGROUND: To evaluate in-silico the performance of a model-based optimization process for volumetric modulated arc therapy (RapidArc) applied to hepatocellular cancer treatments. PATIENTS AND METHODS: 45 clinically accepted RA plans were selected to train a knowledge-based engine for the prediction of individualized dose-volume constraints. The model was validated on the same plans used for training (closed-loop) and on a set of other 25 plans not used for the training (open-loop). Dose prescription, target size, localization in the liver and arc configuration were highly variable in both sets to appraise the power of generalization of the engine. Quantitative dose volume histogram analysis was performed as well as a pass-fail analysis against a set of 8 clinical dose-volume objectives to appraise the quality of the new plans. RESULTS: Qualitative and quantitative equivalence was observed between the clinical and the test plans. The use of model-based optimization lead to a net improvement in the pass-rate of the clinical objectives compared to the plans originally optimized with standard methods (this pass-rate is the frequency of cases where the objectives are respected vs. the cases where constraints are not fulfilled). The increase in the pass-rate resulted of 2.0%, 0.9% and 0.5% in a closed-loop and two different open-loop validation experiments. CONCLUSIONS: A knowledge-based engine for the optimization of RapidArc plans was tested and lead to clinically acceptable plans in the case of hepatocellular cancer radiotherapy. More studies are needed before a broad clinical use.