Advanced imaging in adult diffusely infiltrating low-grade gliomas

Quantitative susceptibility mapping evaluation of glioma

OBJECTIVES

To comprehensively evaluate the glioma using quantitative susceptibility mapping (QSM).

MATERIALS AND METHODS

Forty-two patients (18 women; mean age, 45 years) with pathologically confirmed gliomas were retrospectively included. All the patients underwent conventional and advanced MRI examinations (QSM, DWI, MRS, etc.). Five patients underwent paired QSM (pre- and post-enhancement). Four Visually Accessible Rembrandt Image (VASARI) features and intratumoural susceptibility signal (ITSS) were observed. Three ROIs each were manually drawn separately in the tumour parenchyma with relatively high and low magnetic susceptibility. The association between the tumour's magnetic susceptibility and other MRI parameters was also analysed.

RESULTS

Morphologically, gliomas with heterogeneous ITSS were more similar to high-grade gliomas (p = 0.006, AUC: 0.72, sensitivity: 70%, and specificity: 73%). Heterogeneous ITSS was significantly associated with tumour haemorrhage, necrosis, diffusion restriction, and avid enhancement but did not change between pre- and post-enhanced QSM. Quantitatively, tumour parenchyma magnetic susceptibility had limited value in grading gliomas and identifying IDH mutation status, whereas the relatively low magnetic susceptibility of the tumour parenchyma helped identify oligodendrogliomas in IDH mutated gliomas (AUC = 0.78) with high specificity (100%). The relatively high tumour magnetic susceptibility significantly increased after enhancement (p = 0.039). Additionally, we found that the magnetic susceptibility of the tumour parenchyma was significantly correlated with ADC (r = 0.61) and Cho/NAA (r = 0.40).

CONCLUSIONS

QSM is a promising candidate for the comprehensive evaluation of gliomas, except for IDH mutation status. The magnetic susceptibility of tumour parenchyma may be affected by tumour cell proliferation.

KEY POINTS

• Morphologically, gliomas with a heterogeneous intratumoural susceptibility signal (ITSS) are more similar to high-grade gliomas (p = 0.006; AUC, 0.72; sensitivity, 70%; and specificity, 73%). Heterogeneous ITSS was significantly associated with tumour haemorrhage, necrosis, diffusion restriction, and avid enhancement but did not change between pre- and post-enhanced QSM. • Tumour parenchyma's relatively low magnetic susceptibility helped identify oligodendroglioma with high specificity. • Tumour parenchyma magnetic susceptibility was significantly correlated with ADC (r = 0.61) and Cho/NAA (r = 0.40).

Disclosing transcriptomics network-based signatures of glioma heterogeneity using sparse methods

Gliomas are primary malignant brain tumors with poor survival and high resistance to available treatments. Improving the molecular understanding of glioma and disclosing novel biomarkers of tumor development and progression could help to find novel targeted therapies for this type of cancer. Public databases such as The Cancer Genome Atlas (TCGA) provide an invaluable source of molecular information on cancer tissues. Machine learning tools show promise in dealing with the high dimension of omics data and extracting relevant information from it. In this work, network inference and clustering methods, namely Joint Graphical lasso and Robust Sparse K-means Clustering, were applied to RNA-sequencing data from TCGA glioma patients to identify shared and distinct gene networks among different types of glioma (glioblastoma, astrocytoma, and oligodendroglioma) and disclose new patient groups and the relevant genes behind groups' separation. The results obtained suggest that astrocytoma and oligodendroglioma have more similarities compared with glioblastoma, highlighting the molecular differences between glioblastoma and the others glioma subtypes. After a comprehensive literature search on the relevant genes pointed our from our analysis, we identified potential candidates for biomarkers of glioma. Further molecular validation of these genes is encouraged to understand their potential role in diagnosis and in the design of novel therapies.

