Frequent Alzheimer's disease neuropathological change in patients with glioblastoma.

Background: The incidence of brain cancer and neurodegenerative diseases is increasing with a demographic shift towards aging populations. Biological parallels have been observed between glioblastoma and Alzheimer's disease (AD), which converge on accelerated brain aging. Here, we aimed to map the cooccurrence of AD neuropathological change (ADNC) in the tumor-adjacent cortex of patients with glioblastoma. Methods: Immunohistochemical screening of AD markers amyloid beta (Abeta), amyloid precursor protein (APP), and hyperphosphorylated tau (pTau) was conducted in 420 tumor samples of 205 patients. For each cortex area, we quantified ADNC, neurons, tumor cells, and microglia. Results: Fifty-two percent of patients (N = 106/205) showed ADNC (Abeta and pTau, Abeta or pTau) in the tumor-adjacent cortex, with histological patterns widely consistent with AD. ADNC was positively correlated with patient age and varied spatially according to Thal phases and Braak stages. It decreased with increasing tumor cell infiltration (P < .0001) and was independent of frequent expression of APP in neuronal cell bodies (N = 182/205) and in tumor necrosis-related axonal spheroids (N = 195/205; P = .46). Microglia response was most present in tumor cell infiltration plus ADNC, being further modulated by patient age and sex. ADNC did not impact patient survival in the present cohort. Conclusions: Our findings highlight the frequent presence of ADNC in the glioblastoma vicinity, which was linked to patient age and tumor location. The cooccurrence of AD and glioblastoma seemed stochastic without clear spatial relation. ADNC did not impact patient survival in our cohort.

Fast intraoperative detection of primary CNS lymphoma and differentiation from common CNS tumors using stimulated Raman histology and deep learning.

Accurate intraoperative diagnosis is crucial for differentiating between primary CNS lymphoma (PCNSL) and other CNS entities, guiding surgical decision-making, but represents significant challenges due to overlapping histomorphological features, time constraints, and differing treatment strategies. We combined stimulated Raman histology (SRH) with deep learning to address this challenge. We imaged unprocessed, label-free tissue samples intraoperatively using a portable Raman scattering microscope, generating virtual H&E-like images within less than three minutes. We developed a deep learning pipeline called RapidLymphoma based on a self-supervised learning strategy to (1) detect PCNSL, (2) differentiate from other CNS entities, and (3) test the diagnostic performance in a prospective international multicenter cohort and two additional independent test cohorts. We trained on 54,000 SRH patch images sourced from surgical resections and stereotactic-guided biopsies, including various CNS tumor/non-tumor lesions. Training and test data were collected from four tertiary international medical centers. The final histopathological diagnosis served as ground-truth. In the prospective test cohort of PCNSL and non-PCNSL entities (n=160), RapidLymphoma achieved an overall balanced accuracy of 97.81% ±0.91, non-inferior to frozen section analysis in detecting PCNSL (100% vs. 78.94%). The additional test cohorts (n=420, n=59) reached balanced accuracy rates of 95.44% ±0.74 and 95.57% ±2.47 in differentiating IDH-wildtype diffuse gliomas and various brain metastasis from PCNSL. Visual heatmaps revealed RapidLymphoma's capabilities to detect class-specific histomorphological key features. RapidLymphoma is valid and reliable in detecting PCNSL and differentiating from other CNS entities within three minutes, as well as visual feedback in an intraoperative setting. This leads to fast clinical decision-making and further treatment strategy planning.

Glioblastoma in the real-world setting: patterns of care and outcome in the Austrian population.

