Classification Of Gliomas Research Articles

RANO 2.0: Proposal for an update to the Response Assessment in Neuro-Oncology (RANO) criteria for high- and low-grade gliomas in adults.

2017 Background: Response Assessment in Neuro-Oncology (RANO) criteria for high-grade and low-grade glioma (HGG and LGG) were developed to improve reliability of response assessment in glioma trials. Several limitations of the original RANO criteria have been reported. Methods: To address limitations of the original RANO criteria, a large cohort of patients with newly diagnosed and recurrent glioblastoma were evaluated comparing RANO-HGG with modified RANO (mRANO) and immunotherapy RANO (iRANO) criteria to inform the following proposed updates (RANO 2.0). Results: Based on the 2021 WHO classification of glioma, we recommend a single common set of criteria for all gliomas regardless of WHO grade and IDH mutational status. These criteria will be used for all trials regardless of the treatment modalities being evaluated. In newly diagnosed glioblastoma, the post-radiotherapy MRI, usually obtained approximately 4 weeks after completion of radiotherapy, rather than the post-surgical MRI as proposed in original RANO criteria, will be used as the baseline for comparison of subsequent scans. Since the incidence of pseudoprogression is high in the first 3 months following radiotherapy, confirmation of progression during this period with a repeat MRI (in 4-8 weeks) will be required for determining radiographic progression of disease (PD) if the patient is clinically stable. However, confirmation scans are not mandatory after this period nor for recurrent tumors since these scans do not appear to improve reliability in determining progression. For agents with a high likelihood of producing pseudoprogression such as viral therapies and some other immunotherapies, mandatory confirmation of progression with a repeat MRI is an option. The primary measurement will remain the 2-dimensional maximum cross-sectional area of tumor as the simplest method, but where resources are available, volumetric measurements are an option. For IDH wild-type tumors with contrast enhancement, non-enhancing tumor will no longer be evaluated with the possible exception of patients receiving antiangiogenic therapies. However, in tumors with a significant non-enhancing component, both the contrast enhancing and non-enhancing components will be evaluated as target lesions. Conclusions: We hope that these and other proposed changes by the RANO working group will improve response assessment in glioma clinical trials and help the development of more effective therapy for patients.

The 2021 World Health Organization classification of gliomas: an imaging approach.

The purpose of this pictorial essay is to describe the recommendations of the 2021 World Health Organization classification for adult-type and pediatric-type gliomas and to discuss the main modifications in relation to the previous (2016) classification, exemplified by imaging, histological, and molecular findings in nine patients followed at our institutions. In recent years, molecular biomarkers have gained importance in the diagnosis and classification of gliomas, mainly because they have been shown to correlate with the biological behavior and prognosis of such tumors. It is important for neuroradiologists to familiarize themselves with this new classification of central nervous system tumors, so that they can use this knowledge in evaluating and reporting the imaging examinations of patients with glioma.

The 2021 World Health Organization classification of gliomas: an imaging approach

Resumo O propósito deste ensaio iconográfico é descrever e discutir as novas recomendações da Organização Mundial da Saúde de 2021, referente aos gliomas dos tipos adulto e infantil, e suas principais diferenças com a classificação anterior (2016), exemplificadas com imagens de nove casos de pacientes atendidos nas nossas instituições. Recentemente, há uma crescente significância dos marcadores moleculares no diagnóstico e classificação dos gliomas e tumores do sistema nervoso central, principalmente pela correlação com o comportamento biológico e o prognóstico. É importante que os neurorradiologistas estejam familiarizados com a nova classificação dos tumores do sistema nervoso central para a prática clínica, na avaliação e emissão de laudos e opiniões nas imagens dos pacientes com gliomas.

