Virtual reality-empowered deep-learning analysis of brain cells.

Automated detection of specific cells in three-dimensional datasets such as whole-brain light-sheet image stacks is challenging. Here, we present DELiVR, a virtual reality-trained deep-learning pipeline for detecting c-Fos+ cells as markers for neuronal activity in cleared mouse brains. Virtual reality annotation substantially accelerated training data generation, enabling DELiVR to outperform state-of-the-art cell-segmenting approaches. Our pipeline is available in a user-friendly Docker container that runs with a standalone Fiji plugin. DELiVR features a comprehensive toolkit for data visualization and can be customized to other cell types of interest, as we did here for microglia somata, using Fiji for dataset-specific training. We applied DELiVR to investigate cancer-related brain activity, unveiling an activation pattern that distinguishes weight-stable cancer from cancers associated with weight loss. Overall, DELiVR is a robust deep-learning tool that does not require advanced coding skills to analyze whole-brain imaging data in health and disease.

The glioblastoma multiforme tumor site promotes the commitment of tumor-infiltrating lymphocytes to the TH17 lineage in humans.

Although glioblastoma multiforme (GBM) is not an invariably cold tumor, checkpoint inhibition has largely failed in GBM. In order to investigate T cell-intrinsic properties that contribute to the resistance of GBM to endogenous or therapeutically enhanced adaptive immune responses, we sorted CD4+ and CD8+ T cells from the peripheral blood, normal-appearing brain tissue, and tumor bed of nine treatment-naive patients with GBM. Bulk RNA sequencing of highly pure T cell populations from these different compartments was used to obtain deep transcriptomes of tumor-infiltrating T cells (TILs). While the transcriptome of CD8+ TILs suggested that they were partly locked in a dysfunctional state, CD4+ TILs showed a robust commitment to the type 17 T helper cell (TH17) lineage, which was corroborated by flow cytometry in four additional GBM cases. Therefore, our study illustrates that the brain tumor environment in GBM might instruct TH17 commitment of infiltrating T helper cells. Whether these properties of CD4+ TILs facilitate a tumor-promoting milieu and thus could be a target for adjuvant anti-TH17 cell interventions needs to be further investigated.

Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements.

The development, application, and benchmarking of artificial intelligence (AI) tools to improve diagnosis, prognostication, and therapy in neuro-oncology are increasing at a rapid pace. This Policy Review provides an overview and critical assessment of the work to date in this field, focusing on diagnostic AI models of key genomic markers, predictive AI models of response before and after therapy, and differentiation of true disease progression from treatment-related changes, which is a considerable challenge based on current clinical care in neuro-oncology. Furthermore, promising future directions, including the use of AI for automated response assessment in neuro-oncology, are discussed.

Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice.

Technological advancements have enabled the extended investigation, development, and application of computational approaches in various domains, including health care. A burgeoning number of diagnostic, predictive, prognostic, and monitoring biomarkers are continuously being explored to improve clinical decision making in neuro-oncology. These advancements describe the increasing incorporation of artificial intelligence (AI) algorithms, including the use of radiomics. However, the broad applicability and clinical translation of AI are restricted by concerns about generalisability, reproducibility, scalability, and validation. This Policy Review intends to serve as the leading resource of recommendations for the standardisation and good clinical practice of AI approaches in health care, particularly in neuro-oncology. To this end, we investigate the repeatability, reproducibility, and stability of AI in response assessment in neuro-oncology in studies on factors affecting such computational approaches, and in publicly available open-source data and computational software tools facilitating these goals. The pathway for standardisation and validation of these approaches is discussed with the view of trustworthy AI enabling the next generation of clinical trials. We conclude with an outlook on the future of AI-enabled neuro-oncology.

Deep Learning to Differentiate Benign and Malignant Vertebral Fractures at Multidetector CT.

