68 Ga-DOTATATE and 18 F-FDG PET Imaging of a Rare Chordoid Meningioma With Intracranial Recurrence and Extracranial Metastases.

ABSTRACT: Chordoid meningiomas, rare meningioma variants, are characterized by their histopathological features and clinical behavior resembling that of other chondroid/myxoid neoplasms. We present a case of pathology-proven chordoid meningioma imaged with both 68 Ga-DOTATATE and 18 F-FDG PET images during a complicated postoperative course with multiple episodes of local recurrence and, ultimately, extracranial metastasis. This case underscores the aggressive behavior of chordoid meningiomas while highlighting how molecular imaging plays an important role in clinical monitoring and guidance of management.

Diffuse hemispheric glioma with H3 p.K28M (K27M) mutation: Unusual non-midline presentation of diffuse midline glioma, H3 K27M-altered?

Diffuse midline glioma, H3 K27-altered (DMG-H3 K27) is an aggressive group of diffuse gliomas that predominantly occurs in pediatric patients, involves midline structures, and displays loss of H3 p.K28me3 (K27me3) expression by immunohistochemistry and characteristic genetic/epigenetic profile. Rare examples of a diffuse glioma with an H3 p.K28M (K27M) mutation and without involvement of the midline structures, so-called "diffuse hemispheric glioma with H3 p.K28M (K27M) mutation" (DHG-H3 K27), have been reported. Herein, we describe 2 additional cases of radiologically confirmed DHG-H3 K27 and summarize previously reported cases. We performed histological, immunohistochemical, molecular, and DNA methylation analysis and provided clinical follow-up in both cases. Overall, DHG-H3 K27 is an unusual group of diffuse gliomas that shows similar clinical, histopathological, genomic, and epigenetic features to DMG-H3 K27 as well as enrichment for activating alterations in MAPK pathway genes. These findings suggest that DHG-H3 K27 is closely related to DMG-H3 K27 and may represent an unusual presentation of DMG-H3 K27 without apparent midline involvement and with frequent MAPK pathway activation. Detailed reports of additional cases with clinical follow-up will be important to expand our understanding of this unusual group of diffuse gliomas and to better define the clinical outcome and how to classify DHG-H3 K27.

Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm.

BACKGROUND AND OBJECTIVE: Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcome. METHODS: We proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. Classification accuracy of each gene were compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity. RESULTS: WSO-SVM achieved 0.80 accuracy, 0.79 sensitivity, and 0.81 specificity for classifying EGFR; 0.71 accuracy, 0.70 sensitivity, and 0.72 specificity for classifying PDGFRA; 0.80 accuracy, 0.78 sensitivity, and 0.83 specificity for classifying PTEN; these results significantly outperformed the existing ML algorithms. Using SHAP, we found that the relative contributions of the five contrast images differ between genes, which are consistent with findings in the literature. The prediction maps revealed extensive intra-tumoral region-to-region heterogeneity within each individual tumor in terms of the alteration status of the three genes. CONCLUSIONS: This study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology.

Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures.

Sampling restrictions have hindered the comprehensive study of invasive non-enhancing (NE) high-grade glioma (HGG) cell populations driving tumor progression. Here, we present an integrated multi-omic analysis of spatially matched molecular and multi-parametric magnetic resonance imaging (MRI) profiling across 313 multi-regional tumor biopsies, including 111 from the NE, across 68 HGG patients. Whole exome and RNA sequencing uncover unique genomic alterations to unresectable invasive NE tumor, including subclonal events, which inform genomic models predictive of geographic evolution. Infiltrative NE tumor is alternatively enriched with tumor cells exhibiting neuronal or glycolytic/plurimetabolic cellular states, two principal transcriptomic pathway-based glioma subtypes, which respectively demonstrate abundant private mutations or enrichment in immune cell signatures. These NE phenotypes are non-invasively identified through normalized K2 imaging signatures, which discern cell size heterogeneity on dynamic susceptibility contrast (DSC)-MRI. NE tumor populations predicted to display increased cellular proliferation by mean diffusivity (MD) MRI metrics are uniquely associated with EGFR amplification and CDKN2A homozygous deletion. The biophysical mapping of infiltrative HGG potentially enables the clinical recognition of tumor subpopulations with aggressive molecular signatures driving tumor progression, thereby informing precision medicine targeting.

Image-localized biopsy mapping of brain tumor heterogeneity: A single-center study protocol.

Brain cancers pose a novel set of difficulties due to the limited accessibility of human brain tumor tissue. For this reason, clinical decision-making relies heavily on MR imaging interpretation, yet the mapping between MRI features and underlying biology remains ambiguous. Standard (clinical) tissue sampling fails to capture the full heterogeneity of the disease. Biopsies are required to obtain a pathological diagnosis and are predominantly taken from the tumor core, which often has different traits to the surrounding invasive tumor that typically leads to recurrent disease. One approach to solving this issue is to characterize the spatial heterogeneity of molecular, genetic, and cellular features of glioma through the intraoperative collection of multiple image-localized biopsy samples paired with multi-parametric MRIs. We have adopted this approach and are currently actively enrolling patients for our 'Image-Based Mapping of Brain Tumors' study. Patients are eligible for this research study (IRB #16-002424) if they are 18 years or older and undergoing surgical intervention for a brain lesion. Once identified, candidate patients receive dynamic susceptibility contrast (DSC) perfusion MRI and diffusion tensor imaging (DTI), in addition to standard sequences (T1, T1Gd, T2, T2-FLAIR) at their presurgical scan. During surgery, sample anatomical locations are tracked using neuronavigation. The collected specimens from this research study are used to capture the intra-tumoral heterogeneity across brain tumors including quantification of genetic aberrations through whole-exome and RNA sequencing as well as other tissue analysis techniques. To date, these data (made available through a public portal) have been used to generate, test, and validate predictive regional maps of the spatial distribution of tumor cell density and/or treatment-related key genetic marker status to identify biopsy and/or treatment targets based on insight from the entire tumor makeup. This type of methodology, when delivered within clinically feasible time frames, has the potential to further inform medical decision-making by improving surgical intervention, radiation, and targeted drug therapy for patients with glioma.

