Advanced Imaging in the Diagnosis and Response Assessment of High-Grade Glioma: AJR Expert Panel Narrative Review.

This AJR Expert Panel Narrative explores the current status of advanced MRI and PET techniques for the post-therapeutic response assessment of high-grade adult-type gliomas, focusing on ongoing clinical controversies in current practice. Discussed techniques that complement conventional MRI and aid the differentiation of recurrent tumor from post-treatment effects include DWI and diffusion tensor imaging; perfusion MRI techniques including dynamic susceptibility contrast (DSC), dynamic contrast-enhanced MRI, and arterial spin labeling; MR spectroscopy including assessment of 2-hydroxyglutarate (2HG) concentration; glucose- and amino acid (AA)-based PET; and amide proton transfer imaging. Updated criteria for Response Assessment in Neuro-Oncology are presented. Given the abundant supporting clinical evidence, the panel supports a recommendation that routine response assessment after HGG treatment should include perfusion MRI, particularly given the development of a consensus recommended DSC-MRI protocol. Although published studies support 2HG MRS and AA PET, these techniques' widespread adoption will likely require increased availability (for 2HG MRS) or increased insurance funding in the United States (for AA PET). The article concludes with a series of consensus opinions from the author panel, centered on the clinical integration of the advanced imaging techniques into posttreatment surveillance protocols.

Pathological perspectives in pilocytic astrocytomas: Extent of resection as the sole critical factor for recurrence-free survival, and the challenge of evaluating conclusions derived from limited data.

Introduction: Pilocytic astrocytoma (PA) is one of the most common primary intracranial neoplasms in childhood with an overall favorable prognosis. Despite decades of experience, there are still diagnostic and treatment challenges and unresolved issues regarding risk factors associated with recurrence, most often due to conclusions of publications with limited data. We analyzed 499 patients with PA diagnosed in a single institution over 30 years in order to provide answers to some of the unresolved issues. Materials and Methods: We identified pilocytic astrocytomas diagnosed at the University of California, San Francisco, between 1989 and 2019, confirmed the diagnoses using the WHO 2021 essential and desirable criteria, and performed a retrospective review of the demographic and clinical features of the patients and the radiological, pathologic and molecular features of the tumors. Results: Among the patients identified from pathology archives, 499 cases fulfilled the inclusion criteria. Median age at presentation was 12 years (range 3.5 months - 73 years) and the median follow-up was 78.5 months. Tumors were predominantly located in the posterior fossa (52.6%). There were six deaths, but there were confounding factors that prevented a clear association of death to tumor progression. Extent of resection was the only significant factor for recurrence-free survival. Recurrence-free survival time was 321.0 months for gross total resection, compared to 160.9 months for subtotal resection (log rank, p <0.001). Conclusion: Multivariate analysis was able to identify extent of resection as the only significant variable to influence recurrence-free survival. We did not find a statistically significant association between age, NF1 status, tumor location, molecular alterations, and outcome. Smaller series with apparently significant results may have suffered from limited sample size, limited variables, acceptance of univariate analysis findings as well as a larger p value for biological significance. PA still remains a predominantly surgical disease and every attempt should be made to achieve gross total resection since this appears to be the most reliable predictor of recurrence-free survival.

Clinical Reasoning: A 22-Year-Old Man With Multifocal Brain and Osseous Lesions.

The evaluation of patients with disseminated processes with CNS and osseous involvement is often challenging. A 22-year-old healthy man developed left-sided weakness, paresthesias, and neck pain over several weeks. On clinical examination, he was noted to have decreased right eye visual acuity, left-sided pyramidal weakness and numbness, and bilateral hyperreflexia. MRI revealed multifocal widespread abnormalities: nonenhancing lesions throughout the infratentorial brain, pituitary gland, right frontal lobe, and optic nerves, in addition to an enhancing intramedullary cervical spinal cord lesion, extensive nodular leptomeningeal enhancement of the spine, and numerous enhancing bony lesions throughout the vertebrae and iliac bones. CSF analysis was notable for normal opening pressure, protein 465 mg/dL, glucose 21 mg/dL, and normal cell count. Extensive serum and CSF analysis for infectious, inflammatory, and neoplastic etiologies was unrevealing, and the diagnosis was ultimately revealed after additional staining of tissue biopsy specimen from sacral and cerebellar biopsies. This case highlights the differential diagnoses for widely disseminated disease affecting the CNS and bones and informs pediatric and adult clinicians of important recent developments regarding this diagnostic entity.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn).

