Probabilistic radiographic atlas of glioblastoma phenotypes

Mapping glioblastoma-induced neurological deficits: A brain atlas

BACKGROUND

Identifying radiological characteristics and brain regions associated with neurological deficits in glioblastoma patients can improve diagnostic evaluation and understanding of the disease's impact on neurological function.

METHODS

The retrospective study included 527 newly diagnosed glioblastoma patients. Eligibility criteria included pathologically confirmed IDH-wild type glioblastoma, availability of pre- and post-contrast MRIs, and detailed neurological examination reports. Contrast-enhancing tumors (CET) and non-contrast-enhancing lesions (NEL) were segmented from 3 Tesla MRI scans. Lesion volumes from patients without neurological deficits compared with symptomatic patients using either the Mann-Whitney test or Kruskal-Wallis test. Voxel-wise lesion-symptom mapping was conducted using Fisher-exact-test followed by random permutation analysis (ADIFFI) to identify brain regions with higher occurrences of deficit-associated lesions.

RESULTS

Location of CET and NEL within the brain were associated with specific neurological deficits. Larger CET and NEL volumes were associated with increased neurological deficits (CET: rs = 0.15, p = 0.0006; NEL: rs = 0.22, p < 0.0001). Lesion volumes were smaller in patients without neurological deficits (CET: 4.97 ± 0.69 ml vs. 20.0 ± 0.9 ml, p < 0.0001). Epilepsy-associated lesions were also smaller (CET: 4.59 ± 0.55 ml vs. 22.0 ± 0.9 ml, p < 0.0001).

CONCLUSION

The study highlights that neurological and epilepsy status at pre-treatment provide estimates of glioblastoma lesion volumes and locations. The correlation between lesion volumes and neurological deficits underscores the significance of comprehensive radiological assessments in glioblastoma patients. These findings support the use of detailed lesion-symptom mapping to guide clinical management and prognosis evaluation in glioblastoma.

Anatomical heterogeneity in low-grade and high-grade gliomas: A multiscale perspective

BACKGROUND

Low-grade gliomas (LGGs) and high-grade gliomas (HGGs) often exhibit distinct spatial distributions, a phenomenon that remains incompletely understood. Based on previous research, we hypothesized that functional networks, neurotransmitters, and isocitrate dehydrogenase-1 (IDH-1) status characterize the spatial patterns of LGG and HGG.

METHODS

We analyzed 399 patients diagnosed with primary gliomas. First, we generated glioma frequency maps based on tumor grade, neurotransmitters, and IDH-1 status and constructed a brain functional connectivity network to explore heterogeneity in glioma location. Second, all tumor masks were mirror-symmetrized onto the brain's left hemisphere to facilitate feature extraction. We performed independent component analysis on merged four-dimensional files using Multivariate Exploratory Linear Optimized Decomposition into Independent Component (MELODIC), identifying four IDH-1 wild-type lesion covariance networks (IDHwt-LCNs) and three IDH-1 mutant lesion covariance networks (IDHmut-LCNs) with distinct spatial distributions, and analyzing correlation between the neurotransmitter levels and the IDH-wt/mut specific LCNs. Finally, we compared 42 white matter fibers extracted using XTRACT with 39 functional brain connectivity networks from the multi-subject dictionary learning (MSDL) atlas, revealing significant associations among the frontal aslant tract (FAT) and the intraparietal sulcus (IPS).

RESULTS

Our findings revealed high anatomical heterogeneity between LGG and HGG. Moreover, the high node strength played a critical role in the distinct spatial distribution of glioma. Significant correlations were observed between glioma frequency maps and dopaminergic, cholinergic, μ-opioid, and serotonergic neurotransmission. Furthermore, IDHwt/mut-LCNs analysis demonstrated that IDH-1 status influences glioma distribution, involving key brain structures. Lastly, we also found significant correlations between IDHwt/mut-LCNs and the neurotransmission of dopaminergic, cholinergic, μ-opioid, and serotonergic systems.

CONCLUSION

Our study highlighted the mechanisms by which functional networks, neurotransmitter systems, and IDH-1 status collectively contribute to the anatomical heterogeneity observed in LGG and HGG.

Advanced imaging characterization of post-chemoradiation glioblastoma stratified by diffusion MRI phenotypes known to predict favorable anti-VEGF response

PURPOSE

Recurrent glioblastomas showing a survival benefit from anti-VEGF agents are known to exhibit a distinct diffusion MRI phenotype. We aim to characterize advanced imaging features of this glioblastoma subset.

METHODS

MRI scans from 87 patients with IDH-wildtype glioblastoma were analyzed. All patients had completed standard chemoradiation and were anti-VEGF-naïve. Contrast-enhancing tumor segmentations were used to extract: the lowest peak of the double gaussian distribution of apparent diffusion coefficient values (ADCL) calculated from diffusion MRI, relative cerebral blood flow (rCBV) values from perfusion MRI, MTRasym @ 3ppm from pH-weighted amine CEST MRI, quantitative T2 and T2* relaxation times (qT2 and qT2*), T1w subtraction map values, and contrast-enhancing tumor volume. Lesions were categorized as high- or low-ADCL using a cutoff of 1240 µm2/s, according to previous studies.

RESULTS

High-ADCL lesions showed significantly lower rCBV (1.02 vs. 1.28, p = 0.0057), higher MTRasym @ 3ppm (2.36% vs. 2.10%, p = 0.0043), and higher qT2 (114.8 ms vs. 100.9 ms, p = 0.0094), compared to low-ADCL lesions. No group differences were seen in contrast-enhancing tumor volume, T1w subtraction map values, and qT2*, nor in clinical variables such as sex category, MGMT status, and EGFR status. Finally, no clear group-specific preferential locations were seen.

CONCLUSION

Post-chemoradiation glioblastomas with a diffusion MRI phenotype that is known to predict a favorable response to anti-VEGF (ADCL ≥1240 µm2/s) have distinct biological features, with different perfusion and metabolic characteristics, and T2 relaxation times.

Effects of different hemispheric gliomas on depression and prognosis in neurosurgery patients

BACKGROUND

Depression is common in patients with gliomas, but few studies focused on the association between depression and glioma laterality.

AIMS

This study was purposed to investigate depression difference and prognostic value between patients with left-hemispheric gliomas and right-hemispheric gliomas.

METHODS

This study included 212 patients with left-hemispheric gliomas and 218 patients with right-hemispheric gliomas. Hospital Anxiety and Depression Scale (HADS) and Zung self-rating depression scale (SDS) were independently performed before surgery, 3 months and 6 months after surgery. All patients were followed up to death or 36 months. Overall survival (OS) and progression-free survival (PFS) were performed to evaluate the survival of glioma patients.

RESULTS

The preoperative prevalence and scores of depression in patients with left-hemispheric gliomas were higher than those in patients with right-hemispheric gliomas. But there were no differences in postoperative prevalence and scores of depression between patients with left-hemispheric gliomas and right-hemispheric gliomas. In patients with left-hemispheric gliomas or with right-hemispheric gliomas, the preoperative scores of depression were higher than postoperative scores of depression, whereas there was no difference in depression score between 3 months after surgery and 6 months after surgery. In addition, patients with right-hemispheric gliomas had better PFS and OS than patients with left-hemispheric gliomas.

CONCLUSIONS

Patients with left-hemispheric gliomas are more likely to bring about depression than patients with right-hemispheric gliomas. Besides, patients with right-hemispheric gliomas are more likely to have better survival than patients with left-hemispheric gliomas. Surgery is considered as a useful treatment to alleviate depression of glioma patients.

The radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic drivers

BACKGROUND

Glioblastoma is a highly heterogeneous brain tumor, posing challenges for precision therapies and patient stratification in clinical trials. Understanding how genetic mutations influence tumor imaging may improve patient management and treatment outcomes. This study investigates the relationship between imaging features, spatial patterns of tumor location, and genetic alterations in IDH-wildtype glioblastoma, as well as the likely sequence of mutational events.

METHODS

We conducted a retrospective analysis of 357 IDH-wildtype glioblastomas with pre-operative multiparametric MRI and targeted genetic sequencing data. Radiogenomic signatures and spatial distribution maps were generated for key mutations in genes such as EGFR, PTEN, TP53, and NF1 and their corresponding pathways. Machine and deep learning models were used to identify imaging biomarkers and stratify tumors based on their genetic profiles and molecular heterogeneity.

RESULTS

Here, we show that glioblastoma mutations produce distinctive imaging signatures, which are more pronounced in tumors with less molecular heterogeneity. These signatures provide insights into how mutations affect tumor characteristics such as neovascularization, cell density, invasion, and vascular leakage. We also found that tumor location and spatial distribution correlate with genetic profiles, revealing associations between tumor regions and specific oncogenic drivers. Additionally, imaging features reflect the cross-sectionally inferred evolutionary trajectories of glioblastomas.

CONCLUSIONS

This study establishes clinically accessible imaging biomarkers that capture the molecular composition and oncogenic drivers of glioblastoma. These findings have potential implications for noninvasive tumor profiling, personalized therapies, and improved patient stratification in clinical trials.

Visualizing the association between the location and prognosis of isocitrate dehydrogenase wild-type glioblastoma: a voxel-wise Cox regression analysis with open-source datasets

PURPOSE

This study examined the correlation between tumor location and prognosis in patients with glioblastoma using magnetic resonance images of various isocitrate dehydrogenase (IDH) wild-type glioblastomas from The Cancer Imaging Archive (TCIA). The relationship between tumor location and prognosis was visualized using voxel-wise Cox regression analysis.

METHODS

Participants with IDH wild-type glioblastoma were selected, and their survival and demographic data and tumor characteristics were collected from TCIA datasets. Post-contrast-enhanced T1-weighted imaging, T2-fluid attenuated inversion recovery imaging, and tumor segmentation data were also compiled. Following affine registration of each image and tumor segmentation region of interest to the MNI standard space, a voxel-wise Cox regression analysis was conducted. This analysis determined the association of the presence or absence of the tumor with the prognosis in each voxel after adjusting for the covariates.

RESULTS

The study included 769 participants of 464 men and 305 women (mean age, 63 years ± 12 [standard deviation]). The hazard ratio map indicated that tumors in the medial frontobasal region and around the third and fourth ventricles were associated with poorer prognoses, underscoring the challenges of complete resection and treatment accessibility in these areas regardless of the tumor volume. Conversely, tumors located in the right temporal and occipital lobes had favorable prognoses.

CONCLUSION

This study showed an association between tumor location and prognosis. These findings may assist clinicians in developing more precise and effective treatment plans for patients with glioblastoma to improve their management.

Sequential immunotherapy and bevacizumab treatments in glioblastoma multiforme: A case series and review of the literature

Glioblastoma multiforme (GBM) is a tumor with a high refractory rate to immunotherapy and a low tumor mutational burden phenotype, leading to limited immunogenic neoantigens. The present study aimed to investigate the sequential use of immunotherapy and bevacizumab in patients with GBM, exploring the clinical outcomes and potential complications. Patients received various combinations of immunotherapy and bevacizumab after standard treatment, including surgery, radiotherapy and temozolomide. Clinical courses, radiological findings and treatment outcomes were monitored and documented during each clinical visit through routine physical examinations, imaging studies and review of medical records. The efficacy and side effects of this sequential drug approach remained unclear. The common features of these patients were a marked decline in cognitive function and clinical deterioration, assessed clinically in the absence of obvious tumor progression. Radiological evaluation was also performed, particularly for possible cerebrovascular events. In these cases, the potential for sequential treatment to suppress tumors while inducing cerebrovascular events was also investigated, and patients were not lost to overt tumor progression. Notably, further research is required to clarify the mechanisms of action and complications associated with the sequential use of immunotherapy and bevacizumab in the treatment of GBM.

A bird's eye view to the homeostatic, Alzheimer and Glioblastoma attractors

Dimensional reduction analysis of available data for white matter of the brain allows to locate the normal (homeostatic), Glioblastoma and Alzheimer's disease attractors in gene expression space and to identify paths related to transitions like carcinogenesis or Alzheimer's disease onset. A predefined path for aging is also apparent, which is consistent with the hypothesis of programmatic aging. In addition, reasonable assumptions about the relative strengths of attractors allow to draw a schematic landscape of fitness: a Wright's diagram. These simple diagrams reproduce known relations between aging, Glioblastoma and Alzheimer's disease, and rise interesting questions like the possible connection between programmatic aging and Glioblastoma in this tissue. We anticipate that similar multiple diagrams in other tissues could be useful in the understanding of the biology of apparently unrelated diseases or disorders, and in the discovery of unexpected clues for their treatment.

Deciphering glioblastoma: Unveiling imaging markers for predicting MGMT promoter methylation status

Glioblastoma, the most common primary malignant tumor of the central nervous system in adults, is also among the most lethal. Despite a comprehensive treatment approach which utilizes surgery and postoperative chemoradiation, prognosis typically remains dismal. However certain epigenetic modifications, such as methylation of the MGMT promoter, have been proven to correlate with improved post-treatment outcomes. The 2021 WHO classification emphasizes molecular characteristics, highlighting shared genomic alterations across different grades and positioning MGMT methylation as a key influencer of outcomes. A combined diagnostic approach involving current imaging technology and emerging radiomics and deep learning models may allow for timely and accurate prediction of MGMT methylation status and therefore earlier and more individualized treatment and prognostication. Though these advanced radiomics models are rapidly emerging, additional development, standardization, and implementation may lead to a higher and more individualized level of patient care. This review explores the potential of imaging features in predicting MGMT promoter methylation, a critical determinant of therapeutic response and patient outcomes.

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.

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.

Multi-scale brain attributes contribute to the distribution of diffuse glioma subtypes

Gliomas are primary brain tumors and are among the most malignant types. Adult-type diffuse gliomas can be classified based on their histological and molecular signatures as IDH-wildtype glioblastoma, IDH-mutant astrocytoma, and IDH-mutant and 1p/19q-codeleted oligodendroglioma. Recent studies have shown that each subtype of glioma has its own specific distribution pattern. However, the mechanisms underlying the specific distributions of glioma subtypes are not entirely clear despite partial explanations such as cell origin. To investigate the impact of multi-scale brain attributes on glioma distribution, we constructed cumulative frequency maps for diffuse glioma subtypes based on T1w structural images and evaluated the spatial correlation between tumor frequency and diverse brain attributes, including postmortem gene expression, functional connectivity metrics, cerebral perfusion, glucose metabolism, and neurotransmitter signaling. Regression models were constructed to evaluate the contribution of these factors to the anatomic distribution of different glioma subtypes. Our findings revealed that the three different subtypes of gliomas had distinct distribution patterns, showing spatial preferences toward different brain environmental attributes. Glioblastomas were especially likely to occur in regions enriched with synapse-related pathways and diverse neurotransmitter receptors. Astrocytomas and oligodendrogliomas preferentially occurred in areas enriched with genes associated with neutrophil-mediated immune responses. The functional network characteristics and neurotransmitter distribution also contributed to oligodendroglioma distribution. Our results suggest that different brain transcriptomic, neurotransmitter, and connectomic attributes are the factors that determine the specific distributions of glioma subtypes. These findings highlight the importance of bridging diverse scales of biological organization when studying neurological dysfunction.

