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Purpose The Working Group for Neuro-Oncology of the German Society for Radiation Oncology in cooperation with members of the Neuro-Oncology Working Group of the German Cancer Society aimed to define a practical guideline for the diagnosis and treatment of radiation-induced necrosis (RN) of the central nervous system (CNS). Methods Panel members of the DEGRO working group invited experts, participated in a series of conferences, supplemented their clinical experience, performed a literature review, and formulated recommendations for medical treatment of RN including bevacizumab in clinical routine. Conclusion Diagnosis and treatment of RN requires multidisciplinary structures of care and defined processes. Diagnosis has to be made on an interdisciplinary level with the joint knowledge of a neuroradiologist, radiation oncologist, neurosurgeon, neuropathologist, and neuro-oncologist. A multistep approach as an opportunity to review as many characteristics as possible to improve diagnostic confidence is recommended. Additional information about radiotherapy (RT) techniques is crucial for the diagnosis of RN. Misdiagnosis of untreated and progressive RN can lead to severe neurological deficits. In this practice guideline, we propose a detailed nomenclature of treatment-related changes and a multistep approach for their diagnosis.

Objective: Due to demographic changes and an increased incidence of cancer with age, the number of patients with brain metastases (BM) is constantly increasing, especially among the elderly. Nevertheless, geriatric patients are often overlooked in clinical trials and impact of resection of BM is a frequently [for full text, please go to the a.m. URL]

Solving the inverse problem is the key step in evaluating the capacity of a physical model to describe real phenomena. In medical image computing, it aligns with the classical theme of image-based model personalization. Traditionally, a solution to the problem is obtained by performing either sampling or variational inference based methods. Both approaches aim to identify a set of free physical model parameters that results in a simulation best matching an empirical observation. When applied to brain tumor modeling, one of the instances of image-based model personalization in medical image computing, the overarching drawback of the methods is the time complexity of finding such a set. In a clinical setting with limited time between imaging and diagnosis or even intervention, this time complexity may prove critical. As the history of quantitative science is the history of compression (Schmidhuber and Fridman, 2018), we align in this paper with the historical tendency and propose a method compressing complex traditional strategies for solving an inverse problem into a simple database query task. We evaluated different ways of performing the database query task assessing the trade-off between accuracy and execution time. On the exemplary task of brain tumor growth modeling, we prove that the proposed method achieves one order speed-up compared to existing approaches for solving the inverse problem. The resulting compute time offers critical means for relying on more complex and, hence, realistic models, for integrating image preprocessing and inverse modeling even deeper, or for implementing the current model into a clinical workflow. The code is available at https://github.com/IvanEz/for-loop-tumor.

Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision-support systems for diagnosis, surgery planning, and population-based analysis on spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms towards labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel-level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the the results of this evaluation and further investigate the performance-variation at vertebra-level, scan-level, and at different fields-of-view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The content and code concerning VerSe can be accessed at: https://github.com/anjany/verse.

Diffusion tensor imaging (DTI), and fractional-anisotropy (FA) maps in particular, have shown promise in predicting areas of tumor recurrence in glioblastoma. However, analysis of peritumoral edema, where most recurrences occur, is impeded by free-water contamination. In this study, we evaluated the benefits of a novel, deep-learning-based approach for the free-water correction (FWC) of DTI data for prediction of later recurrence. We investigated 35 glioblastoma cases from our prospective glioma cohort. A preoperative MR image and the first MR scan showing tumor recurrence were semiautomatically segmented into areas of contrast-enhancing tumor, edema, or recurrence of the tumor. The 10th, 50th and 90th percentiles and mean of FA and mean-diffusivity (MD) values (both for the original and FWC−DTI data) were collected for areas with and without recurrence in the peritumoral edema. We found significant differences in the FWC−FA maps between areas of recurrence-free edema and areas with later tumor recurrence, where differences in noncorrected FA maps were less pronounced. Consequently, a generalized mixed-effect model had a significantly higher area under the curve when using FWC−FA maps (AUC = 0.9) compared to noncorrected maps (AUC = 0.77, p < 0.001). This may reflect tumor infiltration that is not visible in conventional imaging, and may therefore reveal important information for personalized treatment decisions.

