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.

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.

An analysis of contributing factors of head and neck space infections of odontogenic origin: A long-term retrospective clinical study (including COVID-19 pandemic period)

BACKGROUND

The purpose of this study is to investigate predisposing factors for the head and neck infections (HNIs), regarding to the demographic data, anatomical spaces, microbiology and antibiotic sensitivity for affected patients.

MATERIAL AND METHODS

A 13-year of retrospective study evaluating 470 patients with HNIs, treated as inpatient management in the Department of Oral and Maxillofacial Surgery of KyungHee University school of Dentistry, Seoul, Korea, from January 2009 to February 2022. Statistical analysis of demographic, time-related, anatomic, microbiologic, and treatment variables were investigated for each patient.

RESULTS

The frequency of HNIs was significantly higher in 50's in males, followed by 70's in females. High Severity score (SS) were significantly associated with increased LOH (Length of hospital stay) and LOM (Length of medication), while LOH showed more intensive relationship compared with LOM. The most frequently involved space in abscess was submandibular space, though incidence and severity of HNIs shows declining tendency throughout 13-year research. Streptococcus viridans was the most predominant species isolated from pus culture growth, and a combination of ampicillin and sulbactam was the 1st choice of antibiotics intravenously. According to the comparison analysis between recommended antibiotics from resistance testing result and clinically administered antibiotics, final coincidence rate was estimated about 55%.

CONCLUSIONS

Due to HNIs being multifactorial, predicting progression and management of HNIs is still a challenge for oral and maxillofacial surgeons. The present study showed several predisposing factors of SHNIs and their correlations, which could contribute to earlier diagnosis and more effective treatment planning for clinicians, thereby leading to the improvement of prognosis for patients, ultimately.

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 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.

First Direct Measurement Constraining the ^{34}Ar(α,p)^{37}K Reaction Cross Section for Mixed Hydrogen and Helium Burning in Accreting Neutron Stars

The rate of the final step in the astrophysical αp process, the ^{34}Ar(α,p)^{37}K reaction, suffers from large uncertainties due to a lack of experimental data, despite having a considerable impact on the observable light curves of x-ray bursts and the composition of the ashes of hydrogen and helium burning on accreting neutron stars. We present the first direct measurement constraining the ^{34}Ar(α,p)^{37}K reaction cross section, using the Jet Experiments in Nuclear Structure and Astrophysics gas jet target. The combined cross section for the ^{34}Ar,Cl(α,p)^{37}K,Ar reaction is found to agree well with Hauser-Feshbach predictions. The ^{34}Ar(α,2p)^{36}Ar cross section, which can be exclusively attributed to the ^{34}Ar beam component, also agrees to within the typical uncertainties quoted for statistical models. This indicates the applicability of the statistical model for predicting astrophysical (α,p) reaction rates in this part of the αp process, in contrast to earlier findings from indirect reaction studies indicating orders-of-magnitude discrepancies. This removes a significant uncertainty in models of hydrogen and helium burning on accreting neutron stars.

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.

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.

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.

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.

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.

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 and 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 & 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 & 10, with receiver operating characteristic area under the curve (ROC AUC) &gt; 0.85 and was fair for all other tested biomarkers with ROC AUC &gt; 0.7. Combined model performance was statistically superior to individual radiomics and CNN feature models for prediction chromosome 7 & 10 aneuploidy, MGMT promoter methylation, and PTEN loss.

Conclusions

Combining radiomics and CNN features from preoperative MRI yields improved non-invasive genetic biomarker prediction performance in patients with grade 4 diffuse gliomas.

ADC Histogram Analysis of Pediatric Low-Grade Glioma Treated with Selumetinib: A Report from the Pediatric Brain Tumor Consortium

BACKGROUND AND PURPOSE

Selumetinib is a promising MAP (mitogen-activated protein) kinase (MEK) 1/2 inhibitor treatment for pediatric low-grade gliomas. We hypothesized that MR imaging-derived ADC histogram metrics would be associated with survival and response to treatment with selumetinib.

MATERIALS AND METHODS

Children with recurrent, refractory, or progressive pediatric low-grade gliomas who had World Health Organization grade I pilocytic astrocytoma with KIAA1549-BRAF fusion or the BRAF V600E mutation (stratum 1), neurofibromatosis type 1-associated pediatric low-grade gliomas (stratum 3), or sporadic non-neurofibromatosis type 1 optic pathway and hypothalamic glioma (OPHG) (stratum 4) were treated with selumetinib for up to 2 years. Quantitative ADC histogram metrics were analyzed for total and enhancing tumor volumes at baseline and during treatment.