The Three Pillars of Glioblastoma: A Systematic Review and Novel Analysis of Multi-Omics and Clinical Data

Glioblastoma is the most fatal and common malignant brain tumor, excluding metastasis and with a median survival of approximately one year. While solid tumors benefit from newly approved drugs, immunotherapy, and prevention, none of these scenarios are opening for glioblastoma. The key to unlocking the peculiar features of glioblastoma is observing its molecular and anatomical features tightly entangled with the host's central nervous system (CNS). In June 2024, we searched the PUBMED electronic database. Data collection and analysis were conducted independently by two reviewers. Results: A total of 215 articles were identified, and 192 were excluded based on inclusion and exclusion criteria. The remaining 23 were used for collecting divergent molecular pathways and anatomical features of glioblastoma. The analysis of the selected papers revealed a multifaced tumor with extreme variability and cellular reprogramming that are observable within the same patient. All the variability of glioblastoma could be clustered into three pillars to dissect the physiology of the tumor: 1. necrotic core; 2. vascular proliferation; 3. CNS infiltration. These three pillars support glioblastoma survival, with a pivotal role of the neurovascular unit, as supported by the most recent paper published by experts in the field.

Glioma lateralization: Focus on the anatomical localization and the distribution of molecular alterations (Review)

It is well known how the precise localization of glioblastoma multiforme (GBM) predicts the direction of tumor spread in the surrounding neuronal structures. The aim of the present review is to reveal the lateralization of GBM by evaluating the anatomical regions where it is frequently located as well as the main molecular alterations observed in different brain regions. According to the literature, the precise or most frequent lateralization of GBM has yet to be determined. However, it can be said that GBM is more frequently observed in the frontal lobe. Tractus and fascicles involved in GBM appear to be focused on the corticospinal tract, superior longitudinal I, II and III fascicles, arcuate fascicle long segment, frontal strait tract, and inferior fronto‑occipital fasciculus. Considering the anatomical features of GBM and its brain involvement, it is logical that the main brain regions involved are the frontal‑temporal‑parietal‑occipital lobes, respectively. Although tumor volumes are higher in the right hemisphere, it has been determined that the prognosis of patients diagnosed with cancer in the left hemisphere is worse, probably reflecting the anatomical distribution of some detrimental alterations such as TP53 mutations, PTEN loss, EGFR amplification, and MGMT promoter methylation. There are theories stating that the right hemisphere is less exposed to external influences in its development as it is responsible for the functions necessary for survival while tumors in the left hemisphere may be more aggressive. To shed light on specific anatomical and molecular features of GBM in different brain regions, the present review article is aimed at describing the main lateralization pathways as well as gene mutations or epigenetic modifications associated with the development of brain tumors.

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.

The Relationship Between the Molecular Phenotypes of Brain Gliomas and the Imaging Features and Sensitivity of Radiotherapy and Chemotherapy

Gliomas are the most common primary malignant tumors of the brain, accounting for about 80% of all central nervous system malignancies. With the development of molecular biology, the molecular phenotypes of gliomas have been shown to be closely related to the process of diagnosis and treatment. The molecular phenotype of glioma also plays an important role in guiding treatment plans and evaluating treatment effects and prognosis. However, due to the heterogeneity of the tumors and the trauma associated with the surgical removal of tumor tissue, the application of molecular phenotyping in glioma is limited. With the development of imaging technology, functional magnetic resonance imaging (MRI) can provide structural and function information about tumors in a noninvasive and radiation-free manner. MRI is very important for the diagnosis of intracranial lesions. In recent years, with the development of the technology for tumor molecular diagnosis and imaging, the use of molecular phenotype information and imaging procedures to evaluate the treatment outcome of tumors has become a hot topic. By reviewing the related literature on glioma treatment and molecular typing that has been published in the past 20 years, and referring to the latest 2020 NCCN treatment guidelines, summarizing the imaging characteristic and sensitivity of radiotherapy and chemotherapy of different molecular phenotypes of glioma. In this article, we briefly review the imaging characteristics of different molecular phenotypes in gliomas and their relationship with radiosensitivity and chemosensitivity of gliomas.

Preoperative prediction of MGMT promoter methylation in glioblastoma based on multiregional and multi-sequence MRI radiomics analysis

O6-methylguanine-DNA methyltransferase (MGMT) has been demonstrated to be an important prognostic and predictive marker in glioblastoma (GBM). To establish a reliable radiomics model based on MRI data to predict the MGMT promoter methylation status of GBM. A total of 183 patients with glioblastoma were included in this retrospective study. The visually accessible Rembrandt images (VASARI) features were extracted for each patient, and a total of 14676 multi-region features were extracted from enhanced, necrotic, "non-enhanced, and edematous" areas on their multiparametric MRI. Twelve individual radiomics models were constructed based on the radiomics features from different subregions and different sequences. Four single-sequence models, three single-region models and the combined radiomics model combining all individual models were constructed. Finally, the predictive performance of adding clinical factors and VASARI characteristics was evaluated. The ComRad model combining all individual radiomics models exhibited the best performance in test set 1 and test set 2, with the area under the receiver operating characteristic curve (AUC) of 0.839 (0.709-0.963) and 0.739 (0.581-0.897), respectively. The results indicated that the radiomics model combining multi-region and multi-parametric MRI features has exhibited promising performance in predicting MGMT methylation status in GBM. The Modeling scheme that combining all individual radiomics models showed best performance among all constructed moels.

Genetic Prognostic Factors in Adult Diffuse Gliomas: A 10-Year Experience at a Single Institution

Gliomas are primary brain lesions involving cerebral structures without well-defined boundaries and constitute the most prevalent central nervous system (CNS) neoplasms. Among gliomas, glioblastoma (GB) is a glioma of the highest grade and is associated with a grim prognosis. We examined how clinical variables and molecular profiles may have affected overall survival (OS) over the past ten years. A retrospective study was conducted at Sina Hospital in Tehran, Iran and examined patients with confirmed glioma diagnoses between 2012 and 2020. We evaluated the correlation between OS in GB patients and sociodemographic as well as clinical factors and molecular profiling based on IDH1, O-6-Methylguanine-DNA Methyltransferase (MGMT), TERTp, and epidermal growth factor receptor (EGFR) amplification (EGFR-amp) status. Kaplan-Meier and multivariate Cox regression models were used to assess patient survival. A total of 178 patients were enrolled in the study. The median OS was 20 months, with a 2-year survival rate of 61.0%. Among the 127 patients with available IDH measurements, 100 (78.7%) exhibited mutated IDH1 (IDH1-mut) tumors. Of the 127 patients with assessed MGMT promoter methylation (MGMTp-met), 89 (70.1%) had MGMT methylated tumors. Mutant TERTp (TERTp-mut) was detected in 20 out of 127 cases (15.7%), while wildtype TERTp (wildtype TERTp-wt) was observed in 107 cases (84.3%). Analyses using multivariable models revealed that age at histological grade (p < 0.0001), adjuvant radiotherapy (p < 0.018), IDH1 status (p < 0.043), and TERT-p status (p < 0.014) were independently associated with OS. Our study demonstrates that patients with higher tumor histological grades who had received adjuvant radiotherapy exhibited IDH1-mut or presented with TERTp-wt experienced improved OS. Besides, an interesting finding showed an association between methylation of MGMTp and TERTp status with tumor location.