PURPOSE: We present results of a retrospective population-based investigation of patterns of care and outcome of glioblastoma patients in Austria. PATIENTS AND METHODS: In this nation-wide cooperative project, all Austrian glioblastoma patients newly diagnosed between 2014 and 2018 and registered in the ABTR-SANOnet database were included. Histological typing used criteria of the WHO classification of CNS tumors, 4th edition 2016. Patterns of care were assessed, and all patients were followed until the end of 2019. RESULTS: 1,420 adult glioblastoma cases were identified. 813 (57.3%) patients were male and 607 (42.7%) female. Median age at diagnosis was 64 years (range: 18-88). Median overall survival (OS) was 11.6 months in the total cohort and 10.9 months in patients with proven IDH-wildtype. Median OS in the patient group ≤ 65 years receiving postoperative standard of care therapy was 16.1 months. In the patient group > 65 years with postoperative therapy, median OS was 11.2 months. Follow-up ≥ 5 years identified 13/264 (4.9%) long-term survivors. Brain tumor surgery frequently was assisted by 5-aminolevulinic acid (5-ALA) fluorescence (up to 55%). Postoperative treatment was initiated around one month after surgery (median: 31 days) following standardized protocols in 1,041/1,420 (73.3%) cases. In 830 patients (58.5%), concomitant radiochemotherapy was started according to the established standard of care. Treatment in case of progressive disease was considerably variable. 170/1,420 patients (12.0%) underwent a second surgical procedure, 467 (33.0%) received systemic treatment after progression, and 173 (12.2%) were re-irradiated. CONCLUSION: Our data illustrate and confirm nation-wide translation of effective standard of care to Austrian glioblastoma patients in the recent past. In the case of progressive disease, highly variable therapeutic approaches were used, most frequently accompanied by anti-angiogenic therapy. Long-term survival was observed in a minor proportion of mostly younger patients who typically had gross total tumor resection, a favorable postoperative ECOG score, and standard of care therapy.

Localization of protoporphyrin IX during glioma-resection surgery via paired stimulated Raman histology and fluorescence microscopy.

The most widely used fluorophore in glioma-resection surgery, 5-aminolevulinic acid (5-ALA), is thought to cause the selective accumulation of fluorescent protoporphyrin IX (PpIX) in tumour cells. Here we show that the clinical detection of PpIX can be improved via a microscope that performs paired stimulated Raman histology and two-photon excitation fluorescence microscopy (TPEF). We validated the technique in fresh tumour specimens from 115 patients with high-grade gliomas across four medical institutions. We found a weak negative correlation between tissue cellularity and the fluorescence intensity of PpIX across all imaged specimens. Semi-supervised clustering of the TPEF images revealed five distinct patterns of PpIX fluorescence, and spatial transcriptomic analyses of the imaged tissue showed that myeloid cells predominate in areas where PpIX accumulates in the intracellular space. Further analysis of external spatially resolved metabolomics, transcriptomics and RNA-sequencing datasets from glioblastoma specimens confirmed that myeloid cells preferentially accumulate and metabolize PpIX. Our findings question 5-ALA-induced fluorescence in glioma cells and show how 5-ALA and TPEF imaging can provide a window into the immune microenvironment of gliomas.

FINAL outcome ANALYSIS FROM THE PHASE II TUXEDO-1 TRIAL OF TRASTUZUMAB-DERUXTECAN IN HER2-positive breast cancer PATIENTS WITH active brain metastases.

BACKGROUND: Brain metastases (BM) are a devastating complication of HER2-positive metastatic breast cancer (BC) and treatment strategies providing optimized local and systemic disease control are urgently required. The antibody-drug conjugate (ADC) trastuzumab deruxtecan (T-DXd) improved progression-free survival (PFS) and overall survival (OS) over trastuzumab emtansine but data regarding intracranial activity is limited. In the primary outcome analysis of TUXEDO-1, a high intracranial response rate (RR) was reported with T-DXd. Here, we report final PFS and OS results. PATIENTS AND METHODS: TUXEDO-1 accrued adult patients with HER2-positive BC and active BM (newly diagnosed or progressing) without indication for immediate local therapy. The primary endpoint was intracranial RR; secondary endpoints included PFS, OS, safety, quality-of-life (QoL), and neurocognitive function. PFS and OS were estimated with the Kaplan-Meier method and analysed in the per-protocol population. RESULTS: At 26.5 months median follow-up, median PFS was 21 months (95% CI 13.3-n.r.) and median OS was not reached (95% CI 22.2-n.r.). With longer follow-up, no new safety signals were observed. The most common grade 3 adverse event was fatigue (20%). Grade 2 interstitial lung disease and a grade 3 symptomatic drop of left-ventricular ejection fraction were observed in one patient each. QoL was maintained over the treatment period. DISCUSSION: T-DXd yielded prolonged intra- and extracranial disease control in patients with active HER2-positive BC BM in line with results from the pivotal trials. These results support the concept of ADCs as systemic therapy for active BM.