Feasibility and utility of the custom RNA-seq for the identification of therapeutic targets and diagnostic biomarkers in glioma.

e14029 Background: The detection of novel therapeutic targets is increasingly important in oncology. The Todai OncoPanel (TOP) RNA panel is a custom target RNA-seq using the junction capture method to maximize the sensitivity in detecting of known 456 fusion gene transcripts, which is scheduled to be approved for clinical application in Japan in April 2023. Above, TOP RNA panel interrogates aberrantly spliced transcripts and analyzes the expression profiles of 1,390 genes. Malignant brain tumors, including gliomas, require various molecular profiles to be classified based on the latest WHO classification criteria, while their poor prognosis also necessitates new therapeutic targets. In this study, we aimed to classify gliomas and identify the molecular targets by TOP RNA panel. Methods: Among 131 frozen tumor samples of malignant gliomas, 124 samples passed quality control for further validation and were subjected to TOP RNA panel to classify based on the molecular profiles and to identify the molecular targets. Fusion detection and RNA expression analysis were conducted with the original pipeline. Cluster analysis and Gene Set Enrichment Analysis (GSEA) were performed to identify gene sets specific for individual subtypes. Copy numbers of tyrosine kinase genes, MDM2 and CDK4 were evaluated by digital droplet PCR (ddPCR). Results: Among 58 cases of glioblastoma, gene fusions were detected in 11 cases (19.0%) including novel MET fusions, and overexpression of seven tyrosine kinase genes were observed in 21 cases (36.2%). Of the 34 overexpressed genes, gene amplification was observed in 17 genes (50%) by ddPCR. In contrast to IDH-wildtype glioblastoma, IDH-mutant tumors including astrocytomas and oligodendrogliomas hardly harbored fusion genes or gene overexpression. GSEA to compare astrocytomas and oligodendrogliomas identified that the gene sets related to 1p36 and 19q were relatively enriched in astrocytoma, suggesting that regional genomic DNA copy number alterations can be evaluated by gene expression analysis. Conclusions: TOP RNA panel is utilized not only for the detection of molecular targets but also for the molecular classification of glioma, both of which is essential for providing treatments optimized for the individual patients.

Improving a neural network model by explanation-guided training for glioma classification based on MRI data

In recent years, artificial intelligence systems have come to the forefront. These systems, mostly based on deep learning, achieve excellent results in areas such as image processing, natural language processing and speech recognition. Despite the statistically high accuracy of deep learning models, their output is often based on ”black box” decisions. Thus, interpretability methods (Reyes et al. in Radiol Artif Intell 2(3):e190043, 2020) have become a popular way to gain insight into the decision-making process of deep learning models (Miller in Artif Intell 267:1–38, 2019). Explanation of deep learning models is desirable in the medical domain since experts have to justify their judgments to the patients. In this work, we proposed a method for explanation-guided training that uses a layer-wise relevance propagation technique to force the model to focus only on the relevant part of the image. We experimentally verified our method on a convolutional neural network model for low-grade and high-grade glioma classification problems. Our experiments produced promising results in the way where we use interpretation techniques in the training process.

From Theory to Practice: Implementing the WHO 2021 Classification of Adult Diffuse Gliomas in Neuropathology Diagnosis.

Diffuse gliomas are the most common type of primary central nervous system (CNS) neoplasm to affect the adult population. The diagnosis of adult diffuse gliomas is dependent upon the integration of morphological features of the tumour with its underlying molecular alterations, and the integrative diagnosis has become of increased importance in the fifth edition of the WHO classification of CNS neoplasms (WHO CNS5). The three major diagnostic entities of adult diffuse gliomas are as follows: (1) astrocytoma, IDH-mutant; (2) oligodendroglioma, IDH-mutant and 1p/19q-codeleted; and (3) glioblastoma, IDH-wildtype. The aim of this review is to summarize the pathophysiology, pathology, molecular characteristics, and major diagnostic updates encountered in WHO CNS5 of adult diffuse gliomas. Finally, the application of implementing the necessary molecular tests for diagnostic workup of these entities in the pathology laboratory setting is discussed.

