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| Home > Publications database > Late contrast enhancing brain lesions in proton treated low-grade glioma patients: clinical evidence for increased periventricular sensitivity and variable RBE. |
| Journal Article | DKFZ-2020-00739 |
; ; ; ; ;
2020
Elsevier Science
Amsterdam [u.a.]
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Please use a persistent id in citations: doi:10.1016/j.ijrobp.2020.03.013
Abstract: Late radiation-induced contrast enhancing brain lesions (CEBL) on magnetic resonance (MR) images after proton therapy of brain tumors have been observed to occur frequently in regions of high linear energy transfer (LET) and in proximity to the ventricular system. We analyzed 110 low-grade glioma patients treated with proton therapy to determine if the risk for CEBLs is increased in proximity to the ventricular system and if there is a relationship between relative biological effectiveness (RBE) and LET.We contoured CEBLs identified on follow-up T1-MR images and computed dose and dose-averaged LET (LETd) distributions for all patients with Monte Carlo. We then performed cross-validated voxel-level logistic regression to predict local risks for image change and to extract model parameters, such as the RBE. From the voxel-level model, we derived a model for patient-level risk prediction based on the treatment plan.Out of 110 patients, 23 exhibited one or several CEBLs on follow-up MR images. The voxel-level logistic model has an accuracy of: AUC = 0.94, Brier score = 2.6x10-5. Model predictions are: a threefold increased risk in the 4 mm region around the ventricular system and an LETd-dependent RBE of e.g. 1.22 for LETd = 2 keV/μm and 1.56 for LETd = 5 keV/μm. The patient-level risk model has an accuracy of: AUC = 0.78, Brier score = 0.13.Our findings present clinical evidence for an increased risk in ventricular proximity and for a proton RBE that increases significantly with increasing LET. We present a voxel-level model that predicts accurately the localization of late MRI contrast change and extrapolate a patient-level model that allows treatment-plan based risk prediction.
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