Feasibility of Individualized, Model-guided Optimization of Proton Beam Treatment Planning in Patients With Low Grade Glioma

Overview

Low-grade glioma (LGG) represent typically slowly growing primary brain tumors with world health organization (WHO) grade I or II who affect young adults around their fourth decade. Radiological feature on MRI is a predominantly T2 hyperintense signal, LGG show typically no contrast uptake. Radiotherapy plays an important role in the treatment of LGG. However, not least because of the good prognosis with long term survivorship the timing of radiotherapy has been discussed controversially. In order to avoid long term sequelae such as neurocognitive impairment, malignant transformation or secondary neoplasms initiation was often postponed as long as possible

Since patients with low grade glioma are expected to become long-term survivors, the prevention of long-term sequelae is particularly important. In addition to disease progression, also treatment related side effects such as decline of neurocognitive function, endocrine impairment or sensorineural deficits can have a negative impact on patient's quality of life. Owing to the biophysical properties of protons with an inverse depth dose profile compared to photons and a steep dose fall of to the normal tissue, there is a strong rationale for the use of PRT in the treatment of patients with low-grade glioma. Although data from large randomized trials are still missing there is increasing evidence from smaller prospective trials and retrospective analyses that the expected advantages indeed transform into clinical advantages. However, in about 20 % of all patients, late contrast-enhancing brain lesions (CEBL) appear on follow-up MR images 6 - 24 months after treatment. At HIT in Heidelberg and at OncoRay in Dresden, CEBLs have been observed to occur at very distinct locations in the brain and relative to the treatment field. Retrospective analysis has elucidated potential key factors that lead to CEBL occurrence. However, avoidance of CEBLs is hardly feasible using conventional treatment planning strategies. Model-aided risk avoidance denotes the use of model-based CEBL risk calculations as an auxiliary tool for clinical treatment planning: Model-based risk calculations and risk reduction via software-based optimization help the clinician to minimize risk of CEBL occurrence during treatment planning.