RESULTS

Each stratum comprised 25 patients. Stratum 1 responders showed lower values of SD of baseline ADC_total as well as a larger decrease with time on treatment in ADC_total mean, mode, and median compared with nonresponders. Stratum 3 responders showed a greater longitudinal decrease in ADC_total. In stratum 4, higher baseline ADC_total skewness and kurtosis were associated with shorter progression-free survival. When all 3 strata were combined, responders showed a greater decrease with time in ADC_total mode and median. Compared with sporadic OPHG, neurofibromatosis type 1-associated OPHG had lower values of ADC_total mean, mode, and median as well as ADC_enhancement mean and median and higher values of ADC_total skewness and kurtosis at baseline. The longitudinal decrease in ADC_total median during treatment was significantly greater in sporadic OPHG compared with neurofibromatosis type 1-associated OPHG.

CONCLUSIONS

ADC histogram metrics are associated with progression-free survival and response to treatment with selumetinib in pediatric low-grade gliomas.

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.

Pseudo-insular glioma syndrome: illustrative cases

BACKGROUND

Lower-grade insular gliomas often appear as expansile and infiltrative masses on magnetic resonance imaging (MRI). However, there are nonneoplastic lesions of the insula, such as demyelinating disease and vasculopathies, that can mimic insular gliomas.

OBSERVATIONS

The authors report two patients who presented with headaches and were found to have mass lesions concerning for lower-grade insular glioma based on MRI obtained at initial presentation. However, on the immediate preoperative MRI obtained a few weeks later, both patients had spontaneous and complete resolution of the insular lesions.

LESSONS

Tumor mimics should always be in the differential diagnosis of brain masses, including those involving the insula. The immediate preoperative MRI (within 24–48 hours of surgery) must be compared carefully with the initial presentation MRI to assess interval change that suggests tumor mimics to avoid unnecessary surgical intervention.

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.

Systemic and Craniospinal Rosai Dorfman Disease with Intraparenchymal, Intramedullary and Leptomeningeal Disease

Rosai Dorfman disease is a rare histiocytic disorder of over-production of non-Langerhans histiocytes, which typically manifests with massive lymphadenopathy and sinonasal involvement. We report a rare case of systemic and disseminated craniospinal Rosai Dorfman disease with intraparenchymal and leptomeningeal involvement, but no sinus or dural-based disease. The diagnosis was established by biopsy of a hypothalamic mass. Additionally, UCSF500 Next Generation Sequencing demonstrated a solitary pathogenic alteration affecting the BRAF oncogene, which supports the morphologic and immunohistochemical diagnosis of Rosai-Dorfman disease.

Prosopagnosia following nonlanguage dominant inferior temporal lobe low-grade glioma resection in which the inferior longitudinal fasciculus was disrupted preoperatively: illustrative case

BACKGROUND

Prosopagnosia is a rare neurological condition characterized by the impairment of face perception with preserved visual processing and cognitive functioning and is associated with injury to the fusiform gyrus and inferior longitudinal fasciculus (ILF). Reports of this clinical impairment following resection of right temporal lobe diffuse gliomas in the absence of contralateral injury are exceedingly scarce and not expected as a complication of surgery.

OBSERVATIONS

The authors describe the case of a young female patient found to have an incidental diffuse glioma in the right inferior temporal lobe despite evidence of preoperative ILF disruption by the tumor. Following resection of the lesion, despite the preoperative disruption to the ILF by the tumor, the patient developed prosopagnosia. There was no evidence of contralateral, left-sided ILF injury.

LESSONS

Given the significant functional impairment associated with prosopagnosia, neurosurgeons should be aware of the exceedingly rare possibility of a visual-processing deficit following unilateral and, in this case, right-sided inferior temporal lobe glioma resections. More investigation is needed to determine whether preoperative testing can determine dominance of facial-processing networks for patients with lesions in the right inferior posterior temporooccipital lobe and whether intraoperative mapping could help prevent this complication.

Pre- and Post-Treatment Imaging of Primary Central Nervous System Tumors in the Molecular and Genetic Era

Recent advances in the molecular and genetic characterization of central nervous system (CNS) tumors have ushered in a new era of tumor classification, diagnosis, and prognostic assessment. In this emerging and rapidly evolving molecular genetic era, imaging plays a critical role in the preoperative diagnosis and surgical planning, molecular marker prediction, targeted treatment planning, and post-therapy assessment of CNS tumors. This review provides an overview of the current imaging methods relevant to the molecular genetic classification of CNS tumors. Specifically, we focused on 1) the correlates between imaging features and specific molecular genetic markers and 2) the post-therapy imaging used for therapeutic assessment.