7 Tesla magnetic resonance spectroscopic imaging predicting IDH status and glioma grading.

INTRODUCTION: With the application of high-resolution 3D 7 Tesla Magnetic Resonance Spectroscopy Imaging (MRSI) in high-grade gliomas, we previously identified intratumoral metabolic heterogeneities. In this study, we evaluated the potential of 3D 7 T-MRSI for the preoperative noninvasive classification of glioma grade and isocitrate dehydrogenase (IDH) status. We demonstrated that IDH mutation and glioma grade are detectable by ultra-high field (UHF) MRI. This technique might potentially optimize the perioperative management of glioma patients. METHODS: We prospectively included 36 patients with WHO 2021 grade 2-4 gliomas (20 IDH mutated, 16 IDH wildtype). Our 7 T 3D MRSI sequence provided high-resolution metabolic maps (e.g., choline, creatine, glutamine, and glycine) of these patients' brains. We employed multivariate random forest and support vector machine models to voxels within a tumor segmentation, for classification of glioma grade and IDH mutation status. RESULTS: Random forest analysis yielded an area under the curve (AUC) of 0.86 for multivariate IDH classification based on metabolic ratios. We distinguished high- and low-grade tumors by total choline (tCho) / total N-acetyl-aspartate (tNAA) ratio difference, yielding an AUC of 0.99. Tumor categorization based on other measured metabolic ratios provided comparable accuracy. CONCLUSIONS: We successfully classified IDH mutation status and high- versus low-grade gliomas preoperatively based on 7 T MRSI and clinical tumor segmentation. With this approach, we demonstrated imaging based tumor marker predictions at least as accurate as comparable studies, highlighting the potential application of MRSI for pre-operative tumor classifications.

The plasma miRNome and venous thromboembolism in high-grade glioma: miRNA Sequencing of a nested case-control cohort.

Patients with high-grade gliomas are at high risk of venous thromboembolism (VTE). MicroRNAs (miRNAs) are small non-coding RNAs with multiple roles in tumour biology, haemostasis and platelet function. Their association with VTE risk in high-grade glioma has not been comprehensively mapped so far. We thus conducted a nested case-control study within 152 patients with WHO grade IV glioma that had been part of a prospective cohort study on VTE risk factors. At inclusion a single blood draw was taken, and patients were thereafter followed for a maximum of 2 years. During that time, 24 patients (16%) developed VTE. Of the other 128 patients, we randomly selected 24 age- and sex-matched controls. After quality control, the final group size was 21 patients with VTE during follow-up and 23 without VTE. Small RNA next-generation sequencing of plasma was performed. We observed that hsa-miR-451a was globally the most abundant miRNA. Notably, 51% of all miRNAs showed a correlation with platelet count. The analysis of miRNAs differentially regulated in VTE patients-with and without platelet adjustment-identified potential VTE biomarker candidates such as has-miR-221-3p. Therewith, we here provide one of the largest and deepest peripheral blood miRNA datasets of high-grade glioma patients so far, in which we identified first VTE biomarker candidates that can serve as the starting point for future research.

A Comparison of 7 Tesla MR Spectroscopic Imaging and 3 Tesla MR Fingerprinting for Tumor Localization in Glioma Patients.

