MRI grading versus histology: predicting survival of World Health Organization grade II-IV astrocytomas

Presurgical diagnosis of diffuse gliomas in adults: Post-WHO 2021 practical perspectives from radiologists in neuro-oncology units

The 2021 World Health Organization classification of CNS tumours was greeted with enthusiasm as well as an initial potential overwhelm. However, with time and experience, our understanding of its key aspects has notably improved. Using our collective expertise gained in neuro-oncology units in hospitals in different countries, we have compiled a practical guide for radiologists that clarifies the classification criteria for diffuse gliomas in adults. Its format is clear and concise to facilitate its incorporation into everyday clinical practice. The document includes a historical overview of the classifications and highlights the most important recent additions. It describes the main types in detail with an emphasis on their appearance on imaging. The authors also address the most debated issues in recent years. It will better prepare radiologists to conduct accurate presurgical diagnoses and collaborate effectively in clinical decision making, thus impacting decisions on treatment, prognosis, and overall patient care.

Deep CNNs for glioma grading on conventional MRIs: Performance analysis, challenges, and future directions

The increasing global incidence of glioma tumors has raised significant healthcare concerns due to their high mortality rates. Traditionally, tumor diagnosis relies on visual analysis of medical imaging and invasive biopsies for precise grading. As an alternative, computer-assisted methods, particularly deep convolutional neural networks (DCNNs), have gained traction. This research paper explores the recent advancements in DCNNs for glioma grading using brain magnetic resonance images (MRIs) from 2015 to 2023. The study evaluated various DCNN architectures and their performance, revealing remarkable results with models such as hybrid and ensemble based DCNNs achieving accuracy levels of up to 98.91%. However, challenges persisted in the form of limited datasets, lack of external validation, and variations in grading formulations across diverse literature sources. Addressing these challenges through expanding datasets, conducting external validation, and standardizing grading formulations can enhance the performance and reliability of DCNNs in glioma grading, thereby advancing brain tumor classification and extending its applications to other neurological disorders.

Evaluation of glial tumors: correlation between magnetic resonance imaging and histopathological analysis

Objective

To determine the correlation of conventional and diffusion-weighted imaging findings on magnetic resonance imaging (MRI) of the brain, based on Visually AcceSAble Rembrandt Images (VASARI) criteria, with the histopathological grading of gliomas: low-grade or high-grade.

Materials and Methods

Preoperative MRI scans of 178 patients with brain gliomas and pathological confirmation were rated by two neuroradiologists for tumor size, location, and tumor morphology, using a standardized imaging feature set based on the VASARI criteria.

Results

In the univariate analysis, more than half of the MRI characteristics evaluated showed a significant association with the tumor grade. The characteristics most significantly associated with the tumor grade were hemorrhage; restricted diffusion; pial invasion; enhancement; and a non-contrast-enhancing tumor crossing the midline. In a multivariable regression model, the presence of enhancement and hemorrhage maintained a significant association with high tumor grade. The absence of contrast enhancement and restricted diffusion were associated with the presence of an isocitrate dehydrogenase gene mutation.

Conclusion

Our data illustrate that VASARI MRI features, especially intratumoral hemorrhage, contrast enhancement, and multicentricity, correlate strongly with glial tumor grade.

Radiogenomics Provides Insights into Gliomas Demonstrating Single-Arm 1p or 19q Deletion

BACKGROUND AND PURPOSE

IDH-mutant gliomas are further divided on the basis of 1p/19q status: oligodendroglioma, IDH-mutant and 1p/19q-codeleted, and astrocytoma, IDH-mutant (without codeletion). Occasionally, testing may reveal single-arm 1p or 19q deletion (unideletion), which remains within the diagnosis of astrocytoma. Molecular assessment has some limitations, however, raising the possibility that some unideleted tumors could actually be codeleted. This study assessed whether unideleted tumors had MR imaging features and survival more consistent with astrocytomas or oligodendrogliomas.

MATERIALS AND METHODS

One hundred twenty-one IDH-mutant grade 2-3 gliomas with 1p/19q results were identified. Two neuroradiologists assessed the T2-FLAIR mismatch sign and calcifications, as differentiators of astrocytomas and oligodendrogliomas. MR imaging features and survival were compared among the unideleted tumors, codeleted tumors, and those without 1p or 19q deletion.

RESULTS

The cohort comprised 65 tumors without 1p or 19q deletion, 12 unideleted tumors, and 44 codeleted. The proportion of unideleted tumors demonstrating the T2-FLAIR mismatch sign (33%) was similar to that in tumors without deletion (49%; P = .39), but significantly higher than codeleted tumors (0%; P = .001). Calcifications were less frequent in unideleted tumors (0%) than in codeleted tumors (25%), but this difference did not reach statistical significance (P = .097). The median survival of patients with unideleted tumors was 7.8 years, which was similar to that in tumors without deletion (8.5 years; P = .72) but significantly shorter than that in codeleted tumors (not reaching median survival after 12 years; P = .013).

CONCLUSIONS

IDH-mutant gliomas with single-arm 1p or 19q deletion have MR imaging appearance and survival that are similar to those of astrocytomas without 1p or 19q deletion and significantly different from those of 1p/19q-codeleted oligodendrogliomas.

