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Background/Objectives: Medulloblastoma is the most common malignant brain tumor in children. In recent decades, the therapeutic landscape has undergone significant changes, with stereotactic radiosurgery (SRS) emerging as a promising treatment for recurrent cases. Our study provides a comprehensive analysis of the long-term efficacy and safety of SRS in recurrent medulloblastomas across both pediatric and adult patients at a single institution. Methods: We retrospectively reviewed the clinical and radiological records of patients who underwent CyberKnife SRS for recurrent cranial medulloblastomas at our institution between 1998 and 2023. Follow-up data were available for 15 medulloblastomas in 10 patients. The cohort comprised eight pediatric patients (ages 3–18) and two adult patients (ages 19–75). The median age at the time of SRS was 13 years, the median tumor volume accounted for 1.9 cc, the median biologically equivalent dose (BED) was 126 Gy, and the single-fraction equivalent dose (SFED) was 18 Gy. The SRS was administered at 75% of the median isodose line. Results: Following a median follow-up of 39 months (range: 6–78), 53.3% of the medulloblastomas progressed, 13.3% regressed, and 33.3% remained stable. The 3-year local tumor control (LTC) rate for all medulloblastomas was 65%, with lower rates observed in the adult cohort (50%) and higher rates in pediatric patients (67%). The 3-year overall survival (OS) rate was 70%, with significantly higher rates in pediatric patients (75%) compared to adult patients (50%). The 3-year progression-free survival (PFS) rate was 58.3%, with higher rates in pediatric patients (60%) compared to adult patients (50%). Two pediatric patients developed radiation-induced edema, while two adult patients experienced radiation necrosis at the latest follow-up, with both adult patients passing away. Conclusions: Our study provides a complex perspective on the efficacy and safety of CyberKnife SRS in treating recurrent cranial medulloblastomas across pediatric and adult populations. The rarity of adverse radiation events (AREs) underscores the safety profile of SRS, reinforcing its role in enhancing treatment outcomes. The intricacies of symptomatic outcomes, intertwined with factors such as age, tumor location, and prior surgeries, emphasize the need for personalized treatment approaches. Our findings underscore the imperative for ongoing research and the development of more refined treatment strategies for recurrent medulloblastomas. Given the observed disparities in treatment outcomes, a more meticulous tailoring of treatment approaches becomes crucial. [ABSTRACT FROM AUTHOR]

Detection and segmentation of brain metastases (BMs) play a pivotal role in diagnosis, treatment planning, and follow-up evaluations for effective BM management. Given the rising prevalence of BM cases and its predominantly multiple onsets, automated segmentation is becoming necessary in stereotactic radiosurgery. It not only alleviates the clinician's manual workload and improves clinical workflow efficiency but also ensures treatment safety, ultimately improving patient care. Recent strides in machine learning, particularly in deep learning (DL), have revolutionized medical image segmentation, achieving state-of-the-art results. This review aims to analyze auto-segmentation strategies, characterize the utilized data, and assess the performance of cutting-edge BM segmentation methodologies. Additionally, we delve into the challenges confronting BM segmentation and share insights gleaned from our algorithmic and clinical implementation experiences. [ABSTRACT FROM AUTHOR]

Purpose: Artificial intelligence (AI)-aided methods have made significant progress in the auto-delineation of normal tissues. However, these approaches struggle with the auto-contouring of radiotherapy target volume. Our goal is to model the delineation of target volume as a clinical decision-making problem, resolved by leveraging large language model-aided multimodal learning approaches., Methods and Materials: A vision-language model, termed Medformer, has been developed, employing the hierarchical vision transformer as its backbone, and incorporating large language models to extract text-rich features. The contextually embedded linguistic features are seamlessly integrated into visual features for language-aware visual encoding through the visual language attention module. Metrics, including Dice similarity coefficient (DSC), intersection over union (IOU), and 95th percentile Hausdorff distance (HD95), were used to quantitatively evaluate the performance of our model. The evaluation was conducted on an in-house prostate cancer dataset and a public oropharyngeal carcinoma (OPC) dataset, totaling 668 subjects., Results: Our Medformer achieved a DSC of 0.81 ± 0.10 versus 0.72 ± 0.10, IOU of 0.73 ± 0.12 versus 0.65 ± 0.09, and HD95 of 9.86 ± 9.77 mm versus 19.13 ± 12.96 mm for delineation of gross tumor volume (GTV) on the prostate cancer dataset. Similarly, on the OPC dataset, it achieved a DSC of 0.77 ± 0.11 versus 0.72 ± 0.09, IOU of 0.70 ± 0.09 versus 0.65 ± 0.07, and HD95 of 7.52 ± 4.8 mm versus 13.63 ± 7.13 mm, representing significant improvements (p < 0.05). For delineating the clinical target volume (CTV), Medformer achieved a DSC of 0.91 ± 0.04, IOU of 0.85 ± 0.05, and HD95 of 2.98 ± 1.60 mm, comparable to other state-of-the-art algorithms., Conclusions: Auto-delineation of the treatment target based on multimodal learning outperforms conventional approaches that rely purely on visual features. Our method could be adopted into routine practice to rapidly contour CTV/GTV., Competing Interests: Declaration of competing interest None, (Copyright © 2024. Published by Elsevier Inc.)

Background and Objectives: Hemangiopericytomas are infrequent vascular tumors originating from Zimmermann pericytes. The conventional treatment involves gross total resection, followed by adjuvant radiotherapy. Nevertheless, their tendency to infiltrate dural sinuses, high vascularity, and anatomic complexity pose challenges for radical resection, leading to a significant risk of recurrence. Stereotactic radiosurgery (SRS) has emerged as a promising adjuvant therapy to address these challenges. Our study provides the largest single-institutional retrospective, aiming to evaluate the effectiveness and safety of SRS as a treatment modality for residual, recurrent, and metastatic hemangiopericytomas., Methods: From 1998 to 2023, 27 patients with 101 tumors underwent CyberKnife SRS at Stanford University Medical Center. The median age was 51 years at the time of treatment. The median follow-up period from SRS was 103 months (range: 6-250). All patients underwent upfront surgical resection. The median tumor volume was 1.5 cc. The median single-fraction equivalent dose was 19 Gy. The SRS was administered at the 76% of the median isodose line (range: 64-89)., Results: Of the 101 treated tumors, 24 (23.8%) progressed with a median time to recurrence of 30 months. At 10 years, the rates of local tumor control (LTC), overall survival (OS), and progression-free survival (PFS) were 74.3%, 80.8%, and 67%, respectively. In patients with metastatic lesions, the LTC rates were significantly greater when compared with those with residual or recurrent tumors. There was no significant difference between patients with residual, recurrent, and metastatic hemangiopericytomas in OS and PFS. Notably, no cases of radiation-induced adverse events were detected., Conclusion: SRS leads to excellent LTC, PFS, and OS at 10 years with negligible risk for adverse events. Therefore, it is an effective and safe management modality for patients with residual, recurrent, and metastatic hemangiopericytomas., (Copyright © Congress of Neurological Surgeons 2024. All rights reserved.)