Relationship between tumor enhancement, edema, IDH1 mutational status, MGMT promoter methylation, and survival in glioblastoma

Structured reporting of gliomas based on VASARI criteria to improve report content and consistency

PURPOSE

Gliomas are the commonest malignant brain tumours. Baseline characteristics on structural MRI, such as size, enhancement proportion and eloquent brain involvement inform grading and treatment planning. Currently, free-text imaging reports depend on the individual style and experience of the radiologist. Standardisation may increase consistency of feature reporting.

METHODS

We compared 100 baseline free-text reports for glioma MRI scans with a structured feature list based on VASARI criteria and performed a full second read to document which VASARI features were in the baseline report.

RESULTS

We found that quantitative features including tumour size and proportion of necrosis and oedema/infiltration were commonly not included in free-text reports. Thirty-three percent of reports gave a description of size only, and 38% of reports did not refer to tumour size at all. Detailed information about tumour location including involvement of eloquent areas and infiltration of deep white matter was also missing from the majority of free-text reports. Overall, we graded 6% of reports as having omitted some key VASARI features that would alter patient management.

CONCLUSIONS

Tumour size and anatomical information is often omitted by neuroradiologists. Comparison with a structured report identified key features that would benefit from standardisation and/or quantification. Structured reporting may improve glioma reporting consistency, clinical communication, and treatment decisions.

Unraveling the heterogeneity of WHO grade 4 gliomas: insights from clinical, imaging, and molecular characterization

PURPOSE

The 2021 WHO classification of central nervous system tumors introduced molecular criteria to stratify Grade 4 gliomas, which remain heterogeneous. This study aims to elucidate the clinical, radiological, and molecular characteristics of WHO Grade 4 gliomas, focusing on their prognostic implications and the development of a predictive model for astrocytoma IDH-mutant WHO Grade 4 (A4).

METHODS

A retrospective cohort of 223 patients from Beijing Tiantan Hospital was analyzed. Clinical, radiological, and histopathological data were combined with molecular profiling, focusing on IDH mutations, TERT promoter mutations, and MGMT methylation. A predictive model was developed using LASSO regression to distinguish A4 from glioblastomas and validated with an external dataset from UCSF.

RESULTS

The cohort included 201 glioblastomas (90.1%) and 22 A4 cases (9.9%). A4 tumors were associated with younger age, higher MGMT promoter methylation, lower rates of TERT mutations, and distinct radiological features, such as cortical non-enhancing tumor infiltration (CnCE). Patients with A4 demonstrated significantly better survival outcomes compared to glioblastoma patients (p < 0.001). The predictive model for A4, incorporating age, tumor margin, and CnCE, achieved an AUC of 0.890 in the training set and 0.951 in the validation set.

CONCLUSION

Integrating molecular and clinical criteria improves prognostication in Grade 4 gliomas. The predictive model developed in this study effectively identifies A4 tumors, facilitating more personalized therapeutic strategies.

Virtual Biopsy for the Prediction of MGMT Promoter Methylation in Gliomas: A Comprehensive Review of Radiomics and Deep Learning Approaches Applied to MRI

Background/Objectives: The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter in gliomas has emerged as a critical biomarker for prognosis and treatment response. Conventional methods for assessing MGMT promoter methylation, such as methylation-specific PCR, are invasive and require tissue sampling. Methods: A comprehensive literature search was performed in compliance with the updated PRISMA 2020 guidelines within electronic databases MEDLINE/PubMed, Scopus, and IEEE Xplore. Search terms, including "MGMT", "methylation", "glioma", "glioblastoma", "machine learning", "deep learning", and "radiomics", were adopted in various MeSH combinations. Original studies in the English, Italian, German, and French languages were considered for inclusion. Results: This review analyzed 34 studies conducted in the last six years, focusing on assessing MGMT methylation status using radiomics (RD), deep learning (DL), or combined approaches. These studies utilized radiological data from the public (e.g., BraTS, TCGA) and private institutional datasets. Sixteen studies focused exclusively on glioblastoma (GBM), while others included low- and high-grade gliomas. Twenty-seven studies reported diagnostic accuracy, with fourteen achieving values above 80%. The combined use of DL and RD generally resulted in higher accuracy, sensitivity, and specificity, although some studies reported lower minimum accuracy compared to studies using a single model. Conclusions: The integration of RD and DL offers a powerful, non-invasive tool for precisely recognizing MGMT promoter methylation status in gliomas, paving the way for enhanced personalized medicine in neuro-oncology. The heterogeneity of study populations, data sources, and methodologies reflected the complexity of the pipeline and machine learning algorithms, which may require general standardization to be implemented in clinical practice.

Intraoperative goal-directed fluid management and postoperative brain edema in patients having high-grade gliomas resections: a randomized trial

INTRODUCTION

Patients with high-grade gliomas often have severe brain edema. Goal-directed fluid management protects neurological function, but whether reduces postoperative brain edema remains unknown.

METHODS

Patients having elective resection of supratentorial malignant gliomas were randomly assigned to goal-directed versus routine fluid management. Patients assigned to goal-directed management group were given 3 ml kg -1 hydroxyethyl starch solution when stroke volume variation exceeded 15% for 5 min. Fluid was managed per routine by attending anesthesiologists in reference patients. The primary outcome was cerebral edema volume after surgery as assessed by computerized tomography.

RESULTS

A total of 480 eligible patients were randomly assigned to the goal-directed ( n = 240) or the routine fluid management group ( n = 240). The amounts of crystalloid (5.4 vs. 7.0 ml kg -1 hour -1 , P < 0.001), colloid (1.1 vs. 1.7 ml kg -1 hour -1 , P < 0.001), and overall fluid balance (0.3 vs. 1.9 ml kg -1 hour -1 , P < 0.001) were significantly lower in goal-directed fluid management group. There was no significant difference in postoperative brain edema volume between groups (36.0 vs. 38.9 cm 3 , mean difference: 0.18 cm 3 , 95% CI: -5.7 to 5.9). Goal-directed patients had lower intraoperative dural tension (risk ratio: 0.63, 95% CI: 0.50-0.80, P < 0.001). There was no significant difference in Karnofsky Performance Status between the two groups at 30 days after surgery.

CONCLUSIONS

Goal-directed fluid therapy substantially reduced intravenous fluid volumes, but did not reduce postoperative brain edema in patients having brain tumor resections.

Solid stress estimations via intraoperative 3D navigation in patients with brain tumors

Background

Physical forces exerted by expanding brain tumors - specifically the compressive stresses propagated through solid tissue structures - reduces brain perfusion and neurological function, but heretofore has not been directly measured in patients in vivo . Solid stress levels estimated from tumor growth patterns are negatively correlated with neurological performance in patients. We hypothesize that measurements of solid stress can be used to inform clinical management of brain tumors.

Methods

We developed an intraoperative technique to quantitatively estimate solid stress and brain replacement by the tumor. In 30 patients we made topographic measurements of brain deformation through the craniotomy site with a neuronavigation system during surgical workflows immediately preceding tumor resection (< 5 minutes in the OR). Utilizing these measurements in conjunction with finite element modeling, we calculated solid stress within the tumor and the brain, and estimated the amount of brain tissue replaced, i.e., lost, by the tumor growth.

Results

Mean solid stresses were in the range of 10 to 600 Pa, and the amount of tissue replacement was up to 10% of the brain. Brain tissue loss in patients delineated glioblastoma from brain metastatic tumors, and in mice solid stress was a sensitive biomarker of chemotherapy response.

Conclusions

We present here a quantitative approach to intraoperatively measure solid stress in patients that can be readily adopted into standard clinical workflows. Brain tissue loss due to tumor growth is a novel mechanical-based biomarker that, in addition to solid stress, may inform personalized management in future clinical studies in brain cancer.

Key Points

Intraoperative and computational technique quantified solid stress and tissue loss in 30 patients Solid stress and tissue loss distinguished tumor types, showing potential as clinical biomarkers.

Importance of the Study

This study addresses a critical gap, as solid stress has been implicated in tumor progression and treatment resistance but not directly measured in patients with brain cancers before. Here, we present a novel intraoperative technique to quantitatively measure solid stress and brain tissue replacement in brain tumor patients. By combining intraoperative neuro-navigation with finite element modeling, we estimate solid stress and quantify the loss of brain tissue replaced by tumor growth. Importantly, higher tissue replacement was associated with glioblastoma compared to metastatic tumors. In mice, solid stress is a sensitive biomarker of treatment response. These findings establish solid stress and tissue replacement as potential physical biomarkers to inform personalized management of brain tumors. Quantifying these mechanical forces during surgery could help predict patient outcomes and guide clinical decision-making.