An interpretable feature-learned model for overall survival classification of High-Grade Gliomas

PurposeAn accurate and well-defined survival prediction of High Grade Gliomas (HGGs) is indispensable because of its high incidence and aggressiveness. Therefore, this paper presents a unified framework for fully automatic overall survival classification and its interpretation. Methods and materialsInitially, a glioma detection model is utilized to detect the tumorous images. A pre-processing module is designed for extracting 2D slices and creating a survival data array for the classification network. Then, the classification pipeline is integrated with two separate pathways: a modality-specific and a modality-concatenated pathway. The modality-specific pathway runs three separate CNNs for extracting rich predictive features from three sub-regions of HGGs (peritumoral edema, enhancing tumor and necrosis) by using three neuro-imaging modalities. In these pathways, the image vectors of the different modalities are also concatenated to the final fusion layer to overcome the loss of lower-level tumor features. Furthermore, to exploit the intra-modality correlations, a modality-concatenated pathway is also added to the classification pipeline. The experiments are conducted on BraTS 2018 and BraTS 2019 benchmarks, demonstrating that the proposed approach performs competitively in classifying HGG patients into three survival groups, namely, short, mid, and long survivors. ResultsThe proposed approach achieves an overall classification accuracy, sensitivity, and specificity of about 0.998, 0.997, and 0.999, respectively, for the BraTS 2018 dataset, and for BraTS 2019, these values correspond to 1.000, 0.999, and 0.999. ConclusionsThe results indicate that the proposed model achieves the highest values of the evaluation metrics for the overall survival classification of HGG.

Adult diffuse gliomas: What the neuroradiologist needs to know.

Diffuse gliomas are the most common primary malignant brain tumors in adults. Advancements in the molecular profiling of diffuse gliomas in recent years have led to a far better understanding of their biology and clinical outcomes. The fifth edition of the World Health Organization Classification of Central Nervous System Tumors, published in 2021, incorporates this genomic information to a much greater degree than prior editions. It is important for radiologists to understand the new glioma classification system and the characteristic neuroimaging features associated with each entity. This review aims to provide an overview of the diffuse gliomas that can present in adults, with an emphasis on their molecular features and associated imaging findings.

The 2021 WHO Classification for Gliomas and Implications on Imaging Diagnosis: Part 1-Key Points of the Fifth Edition and Summary of Imaging Findings on Adult-Type Diffuse Gliomas.

The fifth edition of the World Health Organization (WHO) classification of central nervous system tumors published in 2021 advances the role of molecular diagnostics in the classification of gliomas by emphasizing integrated diagnoses based on histopathology and molecular information and grouping tumors based on genetic alterations. Importantly, molecular biomarkers that provide important prognostic information are now a parameter for establishing tumor grades in gliomas. Understanding the 2021 WHO classification is crucial for radiologists for daily imaging interpretation as well as communication with clinicians. Although imaging features are not included in the 2021 WHO classification, imaging can serve as a powerful tool to impact the clinical practice not only prior to tissue confirmation but beyond. This review represents the first of a three-installment review series on the 2021 WHO classification for gliomas, glioneuronal tumors, and neuronal tumors and implications on imaging diagnosis. This Part 1 Review focuses on the major changes to the classification of gliomas and imaging findings on adult-type diffuse gliomas. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 3.

The 2021 WHO Classification for Gliomas and Implications on Imaging Diagnosis: Part 2-Summary of Imaging Findings on Pediatric-Type Diffuse High-Grade Gliomas, Pediatric-Type Diffuse Low-Grade Gliomas, and Circumscribed Astrocytic Gliomas.

The fifth edition of the World Health Organization (WHO) classification of central nervous system tumors published in 2021 advances the role of molecular diagnostics in the classification of gliomas by emphasizing integrated diagnoses based on histopathology and molecular information and grouping tumors based on genetic alterations. This Part 2 review focuses on the molecular diagnostics and imaging findings of pediatric-type diffuse high-grade gliomas, pediatric-type diffuse low-grade gliomas, and circumscribed astrocytic gliomas. Each tumor type in pediatric-type diffuse high-grade glioma mostly harbors a distinct molecular marker. On the other hand, in pediatric-type diffuse low-grade gliomas and circumscribed astrocytic gliomas, molecular diagnostics may be extremely complicated at a glance in the 2021 WHO classification. It is crucial for radiologists to understand the molecular diagnostics and imaging findings and leverage the knowledge in clinical practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Korean Brain Tumor Society Consensus Review for the Practical Recommendations on Glioma Management in Korea.

