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Simple Summary: Patients with complex brain tumors are regularly treated with proton therapy (PT) to obtain an optimal curative treatment with limited toxicity. Value-based oncological medicine is increasingly important, particularly when long-term survival is to be expected. The aim of our prospective study was to evaluate health-related quality of life (HRQOL) and patient reported outcomes (PROs) in patients treated with PT for brain tumors. PT only temporarily influences PROs and QoL directly following PT and lower QoL and PROs were mostly reported by patients who were already in a worse condition with symptoms related to the tumor and previous treatment, before the start of PT. This study assists in improving patient support in patients with brain tumors receiving PT. Proton therapy (PT) is delivered to complex brain tumors to obtain an optimal curative treatment with limited toxicity. Value-based oncological medicine is increasingly important, particularly when long-term survival is to be expected. This study aims to evaluate health-related quality of life (HRQOL) and patient reported outcomes (PROs) in patients treated with PT for brain tumors. Adult patients with brain tumors treated with PT filled out the EORTC-QLQ-C30 and BN20 questionnaires up to three years following PT. Toxicity was scored using the CTCAE v4.03. QoL and PRO were correlated to clinical factors. Three-year overall survival, distant brain control and local control rates were 98%, 97% and 84%, respectively. No ≥G3 acute toxicity was observed. Late PT-related ≥G3 severe toxicity occurred in seven patients (5.7%). Lower global QoL scores after PT were significantly correlated to low Karnofsky performance status (KPS) before PT (p = 0.001), surgical complications before PT (p = 0.04) and progressive disease (p = 0.017). A low QLQ-30 summary score at one year follow-up was correlated to sex (p = 0.015), low KPS before PT (p < 0.001), and central nervous system symptoms before PT (p = 0.018). Reported QLQ-BN20 neurological symptoms were correlated to lower KPS at baseline (p < 0.001) and surgical complications before PT (p = 0.03). PT-related toxicity only influenced reported symptoms directly following PT, but not QoL. Although global QoL temporarily decreased after treatment, it improved again from one year onwards. Global QoL and reported symptoms over time were not correlated with the proton therapy and were more related to preexisting symptoms and progressive disease. This study assists in improving patient support in patients with brain tumors receiving PT. [ABSTRACT FROM AUTHOR]

Proton therapy (PT) has been administered for many years to a number of cancers, including brain tumours. Due to their remarkable physical properties, delivering their radiation to a very precise brain volume with no exit dose, protons are particularly appropriate for these tumours. The decrease of the brain integral dose may translate with a diminution of neuro-cognitive toxicity and increase of quality of life, particularly so in children. The brain tumour patient's access to PT will be substantially increased in the future, with many new facilities being planned or currently constructed in Europe, Asia and the United States. Although approximately 150'000 patients have been treated with PT, no level I evidence has been demonstrated for this treatment. As such, it is this necessary to generate high-quality data and some new prospective trials will include protons or will be activated to compare photons to protons in a randomized design. PT comes however with an additional cost factor that may contribute to the ever-growing health's expenditure allocated to cancer management. These additional costs and financial toxicity will have to be analysed in the light of a more conformal radiation delivery, non-target brain irradiation and lack of potential for dose escalation when compared to photons. The latter is due to the radiosensitivity of organs at risk in vicinity of the brain tumour, that photons cannot spare optimally. Consequentially, radiation-induced toxicities and tumour recurrences, which are cost-intensive, may decrease with PT resulting in an optimized photon/proton financial ratio in the end. This review details the indication of brain tumors for proton therapy and give a list of the open prospective trials for these challenging tumors. [ABSTRACT FROM AUTHOR]

• High-quality sCT generation from MR images with 3D uncertainty prediction. • Improved plan robustness against network prediction error by uncertainty-aware optimisation. • Clinically acceptable accuracy (+/-3%) for proton therapy planning of 64 brain tumour patients. • Powerful tools for determining the clinical usefulness of synthetic CTs for individual patients. Deep learning techniques excel in MR-based CT synthesis, but missing uncertainty prediction limits its clinical use in proton therapy. We developed an uncertainty-aware framework and evaluated its efficiency in robust proton planning. A conditional generative-adversarial network was trained on 64 brain tumour patients with paired MR-CT images to generate synthetic CTs (sCT) from combined T1-T2 MRs of three orthogonal planes. A Bayesian neural network predicts Laplacian distributions for all voxels with parameters (μ , b). A robust proton plan was optimized using three sCTs of μ and μ ± b. The dosimetric differences between the plan from sCT (sPlan) and the recalculated plan (rPlan) on planning CT (pCT) were quantified for each patient. The uncertainty-aware robust plan was compared to conventional robust (global ± 3 %) and non-robust plans. In 8-fold cross-validation, sCT-pCT image differences (Mean-Absolute-Error) were 80.84 ± 9.84HU (body), 35.78 ± 6.07HU (soft tissues) and 221.88 ± 31.69HU (bones), with Dice scores of 90.33 ± 2.43 %, 95.13 ± 0.80 %, and 85.53 ± 4.16 %, respectively. The uncertainty distribution positively correlated with absolute prediction error (Correlation Coefficient: 0.62 ± 0.01). The uncertainty-conditioned robust optimisation improved the rPlan-sPlan agreement, e.g., D95 absolute difference (CTV) was 1.10 ± 1.24 % compared to conventional (1.64 ± 2.71 %) and non-robust (2.08 ± 2.96 %) optimisation. This trend was consistent across all target and organs-at-risk indexes. The enhanced framework incorporates 3D uncertainty prediction and generates high-quality sCTs from MR images. The framework also facilitates conditioned robust optimisation, bolstering proton plan robustness against network prediction errors. The innovative feature of uncertainty visualisation and robust analyses contribute to evaluating sCT clinical utility for individual patients. [ABSTRACT FROM AUTHOR]