Abstract
Brain metastases affect a significant percentage of patients with advanced extracranial malignancies. Yet, the incidence of brain metastases remains poorly described, largely due to ...limitations of population-based registries, a lack of mandated reporting of brain metastases to federal agencies, and historical difficulties with delineation of metastatic involvement of individual organs using claims data. However, in 2016, the Surveillance Epidemiology and End Results (SEER) program released data relating to the presence vs absence of brain metastases at diagnosis of oncologic disease. In 2020, studies demonstrating the viability of utilizing claims data for identifying the presence of brain metastases, date of diagnosis of intracranial involvement, and initial treatment approach for brain metastases were published, facilitating epidemiologic investigations of brain metastases on a population-based level. Accordingly, in this review, we discuss the incidence, clinical presentation, prognosis, and management patterns of patients with brain metastases. Leptomeningeal disease is also discussed. Considerations regarding individual tumor types that commonly metastasize to the brain are provided.
Radiographic endpoints including response and progression are important for the evaluation of new glioblastoma therapies. The current RANO criteria was developed to overcome many of the challenges ...identified with previous guidelines for response assessment, however, significant challenges and limitations remain. The current recommendations build on the strengths of the current RANO criteria, while addressing many of these limitations. Modifications to the current RANO criteria include suggestions for volumetric response evaluation, use contrast enhanced T1 subtraction maps to increase lesion conspicuity, removal of qualitative non-enhancing tumor assessment requirements, use of the post-radiation time point as the baseline for newly diagnosed glioblastoma response assessment, and “treatment-agnostic” response assessment rubrics for identifying pseudoprogression, pseudoresponse, and a confirmed durable response in newly diagnosed and recurrent glioblastoma trials.
•All six theoretical models have good explanatory power of behavioral intention (BI).•Based on variance explanation, the motivational model (MM) and the technology acceptance model (TAM) have ...stronger explanatory powers.•The theory of planned behavior (TPB) and the TAM have larger effect size compared to other theories.•Perceived usefulness (PU), attitude (ATT), cloud service quality (CSQ), perceived behavior control (PBC), result demonstration (RD), visibility (VIS), and cloud self-efficacy (CSE) are important factors of a unified model.
Cloud computing is an innovative information technology that has been applied to education and has facilitated the development of cloud computing classrooms; however, student behavioral intention (BI) toward cloud computing remains unclear. Most researchers have evaluated, integrated, or compared only few theories to examine user BI. In this study, we tested, compared, and unified six well-known theories, namely service quality (SQ), self-efficacy (SE), the motivational model (MM), the technology acceptance model (TAM), the theory of reasoned action or theory of planned behavior (TRA/TPB), and innovation diffusion theory (IDT), in the context of cloud computing classrooms. This empirical study was conducted using an online survey. The data collected from the samples (n=478) were analyzed using structural equation modeling. We independently analyzed each theory, by formulating a united model. The analysis yielded three valuable findings. First, all six theoretical models and the united model exhibited adequate explanatory power. Second, variance explanation, Chi-squared statistics, effect size, and predictive relevance results revealed the ranking importance of the theoretical models. Third, the united model provided a comprehensive understanding of the factors that significantly affect the college students’ BI toward a cloud computing classroom. The discussions and implications of this study are critical for researchers and practitioners.
A new edition of the WHO classification of tumours of the CNS was published in 2021. Although the previous edition of this classification was published just 5 years earlier, in 2016, rapid advances ...in our understanding of the molecular underpinnings of CNS tumours, including the diversity of clinically relevant molecular types and subtypes, necessitated a new classification system. Compared with the 2016 scheme, the new classification incorporates even more molecular alterations into the diagnosis of many tumours and reorganizes gliomas into adult-type diffuse gliomas, paediatric-type diffuse low-grade and high-grade gliomas, circumscribed astrocytic gliomas, and ependymal tumours. A number of new entities are incorporated into the 2021 classification, especially tumours that preferentially or exclusively arise in the paediatric population. Such a substantial revision of the WHO scheme will have major implications for the diagnosis and treatment of patients with CNS tumours. In this Perspective, we summarize the main changes in the classification of diffuse and circumscribed gliomas, ependymomas, embryonal tumours and meningiomas, and discuss how each change will influence post-surgical treatment, clinical trial enrolment and cooperative studies. Although the 2021 WHO classification of CNS tumours is a major conceptual advance, its implementation on a routine clinical basis presents some challenges that will require innovative solutions.
The clinical management of meningioma is guided by tumor grade and biological behavior. Currently, the assessment of tumor grade follows surgical resection and histopathologic review. Reliable ...techniques for pre-operative determination of tumor grade may enhance clinical decision-making.
A total of 175 meningioma patients (103 low-grade and 72 high-grade) with pre-operative contrast-enhanced T1-MRI were included. Fifteen radiomic (quantitative) and 10 semantic (qualitative) features were applied to quantify the imaging phenotype. Area under the curve (AUC) and odd ratios (OR) were computed with multiple-hypothesis correction. Random-forest classifiers were developed and validated on an independent dataset (n = 44).