Recent updates in genomic-integrated glioma classification have caused confusion in current clinical practice, as management protocols and health insurance systems are based on evidence from previous diagnostic classifications. The Korean Brain Tumor Society conducted an electronic questionnaire for society members, asking for their ideas on risk group categorization and preferred treatment for each individual diagnosis listed in the new World Health Organization (WHO) classification of gliomas. Additionally, the current off-label drug use (OLDU) protocols for glioma management approved by the Health Insurance Review and Assessment Service (HIRA) in Korea were investigated. A total of 24 responses were collected from 20 major institutes in Korea. A consensus was reached on the dichotomic definition of risk groups for glioma prognosis, using age, performance status, and extent of resection. In selecting management protocols, there was general consistency in decisions according to the WHO grade and the risk group, regardless of the individual diagnosis. As of December 2022, there were 22 OLDU protocols available for the management of gliomas in Korea. The consensus and available options described in this report will be temporarily helpful until there is an accumulation of evidence for effective management under the new classification system for gliomas.

Abstract 1506: Decoding multi-omics genetic profiling reveals heterogeneity in adult pan-gliomas

Abstract Adult-type diffuse gliomas (Glioblastoma (GBM), Astrocytoma, Oligodendroglioma) are the most common malignant brain tumor in adults with dismal prognoses. Recent large-scale genomic studies have established molecular classification of glioma based on coding mutations and copy number variations. However, whole-genome landscape and multi-omics profiles of gliomas are not well analyzed. To understand the multi-omics genetic landscape of gliomas, we performed deep whole-genome sequencing (≥ ×120 coverage) of 357 cases with adult-type diffuse gliomas (162 GBMs, 96 astrocytomas, 99 oligodendrogliomas) along with EPIC DNA methylation profiling. RNA-seq, whole-genome bisulfate sequencing, and assay for transposase-accessible chromatin with sequencing (ATAC-seq) were performed on 349, 40, and 40 of these tumors, respectively. Deep WGS delineated a fine view of clonal architecture demonstrating mutational order during tumor initiation of each glioma. Mutational signature varies between clonal and subclonal mutations, supporting a model that tumors acquire genetic alterations by distinct mechanisms based on their developmental stage. Structural variants (SVs) are more frequently detected in higher malignant gliomas. While a complex SV is rare in Astrocytoma and Oligodendroglioma (35% of samples), a large proportion of GBM cases (85%) have complex SV with ≥ 10 breakpoints, which involves multiple different chromosomes including driver genes. CDKN2A homozygous deletions and focal CDK4 amplifications are often induced by complex SVs in GBM. These results suggest that complex SVs could play a pivotal role in the initiation and progression of GBM. Integrative analysis of transcriptomic and epigenomic profiles by similarity network fusion (SNF) identifies homogenous clusters in each glioma where those clusters are associated with mutational and SV signatures, suggesting that specific cell states may have distinct sensitivities to mutational processes. In oligodendrogliomas, the SNF classification detected a cluster with poor prognosis which has an enrichment of stem cell-like signature by CIBERSORTx deconvolution analysis. ATAC-seq demonstrated distinct features of genome-wide chromatin accessibility in each glioma which may reflect the difference of cell of the origin. Most of the focally amplified genes have open chromatin status suggesting that tumors take advantage of not only gene duplication but also a transcriptional activity to activate driver genes. Our integrated analysis uncovers molecular mechanisms of gliomas which will help to understand glioma-genesis. Citation Format: Takuma Nakashima, Yusuke Funakoshi, Atsuhito Uneda, Yuriko Sugihara, Shohei Nambu, Manabu Kinoshita, Yoshiki Arakawa, Shota Tanaka, Joji Ishida, Ryuta Saito, Ryosuke Hanaya, Koji Yoshimoto, Yoshitaka Narita, Hiromichi Suzuki. Decoding multi-omics genetic profiling reveals heterogeneity in adult pan-gliomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1506.