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.

Multiparametric magnetic resonance imaging discerns glioblastoma immune microenvironmental heterogeneity.

RATIONALE AND OBJECTIVE: Poor clinical outcomes for patients with glioblastoma are in part due to dysfunction of the tumor-immune microenvironment. An imaging approach able to characterize immune microenvironmental signatures could provide a framework for biologically based patient stratification and response assessment. We hypothesized spatially distinct gene expression networks can be distinguished by multiparametric Magnetic Resonance Imaging (MRI) phenotypes. MATERIALS AND METHODS: Patients with newly diagnosed glioblastoma underwent image-guided tissue sampling allowing for co-registration of MRI metrics with gene expression profiles. MRI phenotypes based on gadolinium contrast enhancing lesion (CEL) and non-enhancing lesion (NCEL) regions were subdivided based on imaging parameters (relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC)). Gene set enrichment analysis and immune cell type abundance was estimated using CIBERSORT methodology. Significance thresholds were set at a p-value cutoff 0.005 and an FDR q-value cutoff of 0.1. RESULTS: Thirteen patients (eight men, five women, mean age 58 ± 11 years) provided 30 tissue samples (16 CEL and 14 NCEL). Six non-neoplastic gliosis samples differentiated astrocyte repair from tumor associated gene expression. MRI phenotypes displayed extensive transcriptional variance reflecting biological networks, including multiple immune pathways. CEL regions demonstrated higher immunologic signature expression than NCEL, while NCEL regions demonstrated stronger immune signature expression levels than gliotic non-tumor brain. Incorporation of rCBV and ADC metrics identified sample clusters with differing immune microenvironmental signatures. CONCLUSION: Taken together, our study demonstrates that MRI phenotypes provide an approach for non-invasively characterizing tumoral and immune microenvironmental glioblastoma gene expression networks.

Clinical and Radiographic Characteristics of Nocardia vs Non-Nocardia Brain Abscesses.

Background and Objectives: Diagnosis and treatment of CNS nocardiosis is challenging and often delayed, which increases morbidity and mortality. The primary objective was to compare the clinical and radiographic characteristics of patients with CNS nocardiosis with non-Nocardia bacterial brain abscesses. Methods: We performed a case-control study of patients with brain abscesses diagnosed between 1998 and 2018 at a tertiary academic center. We identified 56 patients with brain MRI demonstrating brain abscess from the institutional imaging database: 14 with culture-confirmed nocardiosis and 42 randomly selected prevalent controls with culture-confirmed non-Nocardia bacterial infection. The primary outcomes were the diagnosis of concomitant lung infection and history of immunosuppression. Secondary outcomes included abscess radiographic characteristics: multifocality, occipital lobe and/or infratentorial location, and bilobed morphology. Results: Compared with patients with non-Nocardia brain abscesses, patients with CNS nocardiosis were older (median 61 years [IQR 59-69] vs 48 years [IQR 34-61]; p = 0.03), more likely to be immunosuppressed [71% (10) vs 19% (8); p < 0.001), have diabetes (36% (5) vs 10% [4]; p = 0.03), or a concomitant lung infection (86% [12] vs 2% [1]; p < 0.001). Radiographically, more cases of CNS nocardiosis exhibited multifocal abscesses (29% [4] vs 2% [1]; p = 0.01), which were located in the infratentorial (43% [6] vs 10% (4); p = 0.01) or occipital (36% [5] vs 5% [2]; p = 0.008) regions and had a bilobed (as opposed to unilobed) morphology (79% [11] vs 19% [8]; p < 0.001). Blood and CSF cultures were negative in most of the cases and controls, whereas neurosurgical specimen culture yielded a diagnosis in 100% of specimens. Discussion: Patients with CNS nocardiosis were more likely to be older, have a history of diabetes or immunosuppression, or have a concomitant lung infection compared with those with non-Nocardia brain abscesses. Abscesses because of CNS nocardiosis were more likely to be multifocal, affect the infratentorial region or occipital lobe, or have a bilobed appearance. Neurosurgical specimen culture was most likely to yield a diagnosis for both Nocardia and non-Nocardia abscesses. The combination of clinical and imaging findings may suggest CNS nocardiosis and inform early initiation of targeted empiric treatment.

Pseudoprogression versus true progression in glioblastoma: what neurosurgeons need to know.

