Brain and other central nervous system (CNS) tumors are among the most fatal cancers and account for substantial morbidity and mortality in the United States. Population‐based data from the Central ...Brain Tumor Registry of the United States (a combined data set of the National Program of Cancer Registries NPCR and Surveillance, Epidemiology, and End Results SEER registries), NPCR, National Vital Statistics System and SEER program were analyzed to assess the contemporary burden of malignant and nonmalignant brain and other CNS tumors (hereafter brain) by histology, anatomic site, age, sex, and race/ethnicity. Malignant brain tumor incidence rates declined by 0.8% annually from 2008 to 2017 for all ages combined but increased 0.5% to 0.7% per year among children and adolescents. Malignant brain tumor incidence is highest in males and non‐Hispanic White individuals, whereas the rates for nonmalignant tumors are highest in females and non‐Hispanic Black individuals. Five‐year relative survival for all malignant brain tumors combined increased between 1975 to 1977 and 2009 to 2015 from 23% to 36%, with larger gains among younger age groups. Less improvement among older age groups largely reflects a higher burden of glioblastoma, for which there have been few major advances in prevention, early detection, and treatment the past 4 decades. Specifically, 5‐year glioblastoma survival only increased from 4% to 7% during the same time period. In addition, important survival disparities by race/ethnicity remain for childhood tumors, with the largest Black‐White disparities for diffuse astrocytomas (75% vs 86% for patients diagnosed during 2009‐2015) and embryonal tumors (59% vs 67%). Increased resources for the collection and reporting of timely consistent data are critical for advancing research to elucidate the causes of sex, age, and racial/ethnic differences in brain tumor occurrence, especially for rarer subtypes and among understudied populations.
The prediction of clinical behavior, response to therapy, and outcome of infiltrative glioma is challenging. On the basis of previous studies of tumor biology, we defined five glioma molecular groups ...with the use of three alterations: mutations in the TERT promoter, mutations in IDH, and codeletion of chromosome arms 1p and 19q (1p/19q codeletion). We tested the hypothesis that within groups based on these features, tumors would have similar clinical variables, acquired somatic alterations, and germline variants.
We scored tumors as negative or positive for each of these markers in 1087 gliomas and compared acquired alterations and patient characteristics among the five primary molecular groups. Using 11,590 controls, we assessed associations between these groups and known glioma germline variants.
Among 615 grade II or III gliomas, 29% had all three alterations (i.e., were triple-positive), 5% had TERT and IDH mutations, 45% had only IDH mutations, 7% were triple-negative, and 10% had only TERT mutations; 5% had other combinations. Among 472 grade IV gliomas, less than 1% were triple-positive, 2% had TERT and IDH mutations, 7% had only IDH mutations, 17% were triple-negative, and 74% had only TERT mutations. The mean age at diagnosis was lowest (37 years) among patients who had gliomas with only IDH mutations and was highest (59 years) among patients who had gliomas with only TERT mutations. The molecular groups were independently associated with overall survival among patients with grade II or III gliomas but not among patients with grade IV gliomas. The molecular groups were associated with specific germline variants.
Gliomas were classified into five principal groups on the basis of three tumor markers. The groups had different ages at onset, overall survival, and associations with germline variants, which implies that they are characterized by distinct mechanisms of pathogenesis. (Funded by the National Institutes of Health and others.).
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 (
= 65), hemangioblastoma (
= 44), pilocytic astrocytoma (
= 43), ependymoma (
= 27), and medulloblastoma (
= 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 (
= 0.020); and atypical teratoid/rhabdoid tumor ATRT (
= 0.001) from other types of neoplasms compared to the official clinical report. However, neuroradiologists' interpretations had greater accuracy in differentiating metastases (
= 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.
Abstract
In the management of diffuse gliomas, the identification and removal of tumor at the infiltrative margin remains a central challenge. Prior work has demonstrated that fluorescence labeling ...tools and radiographic imaging are useful surgical adjuvants with macroscopic resolution. However, they lose sensitivity at the tumor margin and have limited clinical utility for lower grade histologies. Fiber-laser based stimulated Raman histology (SRH) is an optical imaging technique that provides microscopic tissue characterization of unprocessed tissues. It remains unknown whether SRH of tissues taken from the infiltrative glioma margin will identify microscopic residual disease. Here we acquired glioma margin specimens for SRH, histology, and tumor specific tissue characterization. Generalized linear mixed models were used to evaluate agreement. We find that SRH identified residual tumor in 82 of 167 margin specimens (49%), compared to IHC confirming residual tumor in 72 of 128 samples (56%), and H&E confirming residual tumor in 82 of 169 samples (49%). Intraobserver agreements between all 3 modalities were confirmed. These data demonstrate that SRH detects residual microscopic tumor at the infiltrative glioma margin and may be a promising tool to enhance extent of resection.