Unraveling glioblastoma diversity: Insights into methylation subtypes and spatial relationships

Background

The purpose of this study was to elucidate the relationship between distinct brain regions and molecular subtypes in glioblastoma (GB), focusing on integrating modern statistical tools and molecular profiling to better understand the heterogeneity of Isocitrate Dehydrogenase wild-type (IDH-wt) gliomas.

Methods

This retrospective study comprised 441 patients diagnosed with new IDH-wt glioma between 2009 and 2020 at Heidelberg University Hospital. The diagnostic process included preoperative magnetic resonance imaging and molecular characterization, encompassing IDH-status determination and subclassification, through DNA-methylation profiling. To discern and map distinct brain regions associated with specific methylation subtypes, a support-vector regression-based lesion-symptom mapping (SVR-LSM) was employed. Lesion maps were adjusted to 2 mm³ resolution. Significance was assessed with beta maps, using a threshold of P < .005, with 10 000 permutations and a cluster size minimum of 100 voxels.

Results

Of 441 initially screened glioma patients, 423 (95.9%) met the inclusion criteria. Following DNA-methylation profiling, patients were classified into RTK II (40.7%), MES (33.8%), RTK I (18%), and other methylation subclasses (7.6%). Between molecular subtypes, there was no difference in tumor volume. Using SVR-LSM, distinct brain regions correlated with each subclass were identified: MES subtypes were associated with left-hemispheric regions involving the superior temporal gyrus and insula cortex, RTK I with right frontal regions, and RTK II with 3 clusters in the left hemisphere.

Conclusions

This study linked molecular diversity and spatial features in glioblastomas using SVR-LSM. Future studies should validate these findings in larger, independent cohorts to confirm the observed patterns.

Coordinatized lesion location analysis empowering ROI-based radiomics diagnosis on brain gliomas

OBJECTIVES

To assess the value of coordinatized lesion location analysis (CLLA), in empowering ROI-based imaging diagnosis of gliomas by improving accuracy and generalization performances.

METHODS

In this retrospective study, pre-operative contrasted T1-weighted and T2-weighted MR images were obtained from patients with gliomas from three centers: Jinling Hospital, Tiantan Hospital, and the Cancer Genome Atlas Program. Based on CLLA and ROI-based radiomic analyses, a fusion location-radiomics model was constructed to predict tumor grades, isocitrate dehydrogenase (IDH) status, and overall survival (OS). An inter-site cross-validation strategy was used for assessing the performances of the fusion model on accuracy and generalization with the value of area under the curve (AUC) and delta accuracy (ACC) (ACCtesting-ACCtraining). Comparisons of diagnostic performances were performed between the fusion model and the other two models constructed with location and radiomics analysis using DeLong's test and Wilcoxon signed ranks test.

RESULTS

A total of 679 patients (mean age, 50 years ± 14 [standard deviation]; 388 men) were enrolled. Based on tumor location probabilistic maps, fusion location-radiomics models (averaged AUC values of grade/IDH/OS: 0.756/0.748/0.768) showed the highest accuracy in contrast to radiomics models (0.731/0.686/0.716) and location models (0.706/0.712/0.740). Notably, fusion models ([median Delta ACC: - 0.125, interquartile range: 0.130]) demonstrated improved generalization than that of radiomics model ([- 0.200, 0.195], p = 0.018).

CONCLUSIONS

CLLA could empower ROI-based radiomics diagnosis of gliomas by improving the accuracy and generalization of the models.

CLINICAL RELEVANCE STATEMENT

This study proposed a coordinatized lesion location analysis for glioma diagnosis, which could improve the performances of the conventional ROI-based radiomics model in accuracy and generalization.

KEY POINTS

• Using coordinatized lesion location analysis, we mapped anatomic distribution patterns of gliomas with specific pathological and clinical features and constructed glioma prediction models. • We integrated coordinatized lesion location analysis into ROI-based analysis of radiomics to propose new fusion location-radiomics models. • Fusion location-radiomics models, with the advantages of being less influenced by variabilities, improved accuracy, and generalization performances of ROI-based radiomics models on predicting the diagnosis of gliomas.

White Matter Tract Density Index Prediction Model of Overall Survival in Glioblastoma

Importance

The prognosis of overall survival (OS) in patients with glioblastoma (GBM) may depend on the underlying structural connectivity of the brain.

Objective

To examine the association between white matter tracts affected by GBM and patients' OS by means of a new tract density index (TDI).

Design, Setting, and Participants

This prognostic study in patients with a histopathologic diagnosis of GBM examined a discovery cohort of 112 patients who underwent surgery between February 1, 2015, and November 30, 2020 (follow-up to May 31, 2023), in Italy and 70 patients in a replicative cohort (n = 70) who underwent surgery between September 1, 2012, and November 30, 2015 (follow-up to May 31, 2023), in Germany. Statistical analyses were performed from June 1, 2021, to May 31, 2023. Thirteen and 12 patients were excluded from the discovery and the replicative sets, respectively, because of magnetic resonance imaging artifacts.

Exposure

The density of white matter tracts encompassing GBM.

Main Outcomes and Measures

Correlation, linear regression, Cox proportional hazards regression, Kaplan-Meier, and prediction analysis were used to assess the association between the TDI and OS. Results were compared with common prognostic factors of GBM, including age, performance status, O6-methylguanine-DNA methyltransferase methylation, and extent of surgery.

Results

In the discovery cohort (n = 99; mean [SD] age, 62.2 [11.5] years; 29 female [29.3%]; 70 male [70.7%]), the TDI was significantly correlated with OS (r = -0.34; P < .001). This association was more stable compared with other prognostic factors. The TDI showed a significant regression pattern (Cox: hazard ratio, 0.28 [95% CI, 0.02-0.55; P = .04]; linear: t = -2.366; P = .02). and a significant Kaplan-Meier stratification of patients as having lower or higher OS based on the TDI (log-rank test = 4.52; P = .03). Results were confirmed in the replicative cohort (n = 58; mean [SD] age, 58.5 [11.1] years, 14 female [24.1%]; 44 male [75.9%]). High (24-month cutoff) and low (18-month cutoff) OS was predicted based on the TDI computed in the discovery cohort (accuracy = 87%).

Conclusions and Relevance

In this study, GBMs encompassing regions with low white matter tract density were associated with longer OS. These findings indicate that the TDI is a reliable presurgical outcome predictor that may be considered in clinical trials and clinical practice. These findings support a framework in which the outcome of GBM depends on the patient's brain organization.

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.

Glioblastomas at the white matter of temporo-parietal junction cause a poor postoperative independence level

INTRODUCTION

Right cerebral hemispheric glioblastomas (GBMs) often decrease the Karnofsky performance status (KPS) score postoperatively, despite the patient having sufficient patient function while performing daily living. This study aimed to evaluate the factors that could cause poor KPS scores during the postoperative chronic phase in patients with right cerebral hemispheric GBMs.