Twelve radiographic features (eight radiomic and four semantic) were significantly associated with meningioma grade. High-grade tumors exhibited necrosis/hemorrhage (ORsem = 6.6, AUCrad = 0.62-0.68), intratumoral heterogeneity (ORsem = 7.9, AUCrad = 0.65), non-spherical shape (AUCrad = 0.61), and larger volumes (AUCrad = 0.69) compared to low-grade tumors. Radiomic and sematic classifiers could significantly predict meningioma grade (AUCsem = 0.76 and AUCrad = 0.78). Furthermore, combining them increased the classification power (AUCradio = 0.86). Clinical variables alone did not effectively predict tumor grade (AUCclin = 0.65) or show complementary value with imaging data (AUCcomb = 0.84).
We found a strong association between imaging features of meningioma and histopathologic grade, with ready application to clinical management. Combining qualitative and quantitative radiographic features significantly improved classification power.
Evolving interest in meningioma, the most common primary brain tumor, has refined contemporary management of these tumors. Problematic, however, is the paucity of prospective clinical trials that ...provide an evidence-based algorithm for managing meningioma. This review summarizes the published literature regarding the treatment of newly diagnosed and recurrent meningioma, with an emphasis on outcomes stratified by WHO tumor grade. Specifically, this review focuses on patient outcomes following treatment (either adjuvant or at recurrence) with surgery or radiation therapy inclusive of radiosurgery and fractionated radiation therapy. Phase II trials for patients with meningioma have recently completed accrual within the Radiation Therapy Oncology Group and the European Organisation for Research and Treatment of Cancer consortia, and Phase III studies are being developed. However, at present, there are no completed prospective, randomized trials assessing the role of either surgery or radiation therapy. Successful completion of future studies will require a multidisciplinary effort, dissemination of the current knowledge base, improved implementation of WHO grading criteria, standardization of response criteria and other outcome end points, and concerted efforts to address weaknesses in present treatment paradigms, particularly for patients with progressive or recurrent low-grade meningioma or with high-grade meningioma. In parallel efforts, Response Assessment in Neuro-Oncology (RANO) subcommittees are developing a paper on systemic therapies for meningioma and a separate article proposing standardized end point and response criteria for meningioma.
High-grade gliomas with mutations in the isocitrate dehydrogenase (IDH) gene family confer longer overall survival relative to their IDH-wild-type counterparts. Accurate determination of the IDH ...genotype preoperatively may have both prognostic and diagnostic value. The current study used a machine-learning algorithm to generate a model predictive of IDH genotype in high-grade gliomas based on clinical variables and multimodal features extracted from conventional MRI.
Preoperative MRIs were obtained for 120 patients with primary grades III (n = 35) and IV (n = 85) glioma in this retrospective study. IDH genotype was confirmed for grade III (32/35, 91%) and IV (22/85, 26%) tumors by immunohistochemistry, spectrometry-based mutation genotyping (OncoMap), or multiplex exome sequencing (OncoPanel). IDH1 and IDH2 mutations were mutually exclusive, and all mutated tumors were collapsed into one IDH-mutated cohort. Cases were randomly assigned to either the training (n = 90) or validation cohort (n = 30). A total of 2970 imaging features were extracted from pre- and postcontrast T1-weighted, T2-weighted, and apparent diffusion coefficient map. Using a random forest algorithm, nonredundant features were integrated with clinical data to generate a model predictive of IDH genotype.
Our model achieved accuracies of 86% (area under the curve AUC = 0.8830) in the training cohort and 89% (AUC = 0.9231) in the validation cohort. Features with the highest predictive value included patient age as well as parametric intensity, texture, and shape features.
Using a machine-learning algorithm, we achieved accurate prediction of IDH genotype in high-grade gliomas with preoperative clinical and MRI features.
Since 1979, the World Health Organization (WHO) has periodically published a consensus classification and grading of tumors of the central nervous system (CNS) to ensure uniform histopathologic ...diagnostic criteria worldwide. In 2016, the WHO published an update of the fourth edition of the classification of CNS tumors. This article summarizes the major changes in the update and discusses their impact on clinical practice.
For the first time, the 2016 revision of the WHO classification uses molecular parameters in addition to traditional histology to diagnose many CNS tumors, resulting in major restructuring of the classification of many tumors, especially gliomas, ependymomas, and medulloblastomas. Accordingly, nomenclature for selected entities now includes both a histopathologic diagnosis and defining molecular features.
The use of integrated phenotypic and genotypic parameters for the classification of CNS tumors introduces greater objectivity to the diagnosis but also requires more widespread availability of molecular testing. It is hoped that these changes will lead to greater diagnostic accuracy with more biologically homogeneous diagnostic entities and improved patient management and determination of prognosis.
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