Aim Achieving maximal and safe tumour resection is a key goal in brain tumour surgery. Confocal laser endomicroscopy (CLE) enables real-time visualisation of the tissue microstructure at a cellular level, potentially helping neurosurgeons distinguish non-neoplastic from neoplastic tissue. The core aim of this study was to determine the baseline diagnostic accuracy that can be achieved with CLE alone, when assessed by neuropathologists without prior CLE training and without any additional clinical or contextual information, and to compare these findings to standard haematoxylin and eosin (H & E)-based histology in a blinded setting.Methods CLE images and corresponding H & E-stained slides from 100 brain tumour patients treated at the University Hospital Bern over a 22-month period were analysed. Five blinded neuropathologists with no prior CLE experience independently evaluated the data sets.Results Based on CLE images, neuropathologists differentiated neoplastic from non-neoplastic tissue in 70.7%. The specific tumour type was correctly identified in 47.5%: gliomas in 59.8%, meningiomas in 43.8%, and metastases in 25.7%. In contrast, H & E slides were correctly classified as neoplastic in 87.6%, with 89.1% tumour-type-level accuracy (gliomas 85.6%, meningiomas 94.6%, metastases 88.2%). Confidence levels for CLE diagnoses were generally low, and no learning curve was observed.Conclusions CLE shows potential to distinguish between neoplastic and non-neoplastic tissue, but diagnostic accuracy remains lower than with H & E-stained slides. Training in CLE image interpretation is recommended to improve diagnostic accuracy. CLE imaging may have the potential to become a valuable tool for delineating brain tumour borders.

Background:Dexamethasone (DEX) is routinely administered perioperatively to manage tumor-associated vasogenic edema in glioblastoma (GBM), yet increasing evidence suggests that corticosteroid exposure may adversely affect survival. The magnitude of this association during the initial neurosurgical phase of care remains unclear. Methods:We performed a retrospective cohort study of patients with histologically confirmed IDH-wildtype GBM treated at a single tertiary center between 2009 and 2020. All perioperative DEX doses from admission to discharge were extracted from daily medical records. Patients were stratified based on the cumulative dose into low-dose exposure (<34 mg) and high-dose exposure (≥34 mg) groups using maximally selected rank statistics. Overall survival was analyzed using Kaplan-Meier estimates with log-rank test and multivariable Cox proportional hazards models. Adjustment variables were selected using a prespecified, causally informed framework to address confounding. Results:A total of 420 patients were included. The majority (n = 341; 81.2%) received ≥34 mg DEX perioperatively. Median OS was 13.6 months in the high-dose group and 15.0 months in the low-dose group, with significantly shorter survival observed in the high-dose cohort (log-rank P = .0103). In the adjusted Cox model, cumulative DEX ≥ 34 mg remained independently associated with increased mortality (HR: 1.40, 95% CI: 1.07-1.83; P = .013). Conclusions:Higher perioperative DEX doses were independently associated with shorter overall survival in GBM patients, emphasizing the need for judicious perioperative use with prompt tapering. Prospective studies are warranted to guide evidence-based DEX management in GBM care.

Accurate delineation of brain tumor borders during neurosurgery is essential for maximizing tumor resection while preserving healthy fiber tracts. Polarized light is sensitive to the optical anisotropy of white matter, caused by densely packed myelinated fibers, and can enhance contrast between anisotropic tumorless white matter of brain and isotropic brain tumor in the images of linear retardance and optical axis azimuth. Using a custom-built wide-field imaging Mueller polarimeter, we previously observed high values of linear retardance and well-aligned azimuth of the optical axis within non-tumoral brain tissue, whereas the tumor regions showed reduced values of linear retardance and randomization of the azimuth. However, crossing of healthy fibers can also disrupt optical anisotropy of brain white matter. To investigate this, we performed polarized Monte Carlo simulations and computed Mueller matrix-derived maps of linear retardance, azimuth of the optical axis, and depolarization. We varied the depth of inclusions, mimicking either crossing fiber or tumor zone, in the optical phantoms of brain tissue.Our results showed that while retardance decreases in both scenarios, depolarization decreases significantly only in tumor-mimicking regions. This depth-dependent depolarization behavior may help differentiate tumor regions from complex fiber architectures during polarimetric image-guided neurosurgery.