Update for astrocytomas: medical and surgical management considerations

Astrocytomas include a wide range of tumors with unique mutations and varying grades of malignancy. These tumors all originate from the astrocyte, a star-shaped glial cell that plays a major role in supporting functions of the central nervous system (CNS), including blood-brain barrier (BBB) development and maintenance, water and ion regulation, influencing neuronal synaptogenesis, and stimulating the immunological response. In terms of epidemiology, glioblastoma (GB), the most common and malignant astrocytoma, generally occur with higher rates in Australia, Western Europe, and Canada, with the lowest rates in Southeast Asia. Additionally, significantly higher rates of GB are observed in males and non-Hispanic whites. It has been suggested that higher levels of testosterone observed in biological males may account for the increased rates of GB. Hereditary syndromes such as Cowden, Lynch, Turcot, Li-Fraumeni, and neurofibromatosis type 1 have been linked to increased rates of astrocytoma development. While there are a number of specific gene mutations that may influence malignancy or be targeted in astrocytoma treatment, O 6-methylguanine-DNA methyltransferase (MGMT) gene function is an important predictor of astrocytoma response to chemotherapeutic agent temozolomide (TMZ). TMZ for primary and bevacizumab in the setting of recurrent tumor formation are two of the main chemotherapeutic agents currently approved in the treatment of astrocytomas. While stereotactic radiosurgery (SRS) has debatable implications for increased survival in comparison to whole-brain radiotherapy (WBRT), SRS demonstrates increased precision with reduced radiation toxicity. When considering surgical resection of astrocytoma, the extent of resection (EoR) is taken into consideration. Subtotal resection (STR) spares the margins of the T1 enhanced magnetic resonance imaging (MRI) region, gross total resection (GTR) includes the margins, and supramaximal resection (SMR) extends beyond the margin of the T1 and into the T2 region. Surgical resection, radiation, and chemotherapy are integral components of astrocytoma treatment.

Sex-specific radiomic features of L-[S-methyl-11C] methionine PET in patients with newly-diagnosed gliomas in relation to IDH1 predictability

Introduction

Amino-acid positron emission tomography (PET) is a validated metabolic imaging approach for the diagnostic work-up of gliomas. This study aimed to evaluate sex-specific radiomic characteristics of L-[S-methyl-¹¹Cmethionine (MET)-PET images of glioma patients in consideration of the prognostically relevant biomarker isocitrate dehydrogenase (IDH) mutation status.

Methods

MET-PET of 35 astrocytic gliomas (13 females, mean age 41 ± 13 yrs. and 22 males, mean age 46 ± 17 yrs.) and known IDH mutation status were included. All patients underwent radiomic analysis following imaging biomarker standardization initiative (IBSI)-conform guidelines both from standardized uptake value (SUV) and tumor-to-background ratio (TBR) PET values. Aligned Monte Carlo (MC) 100-fold split was utilized for SUV and TBR dataset pairs for both sex and IDH-specific analysis. Borderline and outlier scores were calculated for both sex and IDH-specific MC folds. Feature ranking was performed by R-squared ranking and Mann-Whitney U-test together with Bonferroni correction. Correlation of SUV and TBR radiomics in relation to IDH mutational status in male and female patients were also investigated.

Results

There were no significant features in either SUV or TBR radiomics to distinguish female and male patients. In contrast, intensity histogram coefficient of variation (ih.cov) and intensity skewness (stat.skew) were identified as significant to predict IDH +/-. In addition, IDH+ females had significant ih.cov deviation (0.031) and mean stat.skew (-0.327) differences compared to IDH+ male patients (0.068 and -0.123, respectively) with two-times higher standard deviations of the normal brain background MET uptake as well.

Discussion

We demonstrated that female and male glioma patients have significantly different radiomic profiles in MET PET imaging data. Future IDH prediction models shall not be built on mixed female-male cohorts, but shall rely on sex-specific cohorts and radiomic imaging biomarkers.