This paper investigated the correlation between magnetic resonance spectroscopic imaging (MRSI) and magnetic resonance fingerprinting (MRF) in glioma patients by comparing neuro-oncological markers obtained from MRSI to T1/T2 maps from MRF. Data from 12 consenting patients with gliomas were analyzed by defining hotspots for T1, T2, and various metabolic ratios, and comparing them using Sørensen-Dice similarity coefficients (DSCs) and the distances between their centers of intensity (COIDs). The median DSCs between MRF and the tumor segmentation were 0.73 (T1) and 0.79 (T2). The DSCs between MRSI and MRF were the highest for Gln/tNAA (T1: 0.75, T2: 0.80, tumor: 0.78), followed by Gly/tNAA (T1: 0.57, T2: 0.62, tumor: 0.54) and tCho/tNAA (T1: 0.61, T2: 0.58, tumor: 0.45). The median values in the tumor hotspot were T1 = 1724 ms, T2 = 86 ms, Gln/tNAA = 0.61, Gly/tNAA = 0.28, Ins/tNAA = 1.15, and tCho/tNAA = 0.48, and, in the peritumoral region, were T1 = 1756 ms, T2 = 102 ms, Gln/tNAA = 0.38, Gly/tNAA = 0.20, Ins/tNAA = 1.06, and tCho/tNAA = 0.38, and, in the NAWM, were T1 = 950 ms, T2 = 43 ms, Gln/tNAA = 0.16, Gly/tNAA = 0.07, Ins/tNAA = 0.54, and tCho/tNAA = 0.20. The results of this study constitute the first comparison of 7T MRSI and 3T MRF, showing a good correspondence between these methods.

5-ALA localises to the autophagy compartment and increases its fluorescence upon autophagy enhancement through caloric restriction and spermidine treatment in human glioblastoma.

Glioblastoma Multiforme (GBM) is the most invasive and prevalent Central Nervous System (CNS) malignancy. It is characterised by diffuse infiltrative growth and metabolic dysregulation that impairs the extent of surgical resection (EoR), contributing to its poor prognosis. 5-Aminolevulinic acid (5-ALA) fluorescence-guided surgical resection (FGR) takes advantage of the preferential generation of 5-ALA-derived fluorescence signal in glioma cells, thereby improving visualisation and enhancing the EoR. However, despite 5-ALA FGR is a widely used technique in the surgical management of malignant gliomas, the infiltrative tumour margins usually show only vague or no visible fluorescence and thus a significant amount of residual tumour tissue may hence remain in the resection cavity, subsequently driving tumour recurrence. To investigate the molecular mechanisms that govern the preferential accumulation of 5-ALA in glioma cells, we investigated the precise subcellular localisation of 5-ALA signal using Correlative Light and Electron Microscopy (CLEM) and colocalisation analyses in U118MG glioma cells. Our results revealed strong 5-ALA signal localisation in the autophagy compartment - specifically autolysosomes and lysosomes. Flow cytometry was employed to investigate whether autophagy enhancement through spermidine treatment (SPD) or nutrient deprivation/caloric restriction (CR) would enhance 5-ALA fluorescence signal generation. Indeed, SPD, CR and a combination of SPD/CR treatment significantly increased 5-ALA signal intensity, with a most robust increase in signal intensity observed in the combination treatment of SPD/CR. When using 3-D glioma spheroids to assess the effect of 5-ALA on cellular ultrastructure, we demonstrate that 5-ALA exposure leads to cytoplasmic disruption, vacuolarisation and large-scale mitophagy induction. These findings not only suggest a critical role for the autophagy compartment in 5-ALA engagement and signal generation but also point towards a novel and practically feasible approach to enhance 5-ALA fluorescence signal intensity. The findings may highlight that indeed autophagy control may serve as a promising avenue to promote an improved resection and GBM prognosis.

Clinical characteristics, treatment, and outcome of patients with large cell neuroendocrine carcinoma of the lung and brain metastases - data from a tertiary care center.

