Faculty Bibliography

Adolescents and young adults (AYAs; ages 15-39 years) are a vulnerable population facing challenges in oncological care, including access to specialized care, transition of care, unique tumor biology, and poor representation in clinical trials. Brain tumors are the second most common tumor type in AYA, with malignant brain tumors being the most common cause of cancer-related death. The 2021 WHO Classification for central nervous system (CNS) Tumors highlights the importance of integrated molecular characterization with histologic diagnosis in several tumors relevant to the AYA population. In this position paper from the Society for Neuro-Oncology (SNO), the diagnosis and management of CNS tumors in AYA is reviewed, focusing on the most common tumor types in this population, namely glioma, medulloblastoma, ependymoma, and CNS germ cell tumor. Current challenges and future directions specific to AYA are also highlighted. Finally, possible solutions to address barriers in the care of AYA patients are discussed, emphasizing the need for multidisciplinary and collaborative approaches that span the pediatric and adult paradigms of care, and incorporating advanced molecular testing, targeted therapy, and AYA-centered care.

Purpose To develop and validate a deep learning (DL) method to detect and segment enhancing and nonenhancing cellular tumor on pre- and posttreatment MRI scans in patients with glioblastoma and to predict overall survival (OS) and progression-free survival (PFS). Materials and Methods This retrospective study included 1397 MRI scans in 1297 patients with glioblastoma, including an internal set of 243 MRI scans (January 2010 to June 2022) for model training and cross-validation and four external test cohorts. Cellular tumor maps were segmented by two radiologists on the basis of imaging, clinical history, and pathologic findings. Multimodal MRI data with perfusion and multishell diffusion imaging were inputted into a nnU-Net DL model to segment cellular tumor. Segmentation performance (Dice score) and performance in distinguishing recurrent tumor from posttreatment changes (area under the receiver operating characteristic curve [AUC]) were quantified. Model performance in predicting OS and PFS was assessed using Cox multivariable analysis. Results A cohort of 178 patients (mean age, 56 years ± 13 [SD]; 116 male, 62 female) with 243 MRI timepoints, as well as four external datasets with 55, 70, 610, and 419 MRI timepoints, respectively, were evaluated. The median Dice score was 0.79 (IQR, 0.53-0.89), and the AUC for detecting residual or recurrent tumor was 0.84 (95% CI: 0.79, 0.89). In the internal test set, estimated cellular tumor volume was significantly associated with OS (hazard ratio [HR] = 1.04 per milliliter; P < .001) and PFS (HR = 1.04 per milliliter; P < .001) after adjustment for age, sex, and gross total resection (GTR) status. In the external test sets, estimated cellular tumor volume was significantly associated with OS (HR = 1.01 per milliliter; P < .001) after adjustment for age, sex, and GTR status. Conclusion A DL model incorporating advanced imaging could accurately segment enhancing and nonenhancing cellular tumor, distinguish recurrent or residual tumor from posttreatment changes, and predict OS and PFS in patients with glioblastoma. Keywords: Segmentation, Glioblastoma, Multishell Diffusion MRI Supplemental material is available for this article. © RSNA, 2024.

BACKGROUND/UNASSIGNED:Recently recognized as a distinct entity, a myxoid glioneuronal tumor (MGNT) is a rare, low-grade central nervous system tumor. MGNTs are commonly located at the septum pellucidum or in the third ventricle, increasing the likelihood of tumor or treatment-related damage to adjacent structures critical for memory, such as the fornix. Though there have been a handful of case reports of neurosurgical and oncological outcomes of MGNTs, memory outcomes following resection of MGNTs adjacent to the fornix have not been previously reported. METHODS/UNASSIGNED:We present a case of a high functioning female for whom an MRI revealed an incidental finding of an intraventricular tumor adjacent to the fornix bilaterally. The patient underwent resection of the tumor followed by MRI surveillance without additional oncologic intervention. Due to reported cognitive problems, the patient was referred for serial neuropsychological evaluations. RESULTS/UNASSIGNED:Post-operative MRI following resection revealed cytotoxic edema followed by selective, progressive atrophy of the bilateral anterior fornices. Post-surgically, the patient developed an isolated verbal memory impairment, which persisted one-year post resection with minimal improvement. The memory impairment impacted the patient's everyday functioning, including the ability to work in a cognitively demanding job. CONCLUSION/UNASSIGNED:This unique case demonstrates the critical role of the bilateral fornix in verbal memory and underscores the importance of a careful risk/benefit analysis when considering neurosurgical intervention to MGNTs and other intracranial lesions adjacent to this structure during neurosurgical planning.

Meningiomas are the most common non-metastatic brain tumors, and although the majority are relatively slow-growing and histologically benign, a subset of meningiomas are aggressive and remain challenging to treat. Despite a standard of care that includes surgical resection and radiotherapy, and recent advances in meningioma molecular grouping, there are no systemic medical options for patients with meningiomas that are resistant to standard interventions. Misactivation of the cell cycle at the level of CDK4/6 is common in high-grade or molecularly aggressive meningiomas, and CDK4/6 has emerged as a potential target for systemic meningioma treatments. In this review, we describe the preclinical evidence for CDK4/6 inhibitors as a treatment for high-grade meningiomas and summarize evolving clinical experience with these agents. Further, we highlight upcoming clinical trials for patients meningiomas, and discuss future directions aimed at optimizing the efficacy of these therapies and selecting patients most likely to benefit from their use.