The newest revision of the WHO classification of tumors of the central nervous system, also known as WHO 5th edition, introduces substantial changes, especially within the glial tumor category and ...separates adult-type and pediatric-type glial tumors into different categories for the first time. In addition, another category of glial tumors, "Circumscribed Astrocytic Gliomas" were also created. This group includes pilocytic astrocytoma, pleomorphic xanthoastrocytoma, subependymal giant cell astrocytoma, chordoid glioma, astroblastoma, and the highly nebulous novel entity high-grade astrocytoma with piloid features. We present a brief and critical review of the pathological and molecular characteristics of these often well-demarcated tumors that can occur in adults as well as in the pediatric population.
Central nervous system (CNS) lymphoma can present a diagnostic challenge. Currently, there is no consensus regarding what presurgical evaluation is warranted or how to proceed when lesions are not ...surgically accessible. We conducted a review of the literature on CNS lymphoma diagnosis (1966 to October 2011) to determine whether a common diagnostic algorithm can be generated. We extracted data regarding the usefulness of brain and body imaging, serum and cerebrospinal fluid (CSF) studies, ophthalmologic examination, and tissue biopsy in the diagnosis of CNS lymphoma. Contrast enhancement on imaging is highly sensitive at the time of diagnosis: 98.9% in immunocompetent lymphoma and 96.1% in human immunodeficiency virus-related CNS lymphoma. The sensitivity of CSF cytology is low (2%-32%) but increases when combined with flow cytometry. Cerebrospinal fluid lactate dehydrogenase isozyme 5, β2-microglobulin, and immunoglobulin heavy chain rearrangement studies have improved sensitivity over CSF cytology (58%-85%) but have only moderate specificity (85%). New techniques of proteomics and microRNA analysis have more than 95% specificity in the diagnosis of CNS lymphoma. Positive CSF cytology, vitreous biopsy, or brain/leptomeningeal biopsy remain the current standard for diagnosis. A combined stepwise systematic approach outlined here may facilitate an expeditious, comprehensive presurgical evaluation for cases of suspected CNS lymphoma.
The prognostic role of extent of resection (EOR) of low-grade gliomas (LGGs) is a major controversy. We designed a retrospective study to assess the influence of EOR on long-term outcomes of LGGs.
...The study population (N = 216) included adults undergoing initial resection of hemispheric LGG. Region-of-interest analysis was performed to measure tumor volumes based on fluid-attenuated inversion-recovery (FLAIR) imaging.
Median preoperative and postoperative tumor volumes and EOR were 36.6 cm(3) (range, 0.7 to 246.1 cm(3)), 3.7 cm(3) (range, 0 to 197.8 cm(3)) and 88.0% (range, 5% to 100%), respectively. There was no operative mortality. New postoperative deficits were noted in 36 patients (17%); however, all but four had complete recovery. There were 34 deaths (16%; median follow-up, 4.4 years). Progression and malignant progression were identified in 95 (44%) and 44 (20%) cases, respectively. Patients with at least 90% EOR had 5- and 8-year overall survival (OS) rates of 97% and 91%, respectively, whereas patients with less than 90% EOR had 5- and 8-year OS rates of 76% and 60%, respectively. After adjusting each measure of tumor burden for age, Karnofsky performance score (KPS), tumor location, and tumor subtype, OS was predicted by EOR (hazard ratio HR = 0.972; 95% CI, 0.960 to 0.983; P < .001), log preoperative tumor volume (HR = 4.442; 95% CI, 1.601 to 12.320; P = .004), and postoperative tumor volume (HR = 1.010; 95% CI, 1.001 to 1.019; P = .03), progression-free survival was predicted by log preoperative tumor volume (HR = 2.711; 95% CI, 1.590 to 4.623; P <or= .001) and postoperative tumor volume (HR = 1.007; 95% CI, 1.001 to 1.014; P = .035), and malignant progression-free survival was predicted by EOR (HR = 0.983; 95% CI, 0.972 to 0.995; P = .005) and log preoperative tumor volume (HR = 3.826; 95% CI, 1.632 to 8.969; P = .002).
Improved outcome among adult patients with hemispheric LGG is predicted by greater EOR.
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