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Background: 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., Methods: 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 neoplastic/non-neoplastic lesions. Training and test data were collected from four tertiary international medical centers. The final histopathological diagnosis served as ground-truth., Results: 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. 77.77%). 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., Conclusions: RapidLymphoma proves reliable and valid for intraoperative PCNSL detection and differentiation from other CNS entities. It provides visual feedback within three minutes, enabling fast clinical decision-making and subsequent treatment strategy planning., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Following surgery, patients with newly diagnosed glioblastoma frequently enter clinical trials. Nuanced risk assessment is warranted to reduce imbalances between study arms. Here, we aimed (I) to analyze the interactive effects of residual tumor with clinical and molecular factors on outcome and (II) to define a postoperative risk assessment tool., Methods: The RANO resect group retrospectively compiled an international, seven-center training cohort of patients with newly diagnosed glioblastoma. The combined associations of residual tumor with molecular or clinical factors and survival were analyzed, and recursive partitioning analysis was performed for risk modeling. The resulting model was prognostically verified in a separate external validation cohort., Results: Our training cohort compromised 1003 patients with newly diagnosed isocitrate dehydrogenase-wildtype glioblastoma. Residual tumor, O6-methylguanine DNA methyltransferase (MGMT) promotor methylation status, age, and postoperative KPS were prognostic for survival and incorporated into regression tree analysis. By individually weighting the prognostic factors, an additive score (range, 0-9 points) integrating these four variables distinguished patients with low (0-2 points), intermediate (3-5 points), and high risk (6-9 points) for inferior survival. The prognostic value of our risk model was retained in treatment-based subgroups and confirmed in an external validation cohort of 258 patients with glioblastoma. Compared to previously postulated models, goodness-of-fit measurements were superior for our model., Conclusions: The novel RANO risk model serves as an easy-to-use, yet highly prognostic tool for postoperative patient stratification prior to further therapy. The model may serve to guide patient management and reduce imbalances between study arms in prospective trials., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.)

Background: In glioma patients, tumor growth and subsequent treatments are associated with various types of brain lesions. We hypothesized that cognitive functioning in these patients critically depends on the maintained structural connectivity of multiple brain networks., Methods: The study included 121 glioma patients (median age, 52 years; median Eastern Cooperative Oncology Group performance score 1; CNS-WHO Grade 3 or 4) after multimodal therapy. Cognitive performance was assessed by 10 tests in 5 cognitive domains at a median of 14 months after treatment initiation. Hybrid amino acid PET/MRI using the tracer O-(2-[ 18 F]fluoroethyl)-L-tyrosine, a network-based cortical parcellation, and advanced tractography were used to generate whole-brain fiber count-weighted connectivity matrices. The matrices were applied to a cross-validated machine-learning model to identify predictive fiber connections (edges), critical cortical regions (nodes), and the networks underlying cognitive performance., Results: Compared to healthy controls ( n  = 121), patients' cognitive scores were significantly lower in 9 cognitive tests. The models predicted the scores of 7/10 tests (median correlation coefficient, 0.47; range, 0.39-0.57) from 0.6% to 5.4% of the matrix entries; 84% of the predictive edges were between nodes of different networks. Critically involved cortical regions (≥10 adjacent edges) included predominantly left-sided nodes of the visual, somatomotor, dorsal/ventral attention, and default mode networks. Highly critical nodes (≥15 edges) included the default mode network's left temporal and bilateral posterior cingulate cortex., Conclusions: These results suggest that the cognitive performance of pretreated glioma patients is strongly related to structural connectivity between multiple brain networks and depends on the integrity of known network hubs also involved in other neurological disorders., Competing Interests: N.G. has received honoraria from Telix Pharmaceuticals and Blue Earth Diagnostics. The other authors report no conflict of interest., (© The Author(s) 2023. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)

Background: Terminology to describe extent of resection in glioblastoma is inconsistent across clinical trials. A surgical classification system was previously proposed based upon residual contrast-enhancing (CE) tumor. We aimed to (1) explore the prognostic utility of the classification system and (2) define how much removed non-CE tumor translates into a survival benefit., Methods: The international RANO resect group retrospectively searched previously compiled databases from 7 neuro-oncological centers in the USA and Europe for patients with newly diagnosed glioblastoma per WHO 2021 classification. Clinical and volumetric information from pre- and postoperative MRI were collected., Results: We collected 1,008 patients with newly diagnosed IDHwt glioblastoma. 744 IDHwt glioblastomas were treated with radiochemotherapy per EORTC-26981/22981 (TMZ/RT→TMZ) following surgery. Among these homogenously treated patients, lower absolute residual tumor volumes (in cm3) were favorably associated with outcome: patients with "maximal CE resection" (class 2) had superior outcome compared to patients with "submaximal CE resection" (class 3) or "biopsy" (class 4). Extensive resection of non-CE tumor (≤5 cm3 residual non-CE tumor) was associated with better survival among patients with complete CE resection, thus defining class 1 ("supramaximal CE resection"). The prognostic value of the resection classes was retained on multivariate analysis when adjusting for molecular and clinical markers., Conclusions: The proposed "RANO categories for extent of resection in glioblastoma" are highly prognostic and may serve for stratification within clinical trials. Removal of non-CE tumor beyond the CE tumor borders may translate into additional survival benefit, providing a rationale to explicitly denominate such "supramaximal CE resection.", (© The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.)

