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.

Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma.

BACKGROUND: Deep learning has revolutionized medical image analysis in cancer pathology, where it had a substantial clinical impact by supporting the diagnosis and prognostic rating of cancer. Among the first available digital resources in the field of brain cancer is glioblastoma, the most common and fatal brain cancer. At the histologic level, glioblastoma is characterized by abundant phenotypic variability that is poorly linked with patient prognosis. At the transcriptional level, 3 molecular subtypes are distinguished with mesenchymal-subtype tumors being associated with increased immune cell infiltration and worse outcome. RESULTS: We address genotype-phenotype correlations by applying an Xception convolutional neural network to a discovery set of 276 digital hematozylin and eosin (H&E) slides with molecular subtype annotation and an independent The Cancer Genome Atlas-based validation cohort of 178 cases. Using this approach, we achieve high accuracy in H&E-based mapping of molecular subtypes (area under the curve for classical, mesenchymal, and proneural = 0.84, 0.81, and 0.71, respectively; P < 0.001) and regions associated with worse outcome (univariable survival model P < 0.001, multivariable P = 0.01). The latter were characterized by higher tumor cell density (P < 0.001), phenotypic variability of tumor cells (P < 0.001), and decreased T-cell infiltration (P = 0.017). CONCLUSIONS: We modify a well-known convolutional neural network architecture for glioblastoma digital slides to accurately map the spatial distribution of transcriptional subtypes and regions predictive of worse outcome, thereby showcasing the relevance of artificial intelligence-enabled image mining in brain cancer.

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.

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.

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.

Flavin fluorescence lifetime and autofluorescence optical redox ratio for improved visualization and classification of brain tumors.

Purpose: Modern techniques for improved tumor visualization have the aim to maximize the extent of resection during brain tumor surgery and thus improve patient prognosis. Optical imaging of autofluorescence is a powerful and non-invasive tool to monitor metabolic changes and transformation in brain tumors. Cellular redox ratios can be retrieved from fluorescence emitted by the coenzymes reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavin adenine dinucleotide (FAD). Recent studies point out that the influence of flavin mononucleotide (FMN) has been underestimated. Experimental design: Fluorescence lifetime imaging and fluorescence spectroscopy were performed through a modified surgical microscope. We acquired 361 flavin fluorescence lifetime (500-580 nm) and fluorescence spectra (430-740 nm) data points on freshly excised different brain tumors: low-grade gliomas (N=17), high-grade gliomas (N=42), meningiomas (N=23), metastases (N=26) and specimens from the non-tumorous brain (N=3). Results: Protein-bound FMN fluorescence in brain tumors did increase with a shift toward a more glycolytic metabolism (R=-0.87). This increased the average flavin fluorescence lifetime in tumor entities with respect to the non-tumorous brain. Further, these metrics were characteristic for the different tumor entities and showed promise for machine learning based brain tumor classification. Conclusions: Our results shed light on FMN fluorescence in metabolic imaging and outline the potential for supporting the neurosurgeon in visualizing and classifying brain tumor tissue during surgery.

CD34 microvascularity in low-grade glioma: correlation with 5-aminolevulinic acid fluorescence and patient prognosis in a multicenter study at three specialized centers.