Prognostic Factors of Low-Grade Gliomas in Adults

Adult low-grade gliomas are a rare and aggressive pathology of the central nervous system. Some of their characteristics contribute to the patient's life expectancy and to their management. This study aimed to characterize and identify the main prognostic factors of low-grade gliomas. The six-year retrospective study statistically analyzed the demographic, imaging, and morphogenetic characteristics of the patient group through appropriate parameters. Immunohistochemical tests were performed: IDH1, Ki-67, p53, and Nestin, as well as FISH tests on the CDKN2A gene and 1p/19q codeletion. The pathology was prevalent in females, with patients having an average age of 56.31 years. The average tumor volume was 41.61 cm³, producing a midline shift with an average of 7.5 mm. Its displacement had a negative impact on survival. The presence of a residual tumor resulted in decreased survival and is an independent risk factor for mortality. Positivity for p53 identified a low survival rate. CDKN2A mutations were an independent risk factor for mortality. We identified that a negative prognosis is influenced by the association of epilepsy with headache, tumor volume, and immunoreactivity to IDH1 and p53. Independent factors associated with mortality were midline shift, presence of tumor residue, and CDKN2A gene deletions and amplifications.

Stratification by Tumor Grade Groups in a Holistic Evaluation of Machine Learning for Brain Tumor Segmentation

Accurate and consistent segmentation plays an important role in the diagnosis, treatment planning, and monitoring of both High Grade Glioma (HGG), including Glioblastoma Multiforme (GBM), and Low Grade Glioma (LGG). Accuracy of segmentation can be affected by the imaging presentation of glioma, which greatly varies between the two tumor grade groups. In recent years, researchers have used Machine Learning (ML) to segment tumor rapidly and consistently, as compared to manual segmentation. However, existing ML validation relies heavily on computing summary statistics and rarely tests the generalizability of an algorithm on clinically heterogeneous data. In this work, our goal is to investigate how to holistically evaluate the performance of ML algorithms on a brain tumor segmentation task. We address the need for rigorous evaluation of ML algorithms and present four axes of model evaluation-diagnostic performance, model confidence, robustness, and data quality. We perform a comprehensive evaluation of a glioma segmentation ML algorithm by stratifying data by specific tumor grade groups (GBM and LGG) and evaluate these algorithms on each of the four axes. The main takeaways of our work are-(1) ML algorithms need to be evaluated on out-of-distribution data to assess generalizability, reflective of tumor heterogeneity. (2) Segmentation metrics alone are limited to evaluate the errors made by ML algorithms and their describe their consequences. (3) Adoption of tools in other domains such as robustness (adversarial attacks) and model uncertainty (prediction intervals) lead to a more comprehensive performance evaluation. Such a holistic evaluation framework could shed light on an algorithm's clinical utility and help it evolve into a more clinically valuable tool.

A Multiparametric MRI-Based Radiomics Analysis to Efficiently Classify Tumor Subregions of Glioblastoma: A Pilot Study in Machine Learning

Glioblastoma multiforme (GBM) carries a poor prognosis and usually presents with heterogenous regions of a necrotic core, solid part, peritumoral tissue, and peritumoral edema. Accurate demarcation on magnetic resonance imaging (MRI) between the active tumor region and perifocal edematous extension is essential for planning stereotactic biopsy, GBM resection, and radiotherapy. We established a set of radiomics features to efficiently classify patients with GBM and retrieved cerebral multiparametric MRI, including contrast-enhanced T1-weighted (T1-CE), T2-weighted, T2-weighted fluid-attenuated inversion recovery, and apparent diffusion coefficient images from local patients with GBM. A total of 1316 features on the raw MR images were selected for each annotated area. A leave-one-out cross-validation was performed on the whole dataset, the different machine learning and deep learning techniques tested; random forest achieved the best performance (average accuracy: 93.6% necrosis, 90.4% solid part, 95.8% peritumoral tissue, and 90.4% peritumoral edema). The features from the enhancing tumor and the tumor shape elongation of peritumoral edema region for high-risk groups from T1-CE. The multiparametric MRI-based radiomics model showed the efficient classification of tumor subregions of GBM and suggests that prognostic radiomic features from a routine MRI exam may also be significantly associated with key biological processes that affect the response to chemotherapy in GBM.