Topographic correlates of driver mutations and endogenous gene expression in pediatric diffuse midline gliomas and hemispheric high-grade gliomas

We evaluate the topographic distribution of diffuse midline gliomas and hemispheric high-grade gliomas in children with respect to their normal gene expression patterns and pathologic driver mutation patterns. We identified 19 pediatric patients with diffuse midline or high-grade glioma with preoperative MRI from tumor board review. 7 of these had 500 gene panel mutation testing, 11 patients had 50 gene panel mutation testing and one 343 gene panel testing from a separate institution were included as validation set. Tumor imaging features and gene expression patterns were analyzed using Allen Brain Atlas. Twelve patients had diffuse midline gliomas and seven had hemispheric high-grade gliomas. Three diffuse midline gliomas had the K27M mutation in the tail of histone H3 protein. All patients undergoing 500 gene panel testing had additional mutations, the most common being in ACVR1, PPM1D, and p53. Hemispheric high-grade gliomas had either TP53 or IDH1 mutation and diffuse midline gliomas had H3 K27M-mutation. Gene expression analysis in normal brains demonstrated that genes mutated in diffuse midline gliomas had higher expression along midline structures as compared to the cerebral hemispheres. Our study suggests that topographic location of pediatric diffuse midline gliomas and hemispheric high-grade gliomas correlates with driver mutations of tumor to the endogenous gene expression in that location. This correlation suggests that cellular state that is required for increased gene expression predisposes that location to mutations and defines the driver mutations within tumors that arise from that region.

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.

Recurrent tumor and treatment-induced effects have different MR signatures in contrast enhancing and non-enhancing lesions of high-grade gliomas

BACKGROUND

Differentiating treatment-induced injury from recurrent high-grade glioma is an ongoing challenge in neuro-oncology, in part due to lesion heterogeneity. This study aimed to determine whether different MR features were relevant for distinguishing recurrent tumor from the effects of treatment in contrast-enhancing lesions (CEL) and non-enhancing lesions (NEL).

METHODS

This prospective study analyzed 291 tissue samples (222 recurrent tumor, 69 treatment-effect) with known coordinates on imaging from 139 patients who underwent preoperative 3T MRI and surgery for a suspected recurrence. 8 MR parameter values were tested from perfusion-weighted, diffusion-weighted, and MR spectroscopic imaging at each tissue sample location for association with histopathological outcome using generalized estimating equation models for CEL and NEL tissue samples. Individual cutoff values were evaluated using receiver operating characteristic curve analysis with 5-fold cross-validation.

RESULTS

In tissue samples obtained from CEL, elevated relative cerebral blood volume (rCBV) was associated with the presence of recurrent tumor pathology (P < 0.03), while increases in normalized choline (nCho) and choline-to-NAA index (CNI) were associated with the presence of recurrent tumor pathology in NEL tissue samples (P < 0.008). A mean CNI cutoff value of 2.7 had the highest performance, resulting in mean sensitivity and specificity of 0.61 and 0.81 for distinguishing treatment-effect from recurrent tumor within the NEL.

CONCLUSION

Although our results support prior work that underscores the utility of rCBV in distinguishing the effects of treatment from recurrent tumor within the contrast enhancing lesion, we found that metabolic parameters may be better at differentiating recurrent tumor from treatment-related changes in the NEL of high-grade gliomas.

MRI Features May Predict Molecular Features of Glioblastoma in Isocitrate Dehydrogenase Wild-Type Lower-Grade Gliomas

BACKGROUND AND PURPOSE

Isocitrate dehydrogenase (IDH) wild-type lower-grade gliomas (histologic grades II and III) with epidermal growth factor receptor (EGFR) amplification or telomerase reverse transcriptase (TERT) promoter mutation are reported to behave similar to glioblastoma. We aimed to evaluate whether MR imaging features could identify a subset of IDH wild-type lower-grade gliomas that carry molecular features of glioblastoma.

MATERIALS AND METHODS

In this multi-institutional retrospective study, pathologically confirmed IDH wild-type lower-grade gliomas from 2 tertiary institutions and The Cancer Genome Atlas constituted the training set (institution 1 and The Cancer Genome Atlas, 64 patients) and the independent test set (institution 2, 57 patients). Preoperative MRIs were analyzed using the Visually AcceSAble Rembrandt Images and radiomics. The molecular glioblastoma status was determined on the basis of the presence of EGFR amplification and TERT promoter mutation. Molecular glioblastoma was present in 73.4% and 56.1% in the training and test sets, respectively. Models using clinical, Visually AcceSAble Rembrandt Images, and radiomic features were built to predict the molecular glioblastoma status in the training set; then they were validated in the test set.

RESULTS

In the test set, a model using both Visually AcceSAble Rembrandt Images and radiomic features showed superior predictive performance (area under the curve = 0.854) than that with only clinical features or Visually AcceSAble Rembrandt Images (areas under the curve = 0.514 and 0.648, respectively; P < . 001, both). When both Visually AcceSAble Rembrandt Images and radiomics were added to clinical features, the predictive performance significantly increased (areas under the curve = 0.514 versus 0.863, P < .001).

CONCLUSIONS

MR imaging features integrated with machine learning classifiers may predict a subset of IDH wild-type lower-grade gliomas that carry molecular features of glioblastoma.

