Apparent diffusion coefficient for molecular subtyping of non-gadolinium-enhancing WHO grade II/III glioma: volumetric segmentation versus two-dimensional region of interest analysis

Diffusion imaging in gliomas: how ADC values forecast glioma genetics

Purpose

This study investigates the relationship between diffusion-weighted imaging (DWI) and mean apparent diffusion coefficient (ADC) values in predicting the genetic and molecular features of gliomas. The goal is to enhance non-invasive diagnostic methods and support personalised treatment strategies by clarifying the association between imaging biomarkers and tumour genotypes.

Material and methods

A total of 91 glioma patients treated between August 2023 and March 2024 were included in the analysis. All patients underwent preoperative magnetic resonance imaging (MRI), including DWI, and had available histopathological and genetic test results. Clinical data, tumour characteristics, and genetic markers such as IDH1 mutation, MGMT promoter methylation, EGFR amplification, TERT pathogenic variant, and CDKN2A deletion were collected. Statistical analysis was performed to identify correlations between ADC values, MRI perfusion parameters, and genetic characteristics.

Results

Significant associations were found between lower ADC values and aggressive tumour features, including IDH1-wildtype, MGMT unmethylated status, TERT pathogenic variant, and EGFR amplification. Additionally, distinct ADC patterns were observed in gliomas with CDKN2A, TP53, and PTEN gene deletions. These findings were further supported by contrast enhancement and other MRI parameters, indicating their role in tumour characterisation.

Conclusions

DWI and ADC measurements demonstrate strong potential as non-invasive tools for predicting glioma genetics. These imaging biomarkers can aid in tumour characterisation and provide valuable insights for guiding personalised treatment strategies.

Establishment and validation of a nomogram for predicting IDH-wildtype glioblastomas in nonenhancing adult-type diffuse gliomas

Background

Approximately 8.94%-44.44% of nonenhancing adult-type diffuse gliomas are identified as glioblastomas. Our purpose is to develop a nomogram that can predict glioblastomas from nonenhancing adult-type diffuse gliomas.

Methods

Nonenhancing adult-type diffuse gliomas were collected from Beijing Tiantan Hospital and TCIA public database. Univariate and multivariate logistic regression were performed to screen features on the training set. The features with P < .05 in multivariate logistic regression were used to establish the prediction model. The testing and validation sets were used to test the model.

Results

A total of 557 and 67 nonenhancing adult-type diffuse gliomas were collected from Beijing Tiantan Hospital and TCIA, respectively. The T2-FLAIR mismatch sign exhibited 100% specificity but low sensitivity (<30%) in ruling out glioblastoma. Age, tumor location, rADC(kurtosis), and rADC(median) were identified as independent predictors and employed for developing the prediction model. The AUC of the model was 0.901, 0.861, and 0.945 in the training, testing, and validation set, respectively. The best cutoff value of nomoscore was 138.5, which achieved sensitivity of 0.935, 0.714, and 0.895, specificity of 0.777, 0.782, and 0.8775 in the training, testing, and validation sets, respectively. Survival analysis shown that patients with nomoscore above 138.5 had significantly poorer survival time than those with scores below 138.5.

Conclusions

Positive T2-FLAIR mismatch sign can effectively rule out glioblastoma in nonenhancing adult-type diffuse gliomas with high specificity. Nonenhancing adult-type diffuse gliomas with nomoscore above 138.5 are highly suspicious for glioblastoma or nonglioblastoma with a poor prognosis.

Diffusion MRI is superior to quantitative T2-FLAIR mismatch in predicting molecular subtypes of human non-enhancing gliomas

PURPOSE

This study compared the classification performance of normalized apparent diffusion coefficient (nADC) with percentage T2-FLAIR mismatch-volume (%T2FM-volume) for differentiating between IDH-mutant astrocytoma (IDHm-A) and other glioma molecular subtypes.

METHODS

A total of 105 non-enhancing gliomas were studied. T2-FLAIR digital subtraction maps were used to identify T2FM and T2-FLAIR non-mismatch (T2FNM) subregions within tumor volumes of interest (VOIs). Median nADC from the whole tumor, T2FM, and T2NFM subregions and %T2FM-volume were obtained. IDHm-A classification analyses using receiver-operating characteristic curves and multiple logistic regression were performed in addition to exploratory survival analyses.