Management of patients with glioblastoma (GBM) is complex and involves implementing standard therapies including resection, radiation therapy, and chemotherapy, as well as novel immunotherapies and targeted small-molecule inhibitors through clinical trials and precision medicine approaches. As treatments have advanced, the radiological and clinical assessment of patients with GBM has become even more challenging and nuanced. Advances in spatial resolution and both anatomical and physiological information that can be derived from MRI have greatly improved the noninvasive assessment of GBM before, during, and after therapy. Identification of pseudoprogression (PsP), defined as changes concerning for tumor progression that are, in fact, transient and related to treatment response, is critical for successful patient management. These temporary changes can produce new clinical symptoms due to mass effect and edema. Differentiating this entity from true tumor progression is a major decision point in the patient's management and prognosis. Providers may choose to start an alternative therapy, transition to a clinical trial, consider repeat resection, or continue with the current therapy in hopes of resolution. In this review, the authors describe the invasive and noninvasive techniques neurosurgeons need to be aware of to identify PsP and facilitate surgical decision-making.

The University of California San Francisco Preoperative Diffuse Glioma MRI Dataset.

Supplemental material is available for this article. Keywords: Informatics, MR Diffusion Tensor Imaging, MR Perfusion, MR Imaging, Neuro-Oncology, CNS, Brain/Brain Stem, Oncology, Radiogenomics, Radiology-Pathology Integration © RSNA, 2022.

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.

Longitudinal Assessment of Posttreatment Diffuse Glioma Tissue Volumes with Three-dimensional Convolutional Neural Networks.

Neural networks were trained for segmentation and longitudinal assessment of posttreatment diffuse glioma. A retrospective cohort (from January 2018 to December 2019) of 298 patients with diffuse glioma (mean age, 52 years ± 14 [SD]; 177 men; 152 patients with glioblastoma, 72 patients with astrocytoma, and 74 patients with oligodendroglioma) who underwent two consecutive multimodal MRI examinations were randomly selected into training (n = 198) and testing (n = 100) samples. A posttreatment tumor segmentation three-dimensional nnU-Net convolutional neural network with multichannel inputs (T1, T2, and T1 postcontrast and fluid-attenuated inversion recovery [FLAIR]) was trained to segment three multiclass tissue types (peritumoral edematous, infiltrated, or treatment-changed tissue [ED]; active tumor or enhancing tissue [AT]; and necrotic core). Separate longitudinal change nnU-Nets were trained on registered and subtracted FLAIR and T1 postlongitudinal images to localize and better quantify and classify changes in ED and AT. Segmentation Dice scores, volume similarities, and 95th percentile Hausdorff distances ranged from 0.72 to 0.89, 0.90 to 0.96, and 2.5 to 3.6 mm, respectively. Accuracy rates of the posttreatment tumor segmentation and longitudinal change networks being able to classify longitudinal changes in ED and AT as increased, decreased, or unchanged were 76%-79% and 90%-91%, respectively. The accuracy levels of the longitudinal change networks were not significantly different from those of three neuroradiologists (accuracy, 90%-92%; κ, 0.58-0.63; P > .05). The results of this study support the potential clinical value of artificial intelligence-based automated longitudinal assessment of posttreatment diffuse glioma. Keywords: MR Imaging, Neuro-Oncology, Neural Networks, CNS, Brain/Brain Stem, Segmentation, Quantification, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2022.

Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities.

Technological innovation has enabled the development of machine learning (ML) tools that aim to improve the practice of radiologists. In the last decade, ML applications to neuro-oncology have expanded significantly, with the pre-operative prediction of glioma grade using medical imaging as a specific area of interest. We introduce the subject of ML models for glioma grade prediction by remarking upon the models reported in the literature as well as by describing their characteristic developmental workflow and widely used classifier algorithms. The challenges facing these models-including data sources, external validation, and glioma grade classification methods -are highlighted. We also discuss the quality of how these models are reported, explore the present and future of reporting guidelines and risk of bias tools, and provide suggestions for the reporting of prospective works. Finally, this review offers insights into next steps that the field of ML glioma grade prediction can take to facilitate clinical implementation.

Combining radiomics and deep convolutional neural network features from preoperative MRI for predicting clinically relevant genetic biomarkers in glioblastoma.

