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Background and Purpose: Stereotactic linear accelerator-based radiosurgery (LINAC-RS) is increasingly used for microsurgically inaccessible or recurrent pituitary adenomas. This single-center study evaluates the long-term follow-up after LINAC-RS of nonsecreting pituitary adenomas (NSA).Patients and Methods: Between 1992 and August 2008, 65 patients with NSA were treated. Patient treatment and follow-up were conducted according to a prospective protocol. Indications for LINAC-RS were (1) tumor recurrence or (2) residual tumor. Three patients were treated primarily. For analysis of prognostic factors, patients were grouped according to epidemiological or treatment-associated characteristics.Results: A total of 61 patients with a follow-up ≥ 12 months (median 83 months, range 15-186 months, longest follow-up of published radiosurgery series) were evaluated with regard to their clinical, radiological, and endocrinological course. The median tumor volume was 3.5 ml (± 4.3 ml, range 0.3-17.3 ml) treated with a median surface and maximum dose of 13.0 Gy and 29.7 Gy, respectively. Local tumor control was achieved in 98%. One patient died of unrelated cause after 36 months and 1 patient developed a radiation-induced seizure disorder. Visual complications did not occur. In 37 of 41 patients (90.2%), pituitary function remained stable. Maximum dose to the pituitary ≤ 16 Gy and female gender were positive prognostic factors for the preservation of pituitary function.Conclusion: LINAC-RS is a minimally invasive, safe, and effective treatment for recurrent NSA or microsurgically inaccessible residual tumor. LINAC-RS yielded a high rate of local long-term tumor control with a small number of radiation-induced side effects. [ABSTRACT FROM AUTHOR]

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.)

Purpose: Recent artificial intelligence algorithms aided intraoperative decision-making via stimulated Raman histology (SRH) during craniotomy. This study assesses deep learning algorithms for rapid intraoperative diagnosis from SRH images in small stereotactic-guided brain biopsies. It defines a minimum tissue sample size threshold to ensure diagnostic accuracy., Experimental Design: A prospective single-center study examined 121 SRH images from 84 patients with unclear intracranial lesions undergoing stereotactic brain biopsy. Unprocessed, label-free samples were imaged using a portable fiber laser Raman scattering microscope. Three deep learning models were tested to (i) identify tumorous/nontumorous tissue as qualitative biopsy control; (ii) subclassify into high-grade glioma (central nervous system World Health Organization grade 4), diffuse low-grade glioma (central nervous system World Health Organization grades 2-3), metastases, lymphoma, or gliosis; and (iii) molecularly subtype IDH and 1p/19q statuses of adult-type diffuse gliomas. Model predictions were evaluated against frozen section analysis and final neuropathologic diagnoses., Results: The first model identified tumorous/nontumorous tissue with 91.7% accuracy. Sample size on slides impacted accuracy in brain tumor subclassification (81.6%, κ = 0.72 frozen section; 73.9%, κ = 0.61 second model), with SRH images being smaller than hematoxylin and eosin images (4.1 ± 2.5 mm2 vs. 16.7 ± 8.2 mm2, P < 0.001). SRH images with more than 140 high-quality patches and a mean squeezed sample of 5.26 mm2 yielded 89.5% accuracy in subclassification and 93.9% in molecular subtyping of adult-type diffuse gliomas., Conclusions: Artificial intelligence-based SRH image analysis is non-inferior to frozen section analysis in detecting and subclassifying brain tumors during small stereotactic-guided biopsies once a critical squeezed sample size is reached. Beyond frozen section analysis, it enables valid molecular glioma subtyping, allowing faster treatment decisions in the future; however, refinement is needed for long-term application., (©2024 American Association for Cancer Research.)

Brainstem metastases (BSM) present a significant neuro-oncological challenge, resulting in profound neurological deficits and poor survival outcomes. Stereotactic radiosurgery (SRS) and fractionated stereotactic radiotherapy (FSRT) offer promising therapeutic avenues for BSM despite their precarious location. This international multicenter study investigates the efficacy and safety of SRS and FSRT in 136 patients with 144 BSM treated at nine institutions from 2005 to 2022. The median radiographic and clinical follow-up periods were 6.8 and 9.4 months, respectively. Predominantly, patients with BSM were managed with SRS (69.4%). The median prescription dose and isodose line for SRS were 18 Gy and 65%, respectively, while for FSRT, the median prescription dose was 21 Gy with a median isodose line of 70%. The 12-, 24-, and 36-month local control (LC) rates were 82.9%, 71.4%, and 61.2%, respectively. Corresponding overall survival rates at these time points were 61.1%, 34.7%, and 19.3%. In the multivariable Cox regression analysis for LC, only the minimum biologically effective dose was significantly associated with LC, favoring higher doses for improved control (in Gy, hazard ratio [HR]: 0.86, p < .01). Regarding overall survival, good performance status (Karnofsky performance status, ≥90%; HR: 0.43, p < .01) and prior whole brain radiotherapy (HR: 2.52, p < .01) emerged as associated factors. In 14 BSM (9.7%), treatment-related adverse events were noted, with a total of five (3.4%) radiation necrosis. SRS and FSRT for BSM exhibit efficacy and safety, making them suitable treatment options for affected patients., (© 2024 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.)