Background Differentiating between benign and malignant vertebral fractures poses diagnostic challenges. Purpose To investigate the reliability of CT-based deep learning models to differentiate between benign and malignant vertebral fractures. Materials and Methods CT scans acquired in patients with benign or malignant vertebral fractures from June 2005 to December 2022 at two university hospitals were retrospectively identified based on a composite reference standard that included histopathologic and radiologic information. An internal test set was randomly selected, and an external test set was obtained from an additional hospital. Models used a three-dimensional U-Net encoder-classifier architecture and applied data augmentation during training. Performance was evaluated using the area under the receiver operating characteristic curve (AUC) and compared with that of two residents and one fellowship-trained radiologist using the DeLong test. Results The training set included 381 patients (mean age, 69.9 years ± 11.4 [SD]; 193 male) with 1307 vertebrae (378 benign fractures, 447 malignant fractures, 482 malignant lesions). Internal and external test sets included 86 (mean age, 66.9 years ± 12; 45 male) and 65 (mean age, 68.8 years ± 12.5; 39 female) patients, respectively. The better-performing model of two training approaches achieved AUCs of 0.85 (95% CI: 0.77, 0.92) in the internal and 0.75 (95% CI: 0.64, 0.85) in the external test sets. Including an uncertainty category further improved performance to AUCs of 0.91 (95% CI: 0.83, 0.97) in the internal test set and 0.76 (95% CI: 0.64, 0.88) in the external test set. The AUC values of residents were lower than that of the best-performing model in the internal test set (AUC, 0.69 [95% CI: 0.59, 0.78] and 0.71 [95% CI: 0.61, 0.80]) and external test set (AUC, 0.70 [95% CI: 0.58, 0.80] and 0.71 [95% CI: 0.60, 0.82]), with significant differences only for the internal test set (P < .001). The AUCs of the fellowship-trained radiologist were similar to those of the best-performing model (internal test set, 0.86 [95% CI: 0.78, 0.93; P = .39]; external test set, 0.71 [95% CI: 0.60, 0.82; P = .46]). Conclusion Developed models showed a high discriminatory power to differentiate between benign and malignant vertebral fractures, surpassing or matching the performance of radiology residents and matching that of a fellowship-trained radiologist. © RSNA, 2024 See also the editorial by Booz and D'Angelo in this issue.

A Genetic Risk Variant for Multiple Sclerosis Severity is Associated with Brain Atrophy.

The minor allele of the genetic variant rs10191329 in the DYSF-ZNF638 locus is associated with unfavorable long-term clinical outcomes in multiple sclerosis patients. We investigated if rs10191329 is associated with brain atrophy measured by magnetic resonance imaging in a discovery cohort of 748 and a replication cohort of 360 people with relapsing multiple sclerosis. We observed an association with 28% more brain atrophy per rs10191329*A allele. Our results encourage stratification for rs10191329 in clinical trials. Unraveling the underlying mechanisms may enhance our understanding of pathophysiology and identify treatment targets. ANN NEUROL 2023;94:1080-1085.

Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives.

The rapid development of artificial intelligence (AI) has gained importance, with many tools already entering our daily lives. The medical field of radiation oncology is also subject to this development, with AI entering all steps of the patient journey. In this review article, we summarize contemporary AI techniques and explore the clinical applications of AI-based automated segmentation models in radiotherapy planning, focusing on delineation of organs at risk (OARs), the gross tumor volume (GTV), and the clinical target volume (CTV). Emphasizing the need for precise and individualized plans, we review various commercial and freeware segmentation tools and also state-of-the-art approaches. Through our own findings and based on the literature, we demonstrate improved efficiency and consistency as well as time savings in different clinical scenarios. Despite challenges in clinical implementation such as domain shifts, the potential benefits for personalized treatment planning are substantial. The integration of mathematical tumor growth models and AI-based tumor detection further enhances the possibilities for refining target volumes. As advancements continue, the prospect of one-stop-shop segmentation and radiotherapy planning represents an exciting frontier in radiotherapy, potentially enabling fast treatment with enhanced precision and individualization.

Amino acid PET vs. RANO MRI for prediction of overall survival in patients with recurrent high grade glioma under bevacizumab therapy.