Relationship between glioblastoma location and O6-methylguanine-DNA methyltransferase promoter methylation percentage

A large literature assessed the relationships between the O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and glioblastoma location with inconsistent results. Studies assessing this association using the percentage of methylation are lacking. This cross-sectional study aimed at investigating relationships between glioblastoma topology and MGMT promoter methylation, both as categorical (presence/absence) and continuous (percentage) status. We included patients with diagnosis of isocitrate dehydrogenase wild-type glioblastoma [World Health Organization (WHO) 2021 classification], available pre-surgical MRI, known MGMT promoter methylation status. Quantitative methylation assessment was obtained through pyrosequencing. Several analyses were performed for categorical and continuous variables (χ 2, t-tests, ANOVA and Pearson's correlations), investigating relationships between MGMT methylation and glioblastoma location in cortex/white matter/deep grey matter nuclei, lobes, left/right hemispheres and functional grey and white matter network templates. Furthermore, we assessed at the voxel-wise level location differences between (i) methylated and unmethylated glioblastomas and (ii) highly and lowly methylated glioblastomas. Lastly, we investigated the linear relationship between glioblastoma-voxel location and the MGMT methylation percentage. Ninety-three patients were included (66 males; mean age: 62.3 ± 11.3 years), and 42 were MGMT methylated. The mean methylation level was 33.9 ± 18.3%. No differences in glioblastoma volume and location were found between MGMT-methylated and MGMT-unmethylated patients. No specific anatomical regions were associated with MGMT methylation at the voxel-wise level. MGMT methylation percentage positively correlated with cortical localization (R = 0.36, P = 0.021) and negatively with deep grey matter nuclei localization (R = -0.35, P = 0.025). To summarize, we investigated relationships between MGMT methylation status and glioblastoma location through multiple approaches, including voxel-wise analyses. In conclusion, MGMT promoter methylation percentage positively correlated with cortical glioblastoma location, while no specific anatomical regions were associated with MGMT methylation status.

Impact of Sex Hormones on Glioblastoma: Sex-Related Differences and Neuroradiological Insights

Glioblastoma (GBM) displays significant gender disparities, being 1.6 times more prevalent in men, with a median survival time of 15.0 months for males compared to 25.5 months for females. These differences may be linked to gonadal steroid hormones, particularly testosterone, which interacts with the androgen receptor (AR) to promote tumor proliferation. Conversely, estrogen (E2), progesterone (P4), and P4 metabolites exert more complex effects on GBM. Despite these insights, the identification of reliable hormonal tumor markers remains challenging, and studies investigating hormone therapies yield inconclusive results due to small sample sizes and heterogeneous tumor histology. Additionally, genetic, epigenetic, and immunological factors play critical roles in sex disparities, with female patients demonstrating increased O6-Methylguanine-DNA methyltransferase promoter methylation and greater genomic instability. These complexities highlight the need for personalized therapeutic strategies that integrate hormonal influences alongside other sex-specific biological characteristics in the management of GBM. In this review, we present the current understanding of the potential role of sex hormones in the natural history of GBM.

Non-secreting IL12 expressing oncolytic adenovirus Ad-TD-nsIL12 in recurrent high-grade glioma: a phase I trial

Malignant glioma is a highly fatal central nervous system malignancy with high recurrence rates. Oncolytic viruses offer potential treatment but need improvement in efficacy and safety. Here we describe a phase I, dose-escalating, single arm trial (ChiCTR2000032402) to study the safety of Ad-TD-nsIL12, an oncolytic adenovirus expressing non-secreting interleukin-12, in patients with recurrent high-grade glioma that connects with the ventricular system. Eight patients received intratumoral treatment via stereotaxis or an Ommaya reservoir, with doses ranging from 5 × 109 to 5 × 1010vp. The primary end point was to determine the maximal tolerated dose. Secondary endpoints included toxicity and anti-tumour ability. Minimal adverse events were observed at doses of 5 × 109 and 1 × 1010vp. Grade 3 seizure was observed in two patients from Cohort 3 (5 × 1010vp). Therefore, the maximum tolerated dose was determined to be 1 × 1010vp. Four patients developed hydrocephalus during follow-up. Among them, symptoms in two patients were relieved after placement of a ventriculo-peritoneal shunt, and the other two only showed ventriculomegaly on MRI scan without neurological deterioration. Complete response (according to Response Assessment in Neuro-Oncology Criteria) in one patient, a partial response in one patient and post-treatment infiltrations of CD4+ and CD8 + T cells into the tumour were documented during this trial. In conclusion, Ad-TD-nsIL12 has demonstrated safety and preliminary efficacy in patients with recurrent high-grade glioma.

IDH Mutations in Glioma: Molecular, Cellular, Diagnostic, and Clinical Implications

In 2021, the World Health Organization classified isocitrate dehydrogenase (IDH) mutant gliomas as a distinct subgroup of tumors with genetic changes sufficient to enable a complete diagnosis. Patients with an IDH mutant glioma have improved survival which has been further enhanced by the advent of targeted therapies. IDH enzymes contribute to cellular metabolism, and mutations to specific catalytic residues result in the neomorphic production of D-2-hydroxyglutarate (D-2-HG). The accumulation of D-2-HG results in epigenetic alterations, oncogenesis and impacts the tumor microenvironment via immunological modulations. Here, we summarize the molecular, cellular, and clinical implications of IDH mutations in gliomas as well as current diagnostic techniques.

Computational modeling of tumor invasion from limited and diverse data in Glioblastoma

For diseases with high morbidity rates such as Glioblastoma Multiforme, the prognostic and treatment planning pipeline requires a comprehensive analysis of imaging, clinical, and molecular data. Many mutations have been shown to correlate strongly with the median survival rate and response to therapy of patients. Studies have demonstrated that these mutations manifest as specific visual biomarkers in tumor imaging modalities such as MRI. To minimize the number of invasive procedures on a patient and for the overall resource optimization for the prognostic and treatment planning process, the correlation of imaging and molecular features has garnered much interest. While the tumor mass is the most significant feature, the impacted tissue surrounding the tumor is also a significant biomarker contributing to the visual manifestation of mutations - which has not been studied as extensively. The pattern of tumor growth impacts the surrounding tissue accordingly, which is a reflection of tumor properties as well. Modeling how the tumor growth impacts the surrounding tissue can reveal important information about the patterns of tumor enhancement, which in turn has significant diagnostic and prognostic value. This paper presents the first work to automate the computational modeling of the impacted tissue surrounding the tumor using generative deep learning. The paper isolates and quantifies the impact of the Tumor Invasion (TI) on surrounding tissue based on change in mutation status, subsequently assessing its prognostic value. Furthermore, a TI Generative Adversarial Network (TI-GAN) is proposed to model the tumor invasion properties. Extensive qualitative and quantitative analyses, cross-dataset testing, and radiologist blind tests are carried out to demonstrate that TI-GAN can realistically model the tumor invasion under practical challenges of medical datasets such as limited data and high intra-class heterogeneity.

Beyond conventional imaging: Advancements in MRI for glioma malignancy prediction and molecular profiling

This review examines the advancements in magnetic resonance imaging (MRI) techniques and their pivotal role in diagnosing and managing gliomas, the most prevalent primary brain tumors. The paper underscores the importance of integrating modern MRI modalities, such as diffusion-weighted imaging and perfusion MRI, which are essential for assessing glioma malignancy and predicting tumor behavior. Special attention is given to the 2021 WHO Classification of Tumors of the Central Nervous System, emphasizing the integration of molecular diagnostics in glioma classification, significantly impacting treatment decisions. The review also explores radiogenomics, which correlates imaging features with molecular markers to tailor personalized treatment strategies. Despite technological progress, MRI protocol standardization and result interpretation challenges persist, affecting diagnostic consistency across different settings. Furthermore, the review addresses MRI's capacity to distinguish between tumor recurrence and pseudoprogression, which is vital for patient management. The necessity for greater standardization and collaborative research to harness MRI's full potential in glioma diagnosis and personalized therapy is highlighted, advocating for an enhanced understanding of glioma biology and more effective treatment approaches.

Relationship between molecular characteristics of glioblastoma multiforme and the subventricular zone

OBJECTIVE

This study aims to assess the relationship between the molecular characteristics of glioblastoma multiforme (GBM) and the subventricular zone (SVZ).

MATERIAL AND METHODS

Eligible patients had their data anonymously collected from an institutional database, including age, sex, preoperative performance status, the extent of tumour resection, anatomical location, IDH mutation and MGMT methylation status. An Institutional picture archiving and communications system was used for volumetric and morphometric analysis. All measurements were made on T1-weighted magnetic resonance images with gadolinium contrast enhancement. IDH wild-type and mutant GBMs were stratified by MGMT methylation status. The relationship between tumour volume, distance from the tumour's enhancing edge and the tumour's geometric centre to the SVZ and their molecular characteristics were assessed.

RESULTS

Fifty IDH wild-type GBMs were studied. Twenty-three were MGMT methylated, Twenty-seven were unmethylated. IDH wild-type MGMT methylated GBMs were significantly associated with a tumour's enhancing boundary being contiguous to the SVZ (P < 0.001). Ninety percent of tumours contiguous to the SVZ were wild-type methylated (n = 18) and 10% were unmethylated (n = 2). Mean GBM geometric centre distance to SVZ was significantly less for methylated wild-type GBMs compared to unmethylated (P = 0.025) and median GBM distance from the tumour's edge of enhancement to the SVZ was significantly shorter in methylated tumours compared to unmethylated (P < 0.001). Mean and median distances to SVZ from the edge of enhancement was 3.8 millimetres (mm) and 0 mm, respectively, for wild-type methylated GBMs, while for unmethylated wild-types, 14.6 mm, and 12.5 mm. There was no anatomical localisation of IDH wild-type GBMs by MGMT methylation status to a cerebral hemisphere or lobe.