Imaging Surveillance of Gliomas: Role of Basic and Advanced Imaging Techniques

It is essential to be aware of widely accepted criteria for grading of treatment response in both high-grade and low-grade gliomas. These criteria primarily take into account responses of measurable and nonmeasurable lesions on T2-weighted, fluid-attenuated inversion recovery, and postcontrast images to determine a final category of response for the patient. The additional role that other advanced imaging techniques, such as diffusion and perfusion imaging, can play in the surveillance of these tumors is discussed in this article.

Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors

Simple Summary

Advanced neuroimaging is gaining increasing relevance for the characterization and the molecular profiling of brain tumor tissue. On one hand, for some tumor types, the most widespread advanced techniques, investigating diffusion and perfusion features, have been proven clinically feasible and rather robust for diagnosis and prognosis stratification. In addition, 2-hydroxyglutarate spectroscopy, for the first time, offers the possibility to directly measure a crucial molecular marker. On the other hand, numerous innovative approaches have been explored for a refined evaluation of tumor microenvironments, particularly assessing microstructural and microvascular properties, and the potential applications of these techniques are vast and still to be fully explored.

Abstract

In recent years, the clinical assessment of primary brain tumors has been increasingly dependent on advanced magnetic resonance imaging (MRI) techniques in order to infer tumor pathophysiological characteristics, such as hemodynamics, metabolism, and microstructure. Quantitative radiomic data extracted from advanced MRI have risen as potential in vivo noninvasive biomarkers for predicting tumor grades and molecular subtypes, opening the era of “molecular imaging” and radiogenomics. This review presents the most relevant advancements in quantitative neuroimaging of advanced MRI techniques, by means of radiomics analysis, applied to primary brain tumors, including lower-grade glioma and glioblastoma, with a special focus on peculiar oncologic entities of current interest. Novel findings from diffusion MRI (dMRI), perfusion-weighted imaging (PWI), and MR spectroscopy (MRS) are hereby sifted in order to evaluate the role of quantitative imaging in neuro-oncology as a tool for predicting molecular profiles, stratifying prognosis, and characterizing tumor tissue microenvironments. Furthermore, innovative technological approaches are briefly addressed, including artificial intelligence contributions and ultra-high-field imaging new techniques. Lastly, after providing an overview of the advancements, we illustrate current clinical applications and future perspectives.

Magnetic Resonance Imaging Derived Biomarkers of IDH Mutation Status and Overall Survival in Grade III Astrocytomas

The evaluation of the isocitrate dehydrogenase (IDH) mutation status in the glioma decision-making process has diagnostic, prognostic and therapeutic implications. The aim of this study was to evaluate whether conventional magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) can noninvasively predict the most common IDH mutational status (R132H) in GIII-astrocytomas and the overall survival (OS). Hence, twenty-two patients (9-F, 13-M) with a histological diagnosis of GIII-astrocytoma and evaluation of IDH-mutation status (12-wild type, 10-mutant) were retrospectively evaluated. Imaging studies were reviewed for the morphological feature and mean ADC values (ADCm). Statistics included a Fisher’s exact test, Student’s t-test, Spearman’s Test and receiver operating characteristic analysis. A p ≤ 0.05 value was considered statistically significant for all the tests. A younger age and a frontal location were more likely related to mutational status. IDH-wild type (Wt) exhibited a slight enhancement (p = 0.039). The ADCm values in IDH-mutant (Mut) patients were higher than those of IDH-Wt patients (p < 0.0004). The value of ADC ≥ 0.99 × 10⁻³ mm²/s emerged as a “cut-off” to differentiate the mutation state. In the overall group, a positive relationship between the ADCm values and OS was detected (p = 0.003; r = 0.62). Adding quantitative measures of ADC values to conventional MR imaging could be used routinely as a noninvasive marker of specific molecular patterns.