PURPOSE: To summarize evidence on the comparative value of amino acid (AA) PET and conventional MRI for prediction of overall survival (OS) in patients with recurrent high grade glioma (rHGG) under bevacizumab therapy. METHODS: Medical databases were screened for studies with individual data on OS, follow-up MRI, and PET findings in the same patient. MRI images were assessed according to the RANO criteria. A receiver operating characteristic curve analysis was used to predict OS at 9 months. RESULTS: Five studies with a total of 72 patients were included. Median OS was significantly lower in the PET-positive than in the PET-negative group. PET findings predicted OS with a pooled sensitivity and specificity of 76% and 71%, respectively. Corresponding values for MRI were 32% and 82%. Area under the curve and sensitivity were significantly higher for PET than for MRI. CONCLUSION: For monitoring of patients with rHGG under bevacizumab therapy, AA-PET should be preferred over RANO MRI.

Resolving spatial response heterogeneity in glioblastoma.

PURPOSE: Spatial intratumoral heterogeneity poses a significant challenge for accurate response assessment in glioblastoma. Multimodal imaging coupled with advanced image analysis has the potential to unravel this response heterogeneity. METHODS: Based on automated tumor segmentation and longitudinal registration with follow-up imaging, we categorized contrast-enhancing voxels of 61 patients with suspected recurrence of glioblastoma into either true tumor progression (TP) or pseudoprogression (PsP). To allow the unbiased analysis of semantically related image regions, adjacent voxels with similar values of cerebral blood volume (CBV), FET-PET, and contrast-enhanced T1w were automatically grouped into supervoxels. We then extracted first-order statistics as well as texture features from each supervoxel. With these features, a Random Forest classifier was trained and validated employing a 10-fold cross-validation scheme. For model evaluation, the area under the receiver operating curve, as well as classification performance metrics were calculated. RESULTS: Our image analysis pipeline enabled reliable spatial assessment of tumor response. The predictive model reached an accuracy of 80.0% and a macro-weighted AUC of 0.875, which takes class imbalance into account, in the hold-out samples from cross-validation on supervoxel level. Analysis of feature importances confirmed the significant role of FET-PET-derived features. Accordingly, TP- and PsP-labeled supervoxels differed significantly in their 10th and 90th percentile, as well as the median of tumor-to-background normalized FET-PET. However, CBV- and T1c-related features also relevantly contributed to the model's performance. CONCLUSION: Disentangling the intratumoral heterogeneity in glioblastoma holds immense promise for advancing precise local response evaluation and thereby also informing more personalized and localized treatment strategies in the future.

Advanced imaging reveals enhanced malignancy in glioblastomas involving the subventricular zone: evidence of increased infiltrative growth and perfusion.

BACKGROUND: Glioblastoma's infiltrative growth and heterogeneity are influenced by neural, molecular, genetic, and immunological factors, with the precise origin of these tumors remaining elusive. Neurogenic zones might serve as the tumor stem cells' nest, with tumors in contact with these zones exhibiting worse outcomes and more aggressive growth patterns. This study aimed to determine if these characteristics are reflected in advanced imaging, specifically diffusion and perfusion data. METHODS: In this monocentric retrospective study, 137 glioblastoma therapy-naive patients (IDH-wildtype, grade 4) with advanced preoperative MRI, including perfusion and diffusion imaging, were analyzed. Tumors and neurogenic zones were automatically segmented. Advanced imaging metrics, including cerebral blood volume (CBV) from perfusion imaging, tissue volume mask (TVM), and free water corrected fractional anisotropy (FA-FWE) from diffusion imaging, were extracted. RESULTS: SVZ infiltration positively correlated with CBV, indicating higher perfusion in tumors. Significant CBV differences were noted between high and low SVZ infiltration cases at specific percentiles. Negative correlation was observed with TVM and positive correlation with FA-FWE, suggesting more infiltrative tumor growth. Significant differences in TVM and FA-FWE values were found between high and low SVZ infiltration cases. DISCUSSION: Glioblastomas with SVZ infiltration exhibit distinct imaging characteristics, including higher perfusion and lower cell density per voxel, indicating a more infiltrative growth and higher vascularization. Stem cell-like characteristics in SVZ-infiltrating cells could explain the increased infiltration and aggressive behavior. Understanding these imaging and biological correlations could enhance the understanding of glioblastoma evolution.