Spinal intramedullary hemangioblastoma and schwannoma collision tumor: illustrative case.

BACKGROUND: Intramedullary spinal cord tumors represent a minority of intradural tumors. Among intramedullary spinal cord tumors, hemangioblastomas are uncommon, and schwannomas are extremely rare. Collision tumors are histologically distinct tumors that are intermingled and growing together. OBSERVATIONS: In this report, the authors describe a patient with a cervical intramedullary collision tumor involving a hemangioblastoma and schwannoma. To the authors' knowledge, no prior spinal intramedullary collision tumor involving multiple neoplasms has been described. The patient's presentation and management are described. LESSONS: Clinicians should consider the possibility of collision tumors when evaluating intramedullary spinal cord tumors, especially when patient presentation and radiographic findings are atypical. When tumors with similar radiographic characteristics form collision tumors, distinction using preoperative imaging can be extremely challenging. In addition, surgical management of intramedullary collision tumors, like that for all intramedullary spinal cord tumors, should involve meticulous perioperative care and a methodical surgical technique. Maximal safe resection will depend upon histopathological diagnosis, anatomical location of the tumor, presence of distinct dissection planes, and stability of neuromonitoring. Finally, ongoing research on the genetics of intramedullary spinal cord tumors may identify underlying genetic links for intramedullary hemangioblastomas and schwannomas.

Clinicopathologic Features of Diencephalic Neuronal and Glioneuronal Tumors.

Neuronal/mixed glioneuronal tumors are central nervous system neoplasms composed of neoplastic neuronal cell components or a mixture of glial and neuronal elements. They occur in cerebral hemispheres, posterior fossa, and spinal cord. Compared with other tumors at these locations, diencephalic neuronal/glioneuronal tumors are very rare and therefore not well characterized. We hereby performed clinicopathologic evaluation on 10 neuronal/glioneuronal tumors arising from the diencephalic region. Morphologically, these tumors resemble their histologic counterparts in other locations, except that lymphocytic infiltrates and microcalcifications are more common than Rosenthal fibers or eosinophilic granular bodies. The BRAFV600 mutation rate is 75%. Given the high percentage of samples being small biopsy specimens, the subtle histologic features and molecular findings greatly aided in establishing the pathologic diagnosis in several cases. At a median follow-up of 42 months, 71% of the tumors demonstrated radiological recurrence or progression, with median progression-free survival of 18 months. Recurrence/progression is observed in tumors across different histologic subtypes, necessitating additional therapies in 56% of the cases. Despite their bland histology, diencephalic neuronal/glioneuronal tumors are not clinically indolent. Their frequent recurrences warrant a close follow-up, and the prevalent BRAF mutation makes MAPK pathway inhibition a plausible treatment option when conventional therapies fail.

A rare case of giant cell tumor involving the clivus resected through Le Fort I Osteotomy and median maxillotomy.

BACKGROUND: Giant cell tumors (GCTs) are bone tumors that seldom involve the skull. Skull GCTs preferentially occur in the sphenoid and temporal bones with few reported cases involving the clivus. Due to the rarity and complex location, surgical management is not well established for clival GCTs. CASE DESCRIPTION: A 49-year-old male presented with headaches and blurred vision in the right eye for 2 weeks. Computed tomography (CT) with contrast revealed a sellar mass eroding through the sphenoid sinuses with compression of optic chiasm. Biopsy was consistent with GCT. Patient underwent tumor resection by Le Fort I Osteotomy and median maxillotomy for an extended transsphenoidal approach. Upon discharge, patient showed no neurological deficits and intact cranial nerves. CONCLUSION: This case contributes to the limited amount of skull-based GCT cases worldwide. Additionally, the extended transoral approach can be performed safely in the context of a GCT within the clivus with acceptable morbidity and cosmesis.

Pseudoneoplasms of the nervous system.

CONTEXT: Pseudoneoplasms of the nervous system vary greatly in nature. Ranging from inflammatory to autoimmune, infectious, malformative, reactive, degenerative, and radiation induced, they all mimic true tumors. Thus, they have the potential to mislead clinicians, radiologists, and pathologists alike. Their clinical and/or neuroimaging and histologic features are readily misinterpreted as tumor. Knowledge of the pitfalls is essential to avoid mismanagement, specifically overtreatment. In such instances, pathologists must take the entire clinical picture into consideration, acquainting themselves with presenting symptoms, physical findings, and neuroimaging. OBJECTIVE: To present 10 examples of pseudoneoplasms of the nervous system, analyze the basis for their mimicry, and discuss their differential diagnosis. DATA SOURCES: Review of the pertinent literature related to pseudoneoplasms of the nervous system and review of the consultation files of one of the authors (B.W.S.). CONCLUSIONS: The identification of tumor mimics may be difficult under the best of circumstances, and maintaining a broad differential diagnosis as well as application of a variety of immunocytochemical and occasionally ultrastructural and/or molecular genetic methods is essential to arrive at a correct diagnosis.