CONCLUSION

IDH wild-type GBMs contiguous to the SVZ are highly likely to be MGMT methylated. Replication by further studies is required to affirm our results and conclusion.

IDH inhibition in gliomas: from preclinical models to clinical trials

Gliomas are the most common malignant primary brain tumours in adults and cannot usually be cured with standard cancer treatments. Gliomas show intratumoural and intertumoural heterogeneity at the histological and molecular levels, and they frequently contain mutations in the isocitrate dehydrogenase 1 (IDH1) or IDH2 gene. IDH-mutant adult-type diffuse gliomas are subdivided into grade 2, 3 or 4 IDH-mutant astrocytomas and grade 2 or 3 IDH-mutant, 1p19q-codeleted oligodendrogliomas. The product of the mutated IDH genes, D-2-hydroxyglutarate (D-2-HG), induces global DNA hypermethylation and interferes with immunity, leading to stimulation of tumour growth. Selective inhibitors of mutant IDH, such as ivosidenib and vorasidenib, have been shown to reduce D-2-HG levels and induce cellular differentiation in preclinical models and to induce MRI-detectable responses in early clinical trials. The phase III INDIGO trial has demonstrated superiority of vorasidenib, a brain-penetrant pan-mutant IDH inhibitor, over placebo in people with non-enhancing grade 2 IDH-mutant gliomas following surgery. In this Review, we describe the pathway of development of IDH inhibitors in IDH-mutant low-grade gliomas from preclinical models to clinical trials. We discuss the practice-changing implications of the INDIGO trial and consider new avenues of investigation in the field of IDH-mutant gliomas.

Combination of MRI-based prediction and CRISPR/Cas12a-based detection for IDH genotyping in glioma

Early identification of IDH mutation status is of great significance in clinical therapeutic decision-making in the treatment of glioma. We demonstrate a technological solution to improve the accuracy and reliability of IDH mutation detection by combining MRI-based prediction and a CRISPR-based automatic integrated gene detection system (AIGS). A model was constructed to predict the IDH mutation status using whole slices in MRI scans with a Transformer neural network, and the predictive model achieved accuracies of 0.93, 0.87, and 0.84 using the internal and two external test sets, respectively. Additionally, CRISPR/Cas12a-based AIGS was constructed, and AIGS achieved 100% diagnostic accuracy in terms of IDH detection using both frozen tissue and FFPE samples in one hour. Moreover, the feature attribution of our predictive model was assessed using GradCAM, and the highest correlations with tumor cell percentages in enhancing and IDH-wildtype gliomas were found to have GradCAM importance (0.65 and 0.5, respectively). This MRI-based predictive model could, therefore, guide biopsy for tumor-enriched, which would ensure the veracity and stability of the rapid detection results. The combination of our predictive model and AIGS improved the early determination of IDH mutation status in glioma patients. This combined system of MRI-based prediction and CRISPR/Cas12a-based detection can be used to guide biopsy, resection, and radiation for glioma patients to improve patient outcomes.

The 2021 World Health Organization Central Nervous System Tumor Classification: The Spectrum of Diffuse Gliomas

The 2021 edition of the World Health Organization (WHO) classification of central nervous system tumors introduces significant revisions across various tumor types. These updates, encompassing changes in diagnostic techniques, genomic integration, terminology, and grading, are crucial for radiologists, who play a critical role in interpreting brain tumor imaging. Such changes impact the diagnosis and management of nearly all central nervous system tumor categories, including the reclassification, addition, and removal of specific tumor entities. Given their pivotal role in patient care, radiologists must remain conversant with these revisions to effectively contribute to multidisciplinary tumor boards and collaborate with peers in neuro-oncology, neurosurgery, radiation oncology, and neuropathology. This knowledge is essential not only for accurate diagnosis and staging, but also for understanding the molecular and genetic underpinnings of tumors, which can influence treatment decisions and prognostication. This review, therefore, focuses on the most pertinent updates concerning the classification of adult diffuse gliomas, highlighting the aspects most relevant to radiological practice. Emphasis is placed on the implications of new genetic information on tumor behavior and imaging findings, providing necessary tools to stay abreast of advancements in the field. This comprehensive overview aims to enhance the radiologist's ability to integrate new WHO classification criteria into everyday practice, ultimately improving patient outcomes through informed and precise imaging assessments.

Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2-4 glioma patients: a deep Radiomics Approach

BACKGROUND

The role of isocitrate dehydrogenase (IDH) mutation status for glioma stratification and prognosis is established. While structural magnetic resonance image (MRI) is a promising biomarker, it may not be sufficient for non-invasive characterisation of IDH mutation status. We investigated the diagnostic value of combined diffusion tensor imaging (DTI) and structural MRI enhanced by a deep radiomics approach based on convolutional neural networks (CNNs) and support vector machine (SVM), to determine the IDH mutation status in Central Nervous System World Health Organization (CNS WHO) grade 2-4 gliomas.

METHODS

This retrospective study analyzed the DTI-derived fractional anisotropy (FA) and mean diffusivity (MD) images and structural images including fluid attenuated inversion recovery (FLAIR), non-enhanced T1-, and T2-weighted images of 206 treatment-naïve gliomas, including 146 IDH mutant and 60 IDH-wildtype ones. The lesions were manually segmented by experienced neuroradiologists and the masks were applied to the FA and MD maps. Deep radiomics features were extracted from each subject by applying a pre-trained CNN and statistical description. An SVM classifier was applied to predict IDH status using imaging features in combination with demographic data.

RESULTS

We comparatively assessed the CNN-SVM classifier performance in predicting IDH mutation status using standalone and combined structural and DTI-based imaging features. Combined imaging features surpassed stand-alone modalities for the prediction of IDH mutation status [area under the curve (AUC) = 0.846; sensitivity = 0.925; and specificity = 0.567]. Importantly, optimal model performance was noted following the addition of demographic data (patients' age) to structural and DTI imaging features [area under the curve (AUC) = 0.847; sensitivity = 0.911; and specificity = 0.617].

CONCLUSIONS

Imaging features derived from DTI-based FA and MD maps combined with structural MRI, have superior diagnostic value to that provided by standalone structural or DTI sequences. In combination with demographic information, this CNN-SVM model offers a further enhanced non-invasive prediction of IDH mutation status in gliomas.

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.

Survival Outcome Prediction in Glioblastoma: Insights from MRI Radiomics

Background: Extracting multiregional radiomic features from multiparametric MRI for predicting pretreatment survival in isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM) patients is a promising approach. Methods: MRI data from 49 IDH wild-type glioblastoma patients pre-treatment were utilized. Diffusion and perfusion maps were generated, and tumor subregions segmented. Radiomic features were extracted for each tissue type and map. Feature selection on 1862 radiomic features identified 25 significant features. The Cox proportional-hazards model with LASSO regularization was used to perform survival analysis. Internal and external validation used a 38-patient training cohort and an 11-patient validation cohort. Statistical significance was set at p < 0.05. Results: Age and six radiomic features (shape and first and second order) from T1W, diffusion, and perfusion maps contributed to the final model. Findings suggest that a small necrotic subregion, inhomogeneous vascularization in the solid non-enhancing subregion, and edema-related tissue damage in the enhancing and edema subregions are linked to poor survival. The model's C-Index was 0.66 (95% C.I. 0.54-0.80). External validation demonstrated good accuracy (AUC > 0.65) at all time points. Conclusions: Radiomics analysis, utilizing segmented perfusion and diffusion maps, provide predictive indicators of survival in IDH wild-type glioblastoma patients, revealing associations with microstructural and vascular heterogeneity in the tumor.

[18]F-fluoroethyl-l-tyrosine positron emission tomography for radiotherapy target delineation: Results from a Radiation Oncology credentialing program

Background and purpose

The [18]F-fluoroethyl-l-tyrosine (FET) PET in Glioblastoma (FIG) study is an Australian prospective, multi-centre trial evaluating FET PET for newly diagnosed glioblastoma management. The Radiation Oncology credentialing program aimed to assess the feasibility in Radiation Oncologist (RO) derivation of standard-of-care target volumes (TVMR) and hybrid target volumes (TVMR+FET) incorporating pre-defined FET PET biological tumour volumes (BTVs).

Materials and methods

Central review and analysis of TVMR and TVMR+FET was undertaken across three benchmarking cases. BTVs were pre-defined by a sole nuclear medicine expert. Intraclass correlation coefficient (ICC) confidence intervals (CIs) evaluated volume agreement. RO contour spatial and boundary agreement were evaluated (Dice similarity coefficient [DSC], Jaccard index [JAC], overlap volume [OV], Hausdorff distance [HD] and mean absolute surface distance [MASD]). Dose plan generation (one case per site) was assessed.

Results

Data from 19 ROs across 10 trial sites (54 initial submissions, 8 resubmissions requested, 4 conditional passes) was assessed with an initial pass rate of 77.8 %; all resubmissions passed. TVMR+FET were significantly larger than TVMR (p < 0.001) for all cases. RO gross tumour volume (GTV) agreement was moderate-to-excellent for GTVMR (ICC = 0.910; 95 % CI, 0.708-0.997) and good-to-excellent for GTVMR+FET (ICC = 0.965; 95 % CI, 0.871-0.999). GTVMR+FET showed greater spatial overlap and boundary agreement compared to GTVMR. For the clinical target volume (CTV), CTVMR+FET showed lower average boundary agreement versus CTVMR (MASD: 1.73 mm vs. 1.61 mm, p = 0.042). All sites passed the planning exercise.