Maximizing the use of batch production of 18F-FDOPA for imaging of brain tumors to increase availability of hybrid PET/MR imaging in clinical setting

Background

Amino acid PET imaging of brain tumors has been shown to play an important role in predicting tumor grade, delineation of tumor margins, and differentiating tumor recurrence from the background of postradiation changes, but is not commonly used in clinical practice because of high cost. We propose that PET/MRI imaging of patients grouped to the day of tracer radiosynthesis will significantly decrease the cost of PET imaging, which will improve patient access to PET.

Methods

Seventeen patients with either primary brain tumors or metastatic brain tumors were recruited for imaging on 3T PET/MRI and were scanned on 4 separate days in groups of 3 to 5 patients. The first group of consecutively imaged patients contained 3 patients, followed by 2 groups of 5 patients, and a last group of 4 patients.

Results

For each of the patients, standard of care gadolinium-enhanced MRI and dynamic PET imaging with ¹⁸F-FDOPA amino acid tracer was obtained. The total cost savings of scanning 17 patients in batches of 4 as opposed to individual radiosynthesis was 48.5% ($28 321). Semiquantitative analysis of tracer uptake in normal brain were performed with appropriate accumulation and expected subsequent washout.

Conclusion

Amino acid PET tracers have been shown to play a critical role in the characterization of brain tumors but their adaptation to clinical practice has been limited because of the high cost of PET. Scheduling patient imaging to maximally use the radiosynthesis of imaging tracer significantly reduces the cost of PET and results in increased availability of PET tracer use in neuro-oncology.

A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas

Glioblastoma is the most common malignant brain parenchymal tumor yet remains challenging to treat. The current standard of care-resection and chemoradiation-is limited in part due to the genetic heterogeneity of glioblastoma. Previous studies have identified several tumor genetic biomarkers that are frequently present in glioblastoma and can alter clinical management. Currently, genetic biomarker status is confirmed with tissue sampling, which is costly and only available after tumor resection or biopsy. The purpose of this study was to evaluate a fully automated artificial intelligence approach for predicting the status of several common glioblastoma genetic biomarkers on preoperative MRI. We retrospectively analyzed multisequence preoperative brain MRI from 199 adult patients with glioblastoma who subsequently underwent tumor resection and genetic testing. Radiomics features extracted from fully automated deep learning-based tumor segmentations were used to predict nine common glioblastoma genetic biomarkers with random forest regression. The proposed fully automated method was useful for predicting IDH mutations (sensitivity = 0.93, specificity = 0.88), ATRX mutations (sensitivity = 0.94, specificity = 0.92), chromosome 7/10 aneuploidies (sensitivity = 0.90, specificity = 0.88), and CDKN2 family mutations (sensitivity = 0.76, specificity = 0.86).

Involvement of the Olfactory Apparatus by Gliomas

The olfactory bulbs and tracts are central nervous system white matter tracts maintained by central neuroglia. Although rare, gliomas can originate from and progress to involve the olfactory apparatus. Through a Health Insurance Portability and Accountability Act-compliant retrospective review of the institutional teaching files and brain MR imaging reports spanning 10 years, we identified 12 cases of gliomas involving the olfactory bulbs and tracts, including 6 cases of glioblastoma, 2 cases of anaplastic oligodendroglioma, and 1 case each of pilocytic astrocytoma, diffuse (grade II) astrocytoma, anaplastic astrocytoma (grade III), and diffuse midline glioma. All except the pilocytic astrocytoma occurred in patients with known primary glial tumors elsewhere. Imaging findings of olfactory tumor involvement ranged from well-demarcated enhancing masses to ill-defined enhancing infiltrative lesions to nonenhancing masslike FLAIR signal abnormality within the olfactory tracts. Familiarity with the imaging findings of glioma involvement of the olfactory nerves is important for timely diagnosis and treatment of recurrent gliomas and to distinguish them from other disease processes.

Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings

We applied machine learning algorithms for differentiation of posterior fossa tumors using apparent diffusion coefficient (ADC) histogram analysis and structural MRI findings. A total of 256 patients with intra-axial posterior fossa tumors were identified, of whom 248 were included in machine learning analysis, with at least 6 representative subjects per each tumor pathology. The ADC histograms of solid components of tumors, structural MRI findings, and patients' age were applied to construct decision models using Classification and Regression Tree analysis. We also compared different machine learning classification algorithms (i.e., naïve Bayes, random forest, neural networks, support vector machine with linear and polynomial kernel) for dichotomized differentiation of the 5 most common tumors in our cohort: metastasis (n = 65), hemangioblastoma (n = 44), pilocytic astrocytoma (n = 43), ependymoma (n = 27), and medulloblastoma (n = 26). The decision tree model could differentiate seven tumor histopathologies with terminal nodes yielding up to 90% accurate classification rates. In receiver operating characteristics (ROC) analysis, the decision tree model achieved greater area under the curve (AUC) for differentiation of pilocytic astrocytoma (p = 0.020); and atypical teratoid/rhabdoid tumor ATRT (p = 0.001) from other types of neoplasms compared to the official clinical report. However, neuroradiologists' interpretations had greater accuracy in differentiating metastases (p = 0.001). Among different machine learning algorithms, random forest models yielded the highest accuracy in dichotomized classification of the 5 most common tumor types; and in multiclass differentiation of all tumor types random forest yielded an averaged AUC of 0.961 in training datasets, and 0.873 in validation samples. Our study demonstrates the potential application of machine learning algorithms and decision trees for accurate differentiation of brain tumors based on pretreatment MRI. Using easy to apply and understandable imaging metrics, the proposed decision tree model can help radiologists with differentiation of posterior fossa tumors, especially in tumors with similar qualitative imaging characteristics. In particular, our decision tree model provided more accurate differentiation of pilocytic astrocytomas from ATRT than by neuroradiologists in clinical reads.