Objective: Brain tumor-related seizures are common in glioma patients. Many studies investigated risk factors for preoperative seizures as those are associated with improved survival. The role of preoperative tumor volume was discussed controversial in the literature. Aim of this study was to assess[for full text, please go to the a.m. URL], 70. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC), Joint Meeting mit der Skandinavischen Gesellschaft für Neurochirurgie

Objective: PET using radiolabelled amino acids has become a constant tool for preoperative diagnostics and monitoring treatment response in patients with brain metastases. Interestingly, tumor margins in FET-PET and MRI are rarely completely congruent. We investigated which imaging shows more accurately[for full text, please go to the a.m. URL], 69. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC), Joint Meeting mit der Mexikanischen und Kolumbianischen Gesellschaft für Neurochirurgie

Objective: Postoperative infarct volume is a prognostic factor for glioblastoma (GB) patients, postoperative hypoxia is suggested as potential mediatior for invasive tumor growth, resulting in diffuse/multifocal recurrence patterns. Aim of this study was to analyze distinct recurrence patterns[for full text, please go to the a.m. URL], 68. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC), 7. Joint Meeting mit der Society of British Neurological Surgeons (SBNS)

BACKGROUND: Molecular biomarkers, including isocitrate dehydrogenase 1 or 2 (IDH1/2) mutation, 1p/19q codeletion, mutations and (consequently) loss of expression of alpha-thalassemia/mental retardation syndrome X-linked (ATRX) and O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, improve prognostication and may even guide treatment decisions in patients with anaplastic gliomas. Illumina Infinium HumanMethylation450 BeadChip arrays (HM450) allow the determination of large-scale methylation profiles and genome-wide DNA copy number changes, enabling molecular subgrouping of tumors. In addition, algorithms have been developed to detect the glioma CpG island methylator phenotype (G-CIMP) associated with IDH1/2 mutation, 1p/19q codeletion and MGMT promoter methylation using this assay. METHODS: In the biomarker cohort of the NOA-04 trial, the diagnostic and prognostic performance of these molecular markers using single tests, HM450 data and HM450-based algorithms has been investigated to propose biological subgroups, which reflect outcomes and potentially influence treatment decisions. RESULTS: Loss of ATRX expression was detected in 45% of anaplastic astrocytomas (AA), 27% of anaplastic oligoastrocytomas (AOA) and 10% of anaplastic oligodendrogliomas (AO). It was mostly restricted to IDH mutant tumors and almost mutually exclusive with 1p/19q co-deletion. In tumors with IDH1 mutation, MGMT promoter methylation was associated with prolonged progression-free survival (PFS) with chemotherapy or radiotherapy (RT), and thus prognostic. In tumors without IDH1 mutation, MGMT promoter methylation was associated with increased PFS in patients treated with chemotherapy, too, but not in those who received RT alone as the first-line treatment, and is thus chemotherapy-predictive. Comparisons of single assays and HM450-based algorithms revealed a high concordance for IDH and 1p/19q status. The HM450-derived MGMT-STP27 model to calculate MGMT promoter methylation probability revealed this aberration in a significantly higher fraction of cases as conventional methylation-specific PCR, with 87/91 G-CIMP-positive tumors predicted as MGMT promoter-methylated. CONCLUSIONS: ATRX loss is a hallmark and favorable prognosticator of astrocytic tumors allowing a better definition of the clinically and morphologically mixed group of AOA. MGMT promoter methylation is a predictive biomarker for benefit from alkylating agent chemotherapy in patients with IDH1-wildtype, but not IDH1-mutant malignant gliomas of WHO grades III/IV. Combined IDH1/ MGMT assessment may help to individualize clinical decision making in neurooncology. G-CIMP and 1p/19q codeletion are reliably detectable by HM450 analysis and associated with prognosis in the NOA-04 trial. HM450 arrays allowed clustering of anaplastic gliomas into relevant subgroups.