Large cell neuroendocrine carcinoma (LCNEC) of the lung is an aggressive malignancy, with brain metastases (BM) occurring in approximately 20% of cases. There are currently no therapy guidelines for this population as only few data on the management of LCNEC and BM have been published. For this retrospective single center study, patients with LCNEC and BM were identified from the Vienna Brain Metastasis Registry. Data on clinicopathological features, BM-specific characteristics, treatment, and outcome were extracted. In total, 52/6083 (0.09%) patients in the dataset had a diagnosis of LCNEC and radiologically verified BM. Median age at diagnosis of LCNEC and BM was 59.1 and 60.1 years, respectively. Twenty-seven (51.9%) presented with single BM, while 12 (23%) exhibited > 3 BM initially. Neurologic symptoms due to BM were present in n = 40 (76.9%), encompassing neurologic deficits (n = 24), increased intracranial pressure (n = 18), and seizures (n = 6). Initial treatment of BM was resection (n = 13), whole brain radiation therapy (n = 19), and/or stereotactic radiosurgery (n = 25). Median overall survival (mOS) from LCNEC diagnosis was 16 months, and mOS after BM diagnosis was 7 months. Patients with synchronous BM had reduced mOS from LCNEC diagnosis versus patients with metachronous BM (11 versus 27 months, p = 0.003). Median OS after BM diagnosis did not differ between LCNEC patients and a control group of small cell lung cancer patients with BM (7 versus 6 months, p = 0.17). Patients with LCNEC and BM have a poor prognosis, particularly when synchronous BM are present. Prospective trials are required to define optimal therapeutic algorithms.

Comparison of minimal detectable protoporphyrin IX concentrations with a loupe device and conventional 5-ALA fluorescence microscopy: an experimental study.

Significance: The 5-aminolevulinic acid (5-ALA) fluorescence technique is now widely applied for intraoperative visualization of specific central nervous system (CNS) tumors. Previous technical implementations of this technique have relied on specifically modified surgical microscopes to visualize intratumoral fluorescent protoporphyrin (PpIX). While this approach evidently allows for reliable intraoperative tumor visualization, it requires the availability of specifically modified surgical microscopes and their use even in cases where the operating neurosurgeon would prefer to use surgical loupes. Recently, a novel loupe device was introduced that is also capable of visualizing 5-ALA fluorescence. Aim: The aim of this study was therefore to compare the detected PpIX concentrations between the conventional fluorescence microscope and the novel loupe device. Approach: We used fluorescence phantoms of different PpIX concentrations for comparison between a conventional fluorescence microscope and the novel loupe device. For this purpose, we created fluorescence images using the excitation light sources of the conventional fluorescence microscope and the loupe device with both available background illumination modes (low and high). Subsequently, the minimal detectable PpIX concentrations according to each technique were determined by five independent neurosurgeons. Results: Using the conventional fluorescence microscope, the median minimal detectable PpIX concentration was 0.16 µg/ml (range: 0.15 to 0.17 µg/ml). By the loupe device, the median minimal detectable PpIX concentration was 0.12 µg/ml (range: 0.10 to 0.12 µg/ml) and 0.08 µg/ml (range: 0.07 to 0.08 µg/ml) for the high- and low-modes, respectively. Altogether, the minimal detectable PpIX concentrations were significantly lower using the loupe device compared to the conventional fluorescence microscope (p=0.007). Conclusions: Our data indicate that the novel loupe device is able to visualize 5-ALA fluorescence with high sensitivity and thus might serve as a powerful tool for visualization of specific CNS tumors in the future.

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks.

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.

Multi-class glioma segmentation on real-world data with missing MRI sequences: comparison of three deep learning algorithms.