MRI-based deep learning techniques for the prediction of isocitrate dehydrogenase and 1p/19q status in grade 2-4 adult gliomas

Molecular biomarkers are becoming increasingly important in the classification of intracranial gliomas. While tissue sampling remains the gold standard, there is growing interest in the use of deep learning (DL) techniques to predict these markers. This narrative review with a systematic approach identifies and synthesises the current published data on DL techniques using conventional MRI sequences for predicting isocitrate dehydrogenase (IDH) and 1p/19q-codeletion status in World Health Organisation grade 2-4 gliomas. Three databases were searched for relevant studies. In all, 13 studies met the inclusion criteria after exclusions. Key results, limitations and discrepancies between studies were synthesised. High accuracy has been reported in some studies, but the existing literature has several limitations, including generally small cohort sizes, a paucity of studies with independent testing cohorts and a lack of studies assessing IDH and 1p/19q together. While DL shows promise as a non-invasive means of predicting glioma genotype, addressing these limitations in future research will be important for facilitating clinical translation.

Correlating MRI features with additional genetic markers and patient survival in histological grade 2-3 IDH-mutant astrocytomas

PURPOSE

The increasing importance of molecular markers for classification and prognostication of diffuse gliomas has prompted the use of imaging features to predict genotype ("radiogenomics"). CDKN2A/B homozygous deletion has only recently been added to the diagnostic paradigm for IDH[isocitrate dehydrogenase]-mutant astrocytomas; thus, associated radiogenomic literature is sparse. There is also little data on whether different IDH mutations are associated with different imaging appearances. Furthermore, given that molecular status is now generally obtained routinely, the additional prognostic value of radiogenomic features is less clear. This study correlated MRI features with CDKN2A/B status, IDH mutation type and survival in histological grade 2-3 IDH-mutant brain astrocytomas.

METHODS

Fifty-eight grade 2-3 IDH-mutant astrocytomas were identified, 50 with CDKN2A/B results. IDH mutations were stratified into IDH1-R132H and non-canonical mutations. Background and survival data were obtained. Two neuroradiologists independently assessed the following MRI features: T2-FLAIR mismatch (<25%, 25-50%, >50%), well-defined tumour margins, contrast-enhancement (absent, wispy, solid) and central necrosis.

RESULTS

8/50 tumours with CDKN2A/B results demonstrated homozygous deletion; slightly shorter survival was not significant (p=0.571). IDH1-R132H mutations were present in 50/58 (86%). No MRI features correlated with CDKN2A/B status or IDH mutation type. T2-FLAIR mismatch did not predict survival (p=0.977), but well-defined margins predicted longer survival (HR 0.36, p=0.008), while solid enhancement predicted shorter survival (HR 3.86, p=0.004). Both correlations remained significant on multivariate analysis.

CONCLUSION

MRI features did not predict CDKN2A/B homozygous deletion, but provided additional positive and negative prognostic information which correlated more strongly with prognosis than CDKN2A/B status in our cohort.

Imaging features associated with H3 K27-altered and H3 G34-mutant gliomas: a narrative systematic review

Background

Advances in molecular diagnostics accomplished the discovery of two malignant glioma entities harboring alterations in the H3 histone: diffuse midline glioma, H3 K27-altered and diffuse hemispheric glioma, H3 G34-mutant. Radiogenomics research, which aims to correlate tumor imaging features with genotypes, has not comprehensively examined histone-altered gliomas (HAG). The aim of this research was to synthesize the current published data on imaging features associated with HAG.

Methods

A systematic search was performed in March 2022 using PubMed and the Cochrane Library, identifying studies on the imaging features associated with H3 K27-altered and/or H3 G34-mutant gliomas.

Results

Forty-seven studies fulfilled the inclusion criteria, the majority on H3 K27-altered gliomas. Just under half (21/47) were case reports or short series, the remainder being diagnostic accuracy studies. Despite heterogeneous methodology, some themes emerged. In particular, enhancement of H3 K27M-altered gliomas is variable and can be less than expected given their highly malignant behavior. Low apparent diffusion coefficient values have been suggested as a biomarker of H3 K27-alteration, but high values do not exclude this genotype. Promising correlations between high relative cerebral blood volume values and H3 K27-alteration require further validation. Limited data on H3 G34-mutant gliomas suggest some morphologic overlap with 1p/19q-codeleted oligodendrogliomas.

Conclusions

The existing data are limited, especially for H3 G34-mutant gliomas and artificial intelligence techniques. Current evidence indicates that imaging-based predictions of HAG are insufficient to replace histological assessment. In particular, H3 K27-altered gliomas should be considered when occurring in typical midline locations irrespective of enhancement characteristics.

Artificial intelligence for multimodal data integration in oncology

In oncology, the patient state is characterized by a whole spectrum of modalities, ranging from radiology, histology, and genomics to electronic health records. Current artificial intelligence (AI) models operate mainly in the realm of a single modality, neglecting the broader clinical context, which inevitably diminishes their potential. Integration of different data modalities provides opportunities to increase robustness and accuracy of diagnostic and prognostic models, bringing AI closer to clinical practice. AI models are also capable of discovering novel patterns within and across modalities suitable for explaining differences in patient outcomes or treatment resistance. The insights gleaned from such models can guide exploration studies and contribute to the discovery of novel biomarkers and therapeutic targets. To support these advances, here we present a synopsis of AI methods and strategies for multimodal data fusion and association discovery. We outline approaches for AI interpretability and directions for AI-driven exploration through multimodal data interconnections. We examine challenges in clinical adoption and discuss emerging solutions.