Background: Glioblastoma is the most common primary brain malignancy, yet treatment options are limited, and prognosis remains guarded. Individualized tumor genetic assessment has become important for accurate prognosis and for guiding emerging targeted therapies. However, challenges remain for widespread tumor genetic testing due to costs and the need for tissue sampling. The aim of this study is to evaluate a novel artificial intelligence method for predicting clinically relevant genetic biomarkers from preoperative brain MRI in patients with glioblastoma. Methods: We retrospectively analyzed preoperative MRI data from 400 patients with glioblastoma, IDH-wildtype or WHO grade 4 astrocytoma, IDH mutant who underwent resection and genetic testing. Nine genetic biomarkers were assessed: hotspot mutations of IDH1 or TERT promoter, pathogenic mutations of TP53, PTEN, ATRX, or CDKN2A/B, MGMT promoter methylation, EGFR amplification, and combined aneuploidy of chromosomes 7 and 10. Models were developed to predict biomarker status from MRI data using radiomics features, convolutional neural network (CNN) features, and a combination of both. Results: Combined model performance was good for IDH1 and TERT promoter hotspot mutations, pathogenic mutations of ATRX and CDKN2A/B, and combined aneuploidy of chromosomes 7 and 10, with receiver operating characteristic area under the curve (ROC AUC) >0.85 and was fair for all other tested biomarkers with ROC AUC >0.7. Combined model performance was statistically superior to individual radiomics and CNN feature models for prediction chromosome 7 and 10 aneuploidy, MGMT promoter methylation, and PTEN mutation. Conclusions: Combining radiomics and CNN features from preoperative MRI yields improved noninvasive genetic biomarker prediction performance in patients with WHO grade 4 diffuse astrocytic gliomas.

Prospective genomically guided identification of "early/evolving" and "undersampled" IDH-wildtype glioblastoma leads to improved clinical outcomes.

BACKGROUND: Genomic profiling studies of diffuse gliomas have led to new improved classification schemes that better predict patient outcomes compared to conventional histomorphology alone. One example is the recognition that patients with IDH-wildtype diffuse astrocytic gliomas demonstrating lower-grade histologic features but genomic and/or epigenomic profile characteristic of glioblastoma typically have poor outcomes similar to patients with histologically diagnosed glioblastoma. Here we sought to determine the clinical impact of prospective genomic profiling for these IDH-wildtype diffuse astrocytic gliomas lacking high-grade histologic features but with molecular profile of glioblastoma. METHODS: Clinical management and outcomes were analyzed for 38 consecutive adult patients with IDH-wildtype diffuse astrocytic gliomas lacking necrosis or microvascular proliferation on histologic examination that were genomically profiled on a prospective clinical basis revealing criteria for an integrated diagnosis of "diffuse astrocytic glioma, IDH-wildtype, with molecular features of glioblastoma, WHO grade IV" per cIMPACT-NOW criteria. RESULTS: We identified that this diagnosis consists of two divergent clinical scenarios based on integration of radiologic, histologic, and genomic features that we term "early/evolving" and "undersampled" glioblastoma, IDH-wildtype. We found that prospective genomically guided identification of early/evolving and undersampled IDH-wildtype glioblastoma resulted in more aggressive patient management and improved clinical outcomes compared to a biologically matched historical control patient cohort receiving standard-of-care therapy based on histomorphologic diagnosis alone. CONCLUSIONS: These results support routine use of genomic and/or epigenomic profiling to accurately classify glial neoplasms, as these assays not only improve diagnostic classification but critically lead to more appropriate patient management that can improve clinical outcomes.

Advanced Imaging Techniques for Newly Diagnosed and Recurrent Gliomas.

Management of gliomas following initial diagnosis requires thoughtful presurgical planning followed by regular imaging to monitor treatment response and survey for new tumor growth. Traditional MR imaging modalities such as T1 post-contrast and T2-weighted sequences have long been a staple of tumor diagnosis, surgical planning, and post-treatment surveillance. While these sequences remain integral in the management of gliomas, advances in imaging techniques have allowed for a more detailed characterization of tumor characteristics. Advanced MR sequences such as perfusion, diffusion, and susceptibility weighted imaging, as well as PET scans have emerged as valuable tools to inform clinical decision making and provide a non-invasive way to help distinguish between tumor recurrence and pseudoprogression. Furthermore, these advances in imaging have extended to the operating room and assist in making surgical resections safer. Nevertheless, surgery, chemotherapy, and radiation treatment continue to make the interpretation of MR changes difficult for glioma patients. As analytics and machine learning techniques improve, radiomics offers the potential to be more quantitative and personalized in the interpretation of imaging data for gliomas. In this review, we describe the role of these newer imaging modalities during the different stages of management for patients with gliomas, focusing on the pre-operative, post-operative, and surveillance periods. Finally, we discuss radiomics as a means of promoting personalized patient care in the future.