In neurosurgical oncology, maximizing tumor resection while preserving neurological function remains a critical challenge, particularly in glioma surgery where tumor borders are difficult to distinguish intraoperatively. Polarimetry-based imaging has emerged as a promising technique for visualizing white matter fiber tracts and differentiating healthy from neoplastic brain tissue. This study evaluates whether brain edema and temperature variations influence polarimetric parameters, potentially introducing noise and complicating intraoperative imaging. To model edema, fresh ex vivo calf brain samples were immersed in distilled water. Polarimetric parameters including depolarization, linear retardance, and the azimuth of the optical axis, were measured at multiple time points over three hours and a half using a wide-field imaging Mueller polarimetric setup. Results show that water absorption affects all polarimetric parameters and complicates fiber tract visualization. We also investigated whether temperature changes influenced polarimetric markers, but found no significant effect. These findings demonstrate that brain edema might alter polarimetric markers and act as a confounding factor in intraoperative imaging, potentially affecting tumor border visualization. The in vivo impact of brain edema remains unclear, and further studies are needed to refine polarimetric imaging for surgical guidance in glioma resection.

BACKGROUND:Glioblastoma is the most aggressive primary brain tumor. Although temozolomide induces DNA damage, its efficacy is limited by intrinsic resistance mechanisms, leading to tumor relapse. In this study we aimed to identify microRNA-mediated resistance mechanisms. METHODS:MicroRNA screens were performed to identify temozolomide resistance mechanisms. Temozolomide response was evaluated using clonogenic and spheroid assays in glioblastoma cell lines and patient-derived primary cells. DNA damage response was assessed by γH2AX foci formation. Cell cycle distribution, senescence and ferroptosis were assessed following miR-19b attenuation. An orthotopic xenograft and a syngeneic mouse model were used to confirm findings. RESULTS:Our screen and subsequent phosphoprotein analysis identified miR-19b and its target PPP2R5E, a subunit of the phosphatase PP2A, as modulators of temozolomide response. Attenuation of miR-19b upregulated PPP2R5E, inducing genotoxic stress, thereby enhancing temozolomide response. Mechanistically, DNA damage correlated with elevated nuclear ROS, promoting senescence and ferroptosis. Consistently, pharmacological activation of PP2A with FTY720 phenocopied the effects of miR-19b suppression. Conversely, knockdown of PPP2R5E reversed temozolomide sensitisation in vitro, in vivo, and ex vivo models, confirming the critical role of the miR-19b/PPP2R5E axis in modulating drug response. CONCLUSIONS:Therapeutic targeting of the PPP2R5E/PP2A complexes holds promise for enhanced temozolomide efficacy in glioblastoma.

Wide-field imaging Mueller polarimetry has recently emerged as a promising avenue in the development of medical imaging techniques to distinguish tumors from tumor-free tissue during neurosurgery. However, for sterile use in the operating room, the device must be covered with a plastic cap, which is usually birefringent and introduces polarimetric artifacts in the optical path of the device, which propagate into the reconstructed Mueller matrix images of the surgical cavity. In this study, we explore correction of these artifacts in measurements taken with a wide-field imaging Mueller polarimeter operating in reflection mode, and designed for near real-time clinical use to support neurosurgery. We compare two computational strategies to mitigate these artifacts: straightforward matrix inversion and the eigenvalue calibration method. Comparative ex vivo measurements on whole-brain thick sections, acquired with and without the plastic cap, indicate better performance from the eigenvalue calibration method. This simple approach integrates the correction into the imaging pipeline without requiring hardware modifications.

Medulloblastoma (MB) is the most common malignant pediatric brain tumor and comprises molecularly and clinically distinct subgroups with highly variable outcomes. While survival rates exceed 80

Translating wide-field imaging Mueller polarimetry to clinical settings requires fast image acquisition and post-processing. The former can be achieved using polarization-sensitive cameras. However, such detectors typically measure only linear polarization states, which limited to capturing only first three rows of a complete 4x4 Mueller matrix. Testing for physical realizability-a critical step in Mueller polarimetric data post-processing-requires knowledge of all coefficients of the complete Mueller matrix. We developed a machine learning framework based on supervised learning algorithms (XGBoost, CatBoost, and Multi-Layer Perceptron) to directly test the physical realizability of partial 3x4 Mueller matrices. The models were trained and validated on experimental Mueller matrix images of brain tissue specimens measured in reflection geometry, achieving accuracies above 98% when compared to ground truth. Robustness testing on isolated samples and Monte Carlo simulated data confirmed reliable performance across different tissue types and imaging conditions. The framework is sufficiently fast to ensure compatibility with real-time data post-processing requirements.

Real-time brain tumor delineation remains a major challenge in neurosurgery. We present our studies on using wide-field imaging Mueller polarimetry for label-free intraoperative tumor delineation and fiber-tract visualization by revealing the optical anisotropy of tumorless brain white matter. Our compact and fast Mueller polarimetric system demonstrates enhanced contrast between optically anisotropic tumorless white matter and isotropic tumor tissue in the maps of depolarization, linear retardance, and azimuth of the optical axis. By generating such diagnostic maps in real time during neurosurgery, our approach holds promise for neurosurgical guidance, reducing the risk of incomplete tumor resection and postoperative neurological deficits.