Noninvasive Determination of the IDH Status of Gliomas Using MRI and MRI-Based Radiomics: Impact on Diagnosis and Prognosis

Gliomas are the most common primary malignant brain tumors in adults. The fifth edition of the WHO Classification of Tumors of the Central Nervous System, published in 2021, provided molecular and practical approaches to CNS tumor taxonomy. Currently, molecular features are essential for differentiating the histological subtypes of gliomas, and recent studies have emphasized the importance of isocitrate dehydrogenase (IDH) mutations in stratifying biologically distinct subgroups of gliomas. IDH plays a significant role in gliomagenesis, and the association of IDH status with prognosis is very clear. Recently, there has been much progress in conventional MR imaging (cMRI), advanced MR imaging (aMRI), and radiomics, which are widely used in the study of gliomas. These advances have resulted in an improved correlation between MR signs and IDH mutation status, which will complement the prediction of the IDH phenotype. Although imaging cannot currently substitute for genetic tests, imaging findings have shown promising signs of diagnosing glioma subtypes and evaluating the efficacy and prognosis of individualized molecular targeted therapy. This review focuses on the correlation between MRI and MRI-based radiomics and IDH gene-phenotype prediction, discussing the value and application of these techniques in the diagnosis and evaluation of the prognosis of gliomas.

Conventional MRI features can predict the molecular subtype of adult grade 2-3 intracranial diffuse gliomas

Purpose

Molecular biomarkers are important for classifying intracranial gliomas, prompting research into correlating imaging with genotype (“radiogenomics”). A limitation of the existing radiogenomics literature is the paucity of studies specifically characterizing grade 2–3 gliomas into the three key molecular subtypes. Our study investigated the accuracy of multiple different conventional MRI features for genotype prediction.

Methods

Grade 2–3 gliomas diagnosed between 2007 and 2013 were identified. Two neuroradiologists independently assessed nine conventional MRI features. Features with better inter-observer agreement (κ ≥ 0.6) proceeded to consensus assessment. MRI features were correlated with genotype, classified as IDH-mutant and 1p/19q-codeleted (IDH^mut/1p19q^codel), IDH-mutant and 1p/19q-intact (IDH^mut/1p19q^int), or IDH-wildtype (IDH^wt). For IDH^wt tumors, additional molecular markers of glioblastoma were noted.

Results

One hundred nineteen patients were included. T2-FLAIR mismatch (stratified as > 50%, 25–50%, or < 25%) was the most predictive feature across genotypes (p < 0.001). All 30 tumors with > 50% mismatch were IDH^mut/1p19q^int, and all seven with 25–50% mismatch. Well-defined margins correlated with IDH^mut/1p19q^int status on univariate analysis (p < 0.001), but this related to correlation with T2-FLAIR mismatch; there was no longer an association when considering only tumors with < 25% mismatch (p = 0.386). Enhancement (p = 0.001), necrosis (p = 0.002), and hemorrhage (p = 0.027) correlated with IDH^wt status (especially “molecular glioblastoma”). Calcification correlated with IDH^mut/1p19q^codel status (p = 0.003). A simple, step-wise algorithm incorporating these features, when present, correctly predicted genotype with a positive predictive value 91.8%.

Conclusion

T2-FLAIR mismatch strongly predicts IDH^mut/1p19q^int even with a lower threshold of ≥ 25% mismatch and outweighs other features. Secondary features include enhancement, necrosis and hemorrhage (predicting IDH^wt, especially “molecular glioblastoma”), and calcification (predicting IDH^mut/1p19q^codel).

Machine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting Analysis

Objectives

To systematically review, assess the reporting quality of, and discuss improvement opportunities for studies describing machine learning (ML) models for glioma grade prediction.

Methods

This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy (PRISMA-DTA) statement. A systematic search was performed in September 2020, and repeated in January 2021, on four databases: Embase, Medline, CENTRAL, and Web of Science Core Collection. Publications were screened in Covidence, and reporting quality was measured against the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement. Descriptive statistics were calculated using GraphPad Prism 9.

Results

The search identified 11,727 candidate articles with 1,135 articles undergoing full text review and 85 included in analysis. 67 (79%) articles were published between 2018-2021. The mean prediction accuracy of the best performing model in each study was 0.89 ± 0.09. The most common algorithm for conventional machine learning studies was Support Vector Machine (mean accuracy: 0.90 ± 0.07) and for deep learning studies was Convolutional Neural Network (mean accuracy: 0.91 ± 0.10). Only one study used both a large training dataset (n>200) and external validation (accuracy: 0.72) for their model. The mean adherence rate to TRIPOD was 44.5% ± 11.1%, with poor reporting adherence for model performance (0%), abstracts (0%), and titles (0%).