Conclusions

The credentialing program demonstrated feasibility in successful credentialing of 19 ROs across 10 sites, increasing national expertise in TVMR+FET delineation.

Beyond invasive biopsies: using VASARI MRI features to predict grade and molecular parameters in gliomas

BACKGROUND

Gliomas present a significant economic burden and patient management challenge. The 2021 WHO classification incorporates molecular parameters, which guide treatment decisions. However, acquiring these molecular data involves invasive biopsies, prompting a need for non-invasive diagnostic methods. This study aims to assess the potential of Visually AcceSAble Rembrandt Images (VASARI) MRI features to predict glioma characteristics such as grade, IDH mutation, and MGMT methylation status.

METHODS

This study enrolled 107 glioma patients treated between 2017 and 2022, meeting specific criteria including the absence of prior chemotherapy/radiation therapy, and the presence of molecular and MRI data. Images were assessed using the 27 VASARI MRI features by two blinded radiologists. Pathological and molecular assessments were conducted according to WHO 2021 CNS Tumor classification. Cross-validation Least Absolute Shrinkage and Selection Operator (CV-LASSO) logistic regression was applied for statistical analysis to identify significant VASARI features in determining glioma grade, IDH mutation, and MGMT methylation status.

RESULTS

The study demonstrated substantial observer agreement in VASARI feature evaluation (inter- and intra-observer κ = 0.714 - 0.831 and 0.910, respectively). Patient imaging characteristics varied significantly with glioma grade, IDH mutation, and MGMT methylation. A predictive model was established using VASARI features for glioma grade prediction, exhibiting an AUC of 0.995 (95% CI = 0.986 - 0.998), 100% sensitivity, and 92.86% specificity. IDH mutation status was predicted with AUC 0.930 (95% CI = 0.882 - 0.977), and improved slightly to 0.933 with 'age-at-diagnosis' added. A model predicting MGMT methylation had a satisfactory performance (AUC 0.757, 95% CI = 0.645 - 0.868), improving to 0.791 when 'age-at-diagnosis' was added.

CONCLUSIONS

The T1/FLAIR ratio, enhancement quality, hemorrhage, and proportion enhancing predict glioma grade with excellent accuracy. The proportion enhancing, thickness of enhancing margin, and T1/FLAIR ratio are significant predictors for IDH mutation status. Lastly, MGMT methylation is related to the longest diameter of the lesion, edema crossing the midline, and the proportion of the non-enhancing lesion. VASARI MRI features offer non-invasive and accurate predictive models for glioma grade, IDH mutation, and MGMT methylation status, enhancing glioma patient management.

MRI characteristics predict BRAF V600E status in gangliogliomas and pleomorphic xanthoastrocytomas and provide survival prognostication

BACKGROUND

BRAF V600E mutation is a common genomic alteration in gangliogliomas (GGs) and pleomorphic xanthoastrocytomas (PXAs) with prognostic and therapeutic implications.

PURPOSE

To investigate the ability of magnetic resonance imaging (MRI) features to predict BRAF V600E status in GGs and PXAs and their prognostic values.

MATERIAL AND METHODS

A cohort of 44 patients with histologically confirmed GGs and PXAs was reviewed retrospectively. BRAF V600E status was determined by immunohistochemistry (IHC) staining and fluorescence quantitative polymerase chain reaction (PCR). Demographics and MRI characteristics of the two groups were evaluated and compared. Univariate and multivariate Cox regression analyses were performed to identify MRI features that were prognostic for progression-free survival (PFS).

RESULTS

T1/FLAIR ratio, enhancing margin, and mean relative apparent diffusion coefficient (rADCmea) value showed significant differences between the BRAF V600E-mutant and BRAF V600E-wild groups (all P < 0.05). Binary logistic regression analysis revealed only rADCmea value was the independent predictive factor for BRAF V600E status (P = 0.027). Univariate Cox regression analysis showed age at diagnosis (P = 0.032), WHO grade (P = 0.020), enhancing margin (P = 0.029), and rADCmea value (P = 0.005) were significant prognostic factors for PFS. In multivariate Cox regression analysis, increasing age (P = 0.040, hazard ratio [HR] = 1.04, 95% confidence interval [CI] = 1.002-1.079) and lower rADCmea values (P = 0.021, HR = 0.036, 95% CI = 0.002-0.602) were associated with poor PFS in GGs and PXAs.

CONCLUSION

Imaging features are potentially predictive of BRAF V600E status in GGs and PXAs. Furthermore, rADCmea value is a valuable prognostic factor for patients with GGs or PXAs.

Development of A Radiomic Model for MGMT Promoter Methylation Detection in Glioblastoma Using Conventional MRI

The methylation of the O6-methylguanine-DNA methyltransferase (MGMT) promoter is a molecular marker associated with a better response to chemotherapy in patients with glioblastoma (GB). Standard pre-operative magnetic resonance imaging (MRI) analysis is not adequate to detect MGMT promoter methylation. This study aims to evaluate whether the radiomic features extracted from multiple tumor subregions using multiparametric MRI can predict MGMT promoter methylation status in GB patients. This retrospective single-institution study included a cohort of 277 GB patients whose 3D post-contrast T1-weighted images and 3D fluid-attenuated inversion recovery (FLAIR) images were acquired using two MRI scanners. Three separate regions of interest (ROIs) showing tumor enhancement, necrosis, and FLAIR hyperintensities were manually segmented for each patient. Two machine learning algorithms (support vector machine (SVM) and random forest) were built for MGMT promoter methylation prediction from a training cohort (196 patients) and tested on a separate validation cohort (81 patients), based on a set of automatically selected radiomic features, with and without demographic variables (i.e., patients' age and sex). In the training set, SVM based on the selected radiomic features of the three separate ROIs achieved the best performances, with an average of 83.0% (standard deviation: 5.7%) for accuracy and 0.894 (0.056) for the area under the curve (AUC) computed through cross-validation. In the test set, all classification performances dropped: the best was obtained by SVM based on the selected features extracted from the whole tumor lesion constructed by merging the three ROIs, with 64.2% (95% confidence interval: 52.8-74.6%) accuracy and 0.572 (0.439-0.705) for AUC. The performances did not change when the patients' age and sex were included with the radiomic features into the models. Our study confirms the presence of a subtle association between imaging characteristics and MGMT promoter methylation status. However, further verification of the strength of this association is needed, as the low diagnostic performance obtained in this validation cohort is not sufficiently robust to allow clinically meaningful predictions.

Current Status of Cancer Genomics and Imaging Phenotypes: What Radiologists Need to Know

Ongoing discoveries in cancer genomics and epigenomics have revolutionized clinical oncology and precision health care. This knowledge provides unprecedented insights into tumor biology and heterogeneity within a single tumor, among primary and metastatic lesions, and among patients with the same histologic type of cancer. Large-scale genomic sequencing studies also sparked the development of new tumor classifications, biomarkers, and targeted therapies. Because of the central role of imaging in cancer diagnosis and therapy, radiologists need to be familiar with the basic concepts of genomics, which are now becoming the new norm in oncologic clinical practice. By incorporating these concepts into clinical practice, radiologists can make their imaging interpretations more meaningful and specific, facilitate multidisciplinary clinical dialogue and interventions, and provide better patient-centric care. This review article highlights basic concepts of genomics and epigenomics, reviews the most common genetic alterations in cancer, and discusses the implications of these concepts on imaging by organ system in a case-based manner. This information will help stimulate new innovations in imaging research, accelerate the development and validation of new imaging biomarkers, and motivate efforts to bring new molecular and functional imaging methods to clinical radiology. Keywords: Oncology, Cancer Genomics, Epignomics, Radiogenomics, Imaging Markers Supplemental material is available for this article. © RSNA, 2023.

Prediction of Ki-67 labeling index, ATRX mutation, and MGMT promoter methylation status in IDH-mutant astrocytoma by morphological MRI, SWI, DWI, and DSC-PWI

OBJECTIVE

Noninvasive detection of molecular status of astrocytoma is of great clinical significance for predicting therapeutic response and prognosis. We aimed to evaluate whether morphological MRI (mMRI), SWI, DWI, and DSC-PWI could predict Ki-67 labeling index (LI), ATRX mutation, and MGMT promoter methylation status in IDH mutant (IDH-mut) astrocytoma.

METHODS

We retrospectively analyzed mMRI, SWI, DWI, and DSC-PWI in 136 patients with IDH-mut astrocytoma.The features of mMRI and intratumoral susceptibility signals (ITSS) were compared using Fisher exact test or chi-square tests. Wilcoxon rank sum test was used to compare the minimum ADC (ADCmin), and minimum relative ADC (rADCmin) of IDH-mut astrocytoma in different molecular markers status. Mann-Whitney U test was used to compare the rCBVmax of IDH-mut astrocytoma with different molecular markers status. Receiver operating characteristic curves was performed to evaluate their diagnostic performances.

