Predicting Patient-specific Dosimetric Benefits of Proton Therapy for Skull-base Tumors Using a Geometric Knowledge-based Method.
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 relation 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.



Skull-base tumors were chosen to test the method, owing to their geometric complexity and multitude of nearby OARs. By exploiting the correlations 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 dose-volume 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.



The predicted and planned gEUD were in good agreement. Considering all OARs, the prediction error was +1.4 ± 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%).



We trained and validated models that could 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 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.
Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
David C
DC
Hall
Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
Alexei V
AV
Trofimov
Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
Brian A
BA
Winey
Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
Norbert J
NJ
Liebsch
Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts. Electronic address: hpaganetti@mgh.harvard.edu.
Harald
H
Paganetti
D003198:Computer Simulation; D004307:Dose-Response Relationship, Radiation; D006801:Humans; D051188:Knowledge Bases; D000069550:Machine Learning; D008954:Models, Biological; D018802:Patient-Centered Care / Q000379:methods*; D011379:Prognosis; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D019292:Skull Base Neoplasms / Q000175:diagnosis* / Q000532:radiotherapy*