METHODS

Data of 47 patients with newly diagnosed right cerebral hemispheric GBMs were analyzed. All patients were assessed preoperatively and 3 months postoperatively to determine KPS and brain function. To determine tumor location related to the postoperative KPS scores, we used voxel-based lesion symptom mapping (VLSM). The patients were divided into two groups (involvement and non-involvement groups) based on whether their lesion involved a significant region identified by VLSM. We then compared functional factors and prognosis between the groups using the chi-squared and log-rank tests, respectively.

RESULTS

The KPS score significantly decreased after surgery compared to that preoperatively measured (p = 0.023). VLSM revealed that tumors in the white matter of temporo-parietal junction (WM-TPJ) caused a significant decline in the KPS score at three months postoperatively. The patients in the involvement group had a higher probability of impaired attention, visuospatial cognition, emotion recognition, and visual field than did those in the non-involvement group. In addition, tumor in the WM-TPJ were associated with shorter progression-free survival and overall survival (p = 0.039 and 0.023, respectively).

CONCLUSIONS

GBMs involving the right WM-TPJ are more likely to result in poor postoperative KPS scores and prognoses. Impairments of several kinds of brain functions caused by tumor invasion to the WM-TPJ may be associated with lower KPS scores.

Extracellular vesicles in the glioblastoma microenvironment: A diagnostic and therapeutic perspective

Glioblastoma (GBM), is the most malignant form of gliomas and the most common and lethal primary brain tumor in adults. Conventional cancer treatments have limited to no efficacy on GBM. GBM cells respond and adapt to the surrounding brain parenchyma known as tumor microenvironment (TME) to promote tumor preservation. Among specific TME, there are 3 of particular interest for GBM biology: the perivascular niche, the subventricular zone neurogenic niche, and the immune microenvironment. GBM cells and TME cells present a reciprocal feedback which results in tumor maintenance. One way that these cells can communicate is through extracellular vesicles. These vesicles include exosomes and microvesicles that have the ability to carry both cancerous and non-cancerous cargo, such as miRNA, RNA, proteins, lipids, and DNA. In this review we will discuss the booming topic that is extracellular vesicles, and how they have the novelty to be a diagnostic and targetable vehicle for GBM.

Spatial distribution of supratentorial diffuse gliomas: A retrospective study of 990 cases

Background

Gliomas distribute unevenly in the supratentorial brain space. Many factors were linked to tumor locations. This study aims to describe a more detailed distributing pattern of these tumors with age and pathological factors concerned.

Methods

A consecutive series of 990 adult patients with newly-diagnosed supratentorial diffuse gliomas who underwent resection in Beijing Tiantan Hospital between January 2013 and January 2017 were retrospectively reviewed. For each patient, the anatomic locations were identified by the preoperative MRI, and the pathological subtypes were reviewed for histological grade and molecular status (if any) from his medical record. The MNI template was manually segmented to measure each anatomic location's volume, and its invaded ratio was then adjusted by the volume to calculate the frequency density. Factors of age and pathological subtypes were also compared among locations.

Results

The insulae, hippocampi, and corpus callosum were locations of the densest frequencies. The frequency density decreased from the anterior to posterior (frontal - motor region - sensory region - parietal - occipital), while the grade (p < 0.0001) and the proportion of IDH-wt (p < 0.0001) increased. More tumors invading the right basal ganglion were MGMT-mt (p = 0.0007), and more of those invading the left frontal were TERT-wt (p = 0.0256). Age varied among locations and pathological subtypes.

Conclusions

This study demonstrated more detailed spatial disproportions of supratentorial gliomas. There are potential interactions among age, pathological subtypes, and tumor locations.

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.

Prognostic Factors of Survival in Glioblastoma Multiforme Patients-A Retrospective Study

BACKGROUND

Glioblastoma multiforme (GBM) is the most aggressive brain tumor that occurs in adults. In spite of prompt diagnosis and rapidly administered treatment, the survival expectancy is tremendously poor. Extensive research has been performed in order to establish factors to predict the outcome of GBM patients; however, worldwide accepted prognostic markers are still lacking.

METHODS

We retrospectively assessed all adult patients who were diagnosed with primary GBM and underwent surgical treatment during a three-year period (January 2017-December 2019) in the Neurosurgery Department of the Emergency Clinical County Hospital of Târgu Mureș, Romania. Our aim was to find any statistically relevant connections between clinical, imagistic, and histopathological characteristics and patients' survival.

RESULTS

A total of 75 patients were eventually included in our statistical analysis: 40 males and 35 females, with a median age of 61 years. The mean tumor dimension was 45.28 ± 15.52 mm, while the mean survival rate was 4 ± 6.75 months. A univariate analysis demonstrated a statistically significant impact of tumor size, pre-, and postoperative KPSI on survival rate. In addition, a Cox multivariate assessment strengthened previous findings regarding postoperative KPSI (regression coefficient -0.03, HR 0.97, 95% CI (HR) 0.96-0.99, p = 0.002) as a favorable prognostic factor and GBM size (regression coefficient 0.03, HR 1.03, 95% CI (HR) 1.01-1.05, p = 0.005) as a poor prognostic marker for patients' survival.

CONCLUSIONS

The results of our retrospective study are consistent with prior scientific results that provide evidence supporting the importance of clinical (quantified by KPSI) and imagistic (particularly tumor dimensions) features as reliable prognostic factors in GBM patients' survival.

The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics

Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of: a) number of subjects, b) lack of consistent acquisition protocol, c) data quality, or d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute the "University of Pennsylvania Glioblastoma Imaging, Genomics, and Radiomics" (UPenn-GBM) dataset, which describes the currently largest publicly available comprehensive collection of 630 patients diagnosed with de novo glioblastoma. The UPenn-GBM dataset includes (a) advanced multi-parametric magnetic resonance imaging scans acquired during routine clinical practice, at the University of Pennsylvania Health System, (b) accompanying clinical, demographic, and molecular information, (d) perfusion and diffusion derivative volumes, (e) computationally-derived and manually-revised expert annotations of tumor sub-regions, as well as (f) quantitative imaging (also known as radiomic) features corresponding to each of these regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.

An Unusual Case of Glioblastoma Multiforme, Presenting as Skeletal Superscan

Extracranial metastases of glioblastoma multiforme (GBM) are very rare. The estimated incidence is <2%. We report a case of a 49-year-old woman, who was a known case of GBM in the left temporo-occipital lobe. She was operated and had received radiotherapy and adjuvant chemotherapy for the same. Subsequently, the patient underwent bone scan. On 99 m-Tc methylene diphosphonate (MDP) bone scan, homogenously increased tracer uptake was noted in the axial and appendicular skeletal system, suggesting metastatic skeletal superscan.

Olfactory sensory experience regulates gliomagenesis via neuronal IGF1

Animals constantly receive various sensory stimuli, such as odours, sounds, light and touch, from the surrounding environment. These sensory inputs are essential for animals to search for food and avoid predators, but they also affect their physiological status, and may cause diseases such as cancer. Malignant gliomas-the most lethal form of brain tumour¹-are known to intimately communicate with neurons at the cellular level^2,3. However, it remains unclear whether external sensory stimuli can directly affect the development of malignant glioma under normal living conditions. Here we show that olfaction can directly regulate gliomagenesis. In an autochthonous mouse model that recapitulates adult gliomagenesis^4-6 originating in oligodendrocyte precursor cells (OPCs), gliomas preferentially emerge in the olfactory bulb-the first relay of brain olfactory circuitry. Manipulating the activity of olfactory receptor neurons (ORNs) affects the development of glioma. Mechanistically, olfaction excites mitral and tufted (M/T) cells, which receive sensory information from ORNs and release insulin-like growth factor 1 (IGF1) in an activity-dependent manner. Specific knockout of Igf1 in M/T cells suppresses gliomagenesis. In addition, knocking out the IGF1 receptor in pre-cancerous mutant OPCs abolishes the ORN-activity-dependent mitogenic effects. Our findings establish a link between sensory experience and gliomagenesis through their corresponding sensory neuronal circuits.