Conclusions

The application of ML to glioma grade prediction has grown substantially, with ML model studies reporting high predictive accuracies but lacking essential metrics and characteristics for assessing model performance. Several domains, including generalizability and reproducibility, warrant further attention to enable translation into clinical practice.

Systematic Review Registration

PROSPERO, identifier CRD42020209938.

Primary versus recurrent surgery for glioblastoma-a prospective cohort study

Background

There is currently limited evidence for surgery in recurrent glioblastoma (GBM). Our aim was to compare primary and recurrent surgeries, regarding changes in perioperative, generic health-related quality of life (HRQoL), complications, extents of resection and survival.

Methods

Between 2007 and 2018, 65 recurrent and 160 primary GBM resections were prospectively enrolled. HRQoL was recorded with EQ-5D 3L preoperatively and at 1 month postoperatively. Median perioperative change in HRQoL and change greater than the minimal clinically important difference (MCID) were assessed. Tumour volume and extent of resection were obtained from pre- and postoperative MRI scans. Survival was assessed from date of surgery.

Results

Comparing recurrent surgeries and primary resections, most variables were balanced at baseline, but median age (59 vs. 62, p = 0.005) and median preoperative tumour volume (14.9 vs. 25.3 ml, p = 0.001) were lower in recurrent surgeries. There were no statistically significant differences regarding complication rates, neurological deficits, extents of resection or EQ-5D 3L index values at baseline and at follow-up. Twenty (36.4%) recurrent resections vs. 39 (27.5%) primary resections reported clinically significant deterioration in HRQoL at follow-up. Stratified by clinically significant change in EQ-5D 3L, the survival distributions were not statistically significantly different in either group. Survival was associated with extent of resection (p = 0.015) in recurrent surgeries only.

Conclusions

Outcomes after primary and recurrent surgeries were quite similar in our practice. As surgery may prolong life in patients where gross total resection is obtainable with reasonable risk, the indication for surgery in GBM should perhaps not differ that much in primary and recurrent resections.

The histological representativeness of glioblastoma tissue samples

Background

Glioblastomas (GBMs) are known for having a vastly heterogenous histopathology. Several studies have shown that GBMs can be histologically undergraded due to sampling errors of small tissue samples. We sought to explore to what extent histological features in GBMs are dependent on the amount of viable tissue on routine slides from both biopsied and resected tumors.

Methods

In 106 newly diagnosed GBM patients, we investigated associations between the presence or degree of 24 histopathological and two immunohistochemical features and the tissue amount on hematoxylin-eosin (HE) slides. The amount of viable tissue was semiquantitatively categorized as “sparse,” “medium,” or “substantial” for each case. Tissue amount was also assessed for associations with MRI volumetrics and the type of surgical procedure.

Results

About half (46%) of the assessed histological and immunohistochemical features were significantly associated with tissue amount. The significant features were less present or of a lesser degree when the tissue amount was smaller. Among the significant features were most of the features relevant for diffuse astrocytic tumor grading, i.e., small necroses, palisades, microvascular proliferation, atypia, mitotic count, and Ki-67/MIB-1 proliferative index (PI).

Conclusion

A substantial proportion of the assessed histological features were at risk of being underrepresented when the amount of viable tissue on HE slides was limited. Most of the grading features were dependent on tissue amount, which underlines the importance of considering sampling errors in diffuse astrocytic tumor grading. Our findings also highlight the importance of adequate tissue collection to increase the quality of diagnostics and histological research.

DNA methylation profiling for molecular classification of adult diffuse lower-grade gliomas

BACKGROUND

DNA methylation profiling has facilitated and improved the classification of a wide variety of tumors of the central nervous system. In this study, we investigated the potential utility of DNA methylation profiling to achieve molecular diagnosis in adult primary diffuse lower-grade glioma (dLGG) according to WHO 2016 classification system. We also evaluated whether methylation profiling could provide improved molecular characterization and identify prognostic differences beyond the classical histological WHO grade together with IDH mutation status and 1p/19q codeletion status. All patients diagnosed with dLGG in the period 2007-2016 from the Västra Götaland region in Sweden were assessed for inclusion in the study.

RESULTS

A total of 166 dLGG cases were subjected for genome-wide DNA methylation analysis. Of these, 126 (76%) were assigned a defined diagnostic methylation class with a class prediction score ≥ 0.84 and subclass score ≥ 0.50. The assigned methylation classes were highly associated with their IDH mutation status and 1p/19q codeletion status. IDH-wildtype gliomas were further divided into subgroups with distinct molecular features.

CONCLUSION

The stratification of the patients by methylation profiling was as effective as the integrated WHO 2016 molecular reclassification at predicting the clinical outcome of the patients. Our study shows that DNA methylation profiling is a reliable and robust approach for the classification of dLGG into molecular defined subgroups, providing accurate detection of molecular markers according to WHO 2016 classification.

Noninvasive Determination of IDH and 1p19q Status of Lower-grade Gliomas Using MRI Radiomics: A Systematic Review

BACKGROUND

Determination of isocitrate dehydrogenase (IDH) status and, if IDH-mutant, assessing 1p19q codeletion are an important component of diagnosis of World Health Organization grades II/III or lower-grade gliomas. This has led to research into noninvasively correlating imaging features ("radiomics") with genetic status.

