The records of 208.777 (100%) clinical trials registered at ClinicalTrials.gov were downloaded on the 19th of February 2016. Phase II and III trials including patients with glioblastoma were selected for further classification and analysis. Based on the disease settings, trials were classified into three groups: newly diagnosed glioblastoma, recurrent disease and trials with no differentiation according to disease setting. Furthermore, we categorized trials according to the experimental interventions, the primary sponsor, the source of financial support and trial design elements. Trends were evaluated using the autoregressive integrated moving average model. Two hundred sixteen (0.1%) trials were selected for further analysis. Academic centers (investigator initiated trials) were recorded as primary sponsors in 56.9% of trials, followed by industry 25.9%. Industry was the leading source of monetary support for the selected trials in 44.4%, followed by 25% of trials with primarily academic financial support. The number of newly initiated trials between 2005 and 2015 shows a positive trend, mainly through an increase in phase II trials, whereas phase III trials show a negative trend. The vast majority of trials evaluate forms of different systemic treatments (91.2%). In total, one hundred different molecular entities or biologicals were identified. Of those, 60% were involving drugs specifically designed for central nervous system malignancies. Trials that specifically address radiotherapy, surgery, imaging and other therapeutic or diagnostic methods appear to be rare. Current research in glioblastoma is mainly driven or sponsored by industry, academic medical oncologists and neuro-oncologists, with the majority of trials evaluating forms of systemic therapies. Few trials reach phase III. Imaging, radiation therapy and surgical procedures are underrepresented in current trials portfolios. Optimization in research portfolio for glioblastoma is needed.Electronic supplementary materialThe online version of this article (doi:10.1186/s13014-016-0740-5) contains supplementary material, which is available to authorized users.

Background: Automated brain tumor segmentation methods are computational algorithms that yield tumor delineation from, in this case, multimodal magnetic resonance imaging (MRI). We present an automated segmentation method and its results for resection cavity (RC) in glioblastoma multiforme (GBM) patients using deep learning (DL) technologies. Methods: Post-operative, T1w with and without contrast, T2w and fluid attenuated inversion recovery MRI studies of 30 GBM patients were included. Three radiation oncologists manually delineated the RC to obtain a reference segmentation. We developed a DL cavity segmentation method, which utilizes all four MRI sequences and the reference segmentation to learn to perform RC delineations. We evaluated the segmentation method in terms of Dice coefficient (DC) and estimated volume measurements. Results: Median DC of the three radiation oncologist were 0.85 (interquartile range [IQR]: 0.08), 0.84 (IQR: 0.07), and 0.86 (IQR: 0.07). The results of the automatic segmentation compared to the three different raters were 0.83 (IQR: 0.14), 0.81 (IQR: 0.12), and 0.81 (IQR: 0.13) which was significantly lower compared to the DC among raters (chisquare = 11.63, p = 0.04). We did not detect a statistically significant difference of the measured RC volumes for the different raters and the automated method (Kruskal-Wallis test: chi-square = 1.46, p = 0.69). The main sources of error were due to signal inhomogeneity and similar intensity patterns between cavity and brain tissues. Conclusions: The proposed DL approach yields promising results for automated RC segmentation in this proof of concept study. Compared to human experts, the DC are still subpar.

Uncertainty estimation methods are expected to improve the understanding and quality of computer-assisted methods used in medical applications (e.g., neurosurgical interventions, radiotherapy planning), where automated medical image segmentation is crucial. In supervised machine learning, a common practice to generate ground truth label data is to merge observer annotations. However, as many medical image tasks show a high inter-observer variability resulting from factors such as image quality, different levels of user expertise and domain knowledge, little is known as to how inter-observer variability and commonly used fusion methods affect the estimation of uncertainty of automated image segmentation. In this paper we analyze the effect of common image label fusion techniques on uncertainty estimation, and propose to learn the uncertainty among observers. The results highlight the negative effect of fusion methods applied in deep learning, to obtain reliable estimates of segmentation uncertainty. Additionally, we show that the learned observers' uncertainty can be combined with current standard Monte Carlo dropout Bayesian neural networks to characterize uncertainty of model's parameters.

The outbreak of the novel coronavirus disease-19 (COVID-19) has rapidly and drastically impacted worldwide the healthcare system. Despite an increasing number of recommendations becoming available in the last two months, measures adopted in radiationoncology departments to overcome this situation are rapidly changing and may differ largely based on institutional and national practices.We conducted a national survey of all radiation oncology centers in Switzerland to better understand the early impact of the COVID-19 pandemic on our discipline. MethodsA 53-questions online survey was finalized on April 6th, 2020 using available recommendations [1-8] and distributed by email on April 07th, 2020 to the representatives of the 30 Swiss radiation

A treatment technique for DYMBER has been successfully developed and verified for its deliverability. The dosimetric superiority of DYMBER over DTRT and VMAT indicates utilizing increased DoF to be the key to improve brain and head and neck radiation treatments in future.

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Background Sarcopenia, the critical depletion of skeletal muscle mass, is an independent prognostic factor in several tumor entities for treatment-related toxicity and survival. In esophageal cancer, there have been conflicting results regarding the value of sarcopenia as prognostic factor, which may be attributed to the heterogeneous patient populations and the retrospective nature of previous studies. The aim of our study was therefore to determine the impact of sarcopenia on prospectively collected specific outcomes in a subgroup of patients treated within the phase III study SAKK 75/08 with trimodality therapy (induction chemotherapy, radiochemotherapy and surgery) for locally advanced esophageal cancer. Methods Sarcopenia was assessed by skeletal muscle index at the 3rd lumbar vertebra (L3) in cross-sectional computed tomography scans before induction chemotherapy, before radiochemotherapy and after neoadjuvant therapy in a subgroup of 61 patients from four centers in Switzerland. Sarcopenia was determined by previously established cut-off values (Martin et al., PMID: 23530101) and correlated with prospectively collected outcomes including treatment-related toxicity, postoperative morbidity, treatment feasibility and survival. Results Using the published cut-off values, the prevalence of sarcopenia increased from 29.5% before treatment to 63.9% during neoadjuvant therapy (p < 0.001). Feasibility of neoadjuvant therapy and surgery was not different in initially sarcopenic and non-sarcopenic patients. We observed in sarcopenic patients significantly increased grade ≥ 3 toxicities during chemoradiation (83.3% vs 52.4%, p = 0.04) and a non-significant trend towards increased postoperative complications (66.7% vs 42.9%, p = 0.16). No difference in survival according to sarcopenia could be observed in this small study population. Conclusions Trimodality therapy in locally advanced esophageal cancer is feasible in selected patients with sarcopenia. Neoadjuvant chemoradiation increased the percentage of sarcopenia. Sarcopenic patients are at higher risk for increased toxicity during neoadjuvant radiochemotherapy and showed a non-significant trend to more postoperative morbidity.

Primary testicular lymphoma (PTL) is a rare disease accounting for 1% of non-Hodgkin's lymphoma. PTL occurs more frequently in older patients and is a potentially fatal disease. In the early stages (I and II), the treatment consists of orchidectomy followed by chemotherapy (CT) and prophylactic scrotal radiotherapy (RT) with/or without iliac and/or paraaortic lymph node RT. In the advanced stages (III and IV), CT is the treatment of choice whereas the place of scrotal RT is controverted. In both early and advanced disease intrathecal CT is warranted to prevent CNS relapse. New molecular approaches and/or more aggressive treatments are being explored.