Regional Development of Glioblastoma: The Anatomical Conundrum of Cancer Biology and Its Surgical Implication

Glioblastoma (GBM) are among the most common malignant central nervous system (CNS) cancers, they are relatively rare. This evidence suggests that the CNS microenvironment is naturally equipped to control proliferative cells, although, rarely, failure of this system can lead to cancer development. Moreover, the adult CNS is innately non-permissive to glioma cell invasion. Thus, glioma etiology remains largely unknown. In this review, we analyze the anatomical and biological basis of gliomagenesis considering neural stem cells, the spatiotemporal diversity of astrocytes, microglia, neurons and glutamate transporters, extracellular matrix and the peritumoral environment. The precise understanding of subpopulations constituting GBM, particularly astrocytes, is not limited to glioma stem cells (GSC) and could help in the understanding of tumor pathophysiology. The anatomical fingerprint is essential for non-invasive assessment of patients’ prognosis and correct surgical/radiotherapy planning.

Radiogenomic Predictors of Recurrence in Glioblastoma—A Systematic Review

Glioblastoma, as the most aggressive brain tumor, is associated with a poor prognosis and outcome. To optimize prognosis and clinical therapy decisions, there is an urgent need to stratify patients with increased risk for recurrent tumors and low therapeutic success to optimize individual treatment. Radiogenomics establishes a link between radiological and pathological information. This review provides a state-of-the-art picture illustrating the latest developments in the use of radiogenomic markers regarding prognosis and their potential for monitoring recurrence. Databases PubMed, Google Scholar, and Cochrane Library were searched. Inclusion criteria were defined as diagnosis of glioblastoma with histopathological and radiological follow-up. Out of 321 reviewed articles, 43 articles met these inclusion criteria. Included studies were analyzed for the frequency of radiological and molecular tumor markers whereby radiogenomic associations were analyzed. Six main associations were described: radiogenomic prognosis, MGMT status, IDH, EGFR status, molecular subgroups, and tumor location. Prospective studies analyzing prognostic features of glioblastoma together with radiological features are lacking. By reviewing the progress in the development of radiogenomic markers, we provide insights into the potential efficacy of such an approach for clinical routine use eventually enabling early identification of glioblastoma recurrence and therefore supporting a further personalized monitoring and treatment strategy.

Glioma invasion along white matter tracts: A dilemma for neurosurgeons

Invasive growth along white matter (WM) tracts is one of the most prominent clinicopathological features of glioma and is also an important reason for surgical treatment failure in glioma patients. A full understanding of relevant clinical features and mechanisms is of great significance for finding new therapeutic targets and developing new treatment regimens and strategies. Herein, we review the imaging and histological characteristics of glioma patients with WM tracts invasion and summarize the possible molecular mechanism. On this basis, we further discuss the correlation between glioma molecular typing, radiotherapy and tumor treating fields (TTFields) and the invasion of glioma along WM tracts.

MR Fingerprinting-A Radiogenomic Marker for Diffuse Gliomas

(1) Background: Advanced MR imaging (MRI) of brain tumors is mainly based on qualitative contrast images. MR Fingerprinting (MRF) offers a novel approach. The purpose of this study was to use MRF-derived T1 and T2 relaxation maps to differentiate diffuse gliomas according to isocitrate dehydrogenase (IDH) mutation. (2) Methods: Twenty-four patients with histologically verified diffuse gliomas (14 IDH-mutant, four 1p/19q-codeleted, 10 IDH-wildtype) were enrolled. MRF T1 and T2 relaxation times were compared to apparent diffusion coefficient (ADC), relative cerebral blood volume (rCBV) within solid tumor, peritumoral edema, and normal-appearing white matter (NAWM), using contrast-enhanced MRI, diffusion-, perfusion-, and susceptibility-weighted imaging. For perfusion imaging, a T2* weighted perfusion sequence with leakage correction was used. Correlations of MRF T1 and T2 times with two established conventional sequences for T1 and T2 mapping were assessed (a fast double inversion recovery-based MR sequence ('MP2RAGE') for T1 quantification and a multi-contrast spin echo-based sequence for T2 quantification). (3) Results: MRF T1 and T2 relaxation times were significantly higher in the IDH-mutant than in IDH-wildtype gliomas within the solid part of the tumor (p = 0.024 for MRF T1, p = 0.041 for MRF T2). MRF T1 and T2 relaxation times were significantly higher in the IDH-wildtype than in IDH-mutant gliomas within peritumoral edema less than or equal to 1cm adjacent to the tumor (p = 0.038 for MRF T1 mean, p = 0.010 for MRF T2 mean). In the solid part of the tumor, there was a high correlation between MRF and conventionally measured T1 and T2 values (r = 0.913, p < 0.001 for T1, r = 0.775, p < 0.001 for T2), as well as between MRF and ADC values (r = 0.813, p < 0.001 for T2, r = 0.697, p < 0.001 for T1). The correlation was weak between the MRF and rCBV values (r = -0.374, p = 0.005 for T2, r = -0.181, p = 0.181 for T1). (4) Conclusions: MRF enables fast, single-sequence based, multi-parametric, quantitative tissue characterization of diffuse gliomas and may have the potential to differentiate IDH-mutant from IDH-wildtype gliomas.

Radiomics for precision medicine in glioblastoma

INTRODUCTION

Being the most common primary brain tumor, glioblastoma presents as an extremely challenging malignancy to treat with dismal outcomes despite treatment. Varying molecular epidemiology of glioblastoma between patients and intra-tumoral heterogeneity explains the failure of current one-size-fits-all treatment modalities. Radiomics uses machine learning to identify salient features of the tumor on brain imaging and promises patient-specific management in glioblastoma patients.

METHODS

We performed a comprehensive review of the available literature on studies investigating the role of radiomics and radiogenomics models for the diagnosis, stratification, prognostication as well as treatment planning and monitoring of glioblastoma.

RESULTS

Classifiers based on a combination of various MRI sequences, genetic information and clinical data can predict non-invasive tumor diagnosis, overall survival and treatment response with reasonable accuracy. However, the use of radiomics for glioblastoma treatment remains in infancy as larger sample sizes, standardized image acquisition and data extraction techniques are needed to develop machine learning models that can be translated effectively into clinical practice.

CONCLUSION

Radiomics has the potential to transform the scope of glioblastoma management through personalized medicine.