WHO CNS5 2021 includes specific mutations in gliomas that can be identified with MRI quantitative biomarkers

In 2021, the latest version of the World Health Organization classification of central nervous system tumors (WHO CNS5) was published, which is considered an international standard. The first editions of this classification were based on histological characteristics and, subsequently, aspects related to new knowledge were incorporated. In the 2016 revision, molecular characteristics were implemented for the classification and staging of gliomas, such as the presence of mutations in IDH1 or IDH2. Currently, advanced magnetic resonance imaging (MRI) techniques allow assessing for the presence of 2-HG (increased oncometabolite that precedes IDH mutations), whereby IDH mutations can be indirectly identified, without invasive procedures being required. Advanced MRI is a growing field, highly useful for diagnosis and management of different pathologies. This document addresses the implications of WHO CNS5 classification in the evaluation of gliomas, as well as historical aspects, the bases of conventional MRI, and advanced MRI sequences useful in current classification.

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CNS Neuroscience & TherapeuticsVolume 29, Issue 5 p. ii-ii COVER IMAGEOpen Access Back cover First published: 03 April 2023 https://doi.org/10.1111/cns.14202AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Graphical Abstract The cover image is based on the Original Article Altering patterns of sensorimotor network in patients with different pathological diagnoses and glioma-related epilepsy under the latest glioma classification of the central nervous system by Shengyu Fang et al., https://doi.org/10.1111/cns.14109. Volume29, Issue5May 2023Pages ii-ii RelatedInformation

102 AI-Based Molecular Classification of Diffuse Gliomas using Rapid, Label-Free Optical Imaging

Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. Access to timely molecular diagnostic testing for brain tumor patients is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment. By combining stimulated Raman histology (SRH), a rapid, label-free, non-consumptive, optical imaging method, and deep learning-based image classification, we are able to predict the molecular genetic features used by the World Health Organization (WHO) to define the adult-type diffuse glioma taxonomy, including IDH-1/2, 1p19q-codeletion, and ATRX loss. We developed a multimodal deep neural network training strategy that uses both SRH images and large-scale, public diffuse glioma genomic data (i.e. TCGA, CGGA, etc.) in order to achieve optimal molecular classification performance. One institution was used for model training (University of Michigan) and four institutions (NYU, UCSF, Medical University of Vienna, and University Hospital Cologne) were included for patient enrollment in the prospective testing cohort. Using our system, called DeepGlioma, we achieved an average molecular genetic classification accuracy of 93.2% and identified the correct diffuse glioma molecular subgroup with 91.5% accuracy within 2 minutes in the operating room. DeepGlioma outperformed conventional IDH1-R132H immunohistochemistry (94.2% versus 91.4% accuracy) as a first-line molecular diagnostic screening method for diffuse gliomas and can detect canonical and non-canonical IDH mutations. Our results demonstrate how artificial intelligence and optical histology can be used to provide a rapid and scalable alternative to wet lab methods for the molecular diagnosis of brain tumor patients during surgery.

Classification of 3D-DWT Features of Brain Tumours with SVM

Brain tumours are one of the most challenging medical conditions to diagnose and treat. Accurate and timely classification of brain tumours is critical for effective treatment planning and patient management. Machine learning algorithms have shown great promise in improving the accuracy of brain tumour classification. This study implemented high-grade glioma (HGG) and low-grade glioma (LGG) classification on four different 3D-MRI (magnetic resonance imaging) scans (FLAIR, T1, T1c, T2). By using four different scans, 15 different combinations were created for classification process. 3D Discrete Wavelet Transform was used to transform tumour images for feature extraction stage. 36 different wavelet types were used for image transformation. First Order Statistics (mean, variance, kurtosis, skewness, entropy, energy) were extracted from transformed images of 36 wavelet types. Support Vector Machines (SVM) algorithm classified the FOS features that were obtained on BraTS 2017 dataset. The 2-fold, 5-fold, and 10-fold cross-validations are implemented and six metrics (sensitivity, specificity, accuracy, precision, F1-score, AUC) evaluated the performance of proposed method. Consequently, proposed method achieved remarkable scores of 95.23% (sensitivity), 78.81% (specificity), 90.89% (accuracy), 92.59% (precision), 93.89% (F1-score), and 87.02% (AUC) for HGG/LGG classification of 3D brain MRI data on T1+T1c+T2 combination by 2-fold cross validation.

Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging.

Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. However, timely molecular diagnostic testing for patients with brain tumors is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment. In this study, we developed DeepGlioma, a rapid (<90 seconds), artificial-intelligence-based diagnostic screening system to streamline the molecular diagnosis of diffuse gliomas. DeepGlioma is trained using a multimodal dataset that includes stimulated Raman histology (SRH); a rapid, label-free, non-consumptive, optical imaging method; and large-scale, public genomic data. In a prospective, multicenter, international testing cohort of patients with diffuse glioma (n = 153) who underwent real-time SRH imaging, we demonstrate that DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy (IDH mutation, 1p19q co-deletion and ATRX mutation), achieving a mean molecular classification accuracy of 93.3 ± 1.6%. Our results represent how artificial intelligence and optical histology can be used to provide a rapid and scalable adjunct to wet lab methods for the molecular screening of patients with diffuse glioma.

Mutated Isocitrate Dehydrogenase (mIDH) as Target for PET Imaging in Gliomas.

Gliomas are the most common primary brain tumors in adults. A diffuse infiltrative growth pattern and high resistance to therapy make them largely incurable, but there are significant differences in the prognosis of patients with different subtypes of glioma. Mutations in isocitrate dehydrogenase (IDH) have been recognized as an important biomarker for glioma classification and a potential therapeutic target. However, current clinical methods for detecting mutated IDH (mIDH) require invasive tissue sampling and cannot be used for follow-up examinations or longitudinal studies. PET imaging could be a promising approach for non-invasive assessment of the IDH status in gliomas, owing to the availability of various mIDH-selective inhibitors as potential leads for the development of PET tracers. In the present review, we summarize the rationale for the development of mIDH-selective PET probes, describe their potential applications beyond the assessment of the IDH status and highlight potential challenges that may complicate tracer development. In addition, we compile the major chemical classes of mIDH-selective inhibitors that have been described to date and briefly consider possible strategies for radiolabeling of the most promising candidates. Where available, we also summarize previous studies with radiolabeled analogs of mIDH inhibitors and assess their suitability for PET imaging in gliomas.

Detection and classification of glioma, meningioma, pituitary tumor, and normal in brain magnetic resonance imaging using deep learning-based hybrid model

Detection and classification of glioma, meningioma, pituitary tumor, and normal in brain magnetic resonance imaging using deep learning-based hybrid model

Cognitive issues in patients with IDH mutant gliomas: from neuroscience to clinical neuropsychology.

The understanding of cognitive symptoms in patients with IDH-Mutant gliomas (IDH-Mut) is rapidly developing. In this article, we summarize the neuroscientific knowledge base regarding the influence of IDH-Mut tumors and their treatment on cognition and provide guidance regarding the management of these symptoms in patients. We performed a review of peer reviewed publications relevant to IDH-Mut glioma and cognitive outcomes and provide an overview of the literature as well as a case example to clarify management strategies. At the time of presentation, patients with IDH-Mut gliomas have a favorable cognitive profile as compared with those with IDH-wild type (WT) tumors. The relatively low cognitive burden may reflect the slower growth rate of IDH-Mut tumors, which is less disruptive to both local and widespread neural networks. Human connectomic research using a variety of modalities has demonstrated relatively preserved network efficiency in patients with IDH-Mut gliomas as compared with IDH-WT tumors. Risk of cognitive decline from surgery can potentially be mitigated by careful integration of intra-operative mapping. Longer term cognitive risks of tumor treatment, including chemotherapy and radiation, are best managed by instituting neuropsychological assessment as part of the long-term care of patients with IDH-Mutant glioma. A specific timeline for such integrative care is provided. Given the relative recency of the IDH-mutation based classification of gliomas, as well as the long time course of this disease, a thoughtful and comprehensive strategy to studying patient outcomes and devising methods of cognitive risk reduction is required.