RESULTS

T2FM subregions had significantly higher nADC than T2FNM subregions within IDHm-A with ≥ 25% T2FM-volume (P < 0.0001). IDHm-A with ≥ 25% T2FM-volume demonstrated significantly higher whole tumor nADC compared to IDHm-A with < 25% T2FM-volume (P < 0.0001), and both IDHm-A subgroups demonstrated significantly higher nADC compared to IDH-mutant oligodendroglioma and IDH-wild-type gliomas (P < 0.05). For classification of IDHm-A vs. other gliomas, the area under curve (AUC) of nADC was significantly greater compared to the AUC of %T2FM-volume (P = 0.01, nADC AUC = 0.848, %T2FM-volume AUC = 0.714) along with greater sensitivity. In exploratory survival analyses within IDHm-A, %T2FM-volume was not associated with overall survival (P = 0.2), but there were non-significant trends for nADC (P = 0.07) and tumor volume (P = 0.051).

CONCLUSION

T2-FLAIR subtraction maps are useful for characterizing IDHm-A imaging characteristics. nADC outperforms %T2FM-volume for classifying IDHm-A amongst non-enhancing gliomas with preserved high specificity and increased sensitivity, which may be related to inherent diffusivity differences regardless of T2FM. In line with previous findings on visual T2FM-sign, quantitative %T2FM-volume may not be prognostic.

Evolution of Molecular Biomarkers and Precision Molecular Therapeutic Strategies in Glioblastoma

Glioblastoma is the most commonly occurring malignant brain tumor, with a high mortality rate despite current treatments. Its classification has evolved over the years to include not only histopathological features but also molecular findings. Given the heterogeneity of glioblastoma, molecular biomarkers for diagnosis have become essential for initiating treatment with current therapies, while new technologies for detecting specific variations using computational tools are being rapidly developed. Advances in molecular genetics have made possible the creation of tailored therapies based on specific molecular targets, with various degrees of success. This review provides an overview of the latest advances in the fields of histopathology and radiogenomics and the use of molecular markers for management of glioblastoma, as well as the development of new therapies targeting the most common molecular markers. Furthermore, we offer a summary of the results of recent preclinical and clinical trials to recognize the current trends of investigation and understand the possible future directions of molecular targeted therapies in glioblastoma.

Noninvasive prediction of BRAF V600E mutation status of pleomorphic xanthoastrocytomas with MRI morphologic features and diffusion-weighted imaging

OBJECTIVES

Seeking a noninvasive predictor for BRAF V600E mutation status of pleomorphic xanthoastrocytomas (PXAs) is essential for their prognoses and therapeutic use of BRAF inhibitors. We aimed to noninvasively diagnose BRAF V600E-mutated PXAs using MRI morphologic, DWI and clinical parameters.

METHODS

The clinical findings, anatomical MRI characteristics, and diffusion parameters of 36 pathologically confirmed PXAs were retrospectively analyzed, and BRAF V600E-mutated (n = 16) and wild-type (n = 20) groups were compared. A binary logistic-regression analysis was performed, and a ROC curve was calculated to determine the independent predictors of BRAF V600E mutation status, diagnostic accuracy, and optimal cut-off value.

RESULTS

A comparison of findings between groups showed that BRAF V600E-mutated PXAs were more frequent in children and young adults (≤ 35 years; P = 0.042) who often had histories of seizures (P = 0.004). Furthermore, BRAF V600E-mutated PXAs generally presented as solitary masses (P = 0.024), superficial locations with meningeal attachment (P < 0.001), predominantly cystic with mural nodules (P = 0.005), and had greater minimal ADC ratio (ADCratio) values of the tumor and peritumoral edema (P < 0.001). Binary logistic regression showed that age ≤ 35 years, solitary mass, superficial locations with meningeal attachment, and a greater minimal ADCratio of the tumor were independent predictors of BRAF V600E-mutated PXAs. The combination of all four independent predictors resulted in the highest sensitivity (100%) and specificity (90%), with AUC = 0.984.

CONCLUSION

The BRAF V600E mutation status of PXAs could be noninvasively predicted using clinical and MRI characteristics.

CRITICAL RELEVANCE STATEMENT

The noninvasive diagnostic criteria for BRAF V600E-mutated PXAs could offer guidance for the administration of BRAF V600E mutation inhibitors in the future.