This study tests the generalisability of three Brain Tumor Segmentation (BraTS) challenge models using a multi-center dataset of varying image quality and incomplete MRI datasets. In this retrospective study, DeepMedic, no-new-Unet (nn-Unet), and NVIDIA-net (nv-Net) were trained and tested using manual segmentations from preoperative MRI of glioblastoma (GBM) and low-grade gliomas (LGG) from the BraTS 2021 dataset (1251 in total), in addition to 275 GBM and 205 LGG acquired clinically across 12 hospitals worldwide. Data was split into 80% training, 5% validation, and 15% internal test data. An additional external test-set of 158 GBM and 69 LGG was used to assess generalisability to other hospitals' data. All models' median Dice similarity coefficient (DSC) for both test sets were within, or higher than, previously reported human inter-rater agreement (range of 0.74-0.85). For both test sets, nn-Unet achieved the highest DSC (internal = 0.86, external = 0.93) and the lowest Hausdorff distances (10.07, 13.87 mm, respectively) for all tumor classes (p < 0.001). By applying Sparsified training, missing MRI sequences did not statistically affect the performance. nn-Unet achieves accurate segmentations in clinical settings even in the presence of incomplete MRI datasets. This facilitates future clinical adoption of automated glioma segmentation, which could help inform treatment planning and glioma monitoring.

Radiomic features define risk and are linked to DNA methylation attributes in primary CNS lymphoma.

Background: The prognostic roles of clinical and laboratory markers have been exploited to model risk in patients with primary CNS lymphoma, but these approaches do not fully explain the observed variation in outcome. To date, neuroimaging or molecular information is not used. The aim of this study was to determine the utility of radiomic features to capture clinically relevant phenotypes, and to link those to molecular profiles for enhanced risk stratification. Methods: In this retrospective study, we investigated 133 patients across 9 sites in Austria (2005-2018) and an external validation site in South Korea (44 patients, 2013-2016). We used T1-weighted contrast-enhanced MRI and an L1-norm regularized Cox proportional hazard model to derive a radiomic risk score. We integrated radiomic features with DNA methylation profiles using machine learning-based prediction, and validated the most relevant biological associations in tissues and cell lines. Results: The radiomic risk score, consisting of 20 mostly textural features, was a strong and independent predictor of survival (multivariate hazard ratio = 6.56 [3.64-11.81]) that remained valid in the external validation cohort. Radiomic features captured gene regulatory differences such as in BCL6 binding activity, which was put forth as testable treatment target for a subset of patients. Conclusions: The radiomic risk score was a robust and complementary predictor of survival and reflected characteristics in underlying DNA methylation patterns. Leveraging imaging phenotypes to assess risk and inform epigenetic treatment targets provides a concept on which to advance prognostic modeling and precision therapy for this aggressive cancer.

Mapping high-grade glioma immune infiltration to 5-ALA fluorescence levels: TCGA data computation, classical histology, and digital image analysis.

PURPOSE: Resection of high-grade gliomas has been considerably improved by 5-aminolevulinic acid (5-ALA). However, not all neurobiological properties of 5-ALA are fully understood. Specifically, potential differences in immune infiltration have not been conclusively examined, despite recent reports that immune cells might play a role. Thus, we here provide a systematic mapping of immune infiltration of different 5-ALA fluorescence levels. METHODS: Tumor-associated macrophages (CD68, CD163), cytotoxic T cells (CD8), and regulatory T cells (FoxP3) were quantified via three methods. First, data from The Cancer Genome Atlas (TCGA) of 172 patients was examined for correlations between 5-ALA fluorescence-related mRNA expression signatures and immune markers. Second, as classical histology, 508 stained slides from 39 high-grade glioma patients were analysed semi-quantitatively by two independent reviewers, generating 1016 data points. Third, digital image analysis was performed with automated scanning and algorithm-based cell quantification. RESULTS: TCGA mRNA data from 172 patients showed a direct, significant correlation between 5-ALA signatures and immune markers (p < 0.001). However, we were not able to confirm this finding in the here studied initial set of 39 patient histologies where we found a comparable immune infiltration in different fluorescence levels. Digital image analysis correlated excellently with standard histology. CONCLUSION: With mapping the immune infiltration pattern of different 5-ALA categories, we are adding fundamental basic insights to the field of 5-ALA and glioma biology. The observation that a significant correlation in TCGA data did not fully translate to detectable differences in immune infiltration in first histology data warrants further investigation in larger cohorts.