BACKGROUND:Oligodendrogliomas, characterized by isocitrate dehydrogenase (IDH) mutations and 1p/19q codeletion, often exhibit telomerase reverse transcriptase promoter (TERTp) mutations, which have been linked to telomere maintenance (TM) and tumor proliferation. Although there are a few reports on a TERTp-wildtype subset of these tumors in adolescents and young adults, the frequency, molecular characteristics, and prognostic implications of TERTp-wildtype status in oligodendrogliomas remain elusive. METHODS:We retrospectively analyzed 166 IDH-mutant and 1p/19q-codeleted oligodendroglioma cases through comprehensive histopathological review and molecular analyses, including Sanger sequencing, DNA methylation profiling, and whole-exome sequencing (WES). RESULTS:A TERTp-wildtype status was observed in 20/166 cases (12.0%) and was significantly associated with noticeably young age (age range: 14-27, P < .001), CNS WHO grade 2 (P = .003), and the absence of additional DNA copy number variations (CNVs) beyond the pathognomonic 1p/19q codeletion (P < .001). Epigenetic profiling demonstrated TERTp-wildtype tumors shaped a distinct subgroup at the utmost periphery of TERTp-mutant oligodendrogliomas. Methylation analysis of the upstream and proximal TERTp regions revealed that, in line with the absence of genetic alterations, epigenetic regulation does not favor TERT overexpression in TERTp-wildtype oligodendrogliomas. WES showed no TM-related gene alterations in TERTp-wildtype cases. Cox regression analysis confirmed TERTp-wildtype status as an independent prognostic factor for more favorable progression-free survival (PFS) (P = .009). CONCLUSIONS:In conclusion, "oligodendroglioma, IDH-mutant, 1p/19q-codeleted, and TERTp-wildtype" represent a distinct molecular subgroup associated with younger age and a better clinical course compared to CNS WHO grade 2 oligodendrogliomas.

Background Glioblastoma (GBM), the most aggressive primary brain tumor, has a median survival of approximately 15 months. Twenty percent of patients survive beyond three years, but known clinical factors like age, performance status, resection extent, and MGMT promoter methylation status do not fully explain the observed outcomes. Objective Our objective was to identify novel histology derived biomarkers associated with end-of-spectrum overall survival (OS) to provide novel biological insight with a translational potential. Methods We analyzed a total of 748 GBM patients from 3 different cohorts, uniquely enriched in long survivors (n=98 with overall survival (OS) > 5y including n=196 with OS≥3y), with clinical data and H&E slides obtained from the primary tumor at baseline. We propose an interpretable machine learning (ML) methodology for the discovery of histological biomarkers. Our method learned to segment each H&E slide into three distinct regions associated with long-term survival, short-term survival, and non-informative tissue. We characterized these regions by integrating unsupervised learning, nuclei segmentation, blood vessels detection, pathologist annotations, and multimodal data including spatial transcriptomics from n=31 patients of the GBM MOSAIC dataset to discover fully interpretable biomarkers. Results Our OS prediction model using histology and clinical data as input achieved an area under the curve (AUC) of 0.85 for the classification of patients between OS<2 and OS≥3y in external cohort validation, outperforming significantly models trained on clinical data or on histology alone (AUC of 0.76; 0.73, respectively). Two novel biomarkers were predicting poor survival: the presence of regions of lowly infiltrated white matter enriched in malignant cells with a mesenchymal-like phenotype, and lower levels of angiogenesis associated with higher hypoxia response in the main tumor regions. We also found that a subtype of immunosuppressive tumor macrophages - defined by high PLIN2 expression and lipid accumulation- is consistently enriched in histological areas predictive of poor prognosis. Conclusion Our interpretable ML methodology identified a novel prognostic impact of biological processes and cell types according to distinct tumor regions of GBM. These results pave the way for spatially-informed biomarkers to improve risk stratification and for personalized spatially-targeted therapeutic strategies. Key highlights 1. Our ML model identified histological biomarkers predicting prognosis independently from known clinical factors 2. The region of lowly infiltrated white matter enriched in malignant cells including a mesenchymal-like phenotype is predictive of poor prognosis 3. Angiogenesis is increased in areas predictive of long survival in main non-necrotic tumor regions. 4. The subtype of macrophages expressing PLIN2 and associated with increased lipid metabolism was associated with poor prognosis in all GBM regions. ![Figure][1] ### Competing Interest Statement The authors have declared no competing interest. [1]: pending:yes