MRI Scoring Systems for Predicting Isocitrate Dehydrogenase Mutation and Chromosome 1p/19q Codeletion in Adult-type Diffuse Glioma Lacking Contrast Enhancement

Background According to 2021 World Health Organization criteria, adult-type diffuse gliomas include glioblastoma, isocitrate dehydrogenase (IDH)-wildtype; oligodendroglioma, IDH-mutant and 1p/19q-codeleted; and astrocytoma, IDH-mutant, even when contrast enhancement is lacking. Purpose To develop and validate simple scoring systems for predicting IDH and subsequent 1p/19q codeletion status in gliomas without contrast enhancement using standard clinical MRI sequences. Materials and Methods This retrospective study included adult-type diffuse gliomas lacking contrast at contrast-enhanced MRI from two tertiary referral hospitals between January 2012 and April 2022 with diagnoses confirmed at pathology. IDH status was predicted primarily by using T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign, followed by 1p/19q codeletion prediction. A visual rating of MRI features, apparent diffusion coefficient (ADC) ratio, and relative cerebral blood volume was measured. Scoring systems were developed through univariable and multivariable logistic regressions and underwent calibration and discrimination, including internal and external validation. Results For the internal validation cohort, 237 patients were included (mean age, 44.4 years ± 14.4 [SD]; 136 male patients; 193 patients in IDH prediction and 163 patients in 1p/19q prediction). For the external validation cohort, 35 patients were included (46.1 years ± 15.3; 20 male patients; 28 patients in IDH prediction and 24 patients in 1p/19q prediction). The T2-FLAIR mismatch sign demonstrated 100% specificity and 100% positive predictive value for IDH mutation. IDH status prediction scoring system for tumors without mismatch sign included age, ADC ratio, and morphologic characteristics, whereas 1p/19q codeletion prediction for IDH-mutant gliomas included ADC ratio, cortical involvement, and mismatch sign. For IDH status and 1p/19q codeletion prediction, bootstrap-corrected areas under the receiver operating characteristic curve were 0.86 (95% CI: 0.81, 0.90) and 0.73 (95% CI: 0.65, 0.81), respectively, whereas at external validation they were 0.99 (95% CI: 0.98, 1.0) and 0.88 (95% CI: 0.63, 1.0). Conclusion The T2-FLAIR mismatch sign and scoring systems using standard clinical MRI predicted IDH and 1p/19q codeletion status in gliomas lacking contrast enhancement. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Badve and Hodges in this issue.

Standardized brain tumor imaging protocols for clinical trials: current recommendations and tips for integration

Standardized MRI acquisition protocols are crucial for reducing the measurement and interpretation variability associated with response assessment in brain tumor clinical trials. The main challenge is that standardized protocols should ensure high image quality while maximizing the number of institutions meeting the acquisition requirements. In recent years, extensive effort has been made by consensus groups to propose different "ideal" and "minimum requirements" brain tumor imaging protocols (BTIPs) for gliomas, brain metastases (BM), and primary central nervous system lymphomas (PCSNL). In clinical practice, BTIPs for clinical trials can be easily integrated with additional MRI sequences that may be desired for clinical patient management at individual sites. In this review, we summarize the general concepts behind the choice and timing of sequences included in the current recommended BTIPs, we provide a comparative overview, and discuss tips and caveats to integrate additional clinical or research sequences while preserving the recommended BTIPs. Finally, we also reflect on potential future directions for brain tumor imaging in clinical trials.

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

BACKGROUND AND PURPOSE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Evaluation of isocitrate dehydrogenase mutation in 2021 world health organization classification grade 3 and 4 glioma adult-type diffuse gliomas with 18F-fluoromisonidazole PET

PURPOSE

This study aimed to investigate the uptake characteristics of 18F-fluoromisonidazole (FMISO), in mutant-type isocitrate dehydrogenase (IDH-mutant, grade 3 and 4) and wild-type IDH (IDH-wildtype, grade 4) 2021 WHO classification adult-type diffuse gliomas.

MATERIALS AND METHODS

Patients with grade 3 and 4 adult-type diffuse gliomas (n = 35) were included in this prospective study. After registering 18F-FMISO PET and MR images, standardized uptake value (SUV) and apparent diffusion coefficient (ADC) were evaluated in hyperintense areas on fluid-attenuated inversion recovery (FLAIR) imaging (HIA), and in contrast-enhanced tumors (CET) by manually placing 3D volumes of interest. Relative SUVmax (rSUVmax) and SUVmean (rSUVmean), 10th percentile of ADC (ADC10pct), mean ADC (ADCmean) were measured in HIA and CET, respectively.