NIMG-42. RECURRENT TUMOR AND TREATMENT-INDUCED EFFECTS HAVE DIFFERENT MR SIGNATURES IN CONTRAST ENHANCING AND NON-ENHANCING LESIONS OF HIGH-GRADE GLIOMAS

INTRODUCTION

It is estimated that 25 to 35% of patients experience treatment-induced effects that can mimic recurrent high-grade gliomas. This diagnostic challenge is complicated by the coexistence of treatment-related changes and recurrent tumor within the same lesion which limits the accuracy of classification based on summary metrics of multi-parametric MRI. This study aimed to determine whether different MR features were relevant for distinguishing pathological features of recurrent tumor from the effects of treatment in the contrast enhancing and non-enhancing lesions of recurrent high-grade gliomas.

METHODS

Leveraging our unique dataset of image-guided tissue samples that directly maps pathology to MR characteristics, we analyzed 291 tissue samples (222 recurrent tumor; 69 treatment effect) with known coordinates on imaging from 139 patients that underwent preoperative 3T MRI and surgery for a suspected high-grade recurrent tumor. 8 MR parameter values from perfusion-weighted, diffusion-weighted, and MR spectroscopic imaging at each tissue sample location were tested for association with histopathological outcome using univariate and multivariate generalized estimating equation models for enhancing and non-enhancing tissue samples. Individual cutoff values were determined and evaluated using ROC-Curve analysis with 5-fold cross-validation.

RESULTS

In tissue samples obtained from contrast-enhancing lesions, elevated relative cerebral blood volume (rCBV) was significantly associated with the presence of recurrent tumor (p< 0.03), while increases in normalized choline (nCho) and choline-to-NAA index (CNI) were significantly associated with the presence of recurrent tumor in non-enhancing tissue samples (p< 0.008). Cutoff values of 1.6(rCBV), 2.7(CNI), and 2.1(nCho) had the highest performance.

CONCLUSION

Our results confirm the utility of rCBV in distinguishing the effects of treatment from recurrent tumor within the contrast enhancing lesion. We report a novel finding that metabolic parameters can differentiate recurrent tumor from treatment-related changes in the non-enhancing lesion of high-grade gliomas. These results will help improve future management of patients with suspected recurrence.

Diffusion Characteristics of Pediatric Diffuse Midline Gliomas with Histone H3-K27M Mutation Using Apparent Diffusion Coefficient Histogram Analysis

BACKGROUND AND PURPOSE

Diffuse midline gliomas with histone H3 K27M mutation are biologically aggressive tumors with poor prognosis defined as a new diagnostic entity in the 2016 World Health Organization Classification of Tumors of the Central Nervous System. There are no qualitative imaging differences (enhancement, border, or central necrosis) between histone H3 wildtype and H3 K27M-mutant diffuse midline gliomas. Herein, we evaluated the utility of diffusion-weighted imaging to distinguish H3 K27M-mutant from histone H3 wildtype diffuse midline gliomas.

MATERIALS AND METHODS

We identified 31 pediatric patients (younger than 21 years of age) with diffuse gliomas centered in midline structures that had undergone assessment for histone H3 K27M mutation. We measured ADC within these tumors using a voxel-based 3D whole-tumor measurement method.

RESULTS

Our cohort included 18 infratentorial and 13 supratentorial diffuse gliomas centered in midline structures. Twenty-three (74%) tumors carried H3-K27M mutations. There was no difference in ADC histogram parameters (mean, median, minimum, maximum, percentiles) between mutant and wild-type tumors. Subgroup analysis based on tumor location also did not identify a difference in histogram descriptive statistics. Patients who survived <1 year after diagnosis had lower median ADC (1.10 × 10^-3mm²/s; 95% CI, 0.90-1.30) compared with patients who survived >1 year (1.46 × 10^-3mm²/s; 95% CI, 1.19-1.67; P < .06). Average ADC values for diffuse midline gliomas were 1.28 × 10^-3mm²/s (95% CI, 1.21-1.34) and 0.86 × 10^-3mm²/s (95% CI, 0.69-1.01) for hemispheric glioblastomas with P < .05.