OBJECTIVE: Early markers are urgently needed in low-grade glioma (LGG) evaluation to rapidly estimate the individual patient's prognosis and to determine the optimal postoperative management. Generally, visible 5-aminolevulinic acid (5-ALA) fluorescence is present in only a few LGGs. Recently, the authors identified visible 5-ALA fluorescence as a powerful intraoperative marker for unfavorable outcome in LGG treatment. However, its precise histopathological correlate is unclear. Neoangiogenesis represents a crucial event in tumor evolution, and CD34 is an established marker for vascular endothelial progenitors potentially indicating tumor progression. The aim of this study was thus to correlate 5-ALA fluorescence and CD34 microvascularity as well as to investigate the prognostic value of CD34 in a large series of LGGs. METHODS: In this retrospective study including 3 specialized centers, patients with histopathologically confirmed isocitrate dehydrogenase-mutated LGGs (WHO grade II) receiving 5-ALA prior to resection were included. During surgery, the presence of visible fluorescence was analyzed and one representative tumor sample from the area with the maximum fluorescence effect (tumor with focal fluorescence or nonfluorescing tumor) was selected for each LGG. All fluorescing or nonfluorescing tumor samples were stained for CD34 and semiquantitatively analyzed for microvascular proliferation patterns (physiological vessels, branching capillaries, or microvessel clusters) as well as automatically quantified for CD34 microvessel density (MVD) by standardized histomorphometry software. These semiquantitative/quantitative CD34 data were correlated to the fluorescence status and patient outcome including progression-free survival (PFS), malignant transformation-free survival (MTFS), and overall survival (OS). RESULTS: In a total of 86 LGGs, visible fluorescence was found during surgery in 13 (15%) cases. First, the semiquantitative CD34 score significantly correlated with intraoperative fluorescence (p = 0.049). Accordingly, the quantitative CD34 MVD was significantly higher in tumors showing fluorescence (p = 0.03). Altogether, the semiquantitative CD34 score showed a strong correlation with quantitative CD34 MVD (p < 0.001). At a mean follow-up of 5.4 ± 2.6 years, microvessel clusters in semiquantitative analysis were a prognostic marker for poor PFS (p = 0.01) and MTFS (p = 0.006), but not OS (p = 0.28). Finally, quantitative CD34 MVD > 10 vessels/mm2 was a prognostic marker for poor PFS (p = 0.01), MTFS (p = 0.008), and OS (p = 0.049). CONCLUSIONS: The data indicate that CD34 microvascularity is associated with intraoperative 5-ALA fluorescence and outcomes in patients with LGG. Thus, visible fluorescence in LGGs might indicate increased CD34 microvascularity, serving as an early prognostic marker for unfavorable patient outcome that is already available during surgery.

Toward digital histopathological assessment in surgery for central nervous system tumors using stimulated Raman histology.

OBJECTIVE: Intraoperative neuropathological assessment with conventional frozen sections supports the neurosurgeon in optimizing the surgical strategy. However, preparation and review of frozen sections can take as long as 45 minutes. Stimulated Raman histology (SRH) was introduced as a novel technique to provide rapid high-resolution digital images of unprocessed tissue samples directly in the operating room that are comparable to conventional histopathological images. Additionally, SRH images are simultaneously and easily accessible for neuropathological judgment. Recently, the first study showed promising results regarding the accuracy and feasibility of SRH compared with conventional histopathology. Thus, the aim of this study was to compare SRH with conventional H&E images and frozen sections in a large cohort of patients with different suspected central nervous system (CNS) tumors. METHODS: The authors included patients who underwent resection or stereotactic biopsy of suspected CNS neoplasm, including brain and spinal tumors. Intraoperatively, tissue samples were safely collected and SRH analysis was performed directly in the operating room. To enable optimal comparison of SRH with H&E images and frozen sections, the authors created a digital databank that included images obtained with all 3 imaging modalities. Subsequently, 2 neuropathologists investigated the diagnostic accuracy, tumor cellularity, and presence of diagnostic histopathological characteristics (score 0 [not present] through 3 [excellent]) determined with SRH images and compared these data to those of H&E images and frozen sections, if available. RESULTS: In total, 94 patients with various suspected CNS tumors were included, and the application of SRH directly in the operating room was feasible in all cases. The diagnostic accuracy based on SRH images was 99% when compared with the final histopathological diagnosis based on H&E images. Additionally, the same histopathological diagnosis was established in all SRH images (100%) when compared with that of the corresponding frozen sections. Moreover, the authors found a statistically significant correlation in tumor cellularity between SRH images and corresponding H&E images (p < 0.0005 and R = 0.867, Pearson correlation coefficient). Finally, excellent (score 3) or good (2) accordance between diagnostic histopathological characteristics and H&E images was present in 95% of cases. CONCLUSIONS: The results of this retrospective analysis demonstrate the near-perfect diagnostic accuracy and capability of visualizing relevant histopathological characteristics with SRH compared with conventional H&E staining and frozen sections. Therefore, digital SRH histopathology seems especially useful for rapid intraoperative investigation to confirm the presence of diagnostic tumor tissue and the precise tumor entity, as well as to rapidly analyze multiple tissue biopsies from the suspected tumor margin. A real-time analysis comparing SRH images and conventional histological images at the time of surgery should be performed as the next step in future studies.

Heme Biosynthesis Factors and 5-ALA Induced Fluorescence: Analysis of mRNA and Protein Expression in Fluorescing and Non-fluorescing Gliomas.