RESULTS

rSUVmean in HIA and rSUVmean in CET were significantly higher in IDH-wildtype than in IDH-mutant (P = 0.0496 and 0.03, respectively). The combination of FMISO rSUVmean in HIA and ADC10pct in CET, that of rSUVmax and ADC10pct in CET, that of rSUVmean in HIA and ADCmean in CET, were able to differentiate IDH-mutant from IDH-wildtype (AUC 0.80). When confined to astrocytic tumors except for oligodendroglioma, rSUVmax, rSUVmean in HIA and rSUVmean in CET were higher for IDH-wildtype than for IDH-mutant, but not significantly (P = 0.23, 0.13 and 0.14, respectively). The combination of FMISO rSUVmean in HIA and ADC10pct in CET was able to differentiate IDH-mutant (AUC 0.81).

CONCLUSION

PET using 18F-FMISO and ADC might provide a valuable tool for differentiating between IDH mutation status of 2021 WHO classification grade 3 and 4 adult-type diffuse gliomas.

Apparent Diffusion Coefficient as Imaging Biomarker for Identifying IDH Mutation, 1p19q Codeletion, and MGMT Promoter Methylation Status in Patients With Glioma

BACKGROUND

Glioma genotypes are of importance for clinical decision-making. This data can only be acquired through histopathological analysis based on resection or biopsy. Consequently, there is a need for alternative biomarkers that noninvasively provide reliable information for preoperatively identifying molecular characteristics.

PURPOSE

To investigate apparent diffusion coefficient (ADC) as imaging biomarker for preoperatively identifying glioma genotypes based on the 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors.

STUDY TYPE

Retrospective.

SUBJECTS

One hundred and fifty-nine patients (47.6 ± 14.4 years) diagnosed with WHO grade 2-4 glioma including 93 males and 66 females.

FIELD STRENGTH/SEQUENCE

A 3 T/spin echo echo planner imaging.

ASSESSMENT

The ADC measurements were assessed by two neuroradiologists (both with 6 years of experience). Three different lowest portions inside the tumors without overlap were manually drawn on the ADC maps as regions of interest (ROIs). The mean ADC value of the three ROIs was defined as the minimum ADC value (ADCmin ). An ROI was placed in the contralateral normal appearing white matter (NAWM) to obtain the ADC value (ADCNAWM ). The ADCmin to ADCNAWM ratio (ADCratio ) was calculated. Genetics results were retrospectively recorded from pathologic and genetic test reports.

STATISTICAL TESTS

Two-sample independent t-tests, receiver operating characteristic curve analysis, and intraclass correlation coefficient analysis were used. Statistical significance was set at P < 0.05.

RESULTS

Isocitrate dehydrogenase (IDH)-mutated glioma showed higher ADCmin and ADCratio than IDH wild-type glioma. Among IDH-mutated glioma, higher ADCmin and ADCratio were found in 1p19q intact glioma than in 1p19q codeletion glioma. ADC parameters enabled differentiation of IDH mutation status with area under the curve (AUC) of 0.84 and 0.86.

DATA CONCLUSION

ADC has potential discriminative value for IDH mutation and 1p19q codeletion status.

EVIDENCE LEVEL

3.

TECHNICAL EFFICACY

Stage 2.

A multi-reader comparison of normal-appearing white matter normalization techniques for perfusion and diffusion MRI in brain tumors

PURPOSE

There remains no consensus normal-appearing white matter (NAWM) normalization method to compute normalized relative cerebral blood volume (nrCBV) and apparent diffusion coefficient (nADC) in brain tumors. This reader study explored nrCBV and nADC differences using different NAWM normalization methods.

METHODS

Thirty-five newly diagnosed glioma patients were studied. For each patient, two readers created four NAWM regions of interests: (1) a single plane in the centrum semiovale (CSOp), (2) 3 spheres in the centrum semiovale (CSOs), (3) a single plane in the slice of the tumor center (TUMp), and (4) 3 spheres in the slice of the tumor center (TUMs). Readers repeated NAWM segmentations 1 month later. Differences in nrCBV and nADC of the FLAIR hyperintense tumor, inter-/intra-reader variability, and time to segment NAWM were assessed. As a validation step, the diagnostic performance of each method for IDH-status prediction was evaluated.