CONCLUSIONS

Although no statistically significant difference in diffusion characteristics was found between H3-K27M mutant and H3 wildtype diffuse midline gliomas, lower diffusivity corresponds to a lower survival rate at 1 year after diagnosis. These findings can have an impact on the anticipated clinical course for this patient population and offer providers and families guidance on clinical outcomes.

MLTI-18. PRECISION IMAGING OF METASTATIC AND PRIMARY BRAIN TUMORS AFTER RADIATION WITH 18F-FDOPA PET-MRI IS FEASIBLE AND COST EFFECT

PURPOSE: Post-radiation changes in the brain can mimic tumor recurrence on MRI, requiring multiple short term follow-ups to differentiate tumor progression from radiation necrosis. We propose combining functional and anatomic imaging with FDOPA PET tracer in hybrid PET-MRI to improve detection of tumor recurrence. MATERIALS AND METHODS: Seventeen adult patients treated with radiation therapy were identified - four with metastatic disease from breast and lung cancer and thirteen with primary brain glioma (11 IDH wildtype glioblastoma and 2 astrocytoma). Patients were scanned on hybrid PET-MRI (GE Healthcare) with clinical MRI brain sequences and dynamic FDOPA uptake. Dynamic FDOPA uptake within these tumors over 45 minutes after tracer injection was analyzed and compared to ADC histogram analysis. RESULTS: For each of the patients, clinical multi sequence gadolinium enhanced MRI and dynamic PET imaging for up to 45 minutes with ¹⁸F-FDOPA amino acid tracer was obtained. The total cost savings of scanning 17 patients in groups was 51.4% ($28,321), as opposed to the cost of individual radiosynthesis performed for each study. Quantitative analysis of tracer uptake in striatum, internal carotid artery, and superior sagittal sinus were performed with appropriate accumulation and subsequent washout of tracer respectively. Successful dynamic FDOPA uptake within the tumor was seen in all patients and ratio of tumor to contralateral ROI were found to range from 1.8–4.5. While raw SUV values did not differentiate between recurrent tumor and radiation changes, T/C SUVmax ratios were elevated to 4.5 in recurrent glioblastoma, 2.5 in hypoxic treated glioblastoma, and 1.8 in non-recurrent metastatic breast cancer after gamma knife treatment. CONCLUSION: Batch imaging of patients with [¹⁸F]FDOPA PET-MRI is feasible and cost effective. Understanding radionuclide synthesis process is critical for increasing accessibility of novel PET tracers to patients and results in significant cost savings.

A framework for automated conflict detection and resolution in medical guidelines

Common chronic conditions are routinely treated following standardised procedures known as clinical guidelines. For patients suffering from two or more chronic conditions, known as multimorbidity, several guidelines have to be applied simultaneously, which may lead to severe adverse effects when the combined recommendations and prescribed medications are inconsistent or incomplete. This paper presents an automated formal framework to detect, highlight and resolve conflicts in the treatments used for patients with multimorbidities focusing on medications. The presented extended framework has a front-end which takes guidelines captured in a standard modelling language and returns the visualisation of the detected conflicts as well as suggested alternative treatments. Internally, the guidelines are transformed into formal models capturing the possible unfoldings of the guidelines. The back-end takes the formal models associated with multiple guidelines and checks their correctness with a theorem prover, and inherent inconsistencies with a constraint solver. Key to our approach is the use of an optimising constraint solver which enables us to search for the best solution that resolves/minimises conflicts according to medication efficacy and the degree of severity in case of harmful combinations, also taking into account their temporal overlapping. The approach is illustrated throughout with a real medical example.

High-grade neuroepithelial tumor with BCOR exon 15 internal tandem duplication-a comprehensive clinical, radiographic, pathologic, and genomic analysis

High-grade neuroepithelial tumor with BCOR exon 15 internal tandem duplication (HGNET BCOR ex15 ITD) is a recently proposed tumor entity of the central nervous system (CNS) with a distinct methylation profile and characteristic genetic alteration. The complete spectrum of histologic features, accompanying genetic alterations, clinical outcomes, and optimal treatment for this new tumor entity are largely unknown. Here, we performed a comprehensive assessment of 10 new cases of HGNET BCOR ex15 ITD. The tumors mostly occurred in young children and were located in the cerebral or cerebellar hemispheres. On imaging all tumors were large, well-circumscribed, heterogeneous masses with variable enhancement and reduced diffusion. They were histologically characterized by predominantly solid growth, glioma-like fibrillarity, perivascular pseudorosettes, and palisading necrosis, but absence of microvascular proliferation. They demonstrated sparse to absent GFAP expression, no synaptophysin expression, variable OLIG2 and NeuN positivity, and diffuse strong BCOR nuclear positivity. While BCOR exon 15 internal tandem duplication was the solitary pathogenic alteration identified in six cases, four cases contained additional alterations including CDKN2A/B homozygous deletion, TERT amplification or promoter hotspot mutation, and damaging mutations in TP53, BCORL1, EP300, SMARCA2 and STAG2. While the limited clinical follow-up in prior reports had indicated a uniformly dismal prognosis for this tumor entity, this cohort includes multiple long-term survivors. Our study further supports inclusion of HGNET BCOR ex15 ITD as a distinct CNS tumor entity and expands the known clinicopathologic, radiographic, and genetic features.