Generating synthetic high-resolution spinal STIR and T1w images from T2w FSE and low-resolution axial Dixon.

OBJECTIVES: To generate sagittal T1-weighted fast spin echo (T1w FSE) and short tau inversion recovery (STIR) images from sagittal T2-weighted (T2w) FSE and axial T1w gradient echo Dixon technique (T1w-Dixon) sequences. MATERIALS AND METHODS: This retrospective study used three existing datasets: "Study of Health in Pomerania" (SHIP, 3142 subjects, 1.5 Tesla), "German National Cohort" (NAKO, 2000 subjects, 3 Tesla), and an internal dataset (157 patients 1.5/3 Tesla). We generated synthetic sagittal T1w FSE and STIR images from sagittal T2w FSE and low-resolution axial T1w-Dixon sequences based on two successively applied 3D Pix2Pix deep learning models. "Peak signal-to-noise ratio" (PSNR) and "structural similarity index metric" (SSIM) were used to evaluate the generated image quality on an ablations test. A Turing test, where seven radiologists rated 240 images as either natively acquired or generated, was evaluated using misclassification rate and Fleiss kappa interrater agreement. RESULTS: Including axial T1w-Dixon or T1w FSE images resulted in higher image quality in generated T1w FSE (PSNR = 26.942, SSIM = 0.965) and STIR (PSNR = 28.86, SSIM = 0.948) images compared to using only single T2w images as input (PSNR = 23.076/24.677 SSIM = 0.952/0.928). Radiologists had difficulty identifying generated images (misclassification rate: 0.39 ± 0.09 for T1w FSE, 0.42 ± 0.18 for STIR) and showed low interrater agreement on suspicious images (Fleiss kappa: 0.09 for T1w/STIR). CONCLUSIONS: Axial T1w-Dixon and sagittal T2w FSE images contain sufficient information to generate sagittal T1w FSE and STIR images. CLINICAL RELEVANCE STATEMENT: T1w fast spin echo and short tau inversion recovery can be retroactively added to existing datasets, saving MRI time and enabling retrospective analysis, such as evaluating bone marrow pathologies. KEY POINTS: Sagittal T2-weighted images alone were insufficient for differentiating fat and water and to generate T1-weighted images. Axial T1w Dixon technique, together with a T2-weighted sequence, produced realistic sagittal T1-weighted images. Our approach can be used to retrospectively generate STIR and T1-weighted fast spin echo sequences.

Multiscale quantification of morphological heterogeneity with creation of a predictor of longer survival in glioblastoma.

Intratumor heterogeneity is a main cause of the dismal prognosis of glioblastoma (GBM). Yet, there remains a lack of a uniform assessment of the degree of heterogeneity. With a multiscale approach, we addressed the hypothesis that intratumor heterogeneity exists on different levels comprising traditional regional analyses, but also innovative methods including computer-assisted analysis of tumor morphology combined with epigenomic data. With this aim, 157 biopsies of 37 patients with therapy-naive IDH-wildtype GBM were analyzed regarding the intratumor variance of protein expression of glial marker GFAP, microglia marker Iba1 and proliferation marker Mib1. Hematoxylin and eosin stained slides were evaluated for tumor vascularization. For the estimation of pixel intensity and nuclear profiling, automated analysis was used. Additionally, DNA methylation profiling was conducted separately for the single biopsies. Scoring systems were established to integrate several parameters into one score for the four examined modalities of heterogeneity (regional, cellular, pixel-level and epigenomic). As a result, we could show that heterogeneity was detected in all four modalities. Furthermore, for the regional, cellular and epigenomic level, we confirmed the results of earlier studies stating that a higher degree of heterogeneity is associated with poorer overall survival. To integrate all modalities into one score, we designed a predictor of longer survival, which showed a highly significant separation regarding the OS. In conclusion, multiscale intratumor heterogeneity exists in glioblastoma and its degree has an impact on overall survival. In future studies, the implementation of a broadly feasible heterogeneity index should be considered.