RESULTS

Both readers obtained significantly different nrCBV (P < .001), nADC (P < .001), and time to segment NAWM (P < .001) between the four normalization methods. nrCBV and nADC were significantly different between CSO and TUM methods, but not between planar and spherical methods in the same NAWM region. Broadly, CSO methods were quicker than TUM methods, and spherical methods were quicker than planar methods. For all normalization techniques, inter-reader reproducibility and intra-reader repeatability were excellent (intraclass correlation coefficient > 0.9), and the IDH-status predictive performance remained similar.

CONCLUSION

The selected NAWM region significantly impacts nrCBV and nADC values. CSO methods, particularly CSOs, may be preferred because of time reduction, similar reader variability, and similar diagnostic performance compared to TUM methods.

Clinical, Imaging and Neurogenetic Features of Patients with Gliomatosis Cerebri Referred to a Tertiary Neuro-Oncology Centre

INTRODUCTION

Gliomatosis cerebri describes a rare growth pattern of diffusely infiltrating glioma. The treatment options are limited and clinical outcomes remain poor. To characterise this population of patients, we examined referrals to a specialist brain tumour centre.

METHODS

We analysed demographic data, presenting symptoms, imaging, histology and genetics, and survival in individuals referred to a multidisciplinary team meeting over a 10-year period.

RESULTS

In total, 29 patients fulfilled the inclusion criteria with a median age of 64 years. The most common presenting symptoms were neuropsychiatric (31%), seizure (24%) or headache (21%). Of 20 patients with molecular data, 15 had IDH wild-type glioblastoma, with an IDH1 mutation most common in the remainder (5/20). The median length of survival from MDT referral to death was 48 weeks (IQR 23 to 70 weeks). Contrast enhancement patterns varied between and within tumours. In eight patients who had DSC perfusion studies, five (63%) had a measurable region of increased tumour perfusion with rCBV values ranging from 2.8 to 5.7. A minority of patients underwent MR spectroscopy with 2/3 (66.6%) false-negative results.

CONCLUSIONS

Gliomatosis imaging, histological and genetic findings are heterogeneous. Advanced imaging, including MR perfusion, could identify biopsy targets. Negative MR spectroscopy does not exclude the diagnosis of glioma.

Magnetic Resonance Imaging of Primary Adult Brain Tumors: State of the Art and Future Perspectives

MRI is undoubtedly the cornerstone of brain tumor imaging, playing a key role in all phases of patient management, starting from diagnosis, through therapy planning, to treatment response and/or recurrence assessment. Currently, neuroimaging can describe morphologic and non-morphologic (functional, hemodynamic, metabolic, cellular, microstructural, and sometimes even genetic) characteristics of brain tumors, greatly contributing to diagnosis and follow-up. Knowing the technical aspects, strength and limits of each MR technique is crucial to correctly interpret MR brain studies and to address clinicians to the best treatment strategy. This article aimed to provide an overview of neuroimaging in the assessment of adult primary brain tumors. We started from the basilar role of conventional/morphological MR sequences, then analyzed, one by one, the non-morphological techniques, and finally highlighted future perspectives, such as radiomics and artificial intelligence.

Conventional MRI-Derived Biomarkers of Adult-Type Diffuse Glioma Molecular Subtypes: A Comprehensive Review

The introduction of molecular criteria into the classification of diffuse gliomas has added interesting practical implications to glioma management. This has created a new clinical need for correlating imaging characteristics with glioma genotypes, also known as radiogenomics or imaging genomics. Although many studies have primarily focused on the use of advanced magnetic resonance imaging (MRI) techniques for radiogenomics purposes, conventional MRI sequences remain the reference point in the study and characterization of brain tumors. A summary of the conventional imaging features of glioma molecular subtypes should be useful as a tool for daily diagnostic brain tumor management. Hence, this article aims to summarize the conventional MRI features of glioma molecular subtypes in light of the recent literature.

Advanced Neuroimaging Approaches to Pediatric Brain Tumors

Simple Summary

After leukemias, brain tumors are the most common cancers in children, and early, accurate diagnosis is critical to improve patient outcomes. Beyond the conventional imaging methods of computed tomography (CT) and magnetic resonance imaging (MRI), advanced neuroimaging techniques capable of both structural and functional imaging are moving to the forefront to improve the early detection and differential diagnosis of tumors of the central nervous system. Here, we review recent developments in neuroimaging techniques for pediatric brain tumors.