Selumetinib in paediatric patients with BRAF-aberrant or neurofibromatosis type 1-associated recurrent, refractory, or progressive low-grade glioma: a multicentre, phase 2 trial

BACKGROUND

Paediatric low-grade glioma is the most common CNS tumour of childhood. Although overall survival is good, disease often recurs. No single universally accepted treatment exists for these patients; however, standard cytotoxic chemotherapies are generally used. We aimed to assess the activity of selumetinib, a MEK1/2 inhibitor, in these patients.

METHODS

The Pediatric Brain Tumor Consortium performed a multicentre, phase 2 study in patients with paediatric low-grade glioma in 11 hospitals in the USA. Patients aged 3-21 years with a Lansky or Karnofsky performance score greater than 60 and the presence of recurrent, refractory, or progressive paediatric low-grade glioma after at least one standard therapy were eligible for inclusion. Patients were assigned to six unique strata according to histology, tumour location, NF1 status, and BRAF aberration status; herein, we report the results of strata 1 and 3. Stratum 1 comprised patients with WHO grade I pilocytic astrocytoma harbouring either one of the two most common BRAF aberrations (KIAA1549-BRAF fusion or the BRAF^V600E [Val600Glu] mutation). Stratum 3 comprised patients with any neurofibromatosis type 1 (NF1)-associated paediatric low-grade glioma (WHO grades I and II). Selumetinib was provided as capsules given orally at the recommended phase 2 dose of 25 mg/m² twice daily in 28-day courses for up to 26 courses. The primary endpoint was the proportion of patients with a stratum-specific objective response (partial response or complete response), as assessed by the local site and sustained for at least 8 weeks. All responses were reviewed centrally. All eligible patients who initiated treatment were evaluable for the activity and toxicity analyses. Although the trial is ongoing in other strata, enrolment and planned follow-up is complete for strata 1 and 3. This trial is registered with ClinicalTrials.gov, number NCT01089101.

FINDINGS

Between July 25, 2013, and June 12, 2015, 25 eligible and evaluable patients were accrued to stratum 1, and between Aug 28, 2013, and June 25, 2015, 25 eligible and evaluable patients were accrued to stratum 3. In stratum 1, nine (36% [95% CI 18-57]) of 25 patients achieved a sustained partial response. The median follow-up for the 11 patients who had not had a progression event by Aug 9, 2018, was 36·40 months (IQR 21·72-45·59). In stratum 3, ten (40% [21-61]) of 25 patients achieved a sustained partial response; median follow-up was 48·60 months (IQR 39·14-51·31) for the 17 patients without a progression event by Aug 9, 2018. The most frequent grade 3 or worse adverse events were elevated creatine phosphokinase (five [10%]) and maculopapular rash (five [10%]). No treatment-realted deaths were reported.

INTERPRETATION

Selumetinib is active in recurrent, refractory, or progressive pilocytic astrocytoma harbouring common BRAF aberrations and NF1-associated paediatric low-grade glioma. These results show that selumetinib could be an alternative to standard chemotherapy for these subgroups of patients, and have directly led to the development of two Children's Oncology Group phase 3 studies comparing standard chemotherapy to selumetinib in patients with newly diagnosed paediatric low-grade glioma both with and without NF1.

FUNDING

National Cancer Institute Cancer Therapy Evaluation Program, the American Lebanese Syrian Associated Charities, and AstraZeneca.

Targeting iron metabolism in high-grade glioma with 68Ga-citrate PET/MR

Noninvasive tools that target tumor cells could improve the management of glioma. Cancer generally has a high demand for Fe(III), an essential nutrient for a variety of biochemical processes. We tested whether 68Ga-citrate, an Fe(III) biomimetic that binds to apo-transferrin in blood, detects glioma in preclinical models and patients using hybrid PET/MRI. Mouse PET/CT studies showed that 68Ga-citrate accumulates in subcutaneous U87MG xenografts in a transferrin receptor-dependent fashion within 4 hours after injection. Seventeen patients with WHO grade III or IV glioma received 3.7-10.2 mCi 68Ga-citrate and were imaged with PET/MR 123-307 minutes after injection to establish that the radiotracer can localize to human tumors. Multiple contrast-enhancing lesions were PET avid, and tumor to adjacent normal white matter ratios were consistently greater than 10:1. Several contrast-enhancing lesions were not PET avid. One minimally enhancing lesion and another tumor with significantly reduced enhancement following bevacizumab therapy were PET avid. Advanced MR imaging analysis of one patient with contrast-enhancing glioblastoma showed that metabolic hallmarks of viable tumor spatially overlaid with 68Ga-citrate accumulation. These early data underscore that high-grade glioma may be detectable with a radiotracer that targets Fe(III) transport.