PURPOSE

Our aim was to perform a diagnostic test accuracy systematic review for classifying IDH and 1p19q status using MR imaging radiomics, to provide future directions for integration into clinical radiology.

DATA SOURCES

Ovid (MEDLINE), Scopus, and the Web of Science were searched in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Diagnostic Test Accuracy guidelines.

STUDY SELECTION

Fourteen journal articles were selected that included 1655 lower-grade gliomas classified by their IDH and/or 1p19q status from MR imaging radiomic features.

DATA ANALYSIS

For each article, the classification of IDH and/or 1p19q status using MR imaging radiomics was evaluated using the area under curve or descriptive statistics. Quality assessment was performed with the Quality Assessment of Diagnostic Accuracy Studies 2 tool and the radiomics quality score.

DATA SYNTHESIS

The best classifier of IDH status was with conventional radiomics in combination with convolutional neural network-derived features (area under the curve  = 0.95, 94.4% sensitivity, 86.7% specificity). Optimal classification of 1p19q status occurred with texture-based radiomics (area under the curve  = 0.96, 90% sensitivity, 89% specificity).

LIMITATIONS

A meta-analysis showed high heterogeneity due to the uniqueness of radiomic pipelines.

CONCLUSIONS

Radiogenomics is a potential alternative to standard invasive biopsy techniques for determination of IDH and 1p19q status in lower-grade gliomas but requires translational research for clinical uptake.

Neuro-Oncology and Radiogenomics: Time to Integrate?

Radiogenomics aims to predict genetic markers based on imaging features. The critical importance of molecular markers in the diagnosis and management of intracranial gliomas has led to a rapid growth in radiogenomics research, with progressively increasing complexity. Despite the advances in the techniques being examined, there has been little translation into the clinical domain. This has resulted in a growing disconnect between cutting-edge research and assimilation into clinical practice, though the fundamental goal is for these techniques to improve patient care. The goal of this review, therefore, is to discuss possible clinical scenarios in which the addition of radiogenomics may aid patient management. This includes facilitating patient counseling, determining optimal patient management when complete molecular characterization is not possible, reclassifying tumors, and overcoming some of the limitations of histologic assessment. The review also discusses considerations for selecting relevant radiogenomic features based on the clinical setting.

Advancements in predicting outcomes in patients with glioma: a surgical perspective

Introduction: Diffuse glioma is a challenging neurosurgical entity. Although surgery does not provide a cure, it may greatly influence survival, brain function, and quality of life. Surgical treatment is by nature highly personalized and outcome prediction is very complex. To engage and succeed in this balancing act it is important to make best use of the information available to the neurosurgeon.Areas covered: This narrative review provides an update on advancements in predicting outcomes in patients with glioma that are relevant to neurosurgeons.Expert opinion: The classical 'gut feeling' is notoriously unreliable and better prediction strategies for patients with glioma are warranted. There are numerous tools readily available for the neurosurgeon in predicting tumor biology and survival. Predicting extent of resection, functional outcome, and quality of life remains difficult. Although machine-learning approaches are currently not readily available in daily clinical practice, there are several ongoing efforts with the use of big data sets that are likely to create new prediction models and refine the existing models.

T2 mapping of molecular subtypes of WHO grade II/III gliomas

BACKGROUND

According to the new WHO classification from 2016, molecular profiles have shown to provide reliable information about prognosis and treatment response. The purpose of our study is to evaluate the diagnostic potential of non-invasive quantitative T2 mapping in the detection of IDH1/2 mutation status in grade II-III gliomas.

METHODS

Retrospective evaluation of MR examinations in 30 patients with histopathological proven WHO-grade II (n = 9) and III (n = 21) astrocytomas (18 IDH-mutated, 12 IDH-wildtype). Consensus annotation by two observers by use of ROI's in quantitative T2-mapping sequences were performed in all patients. T2 relaxation times were measured pixelwise.

RESULTS

A significant difference (p = 0,0037) between the central region of IDH-mutated tumors (356,83 ± 114,97 ms) and the IDH-wildtype (199,92 ± 53,13 ms) was found. Furthermore, relaxation times between the central region (322,62 ± 127,41 ms) and the peripheral region (211,1 ± 74,16 ms) of WHO grade II and III astrocytomas differed significantly (p = 0,0021). The central regions relaxation time of WHO-grade II (227,44 ± 80,09 ms) and III gliomas (322,62 ± 127,41 ms) did not differ significantly (p = 0,2276). The difference between the smallest and the largest T2 value (so called "range") is significantly larger (p = 0,0017) in IDH-mutated tumors (230,89 ± 121,11 ms) than in the IDH-wildtype (96,33 ± 101,46 ms). Interobserver variability showed no significant differences.

CONCLUSIONS

Quantitative evaluation of T2-mapping relaxation times shows significant differences regarding the IDH-status in WHO grade II and III gliomas adding important information regarding the new 2016 World Health Organization (WHO) Classification of tumors of the central nervous system. This to our knowledge is the first study regarding T2 mapping and the IDH1/2 status shows that the mutational status seems to be more important for the appearance on T2 images than the WHO grade.

