Predicting patient-specific dosimetric benefits of proton therapy for skull-base tumors using a geometric knowledge-based method

Publication date: Available online 14 February 2017
Source:International Journal of Radiation Oncology*Biology*Physics
Author(s): David C. Hall, Alexei V. Trofimov, Brian A. Winey, Norbert J. Liebsch, Harald Paganetti
PurposeTo predict the organ-at-risk (OAR) dose levels achievable with proton beam therapy (PBT), solely based upon the geometric arrangement of the target volume in relation to the OARs. Comparison to an alternative therapy yields a prediction of the patient-specific benefits offered by PBT. This could enable a physician at a hospital without proton capabilities to make a better-informed referral decision, or aid patient selection in model-based clinical trials.Methods and MaterialsSkull-base tumors were chosen to test the method, owing to their geometric complexity and multitude of nearby OARs. By exploiting correlations between dose and distance-to-target in existing PBT plans, models were independently trained for six types of OAR: brainstem, cochlea, optic chiasm, optic nerve, parotid gland and spinal cord. Once trained, the models could estimate the feasible dose-volume histogram and generalized equivalent uniform dose (gEUD) for OAR structures of new patients. Models were trained using 20 patients and validated with a further 21 patients. Validation was achieved by comparing the predicted gEUD to that of the actual PBT plan.ResultsThe predicted and planned gEUD were in good agreement: considering all OARs, the prediction error was +1.4 ± 5.1 Gy (mean ± SD) and Pearson's correlation coefficient was 93%. When compared to an IMRT plan, the model could classify whether an OAR structure would experience a gain with a sensitivity of 93% (95% CI: 87% – 97%) and a specificity of 63% (95% CI: 38% – 84%).ConclusionsWe trained and validated models that quickly and accurately predict the patient-specific benefits of PBT for skull-base tumors. Similar models could be developed for other tumor sites. Such models are useful when an estimation of the feasible benefits of PBT is desired, but the experience and/or resources required for treatment planning are unavailable.

Teaser

This work aims to develop models that can predict the patient-specific benefits offered by proton therapy, solely based upon a patient's geometry. A knowledge-based method trains the models upon geometric patterns observed in a set of existing proton treatment plans for skull base tumors. The models were validated yielding a Pearson's correlation coefficient of 93%; similar models could be trained for other tumor sites and be used to make better-informed referral decisions.


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