Treatment of high-grade glioma patients during the COVID-19 pandemic: Impact on overall survival, tumor size and delay of treatment.

BACKGROUND: Throughout the last years, the coronavirus disease 2019 (COVID-19) pandemic posed a major challenge to the optimal and timely treatment of neurooncological patients around the world. While the importance of prompt surgical treatment in high-grade gliomas is widely accepted, there is sparse data on the impact of the pandemic on patients suffering from this malignant disease. METHODS: We performed a retrospective analysis of patients undergoing surgical high-grade glioma treatment at the Medical University of Vienna between March 2020 and February 2021, as well as a control cohort of patients who received treatment between January and December 2019. Time lag between referral for surgical treatment to actual surgery, preoperative tumor volume and overall patient survival were compared between groups. RESULTS: A total of 118 patients, including 62 cases treated during the first year of the COVID-19 pandemic, as well as 56 control patients, were investigated in this study. Median interval to surgery was significantly shorter in patients treated during COVID-19 compared with the control group (4.00 versus 7.00 days; p = 0.0005). In contrast, patients treated during COVID-19 exhibited marginally larger preoperative tumor volumes, while overall patient survival was comparable between groups. CONCLUSIONS: The COVID-19 pandemic did not negatively affect the overall survival of patients undergoing surgical high-grade glioma treatment at our institution. The significantly shorter treatment delay in patients treated during the pandemic likely reflects increased resource allocation for this critical patient population.

Evaluation of Gliomas with Magnetic Resonance Fingerprinting with PET Correlation-A Comparative Study.

OBJECTIVES: Advanced MR imaging of brain tumors is still mainly based on qualitative imaging. PET imaging offers additive metabolic information, and MR fingerprinting (MRF) offers a novel approach to quantitative data acquisition. The purpose of this study was to evaluate the ability of MRF to predict tumor regions and grading in combination with PET. METHODS: Seventeen patients with histologically verified infiltrating gliomas and available amino-acid PET data were enrolled. ROIs for solid tumor parts (SPo), perifocal edema (ED1), and normal-appearing white matter (NAWM) were selected on conventional MRI sequences and aligned to the MRF and PET images. The predictability of gliomas by region and grading as well as intermodal correlations were assessed. RESULTS: For MRF, we calculated an overall predictability by region (SPo, ED1, and NAWM) for all of the MRF parameters of 76.5%, 47.1%, and 94.1%, respectively. The overall ability to distinguish low- from high-grade gliomas using MRF was 88.9% for LGG and 75% for HGG, with an accuracy of 82.4%, a ppV of 85.71%, and an npV of 80%. PET positivity was found in 13/17 patients for solid tumor parts, and in 3/17 patients for the edema region. However, there was no significant difference in region-specific MRF values between PET positive and PET negative patients. CONCLUSIONS: MRF and PET provide quantitative measurements of the tumor tissue characteristics of gliomas, with good predictability. Nonetheless, the results are dissimilar, reflecting the different underlying mechanisms of each method.

Clinical characteristics, treatment, and long-term outcome of patients with brain metastases from thyroid cancer.

Brain metastases (BM) in patients with thyroid cancer (TC) are rare with an incidence of 1% for papillary and follicular, 3% for medullary and up to 10% for anaplastic TC (PTC, FTC, MTC and ATC). Little is known about the characteristics and management of BM from TC. Thus, we retrospectively analyzed patients with histologically verified TC and radiologically verified BM identified from the Vienna Brain Metastasis Registry. A total of 20/6074 patients included in the database since 1986 had BM from TC and 13/20 were female. Ten patients had FTC, 8 PTC, one MTC and one ATC. The median age at diagnosis of BM was 68 years. All but one had symptomatic BM and 13/20 patients had a singular BM. Synchronous BM at primary diagnosis were found in 6 patients, while the median time to BM diagnosis was 13 years for PTC (range 1.9-24), 4 years for FTC (range 2.1-41) and 22 years for the MTC patient. The overall survival from diagnosis of BM was 13 months for PTC (range 1.8-57), 26 months for FTC (range 3.9-188), 12 years for the MTC and 3 months for the ATC patient. In conclusion, development of BM from TC is exceedingly rare and the most common presentation is a symptomatic single lesion. While BM generally constitute a poor prognostic factor, individual patients experience long-term survival following local therapy.