OBJECTIVE:Spontaneous intracranial hypotension (SIH) with a ventral CSF leak (type 1) is believed to be caused by discogenic microspurs. Recently, this hypothesis was questioned, in which Hofmann's ligament, a fibrous connective tissue between the dura and posterior longitudinal ligament, was claimed to be the cause of a spinal dural tear. The primary objective of this study was to determine whether SIH type 1 lesions arise from a discogenic source or from fibrotic tissue. METHODS:Patients with ventral CSF leaks treated at the authors' institution, in whom histopathological reports on microspurs were available, were included. All histopathological analyses were repeated and tissues classified into either a fibrotic (Hofmann's ligament) or discogenic group. Correlation analysis of microspur localization in the spine and their origin was conducted. Microspur length and Hounsfield units (HUs) on CT were compared between both groups. RESULTS:Twenty-seven patients (19 women, 8 men) with a median age of 57 (IQR 46-64) years were analyzed. Nine microspurs were identified originating from fibrous tissues (Hofmann's ligament) and 13 microspurs were of discogenic origin, while 5 microspurs could not be classified into either group. Nine microspurs were found at the cervicothoracic or thoracolumbar junction, while 18 were located within the midthoracic spine. The location of the microspurs did not correlate with the histopathological origin of the microspur (p = 0.29). The length of a microspur (p = 0.29) as well as its density measured in HUs (p = 0.90) did not show a statistically significant difference between the fibrous and discogenic groups. CONCLUSIONS:These findings confirm that microspurs in patients with ventral CSF leaks originate from both the intervertebral disc and fibrous epidural ligament, suggestive of Hofmann's ligament.

Significance:Mueller polarimetric imaging shows great promise for differentiating neoplastic from healthy brain tissue during neurosurgery. However, validating algorithmic approaches is limited by the scarcity of substantial tumor border zones in ex vivo samples, limiting comprehensive analysis of tumor margins. Aim:We propose a protocol to build a database of histologically annotated polarimetric images from formalin-fixed whole-brain sections. We focus on validating the image alignment pipeline on healthy tissue. Approach:To address the size mismatch between samples and the field of view of imaging instruments, we developed an automatic reconstruction pipeline to create large-scale polarimetric images from smaller raster-scanned tiles. Matching points between reference photographs and tile images allowed precise alignment. Similarly, fractionated histological sections were reconstructed and accurately aligned with the polarimetric data to serve as ground truth. Results:The integrated reconstruction and alignment approach enabled large-scale, spatially co-registered polarimetric and histological imaging, supporting a more detailed investigation of tissue polarimetric parameters. The database thus created will facilitate the training and evaluation of segmentation models. Conclusions:The developed method improved polarimetry-based brain tissue mapping by linking polarimetric parameters with histological features, enhancing the quality and quantity of data available for training and evaluating segmentation models. Although initially applied to brain tissue, the protocol could be extended to other organs to support broader studies of polarimetric tissue characterization.

Mueller matrix polarimetry (MMP) provides valuable structural insights into tissue and holds promise for medical diagnostics. However, its clinical adoption is hindered by laborintensive data collection and annotation. This study examines the use of MMP data collected in reflection from ex vivo human brain tissue to identify neoplastic regions. Using a custom-built single-wavelength MMP imaging system, we compare deep learning models trained on Mueller matrix measurements against Lu-Chipman feature maps. Our networks achieve segmentation accuracy comparable to multi-spectral polarimetry, highlighting the potential of real-time MMP for brain tumor differentiation. We further provide a qualitative analysis discussing challenges and opportunities for neurosurgical MMP applications.

Mueller matrix polarimetry captures essential information about polarized light interactions with a sample, presenting unique challenges for data augmentation in deep learning due to its distinct structure. While augmentations are an effective and affordable way to enhance dataset diversity and reduce overfitting, standard transformations like rotations and flips do not preserve the polarization properties in Mueller matrix images. To this end, we introduce a versatile simulation framework that applies physically consistent rotations and flips to Mueller matrices, tailored to maintain polarization fidelity. Our experimental results across multiple datasets reveal that conventional augmentations can lead to falsified results when applied to polarimetric data, underscoring the necessity of our physics-based approach. In our experiments, we first compare our polarization-specific augmentations against real-world captures to validate their physical consistency. We then apply these augmentations in a semantic segmentation task, achieving substantial improvements in model generalization and performance. This study underscores the necessity of physics-informed data augmentation for polarimetric imaging in deep learning (DL), paving the way for broader adoption and more robust applications across diverse research in the field. In particular, our framework unlocks the potential of DL models for polarimetric datasets with limited sample sizes. Our code implementation is available at github.com/hahnec/polar_augment.