Hypofractionated stereotactic radiotherapy (HFSRT) versus single fraction stereotactic radiosurgery (SRS) to the resection cavity of brain metastases after surgical resection (SATURNUS): study protocol for a randomized phase III trial.

BACKGROUND: The brain is a common site for cancer metastases. In case of large and/or symptomatic brain metastases, neurosurgical resection is performed. Adjuvant radiotherapy is a standard procedure to minimize the risk of local recurrence and is increasingly performed as local stereotactic radiotherapy to the resection cavity. Both hypofractionated stereotactic radiotherapy (HFSRT) and single fraction stereotactic radiosurgery (SRS) can be applied in this case. Although adjuvant stereotactic radiotherapy to the resection cavity is widely used in clinical routine and recommended in international guidelines, the optimal fractionation scheme still remains unclear. The SATURNUS trial prospectively compares adjuvant HFSRT with SRS and seeks to detect the superiority of HFSRT over SRS in terms of local tumor control. METHODS: In this single center two-armed randomized phase III trial, adjuvant radiotherapy to the resection cavity of brain metastases with HFSRT (6 - 7 × 5 Gy prescribed to the surrounding isodose) is compared to SRS (1 × 12-20 Gy prescribed to the surrounding isodose). Patients are randomized 1:1 into the two different treatment arms. The primary endpoint of the trial is local control at the resected site at 12 months. The trial is based on the hypothesis that HFSRT is superior to SRS in terms of local tumor control. DISCUSSION: Although adjuvant stereotactic radiotherapy after resection of brain metastases is considered standard of care treatment, there is a need for further prospective research to determine the optimal fractionation scheme. To the best of our knowledge, the SATURNUS study is the only randomized phase III study comparing different regimes of postoperative stereotactic radiotherapy to the resection cavity adequately powered to detect the superiority of HFSRT regarding local control. TRIAL REGISTRATION: The study was retrospectively registered with ClinicalTrials.gov, number NCT05160818, on December 16, 2021. The trial registry record is available on  https://clinicaltrials.gov/study/NCT05160818 . The presented protocol refers to version V1.3 from March 21, 2021.

MRI-detected intraosseous bone marrow edema recedes after effective therapy of periodontitis.

OBJECTIVES: T2 STIR MRI sequences can detect preclinical changes associated with periodontal inflammation, i.e. intraosseous edema in the tooth-supporting bone. In this study, we assessed whether MRI can be used for monitoring periodontal disease. MATERIAL AND METHODS: In a prospective cohort study, we examined 35 patients with periodontitis between 10/2018 and 04/2019 by using 3D isotropic T2-weighted short tau inversion recovery (STIR) and Fast Field Echo T1-weighted Black bone sequences. All patients received standardized clinical exams before and three months after non-surgical periodontal therapy. Bone marrow edema extent was quantified in the STIR sequence at 922 sites before and after treatment. Results were compared with standard clinical findings. Non-parametric statistical analysis was performed. RESULTS: Non-surgical periodontal treatment caused significant improvement in mean probing depth (p < 0.001) and frequency of bleeding on probing (p < 0.001). The mean depth of osseous edema per site was reduced from a median [IQR] of 2 [1, 3] mm at baseline to 1 [0, 3] mm, (p < 0.001). Periodontal treatment reduced the frequency of sites with edema from 35 to 24% (p < 0.01). CONCLUSION: The decrease of periodontal bone marrow edema, as observed with T2 STIR MR imaging, is indicative of successful periodontal healing. CLINICAL RELEVANCE STATEMENT: T2 STIR hyperintense bone marrow edema in the periodontal bone decreases after treatment and can therefore be used to evaluate treatment success. Furthermore, MRI reveals new options to depict hidden aspects of periodontitis. KEY POINTS: • T2 STIR hyperintense periodontal intraosseous edema was prospectively investigated in 35 patients with periodontitis before and after treatment and compared to clinical outcomes. • The frequency of affected sites was reduced from 35 to 24% (p < 0.001), and mean edema depth was reduced from a median [IQR] of 2 [1, 3] mm at baseline to 1 [0, 3] mm 3 months after treatment. (p < 0.001). • T2 STIR sequences can be used to monitor the posttreatment course of periodontitis.

