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

Citation:

DC Hall, AV Trofimov, BA Winey, NJ Liebsch, and H Paganetti. 2017. “Predicting patient-specific dosimetric benefits of proton therapy for skull-base tumors using a geometric knowledge-based method.” International Journal of Radiation Oncology • Biology • Physics, 97, 5, Pp. 1087-1094. Publisher's Version

Abstract:

Purpose: To predict the organ at risk (OAR) dose levels achievable with proton beam therapy (PBT), solely based on the geometric arrangement of the target volume in rela- tion to the OARs. A comparison with an alternative therapy yields a prediction of the patient-specific benefits offered by PBT. This could enable physicians at hospitals without proton capabilities to make a better-informed referral decision or aid patient selection in model-based clinical trials.

Methods and Materials: Skull-base tumors were chosen to test the method, owing to their geometric complexity and multitude of nearby OARs. By exploiting the correla- tions between the dose and distance-to-target in existing PBT plans, the models were independently trained for 6 types of OARs: brainstem, cochlea, optic chiasm, optic nerve, parotid gland, and spinal cord. Once trained, the models could estimate the feasible doseevolume histogram and generalized equivalent uniform dose (gEUD) for OAR structures of new patients. The models were trained using 20 patients and validated using an additional 21 patients. Validation was achieved by comparing the predicted gEUD to that of the actual PBT plan.

Results: The predicted and planned gEUD were in good agreement. Considering all OARs, the prediction error was þ1.4 ` 5.1 Gy (mean ` standard deviation), and Pearson’s correlation coefficient was 93%. By comparing with an intensity modulated photon treatment plan, the model could classify whether an OAR structure would experience a gain, with a sensitivity of 93% (95% confidence interval: 87%-97%) and specificity of 63% (95% confidence interval: 38%-84%).

Conclusions: We trained and validated models that could quickly and accurately pre- dict the patient-specific benefits of PBT for skull-base tumors. Similar models could be developed for other tumor sites. Such models will be useful when an estimation of the feasible benefits of PBT is desired but the experience and/or resources required for treatment planning are unavailable. 

 

Last updated on 03/15/2017