Objective: The intraoperative visualization of adult-type diffuse gliomas with 5-aminolevulinic acid (5-ALA) induced fluorescence is widely used in the neurosurgical field. While visible 5-ALA induced fluorescence is found in the majority of high-grade gliomas, most low-grade gliomas lack visible fluorescence during surgery. Recently, the heme biosynthesis pathway was identified as crucial influencing factor for presence of visible fluorescence since it metabolizes 5-ALA to fluorescing Protoporphyrin IX (PpIX). However, the exact alterations within the heme biosynthesis pathway resulting in visible 5-ALA induced fluorescence in gliomas are still unclear. The aim of the present study was thus to compare the mRNA and protein expression of promising intramitochondrial heme biosynthesis enzymes/transporters in glioma tissue samples of different fluorescence behavior. Methods: A total of 19 strongly fluorescing and 21 non-fluorescing tissue samples from neurosurgical adult-type diffuse gliomas (WHO grades II-IV) were included in the current analysis. In these samples, we investigated the mRNA expression by quantitative real time PCR and protein expression using immunohistochemistry of the intramitochondrial heme biosynthesis enzymes Coproporphyrinogen Oxidase (CPOX), Protoporphyrinogen Oxidase (PPOX), Ferrochelatase (FECH), and the transporter ATP-binding Cassette Subfamily B Member 2 (ABCG2). Results: Regarding mRNA expression analysis, we found a significantly decreased ABCG2 expression in fluorescing specimens compared to non-fluorescing samples (p = 0.001), whereas no difference in CPOX, PPOX and FECH was present. With respect to protein expression, significantly higher levels of CPOX (p = 0.005), PPOX (p < 0.01) and FECH (p = 0.003) were detected in fluorescing samples. Similar to mRNA expression analysis, the protein expression of ABCG2 (p = 0.001) was significantly lower in fluorescing samples. Conclusion: Distinct alterations of the analyzed heme biosynthesis factors were found primarily on protein level. Our data indicate that heme biosynthesis pathway activity in general is enhanced in fluorescing gliomas with upregulation of PpIX generating enzymes and decreased ABCG2 mediated PpIX efflux outweighing the also increased further metabolization of PpIX to heme. Intramitochondrial heme biosynthesis factors thus constitute promising pharmacological targets to optimize intraoperative 5-ALA fluorescence visualization of usually non-fluorescing tumors such as low-grade gliomas.

7T HR FID-MRSI Compared to Amino Acid PET: Glutamine and Glycine as Promising Biomarkers in Brain Tumors.

(1) Background: Recent developments in 7T magnetic resonance spectroscopic imaging (MRSI) made the acquisition of high-resolution metabolic images in clinically feasible measurement times possible. The amino acids glutamine (Gln) and glycine (Gly) were identified as potential neuro-oncological markers of importance. For the first time, we compared 7T MRSI to amino acid PET in a cohort of glioma patients. (2) Methods: In 24 patients, we co-registered 7T MRSI and routine PET and compared hotspot volumes of interest (VOI). We evaluated dice similarity coefficients (DSC), volume, center of intensity distance (CoI), median and threshold values for VOIs of PET and ratios of total choline (tCho), Gln, Gly, myo-inositol (Ins) to total N-acetylaspartate (tNAA) or total creatine (tCr). (3) Results: We found that Gln and Gly ratios generally resulted in a higher correspondence to PET than tCho. Using cutoffs of 1.6-times median values of a control region, DSCs to PET were 0.53 ± 0.36 for tCho/tNAA, 0.66 ± 0.40 for Gln/tNAA, 0.57 ± 0.36 for Gly/tNAA, and 0.38 ± 0.31 for Ins/tNAA. (4) Conclusions: Our 7T MRSI data corresponded better to PET than previous studies at lower fields. Our results for Gln and Gly highlight the importance of future research (e.g., using Gln PET tracers) into the role of both amino acids.

Does pigmentation, hemosiderin and blood effect visible 5-ALA fluorescence in cerebral melanoma metastasis?