Synthetic T2-weighted fat sat based on a generative adversarial network shows potential for scan time reduction in spine imaging in a multicenter test dataset.

OBJECTIVES: T2-weighted (w) fat sat (fs) sequences, which are important in spine MRI, require a significant amount of scan time. Generative adversarial networks (GANs) can generate synthetic T2-w fs images. We evaluated the potential of synthetic T2-w fs images by comparing them to their true counterpart regarding image and fat saturation quality, and diagnostic agreement in a heterogenous, multicenter dataset. METHODS: A GAN was used to synthesize T2-w fs from T1- and non-fs T2-w. The training dataset comprised scans of 73 patients from two scanners, and the test dataset, scans of 101 patients from 38 multicenter scanners. Apparent signal- and contrast-to-noise ratios (aSNR/aCNR) were measured in true and synthetic T2-w fs. Two neuroradiologists graded image (5-point scale) and fat saturation quality (3-point scale). To evaluate whether the T2-w fs images are indistinguishable, a Turing test was performed by eleven neuroradiologists. Six pathologies were graded on the synthetic protocol (with synthetic T2-w fs) and the original protocol (with true T2-w fs) by the two neuroradiologists. RESULTS: aSNR and aCNR were not significantly different between the synthetic and true T2-w fs images. Subjective image quality was graded higher for synthetic T2-w fs (p = 0.023). In the Turing test, synthetic and true T2-w fs could not be distinguished from each other. The intermethod agreement between synthetic and original protocol ranged from substantial to almost perfect agreement for the evaluated pathologies. DISCUSSION: The synthetic T2-w fs might replace a physical T2-w fs. Our approach validated on a challenging, multicenter dataset is highly generalizable and allows for shorter scan protocols. KEY POINTS: • Generative adversarial networks can be used to generate synthetic T2-weighted fat sat images from T1- and non-fat sat T2-weighted images of the spine. • The synthetic T2-weighted fat sat images might replace a physically acquired T2-weighted fat sat showing a better image quality and excellent diagnostic agreement with the true T2-weighted fat images. • The present approach validated on a challenging, multicenter dataset is highly generalizable and allows for significantly shorter scan protocols.

A rare case of H3K27-altered diffuse midline glioma with multiple osseous and spinal metastases at the time of diagnosis.

BACKGROUND: H3K27-altered diffuse midline gliomas are uncommon central nervous system tumors with extremely poor prognoses. CASE PRESENTATION: We report the case of a 24-year-old man patient with multiple, inter alia osseous metastases who presented with back pain, hemi-hypoesthesia, and hemi-hyperhidrosis. The patient underwent combined radio-chemotherapy and demonstrated temporary improvement before deteriorating. CONCLUSIONS: H3K27-altered diffuse midline glioma presents an infrequent but crucial differential diagnosis and should be considered in cases with rapid neurological deterioration and multiple intracranial and intramedullary tumor lesions in children and young adults. Combined radio-chemotherapy delayed the neurological deterioration, but unfortunately, progression occurred three months after the diagnosis.

Molecular imaging of gliomas.