MRI-based radiomics signature for tumor grading of rectal carcinoma using random forest model

The present study aimed to construct prospective models for tumor grading of rectal carcinoma by using magnetic resonance (MR)-based radiomics features. A set of 118 patients with rectal carcinoma was analyzed. After imbalance-adjustments of the data using Synthetic Minority Oversampling Technique (SMOTE), the final data set was randomized into the training set and validation set at the ratio of 3:1. The radiomics features were captured from manually segmented lesion of magnetic resonance imaging (MRI). The most related radiomics features were selected using the random forest model by calculating the Gini importance of initial extracted characteristics. A random forest classifier model was constructed using the top important features. The classifier model performance was evaluated via receive operator characteristic curve and area under the curve (AUC). A total of 1,131 radiomics features were extracted from segmented lesion. The top 50 most important features were selected to construct a random forest classifier model. The AUC values of grade 1, 2, 3, and 4 for training set were 0.918, 0.822, 0.775, and 1.000, respectively, and the corresponding AUC values for testing set were 0.717, 0.683, 0.690, and 0.827 separately. The developed feature selection method and machine learning-based prediction models using radiomics features of MRI show a relatively acceptable performance in tumor grading of rectal carcinoma and could distinguish the tumor subjects from the healthy ones, which is important for the prognosis of cancer patients.

Tissue-type mapping of gliomas

Purpose

To develop a statistical method of combining multimodal MRI (mMRI) of adult glial brain tumours to generate tissue heterogeneity maps that indicate tumour grade and infiltration margins.

Materials and methods

We performed a retrospective analysis of mMRI from patients with histological diagnosis of glioma (n = 25). ¹H Magnetic Resonance Spectroscopic Imaging (MRSI) was used to label regions of “pure” low- or high-grade tumour across image types. Normal brain and oedema characteristics were defined from healthy controls (n = 10) and brain metastasis patients (n = 10) respectively. Probability density distributions (PDD) for each tissue type were extracted from intensity normalised proton density and T₂-weighted images, and p and q diffusion maps. Superpixel segmentation and Bayesian inference was used to produce whole-brain tissue-type maps.

Results

Total lesion volumes derived automatically from tissue-type maps correlated with those from manual delineation (p < 0.001, r = 0.87). Large high-grade volumes were determined in all grade III & IV (n = 16) tumours, in grade II gemistocytic rich astrocytomas (n = 3) and one astrocytoma with a histological diagnosis of grade II. For patients with known outcome (n = 20), patients with survival time < 2 years (3 grade II, 2 grade III and 10 grade IV) had a high-grade volume significantly greater than zero (Wilcoxon signed rank p < 0.0001) and also significantly greater high grade volume than the 5 grade II patients with survival >2 years (Mann Witney p = 0.0001). Regions classified from mMRI as oedema had non-tumour-like ¹H MRS characteristics.

Conclusions

¹H MRSI can label tumour tissue types to enable development of a mMRI tissue type mapping algorithm, with potential to aid management of patients with glial tumours.

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Non-Gaussian multimodal MRI characteristics of high and low grade glioma tissue.

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Bayesian inference of multimodal MRI derives whole brain tumour tissue-type maps.

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Automated segmentation of normal and tumour tissue volumes.

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Visualisation of glioma heterogeneity, infiltration, necrosis and vasogenic oedema.

MRI Features Can Predict 1p/19q Status in Intracranial Gliomas

BACKGROUND AND PURPOSE

The 2016 revision of the World Health Organization Classification of Tumors of the Central Nervous System mandates codeletion of chromosomes 1p and 19q for the diagnosis of oligodendroglioma. We studied whether conventional MR imaging features could predict 1p/19q status.

MATERIALS AND METHODS

Patients with previous 1p/19q testing were identified through pathology department records, typically performed on the basis of an oligodendroglial component on routine histology; 69 patients met the inclusion criteria. Preoperative imaging of patients with grade II or III gliomas was retrospectively assessed by 2 neuroradiologists, blinded to the 1p/19q status. Thirteen MR imaging features were first assessed in a small initial cohort (n = 10), after which the criteria were narrowed for the remaining patients as a validation cohort.

RESULTS

There was 85% agreement between radiologists for the overall prediction of 1p/19q status in the validation cohort, with an accuracy of 84%. The presence of >50% T2-FLAIR mismatch and calcification was found to be the most useful for predicting 1p/19q status. The >50% T2-FLAIR mismatch variable was demonstrated in 14 tumors and had 100% specificity for identifying a noncodeleted tumor (P = .001), with 97% interobserver correlation. Calcification was visualized in 7 tumors, 6 of which were 1p/19q codeleted (specificity, 97%; P = .006), with 100% interobserver correlation.

CONCLUSIONS

The presence of >50% T2-FLAIR mismatch is highly predictive of a noncodeleted tumor, while calcifications suggest a 1p/19q codeleted tumor. If formal 1p/19q testing is not possible, a combined MR imaging-histologic assessment may improve the diagnostic accuracy over histology alone.

Histogram analysis of T2*-based pharmacokinetic imaging in cerebral glioma grading

BACKGROUND AND OBJECTIVE

To investigate the feasibility of histogram analysis of the T2*-based permeability parameter volume transfer constant (K^trans) for glioma grading and to explore the diagnostic performance of the histogram analysis of K^trans and blood plasma volume (v_p).