RESULTS

ITSS, ADCmin, rADCmin, and rCBVmax were significantly different between high and low Ki-67 LI groups. ITSS, ADCmin, and rADCmin were significantly different between ATRX mutant and wild-type groups. Necrosis, edema, enhancement, and margin pattern were significantly different between low and high Ki-67 LI groups. Peritumoral edema was significantly different between ATRX mutant and wild-type groups. Grade 3 IDH-mut astrocytoma with unmethylated MGMT promoter was more likely to show enhancement compared to the methylated group.

CONCLUSIONS

mMRI, SWI, DWI, and DSC-PWI were shown to have the potential to predict Ki-67 LI and ATRX mutation status in IDH-mut astrocytoma. A combination of mMRI and SWI may improve diagnostic performance for predicting Ki-67 LI and ATRX mutation status.

CLINICAL RELEVANCE STATEMENT

Conventional MRI and functional MRI (SWI, DWI, and DSC-PWI) can predict Ki-67 expression and ATRX mutation status of IDH mutant astrocytoma, which may help clinicians determine personalized treatment plans and predict patient outcomes.

KEY POINTS

• A combination of multimodal MRI may improve the diagnostic performance to predict Ki-67 LI and ATRX mutation status. • Compared with IDH-mutant astrocytoma with low Ki-67 LI, IDH-mutant astrocytoma with high Ki-67 LI was more likely to show necrosis, edema, enhancement, poorly defined margin, higher ITSS levels, lower ADC, and higher rCBV. • ATRX wild-type IDH-mutant astrocytoma was more likely to show edema, higher ITSS levels, and lower ADC compared to ATRX mutant IDH-mutant astrocytoma.

Non-Invasive Assessment of Isocitrate Dehydrogenase-Mutant Gliomas Using Optimized Proton Magnetic Resonance Spectroscopy on a Routine Clinical 3-Tesla MRI

PURPOSE

The isocitrate dehydrogenase (IDH) mutation has become one of the most important prognostic biomarkers in glioma management, indicating better treatment response and prognosis. IDH mutations confer neomorphic activity leading to the conversion of alpha-ketoglutarate (α-KG) to 2-hydroxyglutarate (2HG). The purpose of this study was to investigate the clinical potential of proton MR spectroscopy (1H-MRS) in identifying IDH-mutant gliomas by detecting characteristic resonances of 2HG and its complex interplay with other clinically relevant metabolites.

MATERIALS AND METHODS

Thirty-two patients with suspected infiltrative glioma underwent a single-voxel (SVS, n = 17) and/or single-slice-multivoxel (1H-MRSI, n = 15) proton MR spectroscopy (1H-MRS) sequence with an optimized echo-time (97 ms) on 3T-MRI. Spectroscopy data were analyzed using the linear combination (LC) model. Cramér-Rao lower bound (CRLB) values of <40% were considered acceptable for detecting 2HG and <20% for other metabolites. Immunohistochemical analyses for determining IDH mutational status were subsequently performed from resected tumor specimens and findings were compared with the results from spectral data. Mann-Whitney and chi-squared tests were performed to ascertain differences in metabolite levels between IDH-mutant and IDH-wild-type gliomas. Receiver operating characteristic (ROC) curve analyses were also performed.

RESULTS

Data from eight cases were excluded due to poor spectral quality or non-tumor-related etiology, and final data analyses were performed from 24 cases. Of these cases, 9/12 (75%) were correctly identified as IDH-mutant or IDH-wildtype gliomas through SVS and 10/12 (83%) through 1H-MRSI with an overall concordance rate of 79% (19/24). The sensitivity, specificity, positive predictive value, and negative predictive value were 80%, 77%, 86%, and 70%, respectively. The metabolite 2HG was found to be significant in predicting IDH-mutant gliomas through the chi-squared test (p < 0.01). The IDH-mutant gliomas also had a significantly higher NAA/Cr ratio (1.20 ± 0.09 vs. 0.75 ± 0.12 p = 0.016) and lower Glx/Cr ratio (0.86 ± 0.078 vs. 1.88 ± 0.66; p = 0.029) than those with IDH wild-type gliomas. The areas under the ROC curves for NAA/Cr and Glx/Cr were 0.808 and 0.786, respectively.

CONCLUSIONS

Noninvasive optimized 1H-MRS may be useful in predicting IDH mutational status and 2HG may serve as a valuable diagnostic and prognostic biomarker in patients with gliomas.

Association of partial T2-FLAIR mismatch sign and isocitrate dehydrogenase mutation in WHO grade 4 gliomas: results from the ReSPOND consortium

PURPOSE

While the T2-FLAIR mismatch sign is highly specific for isocitrate dehydrogenase (IDH)-mutant, 1p/19q-noncodeleted astrocytomas among lower-grade gliomas, its utility in WHO grade 4 gliomas is not well-studied. We derived the partial T2-FLAIR mismatch sign as an imaging biomarker for IDH mutation in WHO grade 4 gliomas.

METHODS

Preoperative MRI scans of adult WHO grade 4 glioma patients (n = 2165) from the multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium were analyzed. Diagnostic performance of the partial T2-FLAIR mismatch sign was evaluated. Subset analyses were performed to assess associations of imaging markers with overall survival (OS).

RESULTS

One hundred twenty-one (5.6%) of 2165 grade 4 gliomas were IDH-mutant. Partial T2-FLAIR mismatch was present in 40 (1.8%) cases, 32 of which were IDH-mutant, yielding 26.4% sensitivity, 99.6% specificity, 80.0% positive predictive value, and 95.8% negative predictive value. Multivariate logistic regression demonstrated IDH mutation was significantly associated with partial T2-FLAIR mismatch (odds ratio [OR] 5.715, 95% CI [1.896, 17.221], p = 0.002), younger age (OR 0.911 [0.895, 0.927], p < 0.001), tumor centered in frontal lobe (OR 3.842, [2.361, 6.251], p < 0.001), absence of multicentricity (OR 0.173, [0.049, 0.612], p = 0.007), and presence of cystic (OR 6.596, [3.023, 14.391], p < 0.001) or non-enhancing solid components (OR 6.069, [3.371, 10.928], p < 0.001). Multivariate Cox analysis demonstrated cystic components (p = 0.024) and non-enhancing solid components (p = 0.003) were associated with longer OS, while older age (p < 0.001), frontal lobe center (p = 0.008), multifocality (p < 0.001), and multicentricity (p < 0.001) were associated with shorter OS.

CONCLUSION

Partial T2-FLAIR mismatch sign is highly specific for IDH mutation in WHO grade 4 gliomas.

Diagnostic Approaches to Adult-Type Diffuse Glial Tumors: Comparative Literature and Clinical Practice Study

Gliomas are the most frequent intrinsic central nervous system tumors. The new 2021 WHO Classification of Central Nervous System Tumors brought significant changes into the classification of gliomas, that underline the role of molecular diagnostics, with the adult-type diffuse glial tumors now identified primarily by their biomarkers rather than histology. The status of the isocitrate dehydrogenase (IDH) 1 or 2 describes tumors at their molecular level and together with the presence or absence of 1p/19q codeletion are the most important biomarkers used for the classification of adult-type diffuse glial tumors. In recent years terminology has also changed. IDH-mutant, as previously known, is diagnostically used as astrocytoma and IDH-wildtype is used as glioblastoma. A comprehensive understanding of these tumors not only gives patients a more proper treatment and better prognosis but also highlights new difficulties. MR imaging is of the utmost importance for diagnosing and supervising the response to treatment. By monitoring the tumor on followup exams better results can be achieved. Correlations are seen between tumor diagnostic and clinical manifestation and surgical administration, followup care, oncologic treatment, and outcomes. Minimal resection site use of functional imaging (fMRI) and diffusion tensor imaging (DTI) have become indispensable tools in invasive treatment. Perfusion imaging provides insightful information about the vascularity of the tumor, spectroscopy shows metabolic activity, and nuclear medicine imaging displays tumor metabolism. To accommodate better treatment the differentiation of pseudoprogression, pseudoresponse, or radiation necrosis is needed. In this report, we present a literature review of diagnostics of gliomas, the differences in their imaging features, and our radiology's departments accumulated experience concerning gliomas.

Imaging-Based Versus Pathologic Survival Stratifications of Diffuse Glioma According to the 2021 WHO Classification System

OBJECTIVE

Imaging-based survival stratification of patients with gliomas is important for their management, and the 2021 WHO classification system must be clinically tested. The aim of this study was to compare integrative imaging- and pathology-based methods for survival stratification of patients with diffuse glioma.

MATERIALS AND METHODS

This study included diffuse glioma cases from The Cancer Genome Atlas (training set: 141 patients) and Asan Medical Center (validation set: 131 patients). Two neuroradiologists analyzed presurgical CT and MRI to assign gliomas to five imaging-based risk subgroups (1 to 5) according to well-known imaging phenotypes (e.g., T2/FLAIR mismatch) and recategorized them into three imaging-based risk groups, according to the 2021 WHO classification: group 1 (corresponding to risk subgroup 1, indicating oligodendroglioma, isocitrate dehydrogenase [IDH]-mutant, and 1p19q-co-deleted), group 2 (risk subgroups 2 and 3, indicating astrocytoma, IDH-mutant), and group 3 (risk subgroups 4 and 5, indicating glioblastoma, IDHwt). The progression-free survival (PFS) and overall survival (OS) were estimated for each imaging risk group, subgroup, and pathological diagnosis. Time-dependent area-under-the receiver operating characteristic analysis (AUC) was used to compare the performance between imaging-based and pathology-based survival model.