Background: The BRAF V600E mutation is present in approximately 50% of patients with melanoma brain metastases and an important prerequisite for response to targeted therapies, particularly BRAF inhibitors. As heterogeneity in terms of BRAF mutation status may occur in melanoma patients, a wild-type extracranial primary tumor does not necessarily rule out a targetable mutation in brain metastases using BRAF inhibitors. We evaluated the potential of MRI radiomics for a noninvasive prediction of the intracranial BRAF mutation status., Methods: Fifty-nine patients with melanoma brain metastases from two university brain tumor centers (group 1, 45 patients; group 2, 14 patients) underwent tumor resection with subsequent genetic analysis of the intracranial BRAF mutation status. Preoperative contrast-enhanced MRI was manually segmented and analyzed. Group 1 was used for model training and validation, group 2 for model testing. After radiomics feature extraction, a test-retest analysis was performed to identify robust features prior to feature selection. Finally, the best performing radiomics model was applied to the test data. Diagnostic performances were evaluated using receiver operating characteristic (ROC) analyses., Results: Twenty-two of 45 patients (49%) in group 1, and 8 of 14 patients (57%) in group 2 had an intracranial BRAF V600E mutation. A linear support vector machine classifier using a six-parameter radiomics signature yielded an area under the ROC curve of 0.92 (sensitivity, 83%; specificity, 88%) in the test data., Conclusions: The developed radiomics classifier allows a noninvasive prediction of the intracranial BRAF V600E mutation status in patients with melanoma brain metastases with high diagnostic performance., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: The aim of this study was to investigate the potential of dynamic O-(2-[18F]fluoroethyl)-L-tyrosine (18F-FET) PET for differentiating local recurrent brain metastasis from radiation injury after radiotherapy since contrast-enhanced MRI often remains inconclusive., Methods: Sixty-two patients (mean age, 55 ± 11 y) with single or multiple contrast-enhancing brain lesions (n = 76) on MRI after radiotherapy of brain metastases (predominantly stereotactic radiosurgery) were investigated with dynamic 18F-FET PET. Maximum and mean tumor-to-brain ratios (TBRmax, TBRmean) of 18F-FET uptake were determined (20-40 min postinjection) as well as tracer uptake kinetics (ie, time-to-peak and slope of time-activity curves). Diagnoses were confirmed histologically (34%; 26 lesions in 25 patients) or by clinical follow-up (66%; 50 lesions in 37 patients). Diagnostic accuracies of PET parameters for the correct identification of recurrent brain metastasis were evaluated by receiver-operating-characteristic analyses or the chi-square test., Results: TBRs were significantly higher in recurrent metastases (n = 36) than in radiation injuries (n = 40) (TBRmax 3.3 ± 1.0 vs 2.2 ± 0.4, P < .001; TBRmean 2.2 ± 0.4 vs 1.7 ± 0.3, P < .001). The highest accuracy (88%) for diagnosing local recurrent metastasis could be obtained with TBRs in combination with the slope of time-activity curves (P < .001)., Conclusions: The results of this study confirm previous preliminary observations that the combined evaluation of the TBRs of 18F-FET uptake and the slope of time-activity curves can differentiate local brain metastasis recurrence from radiation-induced changes with high accuracy. 18F-FET PET may thus contribute significantly to the management of patients with brain metastases., (© The Author(s) 2016. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com)

Background: Resection of gliomas located in eloquent brain areas remains a neurosurgical challenge. The reported incidence of transient or permanent neurological deficits after microsurgery in eloquent brain ranges 20%-100%, or 0%-47% among contemporary neurosurgical series. The aim of this study was to assess the feasibility of stereotactic brachytherapy (SBT) as a local treatment alternative to microsurgical resection for patients with gliomas in highly eloquent areas, located in the central sulcus region (CSR)., Method: Between 1997 and 2010, 60 patients with World Health Organization (WHO) grades II and III gliomas located in the CSR were treated with SBT (iodine-125 seeds; cumulative therapeutic dose, 50-65 Gy). Following SBT, WHO grade III glioma patients additionally received percutaneous radiotherapy (median boost dose, 25.2 Gy). We evaluated procedure-related complications, clinical outcome, and progression-free survival., Results: Procedure-related mortality was zero. Within 30 days of SBT, 3 patients (5%) had transient neurological deficits, and 8 patients (13%) had temporarily increased seizure activity. One patient (1.6%) deteriorated permanently. Space-occupying cysts (6 patients) and radiation necrosis (1 patient) developed after a median of 38 months and required surgical intervention. Seizure activity, rated 12 months following SBT, decreased in 82% of patients (Engel classes I-III). Median progression-free survivals were 62.2 ± 19.7 months (grade II gliomas) and 26.1 ± 17.9 months (grade III gliomas)., Conclusions: Compared with microsurgical resection, SBT harbors a low risk of procedural complications, is minimally invasive, and seems to be an effective local treatment option for patients with inoperable, eloquent WHO grade II and III gliomas in the CSR. However, the value of SBT for treating gliomas still needs to be determined in prospective, randomized studies.