Molecular characterization has become a key diagnostic tool for the classification and grading of primary brain tumors. Molecular markers, such as isocitrate dehydrogenase (IDH) mutation status, 1p/19q codeletion, methylation of the O(6)-methylguanine-DNA methyltransferase (MGMT) promoter, or CDKN2A/B homozygous deletion discriminate different tumor entities and grades, and play a crucial role for treatment response and prognosis. In recent years, magnetic resonance imaging (MRI), whose main functions has been to detect a tumor, to provide spatial information for neurosurgical and radiotherapy planning, and to monitor treatment response, has shown potential in assessing molecular features of gliomas from image-based biomarkers. As an outstanding example, numerous studies have proven that the T2/FLAIR mismatch sign can identify IDH-mutant, 1p/19q non-codeleted astrocytomas with a specificity of up to 100%. For other purposes, multiparametric MRI, often coupled with machine learning methods, seems to achieve the highest accuracy in predicting molecular markers. Relevant future applications might be anticipating changes in the molecular composition of gliomas and providing useful information about the cellular and genetic heterogeneity of gliomas, especially in the non-resected tumor parts.

Elucidating the structural-functional connectome of language in glioma-induced aphasia using nTMS and DTI.

Glioma-induced aphasia (GIA) is frequently observed in patients with newly diagnosed gliomas. Previous studies showed an impact of gliomas not only on local brain regions but also on the functionality and structure of brain networks. The current study used navigated transcranial magnetic stimulation (nTMS) to localize language-related regions and to explore language function at the network level in combination with connectome analysis. Thirty glioma patients without aphasia (NA) and 30 patients with GIA were prospectively enrolled. Tumors were located in the vicinity of arcuate fasciculus-related cortical and subcortical regions. The visualized ratio (VR) of each tract was calculated based on their respective fractional anisotropy (FA) and maximal FA. Using a thresholding method of each tract at 25% VR and 50% VR, DTI-based tractography was performed to construct structural brain networks for graph-based connectome analysis, containing functional data acquired by nTMS. The average degree of left hemispheric networks (Mleft ) was higher in the NA group than in the GIA group for both VR thresholds. Differences of global and local efficiency between 25% and 50% VR thresholds were significantly lower in the NA group than in the GIA group. Aphasia levels correlated with connectome properties in Mleft and networks based on positive nTMS mapping regions (Mpos ). A more substantial relation to language performance was found in Mpos and Mleft compared to the network of negative mapping regions (Mneg ). Gliomas causing deterioration of language are related to various cerebral networks. In NA patients, mainly Mneg was impacted, while Mpos was impacted in GIA patients.

Preoperative function-specific connectome analysis predicts surgery-related aphasia after glioma resection.

Glioma resection within language-eloquent regions poses a high risk of surgery-related aphasia (SRA). Preoperative functional mapping by navigated transcranial magnetic stimulation (nTMS) combined with diffusion tensor imaging (DTI) is increasingly used to localize cortical and subcortical language-eloquent areas. This study enrolled 60 nonaphasic patients with left hemispheric perisylvian gliomas to investigate the prediction of SRA based on function-specific connectome network properties under different fractional anisotropy (FA) thresholds. Moreover, we applied a machine learning model for training and cross-validation to predict SRA based on preoperative connectome parameters. Preoperative connectome analysis helps predict SRA development with an accuracy of 73.3% and sensitivity of 78.3%. The current study provides a new perspective of combining nTMS and function-specific connectome analysis applied in a machine learning model to investigate language in neurooncological patients and promises to advance our understanding of the intricate networks.

DEGRO practical guideline for central nervous system radiation necrosis part 2: treatment.

PURPOSE: The Working Group for Neurooncology of the German Society for Radiation Oncology (DEGRO; AG NRO) in cooperation with members of the Neurooncological Working Group of the German Cancer Society (DKG-NOA) 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 neurooncologist. If the diagnosis of blood-brain barrier disruptions (BBD) or RN is likely, treatment should be initiated depending on the symptoms, location, and dynamic of the lesion. Multiple treatment options are available (such as observation, surgery, steroids, and bevacizumab) and the optimal approach should be discussed in an interdisciplinary setting. In this practice guideline, we offer detailed treatment strategies for various scenarios.