RESULTS

Both OS and PFS were stratified according to the five imaging-based risk subgroups (P < 0.001) and three imaging-based risk groups (P < 0.001). The three imaging-based groups showed high performance in predicting PFS at one-year (AUC, 0.787) and five-years (AUC, 0.823), which was similar to that of the pathology-based prediction of PFS (AUC of 0.785 and 0.837). Combined with clinical predictors, the performance of the imaging-based survival model for 1- and 3-year PFS (AUC 0.813 and 0.921) was similar to that of the pathology-based survival model (AUC 0.839 and 0.889).

CONCLUSION

Imaging-based survival stratification according to the 2021 WHO classification demonstrated a performance similar to that of pathology-based survival stratification, especially in predicting PFS.

Conventional and Advanced Magnetic Resonance Imaging Assessment of Non-Enhancing Peritumoral Area in Brain Tumor

The non-enhancing peritumoral area (NEPA) is defined as the hyperintense region in T2-weighted and fluid-attenuated inversion recovery (FLAIR) images surrounding a brain tumor. The NEPA corresponds to different pathological processes, including vasogenic edema and infiltrative edema. The analysis of the NEPA with conventional and advanced magnetic resonance imaging (MRI) was proposed in the differential diagnosis of solid brain tumors, showing higher accuracy than MRI evaluation of the enhancing part of the tumor. In particular, MRI assessment of the NEPA was demonstrated to be a promising tool for distinguishing high-grade gliomas from primary lymphoma and brain metastases. Additionally, the MRI characteristics of the NEPA were found to correlate with prognosis and treatment response. The purpose of this narrative review was to describe MRI features of the NEPA obtained with conventional and advanced MRI techniques to better understand their potential in identifying the different characteristics of high-grade gliomas, primary lymphoma and brain metastases and in predicting clinical outcome and response to surgery and chemo-irradiation. Diffusion and perfusion techniques, such as diffusion tensor imaging (DTI), diffusional kurtosis imaging (DKI), dynamic susceptibility contrast-enhanced (DSC) perfusion imaging, dynamic contrast-enhanced (DCE) perfusion imaging, arterial spin labeling (ASL), spectroscopy and amide proton transfer (APT), were the advanced MRI procedures we reviewed.

Mutant IDH in Gliomas: Role in Cancer and Treatment Options

Altered metabolism is a common feature of many cancers and, in some cases, is a consequence of mutation in metabolic genes, such as the ones involved in the TCA cycle. Isocitrate dehydrogenase (IDH) is mutated in many gliomas and other cancers. Physiologically, IDH converts isocitrate to α-ketoglutarate (α-KG), but when mutated, IDH reduces α-KG to D2-hydroxyglutarate (D2-HG). D2-HG accumulates at elevated levels in IDH mutant tumours, and in the last decade, a massive effort has been made to develop small inhibitors targeting mutant IDH. In this review, we summarise the current knowledge about the cellular and molecular consequences of IDH mutations and the therapeutic approaches developed to target IDH mutant tumours, focusing on gliomas.

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.

BOLD fMRI and DTI fiber tracking for preoperative mapping of eloquent cerebral regions in brain tumor patients: impact on surgical approach and outcome

PURPOSE

Task-based BOLD fMRI and DTI-fiber tracking have become part of the routine presurgical work-up of brain tumor patients in many institutions. However, their potential impact on both surgical treatment and neurologic outcome remains unclear, in despite of the high costs and complex implementation.

METHODS

We retrospectively investigated whether performing fMRI and DTI-ft preoperatively substantially impacted surgical planning and patient outcome in a series of brain tumor patients. We assessed (i) the quality of fMRI and DTI-ft results, by using a scale of 0-2 (0 = failed mapping; 1 = intermediate confidence; 2 = good confidence), (ii) whether functional planning substantially contributed to defining the surgical strategy to be undertaken (i.e., no surgery, biopsy, or resection, with or without ESM), the surgical entry point and extent of resection, and (iii) the incidence of neurological deficits post-operatively.

RESULTS

Twenty-seven patients constituted the study population. The mean confidence rating was 1.9/2 for fMRI localization of the eloquent cortex and lateralization of the language function and 1.7/2 for DTI-ft results. Treatment strategy was altered in 33% (9/27) of cases. Surgical entry point was modified in 8% (2/25) of cases. The extent of resection was modified in 40% (10/25). One patient (1/25, 4%) developed one new functional deficit post-operatively.

CONCLUSION

Functional MR mapping - which must not be considered an alternative to ESM - has a critical role preoperatively, potentially modifying treatment strategy or increasing the neurosurgeons' confidence in the surgical approach hypothesized based on conventional imaging.

Importance of Age and Noncontrast-Enhancing Tumor as Biomarkers for Isocitrate Dehydrogenase-Mutant Glioblastoma: A Multicenter Study

PURPOSE

This study aimed to investigate the most useful clinical and magnetic resonance imaging (MRI) parameters for differentiating isocitrate dehydrogenase (IDH)-mutant and -wildtype glioblastomas in the 2016 World Health Organization Classification of Tumors of the Central Nervous System.

METHODS

This multicenter study included 327 patients with IDH-mutant or IDH-wildtype glioblastoma in the 2016 World Health Organization classification who preoperatively underwent MRI. Isocitrate dehydrogenase mutation status was determined by immunohistochemistry, high-resolution melting analysis, and/or IDH1/2 sequencing. Three radiologists independently reviewed the tumor location, tumor contrast enhancement, noncontrast-enhancing tumor (nCET), and peritumoral edema. Two radiologists independently measured the maximum tumor size and mean and minimum apparent diffusion coefficients of the tumor. Univariate and multivariate logistic regression analyses with an odds ratio (OR) were performed.

RESULTS

The tumors were IDH-wildtype glioblastoma in 306 cases and IDH-mutant glioblastoma in 21. Interobserver agreement for both qualitative and quantitative evaluations was moderate to excellent. The univariate analyses revealed a significant difference in age, seizure, tumor contrast enhancement, and nCET (P < 0.05). The multivariate analysis revealed significant difference in age for all 3 readers (reader 1, odds ratio [OR] = 0.960, P = 0.012; reader 2, OR = 0.966, P = 0.048; reader 3, OR = 0.964, P = 0.026) and nCET for 2 readers (reader 1, OR = 3.082, P = 0.080; reader 2, OR = 4.500, P = 0.003; reader 3, OR = 3.078, P = 0.022).

CONCLUSIONS

Age and nCET are the most useful parameters among the clinical and MRI parameters for differentiating IDH-mutant and IDH-wildtype glioblastomas.

Diffusion Weighted Imaging in Neuro-Oncology: Diagnosis, Post-Treatment Changes, and Advanced Sequences-An Updated Review

DWI is an imaging technique commonly used for the assessment of acute ischemia, inflammatory disorders, and CNS neoplasia. It has several benefits since it is a quick, easily replicable sequence that is widely used on many standard scanners. In addition to its normal clinical purpose, DWI offers crucial functional and physiological information regarding brain neoplasia and the surrounding milieu. A narrative review of the literature was conducted based on the PubMed database with the purpose of investigating the potential role of DWI in the neuro-oncology field. A total of 179 articles were included in the study.

MRI features predict tumor grade in isocitrate dehydrogenase (IDH)-mutant astrocytoma and oligodendroglioma

PURPOSE

Nearly all literature for predicting tumor grade in astrocytoma and oligodendroglioma pre-dates the molecular classification system. We investigated the association between contrast enhancement, ADC, and rCBV with tumor grade separately for IDH-mutant astrocytomas and molecularly-defined oligodendrogliomas.

METHODS

For this retrospective study, 44 patients with IDH-mutant astrocytomas (WHO grades II, III, or IV) and 39 patients with oligodendrogliomas (IDH-mutant and 1p/19q codeleted) (WHO grade II or III) were enrolled. Two readers independently assessed preoperative MRI for contrast enhancement, ADC, and rCBV. Inter-reader agreement was calculated, and statistical associations between MRI metrics and WHO grade were determined per reader.

RESULTS

For IDH-mutant astrocytomas, both readers found a stepwise positive association between contrast enhancement and WHO grade (Reader A: OR 7.79 [1.97, 30.80], p = 0.003; Reader B: OR 6.62 [1.70, 25.82], p = 0.006); both readers found that ADC was negatively associated with WHO grade (Reader A: OR 0.74 [0.61, 0.90], p = 0.002); Reader B: OR 0.80 [0.66, 0.96], p = 0.017), and both readers found that rCBV was positively associated with WHO grade (Reader A: OR 2.33 [1.35, 4.00], p = 0.002; Reader B: OR 2.13 [1.30, 3.57], p = 0.003). For oligodendrogliomas, both readers found a positive association between contrast enhancement and WHO grade (Reader A: OR 15.33 [2.56, 91.95], p = 0.003; Reader B: OR 20.00 [2.19, 182.45], p = 0.008), but neither reader found an association between ADC or rCBV and WHO grade.