Frequent Overexpression of HER3 in Brain Metastases from Breast and Lung Cancer.

PURPOSE: HER3 belongs to a family of receptor tyrosine kinases with oncogenic properties and is targeted by a variety of novel anticancer agents. There is a huge unmet medical need for systemic treatment options in patients with brain metastases (BM). Therefore, we aimed to investigate HER3 expression in BM of breast (BCa) and non-small cell lung cancer (NSCLC) as the basis for future clinical trial design. EXPERIMENTAL DESIGN: We analyzed 180 BM samples of breast cancer or NSCLC and 47 corresponding NSCLC extracranial tissue. IHC was performed to evaluate protein expression of HER3, and immune cells based on CD3, CD8, and CD68. To identify dysregulated pathways based on differential DNA methylation patterns, we used Infinium MethylationEPIC microarrays. RESULTS: A total of 99/132 (75.0%) of BCa-BM and 35/48 (72.9%) of NSCLC-BM presented with HER3 expression. Among breast cancer, HER2-positive and HER2-low BM showed significantly higher rates of HER3 coexpression than HER2-negative BM (87.1%/85.7% vs. 61.0%, P = 0.004). Among NSCLC, HER3 was more abundantly expressed in BM than in matched extracranial samples (72.9% vs. 41.3%, P = 0.003). No correlation of HER3 expression and intratumoral immune cell density was observed. HER3 expression did not correlate with overall survival from BM diagnosis. Methylation signatures differed according to HER3 status in BCa-BM samples. Pathway analysis revealed subtype-specific differences, such as TrkB and Wnt signaling pathways dysregulated in HER2-positive and triple-negative breast cancer BM, respectively. CONCLUSIONS: HER3 is highly abundant in BM of breast cancer and NSCLC. Given the promising results of antibody-drug conjugates in extracranial disease, BM-specific trials that target HER3 are warranted. See related commentary by Kabraji and Lin, p. 2961.

102 AI-Based Molecular Classification of Diffuse Gliomas using Rapid, Label-Free Optical Imaging.

INTRODUCTION: Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. Access to timely molecular diagnostic testing for brain tumor patients is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment. METHODS: By combining stimulated Raman histology (SRH), a rapid, label-free, non-consumptive, optical imaging method, and deep learning-based image classification, we are able to predict the molecular genetic features used by the World Health Organization (WHO) to define the adult-type diffuse glioma taxonomy, including IDH-1/2, 1p19q-codeletion, and ATRX loss. We developed a multimodal deep neural network training strategy that uses both SRH images and large-scale, public diffuse glioma genomic data (i.e. TCGA, CGGA, etc.) in order to achieve optimal molecular classification performance. RESULTS: One institution was used for model training (University of Michigan) and four institutions (NYU, UCSF, Medical University of Vienna, and University Hospital Cologne) were included for patient enrollment in the prospective testing cohort. Using our system, called DeepGlioma, we achieved an average molecular genetic classification accuracy of 93.2% and identified the correct diffuse glioma molecular subgroup with 91.5% accuracy within 2 minutes in the operating room. DeepGlioma outperformed conventional IDH1-R132H immunohistochemistry (94.2% versus 91.4% accuracy) as a first-line molecular diagnostic screening method for diffuse gliomas and can detect canonical and non-canonical IDH mutations. CONCLUSIONS: Our results demonstrate how artificial intelligence and optical histology can be used to provide a rapid and scalable alternative to wet lab methods for the molecular diagnosis of brain tumor patients during surgery.