Abstract

Central nervous system tumors are the most common pediatric solid tumors; they are also the most lethal. Unlike adults, childhood brain tumors are mostly primary in origin and differ in type, location and molecular signature. Tumor characteristics (incidence, location, and type) vary with age. Children present with a variety of symptoms, making early accurate diagnosis challenging. Neuroimaging is key in the initial diagnosis and monitoring of pediatric brain tumors. Conventional anatomic imaging approaches (computed tomography (CT) and magnetic resonance imaging (MRI)) are useful for tumor detection but have limited utility differentiating tumor types and grades. Advanced MRI techniques (diffusion-weighed imaging, diffusion tensor imaging, functional MRI, arterial spin labeling perfusion imaging, MR spectroscopy, and MR elastography) provide additional and improved structural and functional information. Combined with positron emission tomography (PET) and single-photon emission CT (SPECT), advanced techniques provide functional information on tumor metabolism and physiology through the use of radiotracer probes. Radiomics and radiogenomics offer promising insight into the prediction of tumor subtype, post-treatment response to treatment, and prognostication. In this paper, a brief review of pediatric brain cancers, by type, is provided with a comprehensive description of advanced imaging techniques including clinical applications that are currently utilized for the assessment and evaluation of pediatric brain tumors.

Integrated MRI–Immune–Genomic Features Enclose a Risk Stratification Model in Patients Affected by Glioblastoma

Simple Summary

Despite crucial scientific advances, Glioblastoma (GB) remains a fatal disease with limited therapeutic options and a lack of suitable biomarkers. The unveiled competence of the brain immune system together with the breakthrough advent of immunotherapy has shifted the present translational research on GB towards an immune-focused perspective. Several clinical trials targeting the immunosuppressive GB background are ongoing. So far, results are inconclusive, underpinning our partial understanding of the complex cancer-immune interplay in brain tumors. High throughput Magnetic Resonance (MR) imaging has shown the potential to decipher GB heterogeneity, including pathologic and genomic clues. However, whether distinct GB immune contextures can be deciphered at an imaging scale is still elusive, leaving unattained the non-invasive achievement of prognostic and predictive biomarkers. Along these lines, we integrated genetic, immunopathologic and imaging features in a series of GB patients. Our results suggest that multiparametric approaches might offer new efficient risk stratification models, opening the possibility to intercept the critical events implicated in the dismal prognosis of GB.

Abstract

Background: The aim of the present study was to dissect the clinical outcome of GB patients through the integration of molecular, immunophenotypic and MR imaging features. Methods: We enrolled 57 histologically proven and molecularly tested GB patients (5.3% IDH-1 mutant). Two-Dimensional Free ROI on the Biggest Enhancing Tumoral Diameter (TDFRBETD) acquired by MRI sequences were used to perform a manual evaluation of multiple quantitative variables, among which we selected: SD Fluid Attenuated Inversion Recovery (FLAIR), SD and mean Apparent Diffusion Coefficient (ADC). Characterization of the Tumor Immune Microenvironment (TIME) involved the immunohistochemical analysis of PD-L1, and number and distribution of CD3+, CD4+, CD8+ Tumor Infiltrating Lymphocytes (TILs) and CD163+ Tumor Associated Macrophages (TAMs), focusing on immune-vascular localization. Genetic, MR imaging and TIME descriptors were correlated with overall survival (OS). Results: MGMT methylation was associated with a significantly prolonged OS (median OS = 20 months), while no impact of p53 and EGFR status was apparent. GB cases with high mean ADC at MRI, indicative of low cellularity and soft consistency, exhibited increased OS (median OS = 24 months). PD-L1 and the overall number of TILs and CD163+TAMs had a marginal impact on patient outcome. Conversely, the density of vascular-associated (V) CD4+ lymphocytes emerged as the most significant prognostic factor (median OS = 23 months in V-CD4^high vs. 13 months in V-CD4^low, p = 0.015). High V-CD4+TILs also characterized TIME of MGMT^meth GB, while p53^mut appeared to condition a desert immune background. When individual genetic (MGMT^unmeth), MR imaging (mean ADC^low) and TIME (V-CD4+TILs^low) negative predictors were combined, median OS was 21 months (95% CI, 0–47.37) in patients displaying 0–1 risk factor and 13 months (95% CI 7.22–19.22) in the presence of 2–3 risk factors (p = 0.010, HR = 3.39, 95% CI 1.26–9.09). Conclusion: Interlacing MRI–immune–genetic features may provide highly significant risk-stratification models in GB patients.