INTRODUCTION: The clinical impact of 5-aminolevulinic acid (5-ALA) fluorescence during resection of brain metastases is not yet clear.. Recent data demonstrated significantly lower incidence of visible fluorescence in cerebral melanoma metastases (CMM) compared to other brain metastases (BM). The aim of this study was to investigate if characteristic melanoma features such as pigmentation, intratumoural hemosiderin and bleeding have an influence on visible fluorescence in CMM. MATERIALS AND METHODS: A retrospective study of two neurosurgical centers was performed including adult patients with resection of CMM after preoperative administration of 5-ALA. Data on the fluorescence status (visible or no fluorescence), the fluorescence quality (strong, vague, none) and fluorescence homogeneity (homogeneous or heterogeneous) of each CMM were collected. The amount of melanin, hemosiderin and intratumoural bleeding was semi-quantitatively determined and automated computer-based calculation of the relative pigmented area was performed in fluorescing and non-fluorescing CMM samples. RESULTS: Altogether, 29 CMM were surgically removed after 5-ALA administration. Visible fluorescence was detected in 8 CMM (28%), whereas no fluorescence was detected in 21 CMM (72%). In detail, 3 tumors (10%) showed strong fluorescence, 5 tumors (17%) revealed vague fluorescence and in 21 tumors (72%) no fluorescence was found. In total, 8 fluorescing and 25 non-fluorescing CMM samples were investigated. According to the semi-quantitatively calculated fluorescence status, no statistically significant difference in the median amount of melanin (p = 0.242), hemosiderin (p = 0.603) and bleeding (p = 0.762) between CMM samples with and without visible fluorescence was found. Moreover, the automatically assessed relative pigmented area did not show a statistically significant difference between samples with visible and no fluorescence (p = 0.966). CONCLUSION: Our data indicate that 5-ALA fluorescence is not dependent on the amount of pigmentation, intratumoural hemosiderin and bleeding in CMM. We thus assume that other factors are responsible for the low rate of visible fluorescence in CMM.

The Digital Brain Tumour Atlas, an open histopathology resource.

Currently, approximately 150 different brain tumour types are defined by the WHO. Recent endeavours to exploit machine learning and deep learning methods for supporting more precise diagnostics based on the histological tumour appearance have been hampered by the relative paucity of accessible digital histopathological datasets. While freely available datasets are relatively common in many medical specialties such as radiology and genomic medicine, there is still an unmet need regarding histopathological data. Thus, we digitized a significant portion of a large dedicated brain tumour bank based at the Division of Neuropathology and Neurochemistry of the Medical University of Vienna, covering brain tumour cases from 1995-2019. A total of 3,115 slides of 126 brain tumour types (including 47 control tissue slides) have been scanned. Additionally, complementary clinical annotations have been collected for each case. In the present manuscript, we thoroughly discuss this unique dataset and make it publicly available for potential use cases in machine learning and digital image analysis, teaching and as a reference for external validation.

Optimizing maximum resection of glioblastoma: Raman spectroscopy versus 5-aminolevulinic acid.

OBJECTIVE: The objective of this study was to assess and compare the potential of 5-aminolevulinic acid (5-ALA) and Raman spectroscopy (RS) in detecting tumor-infiltrated brain in patients with glioblastoma (GBM). METHODS: Between July 2020 and October 2021, the authors conducted a prospective clinical trial with 15 patients who underwent neurosurgical treatment of newly diagnosed and histologically verified GBM. A solid contrast-enhancing tumor core and peritumoral tissue were investigated intraoperatively for cancer cells by using 5-ALA and RS to achieve pathology-tailored maximum resection. In each case, a minimum of 10 biopsies were sampled from navigation-guided areas. Two neuropathologists examined the biopsies for the presence of neoplastic cells. The detection performance of 5-ALA and RS alone and in combination was assessed. Pre- and postoperative MRI, Karnofsky Performance Status (KPS), and National Institutes of Health Stroke Scale (NIHSS) scores were compared, and median progression-free survival (PFS) was evaluated. RESULTS: A total of 185 biopsy samples were harvested from the contrast-enhancing tumor core (n = 19) and peritumoral tissue (n = 166). In the tumor core, 5-ALA and RS each showed a sensitivity of 100%. In the peritumoral tissue, 5-ALA was less sensitive than RS in detecting cancer (46% vs 69%) but showed higher specificity (81% vs 57%). When the two methods were combined, the accuracy of tumor detection was increased by about 10%. Pathology-tailored resection led to a 52% increase in resection volume comparing the volume of preoperative contrast enhancement with the postoperative resection cavity on MRI (p = 0.0123). Eloquent brain involvement prevented gross-total resection in 4 patients. Four weeks after surgery, mean KPS (p = 0.7637) and NIHSS scores (p = 0.3146) were not significantly different from preoperative values. Of the 13 patients who had received postoperative chemoradiotherapy, 4 did not show any progression after a median follow-up of 14 months. The remaining 9 patients had a median PFS of 8 months. CONCLUSIONS: According to the study data, RS is capable of detecting tumor-infiltrated brain with higher sensitivity but lower specificity than the current standard of 5-ALA. With further technological and workflow advancements, RS in combination with protoporphyrin IX fluorescence may contribute to pathology-tailored glioma resection in the future.