CONCLUSIONS

Contrast enhancement predicts WHO grade for IDH-mutant astrocytomas and oligodendrogliomas. ADC and rCBV predict WHO grade for IDH-mutant astrocytomas, but not for oligodendrogliomas.

Challenges and opportunities for advanced neuroimaging of glioblastoma

Glioblastoma is the most aggressive of glial tumours in adults. On conventional magnetic resonance (MR) imaging, these tumours are observed as irregular enhancing lesions with areas of infiltrating tumour and cortical expansion. More advanced imaging techniques including diffusion-weighted MRI, perfusion-weighted MRI, MR spectroscopy and positron emission tomography (PET) imaging have found widespread application to diagnostic challenges in the setting of first diagnosis, treatment planning and follow-up. This review aims to educate readers with regard to the strengths and weaknesses of the clinical application of these imaging techniques. For example, this review shows that the (semi)quantitative analysis of the mentioned advanced imaging tools was found useful for assessing tumour aggressiveness and tumour extent, and aids in the differentiation of tumour progression from treatment-related effects. Although these techniques may aid in the diagnostic work-up and (post-)treatment phase of glioblastoma, so far no unequivocal imaging strategy is available. Furthermore, the use and further development of artificial intelligence (AI)-based tools could greatly enhance neuroradiological practice by automating labour-intensive tasks such as tumour measurements, and by providing additional diagnostic information such as prediction of tumour genotype. Nevertheless, due to the fact that advanced imaging and AI-diagnostics is not part of response assessment criteria, there is no harmonised guidance on their use, while at the same time the lack of standardisation severely hampers the definition of uniform guidelines.

Multiparametric Magnetic Resonance Imaging Correlates of Isocitrate Dehydrogenase Mutation in WHO high-Grade Astrocytomas

Purpose and background: Isocitrate dehydrogenase (IDH) mutation and O-6 methyl guanine methyl transferase (MGMT) methylation are surrogate biomarkers of improved survival in gliomas. This study aims at studying the ability of semantic magnetic resonance imaging (MRI) features to predict the IDH mutation status confirmed by the gold standard molecular tests. Methods: The MRI of 148 patients were reviewed for various imaging parameters based on the Visually AcceSAble Rembrandt Images (VASARI) study. Their IDH status was determined using immunohistochemistry (IHC). Fisher’s exact or chi-square tests for univariate and logistic regression for multivariate analysis were used. Results: Parameters such as mild and patchy enhancement, minimal edema, necrosis < 25%, presence of cysts, and less rCBV (relative cerebral blood volume) correlated with IDH mutation. The median age of IDH-mutant and IDH-wild patients were 34 years (IQR: 29−43) and 52 years (IQR: 45−59), respectively. Mild to moderate enhancement was observed in 15/19 IDH-mutant patients (79%), while 99/129 IDH-wildtype (77%) had severe enhancement (p-value <0.001). The volume of edema with respect to tumor volume distinguished IDH-mutants from wild phenotypes (peritumoral edema volume < tumor volume was associated with higher IDH-mutant phenotypes; p-value < 0.025). IDH-mutant patients had a median rCBV value of 1.8 (IQR: 1.4−2.0), while for IDH-wild phenotypes, it was 2.6 (IQR: 1.9−3.5) {p-value = 0.001}. On multivariate analysis, a cut-off of 25% necrosis was able to differentiate IDH-mutant from IDH-wildtype (p-value < 0.001), and a cut-off rCBV of 2.0 could differentiate IDH-mutant from IDH-wild phenotypes (p-value < 0.007). Conclusion: Semantic imaging features could reliably predict the IDH mutation status in high-grade gliomas. Presurgical prediction of IDH mutation status could help the treating oncologist to tailor the adjuvant therapy or use novel IDH inhibitors.

Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology

Glioblastoma (GBM) is a malignant brain tumor exhibiting rapid and infiltrative growth, with less than 10% of patients surviving over 5 years, despite aggressive and multimodal treatments. The poor prognosis and the lack of effective pharmacological treatments are imputable to a remarkable histological and molecular heterogeneity of GBM, which has led, to date, to the failure of precision oncology and targeted therapies. Identification of molecular biomarkers is a paradigm for comprehensive and tailored treatments; nevertheless, biopsy sampling has proved to be invasive and limited. Radiogenomics is an emerging translational field of research aiming to study the correlation between radiographic signature and underlying gene expression. Although a research field still under development, not yet incorporated into routine clinical practice, it promises to be a useful non-invasive tool for future personalized/adaptive neuro-oncology. This review provides an up-to-date summary of the recent advancements in the use of magnetic resonance imaging (MRI) radiogenomics for the assessment of molecular markers of interest in GBM regarding prognosis and response to treatments, for monitoring recurrence, also providing insights into the potential efficacy of such an approach for survival prognostication. Despite a high sensitivity and specificity in almost all studies, accuracy, reproducibility and clinical value of radiomic features are the Achilles heel of this newborn tool. Looking into the future, investigators' efforts should be directed towards standardization and a disciplined approach to data collection, algorithms, and statistical analysis.

Noninvasive Classification of Glioma Subtypes Using Multiparametric MRI to Improve Deep Learning

Background: Deep learning (DL) methods can noninvasively predict glioma subtypes; however, there is no set paradigm for the selection of network structures and input data, including the image combination method, image processing strategy, type of numeric data, and others. Purpose: To compare different combinations of DL frameworks (ResNet, ConvNext, and vision transformer (VIT)), image preprocessing strategies, magnetic resonance imaging (MRI) sequences, and numerical data for increasing the accuracy of DL models for differentiating glioma subtypes prior to surgery. Methods: Our dataset consisted of 211 patients with newly diagnosed gliomas who underwent preoperative MRI with standard and diffusion-weighted imaging methods. Different data combinations were used as input for the three different DL classifiers. Results: The accuracy of the image preprocessing strategies, including skull stripping, segment addition, and individual treatment of slices, was 5%, 10%, and 12.5% higher, respectively, than that of the other strategies. The accuracy increased by 7.5% and 10% following the addition of ADC and numeric data, respectively. ResNet34 exhibited the best performance, which was 5% and 17.5% higher than that of ConvNext tiny and VIT-base, respectively. Data Conclusions: The findings demonstrated that the addition of quantitatively numeric data, ADC images, and effective image preprocessing strategies improved model accuracy for datasets of similar size. The performance of ResNet was superior for small or medium datasets.

Standard deviations of MR signal intensities show a consistent trend during imaging follow-ups for glioblastoma patients when corrected for non-biological heterogeneity due to hardware and software variation

INTRODUCTION

Glioblastoma multiforme (GBM) has a poor prognosis in spite of advanced MRI guided treatments today. Routine MRI using conventional T₁ or advanced permeability based MRI of GBM often does not adequately represent changing tumor phases or overall survival. In this work, region of interest (ROI) based tissue MR standard deviation (SD) is demonstrated as an important MRI variable that could be a potential biomarker of GBM heterogeneity and radioresistance.

MATERIALS AND METHODS

MRI characterization is often qualitative and lacks reproducibility. Using standardized MRI phantoms we have normalized retrospective records of 12 radioresistant GBM patients that underwent radiation therapy (RT) with concomitant and adjuvant temozolomide (TMZ) chemotherapy followed by serial MR imaging with gadolinium contrast.

RESULTS AND DISCUSSION

We have identified key variables like hardware, software and protocol variation and have standardized those using test phantoms at five MR systems. We suggest GBM growth during the treatment period can be linked to normalized MRI signal and its fluctuations from session to session and from magnet to magnet by using an ROI derived standard deviation that corresponds to heterogeneity of the tumor MRI signal and changes in magnetic susceptibility. The time period observed in our patient group for peak standard deviations is approximately halfway through the tumor course and may correspond to a growth of more aggressive MES subtype of cells. To model the GBM heterogeneity we performed in vitro T₁ weighted inversion recovery MRI experiments at 3 T for porous media of silicate particles in 1% aq solution of Gadavist and linked SD with particle size and local gadolinium volume within porous media. Such in vitro models mimic the increased SD in radioresistant GBM and as a novel contribution suggest that finer texture with high surface area might arise approximately halfway through the overall survival duration in GBM.

CONCLUSION

Standard deviation as a measure of magnetic susceptibility may be collectively linked to the changes in texture, cell fractions (biological) and trapped contrast media (vascular as well as artifactual consequences) and should be evaluated as a potential biomarker of GBM aggressiveness than the overall MRI signal intensity from a GBM.

Immune landscape-based machine-learning-assisted subclassification, prognosis, and immunotherapy prediction for glioblastoma

Introduction

As a malignant brain tumor, glioblastoma (GBM) is characterized by intratumor heterogeneity, a worse prognosis, and highly invasive, lethal, and refractory natures. Immunotherapy has been becoming a promising strategy to treat diverse cancers. It has been known that there are highly heterogeneous immunosuppressive microenvironments among different GBM molecular subtypes that mainly include classical (CL), mesenchymal (MES), and proneural (PN), respectively. Therefore, an in-depth understanding of immune landscapes among them is essential for identifying novel immune markers of GBM.