METHODS

We recruited 31 and 11 patients with high- and low-grade gliomas, respectively. The histogram parameters of K^trans and v_p, derived from the first-pass pharmacokinetic modeling based on the T2* dynamic susceptibility-weighted contrast-enhanced perfusion-weighted magnetic resonance imaging (T2* DSC-PW-MRI) from the entire tumor volume, were evaluated for differentiating glioma grades.

RESULTS

Histogram parameters of K^trans and v_p showed significant differences between high- and low-grade gliomas and exhibited significant correlations with tumor grades. The mean K^trans derived from the T2* DSC-PW-MRI had the highest sensitivity and specificity for differentiating high-grade gliomas from low-grade gliomas compared with other histogram parameters of K^trans and v_p.

CONCLUSIONS

Histogram analysis of T2*-based pharmacokinetic imaging is useful for cerebral glioma grading. The histogram parameters of the entire tumor K^trans measurement can provide increased accuracy with additional information regarding microvascular permeability changes for identifying high-grade brain tumors.

Computer-assisted brain tumor type discrimination using magnetic resonance imaging features

Medical imaging plays an integral role in the identification, segmentation, and classification of brain tumors. The invention of MRI has opened new horizons for brain-related research. Recently, researchers have shifted their focus towards applying digital image processing techniques to extract, analyze and categorize brain tumors from MRI. Categorization of brain tumors is defined in a hierarchical way moving from major to minor ones. A plethora of work could be seen in literature related to the classification of brain tumors in categories such as benign and malignant. However, there are only a few works reported on the multiclass classification of brain images where each part of the image containing tumor is tagged with major and minor categories. The precise classification is difficult to achieve due to ambiguities in images and overlapping characteristics of different type of tumors. In the current study, a comprehensive review of recent research on brain tumors multiclass classification using MRI is provided. These multiclass classification studies are categorized into two major groups: XX and YY and each group are further divided into three sub-groups. A set of common parameters from the reviewed works is extracted and compared to highlight the merits and demerits of individual works. Based on our analysis, we provide a set of recommendations for researchers and professionals working in the area of brain tumors classification.

The 2016 revision of the WHO Classification of Central Nervous System Tumours: retrospective application to a cohort of diffuse gliomas

The classification of central nervous system tumours has more recently been shaped by a focus on molecular pathology rather than histopathology. We re-classified 82 glial tumours according to the molecular-genetic criteria of the 2016 revision of the World Health Organization (WHO) Classification of Tumours of the Central Nervous System. Initial diagnoses and grading were based on the morphological criteria of the 2007 WHO scheme. Because of the impression of an oligodendroglial component on initial histological assessment, each tumour was tested for co-deletion of chromosomes 1p and 19q and mutations of isocitrate dehydrogenase (IDH-1 and 2) genes. Additionally, expression of proteins encoded by alpha-thalassemia X-linked mental retardation (ATRX) and TP53 genes was assessed by immunohistochemistry. We found that all but two tumours could be assigned to a specific category in the 2016 revision. The most common change in diagnosis was from oligoastrocytoma to specifically astrocytoma or oligodendroglioma. Analysis of progression free survival (PFS) for WHO grade II and III tumours showed that the objective criteria of the 2016 revision separated diffuse gliomas into three distinct molecular categories: chromosome 1p/19q co-deleted/IDH mutant, intact 1p/19q/IDH mutant and IDH wild type. No significant difference in PFS was found when comparing IDH mutant grade II and III tumours suggesting that IDH status is more informative than tumour grade. The segregation into distinct molecular sub-types that is achieved by the 2016 revision provides an objective evidence base for managing patients with grade II and III diffuse gliomas based on prognosis.

Preoperative relative cerebral blood volume analysis in gliomas predicts survival and mitigates risk of biopsy sampling error

Appropriate management of adult gliomas requires an accurate histopathological diagnosis. However, the heterogeneity of gliomas can lead to misdiagnosis and undergrading, especially with biopsy. We evaluated the role of preoperative relative cerebral blood volume (rCBV) analysis in conjunction with histopathological analysis as a predictor of overall survival and risk of undergrading. We retrospectively identified 146 patients with newly diagnosed gliomas (WHO grade II-IV) that had undergone preoperative MRI with rCBV analysis. We compared overall survival by histopathologically determined WHO tumor grade and by rCBV using Kaplan-Meier survival curves and the Cox proportional hazards model. We also compared preoperative imaging findings and initial histopathological diagnosis in 13 patients who underwent biopsy followed by subsequent resection. Survival curves by WHO grade and rCBV tier similarly separated patients into low, intermediate, and high-risk groups with shorter survival corresponding to higher grade or rCBV tier. The hazard ratio for WHO grade III versus II was 3.91 (p = 0.018) and for grade IV versus II was 11.26 (p < 0.0001) and the hazard ratio for each increase in 1.0 rCBV units was 1.12 (p < 0.002). Additionally, 3 of 13 (23%) patients initially diagnosed by biopsy were upgraded on subsequent resection. Preoperative rCBV was elevated at least one standard deviation above the mean in the 3 upgraded patients, suggestive of undergrading, but not in the ten concordant diagnoses. In conclusion, rCBV can predict overall survival similarly to pathologically determined WHO grade in patients with gliomas. Discordant rCBV analysis and histopathology may help identify patients at higher risk for undergrading.