Clinically Relevant and Minimally Invasive Tumor Surveillance of Pediatric Diffuse Midline Gliomas Using Patient-Derived Liquid Biopsy

PURPOSE

Pediatric diffuse midline glioma (DMG) are highly malignant tumors with poor clinical outcomes. Over 70% of patients with DMG harbor the histone 3 p.K27M (H3K27M) mutation, which correlates with a poorer clinical outcome, and is also used as a criterion for enrollment in clinical trials. Because complete surgical resection of DMG is not an option, biopsy at presentation is feasible, but rebiopsy at time of progression is rare. While imaging and clinical-based disease monitoring is the standard of care, molecular-based longitudinal characterization of these tumors is almost nonexistent. To overcome these hurdles, we examined whether liquid biopsy allows measurement of disease response to precision therapy.

EXPERIMENTAL DESIGN

We established a sensitive and specific methodology that detects major driver mutations associated with pediatric DMGs using droplet digital PCR (n = 48 subjects, n = 110 specimens). Quantification of circulating tumor DNA (ctDNA) for H3K27M was used for longitudinal assessment of disease response compared with centrally reviewed MRI data.

RESULTS

H3K27M was identified in cerebrospinal fluid (CSF) and plasma in 88% of patients with DMG, with CSF being the most enriched for ctDNA. We demonstrated the feasibility of multiplexing for detection of H3K27M, and additional driver mutations in patient's tumor and matched CSF, maximizing the utility of a single source of liquid biome. A significant decrease in H3K27M plasma ctDNA agreed with MRI assessment of tumor response to radiotherapy in 83% (10/12) of patients.

CONCLUSIONS

Our liquid biopsy approach provides a molecularly based tool for tumor characterization, and is the first to indicate clinical utility of ctDNA for longitudinal surveillance of DMGs.

Volumetric voxelwise apparent diffusion coefficient histogram analysis for differentiation of the fourth ventricular tumors

PURPOSE

We applied voxelwise apparent diffusion coefficient (ADC) histogram analysis in addition to structural magnetic resonance imaging (MRI) findings and patients' age for differentiation of intraaxial posterior fossa tumors involving the fourth ventricle.

PARTICIPANTS AND METHODS

Pretreatment MRIs of 74 patients with intraaxial brain neoplasm involving the fourth ventricle, from January 1, 2004 to December 31, 2015, were reviewed. The tumor solid components were segmented and voxelwise ADC histogram variables were determined. Histogram-driven variables, structural MRI findings, and patient age were combined to devise a differential diagnosis algorithm.

RESULTS

The most common neoplasms were ependymomas ( n = 21), medulloblastoma ( n = 17), and pilocytic astrocytomas ( n = 13). Medulloblastomas followed by atypical teratoid/rhabdoid tumors had the lowest ADC histogram percentile values; whereas pilocytic astrocytomas and choroid plexus papillomas had the highest ADC histogram percentile values. In a multivariable multinominal regression analysis, the ADC 10th percentile value from voxelwise histogram was the only independent predictor of tumor type ( p < 0.001). In separate binary logistic regression analyses, the 10th percentile ADC value, tumor morphology, enhancement pattern, extension into Luschka/Magendie foramina, and patient age were predictors of different tumor types. Combining these variables, we devised a stepwise diagnostic model yielding 71% to 82% sensitivity, 91% to 95% specificity, 75% to 78% positive predictive value, and 89% to 95% negative predictive value for differentiation of ependymoma, medulloblastoma, and pilocytic astrocytoma.

CONCLUSION

We have shown how the addition of quantitative voxelwise ADC histogram analysis of the tumor solid component to structural findings and patient age can help with accurate differentiation of intraaxial posterior fossa neoplasms involving the fourth ventricle based on pretreatment MRI.

Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas

BACKGROUND AND PURPOSE

The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation.

MATERIALS AND METHODS

MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify isocitrate dehydrogenase 1 (IDH1) mutation status, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation status. Principal component analysis of the final convolutional neural network layer was used to extract the key imaging features critical for successful classification.

RESULTS

Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. Each genetic category was also associated with distinctive imaging features such as definition of tumor margins, T1 and FLAIR suppression, extent of edema, extent of necrosis, and textural features.

CONCLUSIONS

Our results indicate that for The Cancer Imaging Archives dataset, machine-learning approaches allow classification of individual genetic mutations of both low- and high-grade gliomas. We show that relevant MR imaging features acquired from an added dimensionality-reduction technique demonstrate that neural networks are capable of learning key imaging components without prior feature selection or human-directed training.