New phase therapeutic pursuits for targeted drug delivery in glioblastoma multiforme

Glioblastoma multiforme (GBM) is known as the most aggressive and prevalent brain tumor with a high mortality rate. It is reported in people who are as young as 10 years old to as old as over 70 years old, exhibiting inter and intra tumor heterogeneity. There are several genomic and proteomic investigations that have been performed to find the unexplored potential targets of the drug against GBM. Therefore, certain effective targets have been taken to further validate the studies embarking on the robustness in the field of medicinal chemistry followed by testing in clinical trials. Also, The Cancer Genome Atlas (TCGA) project has identified certain overexpressed targets involved in the pathogenesis of GBM in three major pathways, i.e., tumor protein 53 (p53), retinoblastoma (RB), and receptor tyrosine kinase (RTK)/rat sarcoma virus (Ras)/phosphoinositide 3-kinase (PI3K) pathways. This review focuses on the compilation of recent developments in the fight against GBM thus, directing future research into the elucidation of pathogenesis and potential cure for GBM. Also, it highlights the potential biomarkers that have undergone extensive research and have promising prognostic and predictive values. Additionally, this manuscript analyses the advent of gene therapy and immunotherapy, unlocking the way to consider treatment approaches other than, or in addition to, conventional chemo-radiation therapies. This review study encompasses all the relevant research studies associated with the pathophysiology, occurrence, diagnostic tools, and therapeutic intervention for GBM. It highlights the evolution of various therapeutic perspectives against GBM from the most conventional form of radiotherapy to the recent advancement of gene/cell/immune therapy. Further, the review focuses on various targeted therapies for GBM including chemotherapy sensitization, radiotherapy, nanoparticles based, immunotherapy, cell therapy, and gene therapy which would offer a comprehensive account for exploring several facets related to GBM prognostics.

Lesion covariance networks reveal proposed origins and pathways of diffuse gliomas

Diffuse gliomas have been hypothesized to originate from neural stem cells in the subventricular zone and develop along previously healthy brain networks. Here, we evaluated these hypotheses by mapping independent sources of glioma localization and determining their relationships with neurogenic niches, genetic markers and large-scale connectivity networks. By applying independent component analysis to lesion data from 242 adult patients with high- and low-grade glioma, we identified three lesion covariance networks, which reflect clusters of frequent glioma localization. Replicability of the lesion covariance networks was assessed in an independent sample of 168 glioma patients. We related the lesion covariance networks to important clinical variables, including tumour grade and patient survival, as well as genomic information such as molecular genetic subtype and bulk transcriptomic profiles. Finally, we systematically cross-correlated the lesion covariance networks with structural and functional connectivity networks derived from neuroimaging data of over 4000 healthy UK BioBank participants to uncover intrinsic brain networks that may that underlie tumour development. The three lesion covariance networks overlapped with the anterior, posterior and inferior horns of the lateral ventricles respectively, extending into the frontal, parietal and temporal cortices. These locations were independently replicated. The first lesion covariance network, which overlapped with the anterior horn, was associated with low-grade, isocitrate dehydrogenase -mutated/1p19q-codeleted tumours, as well as a neural transcriptomic signature and improved overall survival. Each lesion covariance network significantly coincided with multiple structural and functional connectivity networks, with the first bearing an especially strong relationship with brain connectivity, consistent with its neural transcriptomic profile. Finally, we identified subcortical, periventricular structures with functional connectivity patterns to the cortex that significantly matched each lesion covariance network. In conclusion, we demonstrated replicable patterns of glioma localization with clinical relevance and spatial correspondence with large-scale functional and structural connectivity networks. These results are consistent with prior reports of glioma growth along white matter pathways, as well as evidence for the coordination of glioma stem cell proliferation by neuronal activity. Our findings describe how the locations of gliomas relate to their proposed subventricular origins, suggesting a model wherein periventricular brain connectivity guides tumour development.

Exposure of the eye lens and brain for interventional cardiology staff

INTRODUCTION

Occupational exposure to ionizing radiation for people working with an X-ray treatment unit is one of the highest in medicine. The epidemiological data analyzed by the International Commission on Radiological Protection (ICRP) indicate that the dose threshold for tissues located in the eye lens is or may be lower than previously thought. The new ICRP recommendations reduce the currently used threshold 7.5 times to the limit of 20 mSv per year.

AIM

To carry out measurements of equivalent doses for the lenses and scalps of cardiology interventional staff to determine the actual exposure.

MATERIAL AND METHODS

Personnel performing interventional cardiology procedures participated in the measurements. The measurements were performed using thermoluminescence dosimetry in two measurement periods. The operational quantities used in individual dosimetry were determined (dose equivalent for the scalp, dose equivalent for the eye lens). In both measurement periods, 69 operators and 12 nurses took part.

RESULTS

The maximum value of eye doses for cardiologists was 18.80 mSv per year, with a mean of 9.83 ±6.47 mSv/year (for all cases), 5.70 ±4.26 mSv/year (with safety glasses/headgear), 13.14 ±6.28 mSv/year (without safety glasses/headgear), and 6.28 ±1.76 mSv per year for the nurses. The values of brain doses fluctuate around 1 mSv per quarter.

CONCLUSIONS

Dose equivalents for the lenses of the eyes obtained by cardiologists may be close to or exceed the current dose limits.

On the cutting edge of glioblastoma surgery: where neurosurgeons agree and disagree on surgical decisions

OBJECTIVE

The aim of glioblastoma surgery is to maximize the extent of resection while preserving functional integrity. Standards are lacking for surgical decision-making, and previous studies indicate treatment variations. These shortcomings reflect the need to evaluate larger populations from different care teams. In this study, the authors used probability maps to quantify and compare surgical decision-making throughout the brain by 12 neurosurgical teams for patients with glioblastoma.

METHODS

The study included all adult patients who underwent first-time glioblastoma surgery in 2012-2013 and were treated by 1 of the 12 participating neurosurgical teams. Voxel-wise probability maps of tumor location, biopsy, and resection were constructed for each team to identify and compare patient treatment variations. Brain regions with different biopsy and resection results between teams were identified and analyzed for patient functional outcome and survival.

RESULTS

The study cohort consisted of 1087 patients, of whom 363 underwent a biopsy and 724 a resection. Biopsy and resection decisions were generally comparable between teams, providing benchmarks for probability maps of resections and biopsies for glioblastoma. Differences in biopsy rates were identified for the right superior frontal gyrus and indicated variation in biopsy decisions. Differences in resection rates were identified for the left superior parietal lobule, indicating variations in resection decisions.

CONCLUSIONS

Probability maps of glioblastoma surgery enabled capture of clinical practice decisions and indicated that teams generally agreed on which region to biopsy or to resect. However, treatment variations reflecting clinical dilemmas were observed and pinpointed by using the probability maps, which could therefore be useful for quality-of-care discussions between surgical teams for patients with glioblastoma.

Cortical and Subcortical Anatomy of the Orbitofrontal Cortex: A White Matter Microfiberdissection Study and Case Series

BACKGROUND

The literature on white matter anatomy underlying the human orbitofrontal cortex (OFC) is scarce in spite of its relevance for glioma surgery.

OBJECTIVE

To describe the anatomy of the OFC and of the underlying white matter fiber anatomy, with a particular focus on the surgical structures relevant for a safe and efficient orbitofrontal glioma resection. Based on anatomical and radiological data, the secondary objective was to describe the growth pattern of OFC gliomas.

METHODS

The study was performed on 10 brain specimens prepared according to Klingler's protocol and dissected using the fiber microdissection technique modified according to U.T., under the microscope at high magnification.