Histopathologic Features in Relation to Pretreatment Tumor Growth in Patients with Glioblastoma

BACKGROUND

Rapid growth is a well-known property of glioblastoma (GBM); however, growth rates vary among patients. Mechanisms behind such variation have not been widely studied in human patients. We sought to investigate relationships between histopathologic features and tumor growth estimated from pretreatment magnetic resonance imaging scans.

METHODS

In 106 patients with GBM, 2 preoperative T1-weighted magnetic resonance imaging scans obtained at least 14 days apart were segmented to assess tumor growth. A fitted Gompertzian growth curve based on the segmented volumes divided the tumors into 2 groups: faster and slower growth than expected based on the initial tumor volume. Histopathologic features were investigated for associations with these groups, using univariable and multivariable logistic regression analyses.

RESULTS

The presence of high cellular density and thromboses was significantly associated with radiologic growth in the multivariable analysis (P = 0.018 and 0.019, respectively), with respective odds ratios of 3.0 (95% confidence interval, 1.2-7.4) and 4.3 (95% confidence interval, 1.3-14.5) for faster growing tumors.

CONCLUSIONS

Our findings show that high cellular density and thromboses are significant independent predictors of faster growth in human GBM. This finding underlines the importance of hypercellularity as a criterion in glioma grading. Furthermore, our findings are concordant with hypotheses suggesting hypoxia triggered by thromboses to be relevant for growth of GBM.

Multifocal and multicentric glioblastoma: Improved characterisation with FLAIR imaging and prognostic implications

Glioblastoma usually presents on imaging as a single peripherally enhancing lesion, but multiple enhancing lesions can occur, termed multifocal if there is a connection between enhancing lesions, or multicentric when no communication is demonstrated. We aim to determine the incidence and prognostic implications of multifocal and multicentric glioblastoma in the era of modern MRI, focusing on the added benefit of T2-weighted fluid-attenuated inversion recovery (FLAIR) imaging. Patients with a new diagnosis of glioblastoma were identified. Preoperative MRI were reviewed to determine whether more than one distinct enhancing lesion was present, and whether there was communication between lesions. The findings were compared against survival data. More than one discrete contrast-enhancing lesion was present in 51 of the 151 patients (34%). Communication between lesions was identified in 47 of these, most commonly direct parenchymal spread (41 patients). The patients with multiple lesions had worse survival (median 176days, compared to 346days), but this difference was not statistically significant (p=0.253). These tumours more frequently involved deep structures (p<0.001) and the posterior fossa (p=0.045), both of which were associated with worse survival. The presence of multiple enhancing foci in glioblastoma is common, occurring in about one-third of patients, and the majority have multifocal disease. The FLAIR sequence is the crucial sequence for demonstrating a communication between lesions. The worse survival of these patients is, at least in large part related to more extensive tumour dissemination and more frequent involvement of key structures, rather than multiplicity per se.

Diagnostic and Prognostic Value of 11C-Methionine PET for Nonenhancing Gliomas

BACKGROUND AND PURPOSE

Noninvasive radiologic evaluation of glioma can facilitate correct diagnosis and detection of malignant transformation. Although positron-emission tomography is considered valuable in the care of patients with gliomas, (18)F-fluorodeoxyglucose and (11)C-methionine have reportedly shown ambiguous results in terms of grading and prognostication. The present study compared the diagnostic and prognostic capabilities of diffusion tensor imaging, FDG, and (11)C-methionine PET in nonenhancing gliomas.

MATERIALS AND METHODS

Thirty-five consecutive newly diagnosed, histologically confirmed nonenhancing gliomas that underwent both FDG and (11)C-methionine PET were retrospectively investigated (23 grade II and 12 grade III gliomas). Apparent diffusion coefficient, fractional anisotropy, and tumor-to-normal tissue ratios of both FDG and (11)C-methionine PET were compared between grade II and III gliomas. Prognostic values of these parameters were also tested by using progression-free survival.

RESULTS

Grade III gliomas showed significantly higher average tumor-to-normal tissue and maximum tumor2-to-normal tissue than grade II gliomas in (11)C-methionine (P = .013, P = .0017, respectively), but not in FDG-PET imaging. There was no significant difference in average ADC, minimum ADC, average fractional anisotropy, and maximum fractional anisotropy. (11)C-methionine PET maximum tumor-to-normal tissue ratio of 2.0 was most suitable for detecting grade III gliomas among nonenhancing gliomas (sensitivity, 83.3%; specificity, 73.9%). Among patients not receiving any adjuvant therapy, median progression-free survival was 64.2 ± 7.2 months in patients with maximum tumor-to-normal tissue ratio of <2.0 for (11)C-methionine PET and 18.6 ± 6.9 months in patients with maximum tumor-to-normal tissue ratio of >2.0 (P = .0044).

CONCLUSIONS

(11)C-methionine PET holds promise for World Health Organization grading and could offer a prognostic imaging biomarker for nonenhancing gliomas.