Methods and results

In the present study, based on collecting the largest number of 109 immune signatures, we aim to achieve a precise diagnosis, prognosis, and immunotherapy prediction for GBM by performing a comprehensive immunogenomic analysis. Firstly, machine-learning (ML) methods were proposed to evaluate the diagnostic values of these immune signatures, and the optimal classifier was constructed for accurate recognition of three GBM subtypes with robust and promising performance. The prognostic values of these signatures were then confirmed, and a risk score was established to divide all GBM patients into high-, medium-, and low-risk groups with a high predictive accuracy for overall survival (OS). Therefore, complete differential analysis across GBM subtypes was performed in terms of the immune characteristics along with clinicopathological and molecular features, which indicates that MES shows much higher immune heterogeneity compared to CL and PN but has significantly better immunotherapy responses, although MES patients may have an immunosuppressive microenvironment and be more proinflammatory and invasive. Finally, the MES subtype is proved to be more sensitive to 17-AAG, docetaxel, and erlotinib using drug sensitivity analysis and three compounds of AS-703026, PD-0325901, and MEK1-2-inhibitor might be potential therapeutic agents.

Conclusion

Overall, the findings of this research could help enhance our understanding of the tumor immune microenvironment and provide new insights for improving the prognosis and immunotherapy of GBM patients.

Effect of intraoperative goal-directed fluid therapy on the postoperative brain edema in patients undergoing high-grade glioma resections: a study protocol of randomized control trial

Introduction

Brain edema is the most frequent postoperative complication after brain tumor resection, especially in patients with high-grade glioma. However, the effect of SVV-based goal-directed fluid therapy (GDFT) on postoperative brain edema and the prognosis remain unclear.

Methods and analysis

This is a prospective, randomized, double-blinded, parallel-controlled trial aiming to observe whether stroke volume variation (SVV)-based GDFT could improve the postoperative brain edema in patients undergoing supratentorial high-grade gliomas compared with traditional fluid therapy. The patient will be given 3 ml/kg hydroxyethyl starch solution when the SVV is greater than 15% continuously for more than 5 min intraoperatively. The primary outcome will be postoperative cerebral edema volume on brain CT within 24 h.

Ethics and dissemination

This trial has been registered at ClinicalTrials.gov (NCT03323580) and approved by the Ethics Committee of Beijing Tiantan Hospital, Capital Medical University (reference number: KY2017-067-02). The findings will be disseminated in peer-reviewed journals and presented at national or international conferences relevant to the subject fields.

Trial registration

ClinicalTrials.gov NCT03323580 (First posted: October 27, 2017; Last update posted: February 11, 2022).

Supplementary Information

The online version contains supplementary material available at 10.1186/s13063-022-06859-9.

Diffuse glioma, not otherwise specified: imaging-based risk stratification achieves histomolecular-level prognostication

OBJECTIVES

To determine whether imaging-based risk stratification enables prognostication in diffuse glioma, NOS (not otherwise specified).

METHODS

Data from 220 patients classified as diffuse glioma, NOS, between January 2011 and December 2020 were retrospectively included. Two neuroradiologists analyzed pre-surgical CT and MRI to assign gliomas to the three imaging-based risk types considering well-known imaging phenotypes (e.g., T2/FLAIR mismatch). According to the 2021 World Health Organization classification, the three risk types included (1) low-risk, expecting oligodendroglioma, isocitrate dehydrogenase (IDH)-mutant, and 1p/19q-codeleted; (2) intermediate-risk, expecting astrocytoma, IDH-mutant; and (3) high-risk, expecting glioblastoma, IDH-wildtype. Progression-free survival (PFS) and overall survival (OS) were estimated for each risk type. Time-dependent receiver operating characteristic analysis using 10-fold cross-validation with 100-fold bootstrapping was used to compare the performance of an imaging-based survival model with that of a historical molecular-based survival model published in 2015, created using The Cancer Genome Archive data.

RESULTS

Prognostication according to the three imaging-based risk types was achieved for both PFS and OS (log-rank test, p < 0.001). The imaging-based survival model showed high prognostic value, with areas under the curves (AUCs) of 0.772 and 0.650 for 1-year PFS and OS, respectively, similar to the historical molecular-based survival model (AUC = 0.74 for PFS and 0.87 for OS). The imaging-based survival model achieved high long-term performance in both 3-year PFS (AUC = 0.806) and 5-year OS (AUC = 0.812).

CONCLUSION

Imaging-based risk stratification achieved histomolecular-level prognostication in diffuse glioma, NOS, and could aid in guiding patient referral for insufficient or unsuccessful molecular diagnosis.

KEY POINTS

• Three imaging-based risk types enable distinct prognostication in diffuse glioma, NOS (not otherwise specified). • The imaging-based survival model achieved similar prognostic performance as a historical molecular-based survival model. • For long-term prognostication of 3 and 5 years, the imaging-based survival model showed high performance.

Factors Involved in Preoperative Edema in High-Grade Gliomas

Background Expansion of preoperative edema (PE) is an independent poor prognostic factor in high-grade gliomas. Evaluation of PE provides important information that can be readily obtained from magnetic resonance imaging (MRI), but there are few reports on factors associated with PE. The goal of this study was to identify factors contributing to PE in Grade 3 (G3) and Grade 4 (G4) gliomas. Methodology PE was measured in 141 pathologically proven G3 and G4 gliomas, and factors with a potential relationship with PE were examined in univariate and multivariate analyses. The following eight explanatory variables were used: age, sex, Karnofsky performance status (KPS), location of the glioma, tumor diameter, pathological grade, isocitrate dehydrogenase (IDH)-1-R132H status, and Ki-67 index. Overall survival (OS) and progression-free survival (PFS) were calculated in groups divided by PE (<1 vs. ≥1 cm) and by factors with a significant correlation with PE in multivariate analysis. Results In univariate analysis, age (p = 0.013), KPS (p = 0.012), pathology grade (p = 0.004), and IDH1-R132H status (p = 0.0003) were significantly correlated with PE. In multivariate analysis, only IDH1-R132H status showed a significant correlation (p = 0.036), with a regression coefficient of -0.42. The median follow-up period in survivors was 38.9 months (range: 1.2-131.7 months). The one-, two-, and three-year OS rates for PE <1 vs. ≥1 cm were 77% vs. 68%, 67% vs. 44%, and 63% vs. 24% (p = 0.0001), respectively, and those for IDH1-R132H mutated vs. wild-type cases were 85% vs. 67%, 85% vs. 40%, and 81% vs. 21% (p < 0.0001), respectively. The one-, two-, and three-year PFS rates for PE <1 vs. ≥1 cm were 77% vs. 49%, 64% vs. 24%, and 50% vs. 18% (p = 0.0002), respectively, and those for IDH1-R132H mutated vs. wild-type cases were 85% vs. 48%, 77% vs. 23%, and 73% vs. 14% (p < 0.0001), respectively. Conclusions IDH1-R132H status was found to be a significant contributor to PE. Cases with PE <1 cm and those with the IDH1-R132H mutation clearly had a better prognosis.

Classification and Diagnosis of Adult Glioma: A Scoping Review

Gliomas are primary central nervous system tumors that arise from glial progenitor cells. Gliomas have been classically classified morphologically based on their histopathological characteristics. However, with recent advances in cancer genomics, molecular profiles have now been integrated into the classification and diagnosis of gliomas. In this review article, we discuss the clinical features, imaging findings, and molecular profiles of adult-type diffuse gliomas based on the new 2021 World Health Organization Classifications of Tumors of the central nervous system.

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.

The early infiltrative phase of GBM hypothesis: are molecular glioblastomas histological glioblastomas in the making? A preliminary multicenter study

PURPOSE

The presence of necrosis or microvascular proliferation was previously the hallmark for glioblastoma (GBM) diagnosis. The 2021 WHO classification now considers IDH-wildtype diffuse astrocytic tumors without the histological features of glioblastoma (that would have otherwise been classified as grade 2 or 3) as molecular GBM (molGBM) if they harbor any of the following molecular abnormalities: TERT promoter mutation, EGFR amplification, or chromosomal + 7/-10 copy changes. We hypothesize that these tumors are early histological GBM and will eventually develop the classic histological features.

METHODS

Medical records from 65 consecutive patients diagnosed with molGBM at three tertiary-care centers from our institution were retrospectively reviewed from November 2017-October 2021. Only patients who underwent reoperation for tumor recurrence and whose tissue at initial diagnosis and recurrence was available were included in this study. The detailed clinical, histopathological, and radiographic scenarios are presented.

RESULTS

Five patients were included in our final cohort. Three (60%) patients underwent reoperation for recurrence in the primary site and 2 (40%) underwent reoperation for distal recurrence. Microvascular proliferation and pseudopalisading necrosis were absent at initial diagnosis but present at recurrence in 4 (80%) patients. Radiographically, all tumors showed contrast enhancement, however none of them showed the classic radiographic features of GBM at initial diagnosis.

CONCLUSIONS

In this manuscript we present preliminary data for a hypothesis that molGBMs are early histological GBMs diagnosed early in their natural history of disease and will eventually develop necrosis and microvascular proliferation. Further correlative studies are needed in support of this hypothesis.