RESULTS

A detailed stratigraphy of the OFC was performed, from the cortex up to the frontal horn of the lateral ventricle. The interposed neural structures are described together with relevant neighboring topographic areas and nuclei. Combining anatomical and radiological data, it appears that the anatomical boundaries delimiting and guiding the macroscopical growth of OFC gliomas are as follows: the corpus callosum superiorly, the external capsule laterally, the basal forebrain and lentiform nucleus posteriorly, and the gyrus rectus medially. Thus, OFC gliomas seem to grow ventriculopetally, avoiding the laterally located neocortex.

CONCLUSION

The findings in our study supplement available anatomical knowledge of the OFC, providing reliable landmarks for a precise topographical diagnosis of OFC lesions and for perioperative orientation. The relationships between deep anatomic structures and glioma formations described in this study are relevant for surgery in this highly interconnected area.

Predicting BRAF V600E mutation in glioblastoma: utility of radiographic features

Detection of BRAF V600E mutation in glioblastomas (GBMs) is important because of potential therapeutic implications. Still, the relative paucity of these mutations makes molecular detection in all GBMs controversial. In the present study, we analyzed clinical, radiographic and pathologic features of 12 BRAF V600E-mutant GBMs and 12 matched controls from 2 institutions. We found that a majority of BRAF V600E-mutant GBMs displayed a combination of well-circumscribed lesions, large cystic components with thin walls and solid cortical component on MRI, but with some overlap with matched BRAF wildtype controls (p = 0.069). BRAF V600E-mutant GBMs were also apt to gross total resection (83% vs 17%, p = 0.016) and morphologically displayed epithelioid features (83% vs 0%, p < 0.0001). Identification of these clinical, radiographic, and pathologic characteristics should prompt testing for BRAF V600E in IDH-wildtype GBM.

Survival of glioblastoma in relation to tumor location: a statistical tumor atlas of a population-based cohort

Purpose

Previous studies on the effect of tumor location on overall survival in glioblastoma have found conflicting results. Based on statistical maps, we sought to explore the effect of tumor location on overall survival in a population-based cohort of patients with glioblastoma and IDH wild-type astrocytoma WHO grade II–III with radiological necrosis.

Methods

Patients were divided into three groups based on overall survival: < 6 months, 6–24 months, and > 24 months. Statistical maps exploring differences in tumor location between these three groups were calculated from pre-treatment magnetic resonance imaging scans. Based on the results, multivariable Cox regression analyses were performed to explore the possible independent effect of centrally located tumors compared to known prognostic factors by use of distance from center of the third ventricle to contrast-enhancing tumor border in centimeters as a continuous variable.

Results

A total of 215 patients were included in the statistical maps. Central tumor location (corpus callosum, basal ganglia) was associated with overall survival < 6 months. There was also a reduced overall survival in patients with tumors in the left temporal lobe pole. Tumors in the dorsomedial right temporal lobe and the white matter region involving the left anterior paracentral gyrus/dorsal supplementary motor area/medial precentral gyrus were associated with overall survival > 24 months. Increased distance from center of the third ventricle to contrast-enhancing tumor border was a positive prognostic factor for survival in elderly patients, but less so in younger patients.

Conclusions

Central tumor location was associated with worse prognosis. Distance from center of the third ventricle to contrast-enhancing tumor border may be a pragmatic prognostic factor in elderly patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00701-021-04802-6.

Identification of magnetic resonance imaging features for the prediction of molecular profiles of newly diagnosed glioblastoma

PURPOSE

We predicted molecular profiles in newly diagnosed glioblastoma patients using magnetic resonance (MR) imaging features and explored the associations between imaging features and major molecular alterations.

METHODS

This retrospective study included patients with newly diagnosed glioblastoma and available next-generation sequencing results. From preoperative MR imaging, Visually AcceSAble Rembrandt Images (VASARI) features, volumetric parameters, and apparent diffusion coefficient (ADC) values were obtained. First, univariate random forest was performed to identify gene abnormalities that could be predicted by imaging features with high accuracy and stability. Next, multivariate random forest was trained to predict the selected genes in the discovery cohort and was validated in the external cohort. Univariable logistic regression was performed to further explore the associations between imaging features and genes.

RESULTS

Univariate random forest identified nine genes predicted by imaging features, with high accuracy and stability. The multivariate random forest model showed excellent performance in predicting IDH and PTPN11 mutations in the discovery cohort, which were validated in the external validation cohorts (areas under the receiver operator characteristic curve [AUCs] of 0.855 for IDH and 0.88 for PTPN11). ATRX loss and EGFR mutation were predicted with AUCs of 0.753 and 0.739, respectively, whereas PTEN could not be reliably predicted. Based on univariable logistic regression analyses, IDH, ATRX, and TP53 were clustered according to their shared imaging features, whereas EGFR and CDKN2A/B were clustered in the opposite direction.

CONCLUSIONS

MR imaging features are related to specific molecular alterations and can be used to predict molecular profiles in patients with newly diagnosed glioblastoma.

MRI Imaging Characteristics of Glioblastoma with Concurrent Gain of Chromosomes 19 and 20

Glioblastoma (GBM) is the most common and deadly primary brain tumor in adults. Some of the genetic variations identified thus far, such as IDH mutation and MGMT promotor methylation, have implications for survival and response to therapy. A recent analysis of long-term GBM survivors showed that concurrent gain of chromosomes 19 and 20 (19/20 co-gain) is a positive prognostic factor that is independent of IDH mutation status. In this study, we retrospectively identified 18 patients with 19/20 co-gain and compared their imaging features to a control cohort without 19/20 co-gain. Imaging features such as tumor location, size, pial invasion, and ependymal extension were examined manually. When compared without further genetic subclassification, both groups showed similar imaging features except for rates of pial invasion. When each group was subclassified by MGMT promotor methylation status however, the two groups showed different imaging features in a number of additional ways including tumor location, size, and ependymal extension. Our results indicate that different permutations of various genetic mutations that coexist in GBM may interact in unpredictable ways to affect imaging appearance, and that imaging prognostication may be better approached in the context of the global genomic profile rather than individual genetic alterations.

Radiogenomics of Gliomas

The 2016 World Health Organization brain tumor classification is based on genomic and molecular profile of tumor tissue. These characteristics have improved understanding of the brain tumor and played an important role in treatment planning and prognostication. There is an ongoing effort to develop noninvasive imaging techniques that provide insight into tissue characteristics at the cellular and molecular levels. This article focuses on the molecular characteristics of gliomas, transcriptomic subtypes, and radiogenomic studies using semantic and radiomic features. The limitations and future directions of radiogenomics as a standalone diagnostic tool also are discussed.

"Real world" use of a highly reliable imaging sign: "T2-FLAIR mismatch" for identification of IDH mutant astrocytomas

AbstractThe T2-FLAIR (fluid attenuated inversion recovery) mismatch sign is an easily detectable imaging sign on routine clinical MRI studies that suggests diagnosis of isocitrate dehydrogenase (IDH)-mutant 1p/19q non-codeleted gliomas. Multiple independent studies show that the T2-FLAIR mismatch sign has near-perfect specificity, but low sensitivity for diagnosing IDH-mutant astrocytomas. Thus, the T2-FLAIR mismatch sign represents a non-invasive radiogenomic diagnostic finding with potential clinical impact. Recently, false positive cases have been reported, many related to variable application of the sign's imaging criteria and differences in image acquisition, as well as to differences in the included patient populations. Here we summarize the imaging criteria for the T2-FLAIR mismatch sign, review similarities and differences between the multiple validation studies, outline strategies to optimize its clinical use, and discuss potential opportunities to refine imaging criteria in order to maximize its impact in glioma diagnostics.