Functional outcomes after resection of middle frontal gyrus diffuse gliomas.

OBJECTIVE: The clinical outcomes for patients undergoing resection of diffuse glioma within the middle frontal gyrus (MFG) are understudied. Anatomically, the MFG is richly interconnected to known language areas, and nearby subcortical fibers are at risk during resection. The goal of this study was to determine the functional outcomes and intraoperative mapping results related to resection of MFG gliomas. Additionally, the study aimed to evaluate if subcortical tract disruption on imaging correlated with functional outcomes. METHODS: The authors performed a retrospective review of 39 patients with WHO grade II-IV diffuse gliomas restricted to only the MFG and underlying subcortical region that were treated with resection and had no prior treatment. Intraoperative mapping results and postoperative neurological deficits by discharge and 90 days were assessed. Diffusion tensor imaging (DTI) tractography was used to assess subcortical tract integrity on pre- and postoperative imaging. RESULTS: The mean age of the cohort was 37.9 years at surgery, and the median follow-up was 5.1 years. The mean extent of resection was 98.9% for the cohort. Of the 39 tumors, 24 were left sided (61.5%). Thirty-six patients (92.3%) underwent intraoperative mapping, with 59% of patients undergoing an awake craniotomy. No patients had positive cortical mapping sites overlying the tumor, and 12 patients (33.3%) had positive subcortical stimulation sites. By discharge, 8 patients had language dysfunction, and 5 patients had mild weakness. By 90 days, 2 patients (5.1%) had persistent mild hand weakness only. There were no persistent language deficits by 90 days. On univariate analysis, preoperative tumor size (p = 0.0001), positive subcortical mapping (p = 0.03), preoperative tumor invasion of neighboring subcortical tracts on DTI tractography (p = 0.0003), and resection cavity interruption of subcortical tracts on DTI tractography (p < 0.0001) were associated with an increased risk of having a postoperative deficit by discharge. There were no instances of complete subcortical tract transections in the cohort. CONCLUSIONS: MFG diffuse gliomas may undergo extensive resection with minimal risk for long-term morbidity. Partial subcortical tract interruption may lead to transient but not permanent deficits. Subcortical mapping is essential to reduce permanent morbidity during resection of MFG tumors by avoiding complete transection of critical subcortical tracts.

Feasibility of Simulated Postcontrast MRI of Glioblastomas and Lower-Grade Gliomas by Using Three-dimensional Fully Convolutional Neural Networks.

PURPOSE: To evaluate the feasibility and accuracy of simulated postcontrast T1-weighted brain MR images generated by using precontrast MR images in patients with brain glioma. MATERIALS AND METHODS: In this retrospective study, a three-dimensional deep convolutional neural network was developed to simulate T1-weighted postcontrast images from eight precontrast sequences in 400 patients (mean age, 57 years; 239 men; from 2015 to 2020), including 332 with glioblastoma and 68 with lower-grade gliomas. Performance was evaluated by using quantitative image similarity and error metrics and enhancing tumor overlap analysis. Performance was also assessed on a multicenter external dataset (n = 286 from the 2019 Multimodal Brain Tumor Segmentation Challenge; mean age, 60 years; ratio of men to women unknown) by using transfer learning. A subset of cases was reviewed by neuroradiologist readers to assess whether simulated images affected the ability to determine the tumor grade. RESULTS: Simulated whole-brain postcontrast images were both qualitatively and quantitatively similar to the real postcontrast images in terms of quantitative image similarity (structural similarity index of 0.84 ± 0.05), pixelwise error (symmetric mean absolute percent error of 3.65%), and enhancing tumor compartment overlap (Dice coefficient, 0.65 ± 0.25). Similar results were achieved with the external dataset (Dice coefficient, 0.62 ± 0.27). There was no difference in the ability of the neuroradiologist readers to determine the tumor grade in real versus simulated images (accuracy, 87.7% vs 90.6%; P = .87). CONCLUSION: The developed model was capable of producing simulated postcontrast T1-weighted MR images that were similar to real acquired images as determined by both quantitative analysis and radiologist assessment.Keywords: MR-Contrast Agent, MR-Imaging, CNS, Brain/Brain Stem, Contrast Agents-Intravenous, Neoplasms-Primary, Experimental Investigations, Technology Assessment, Supervised Learning, Transfer Learning, Convolutional Neural Network, Deep Learning Algorithms, Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2021.