CEST MRI provides amide/amine surrogate biomarkers for treatment-naïve glioma sub-typing

PURPOSE

Accurate glioma classification affects patient management and is challenging on non- or low-enhancing gliomas. This study investigated the clinical value of different chemical exchange saturation transfer (CEST) metrics for glioma classification and assessed the diagnostic effect of the presence of abundant fluid in glioma subpopulations.

METHODS

Forty-five treatment-naïve glioma patients with known isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status received CEST MRI (B1rms = 2μT, Tsat = 3.5 s) at 3 T. Magnetization transfer ratio asymmetry and CEST metrics (amides: offset range 3-4 ppm, amines: 1.5-2.5 ppm, amide/amine ratio) were calculated with two models: 'asymmetry-based' (AB) and 'fluid-suppressed' (FS). The presence of T2/FLAIR mismatch was noted.

RESULTS

IDH-wild type had higher amide/amine ratio than IDH-mutant_1p/19qcodel (p < 0.022). Amide/amine ratio and amine levels differentiated IDH-wild type from IDH-mutant (p < 0.0045) and from IDH-mutant_1p/19qret (p < 0.021). IDH-mutant_1p/19qret had higher amides and amines than IDH-mutant_1p/19qcodel (p < 0.035). IDH-mutant_1p/19qret with AB/FS mismatch had higher amines than IDH-mutant_1p/19qret without AB/FS mismatch ( < 0.016). In IDH-mutant_1p/19qret, the presence of AB/FS mismatch was closely related to the presence of T2/FLAIR mismatch (p = 0.014).

CONCLUSIONS

CEST-derived biomarkers for amides, amines, and their ratio can help with histomolecular staging in gliomas without intense contrast enhancement. T2/FLAIR mismatch is reflected in the presence of AB/FS CEST mismatch. The AB/FS CEST mismatch identifies glioma subgroups that may have prognostic and clinical relevance.

Standard clinical approaches and emerging modalities for glioblastoma imaging

Glioblastoma (GBM) is the most common primary adult intracranial malignancy and carries a dismal prognosis despite an aggressive multimodal treatment regimen that consists of surgical resection, radiation, and adjuvant chemotherapy. Radiographic evaluation, largely informed by magnetic resonance imaging (MRI), is a critical component of initial diagnosis, surgical planning, and post-treatment monitoring. However, conventional MRI does not provide information regarding tumor microvasculature, necrosis, or neoangiogenesis. In addition, traditional MRI imaging can be further confounded by treatment-related effects such as pseudoprogression, radiation necrosis, and/or pseudoresponse(s) that preclude clinicians from making fully informed decisions when structuring a therapeutic approach. A myriad of novel imaging modalities have been developed to address these deficits. Herein, we provide a clinically oriented review of standard techniques for imaging GBM and highlight emerging technologies utilized in disease characterization and therapeutic development.

The Use of 18F-FET-PET-MRI in Neuro-Oncology: The Best of Both Worlds—A Narrative Review

Gliomas are the most frequent primary tumors of the brain. They can be divided into grade II-IV astrocytomas and grade II-III oligodendrogliomas, based on their histomolecular profile. The prognosis and treatment is highly dependent on grade and well-identified prognostic and/or predictive molecular markers. Multi-parametric MRI, including diffusion weighted imaging, perfusion, and MR spectroscopy, showed increasing value in the non-invasive characterization of specific molecular subsets of gliomas. Radiolabeled amino-acid analogues, such as 18F-FET, have also been proven valuable in glioma imaging. These tracers not only contribute in the diagnostic process by detecting areas of dedifferentiation in diffuse gliomas, but this technique is also valuable in the follow-up of gliomas, as it can differentiate pseudo-progression from real tumor progression. Since multi-parametric MRI and 18F-FET PET are complementary imaging techniques, there may be a synergistic role for PET-MRI imaging in the neuro-oncological imaging of primary brain tumors. This could be of value for both primary staging, as well as during treatment and follow-up.

Prediction of IDH Mutation Status in High-grade Gliomas Using DWI and High T1-weight DSC-MRI

RATIONALE AND OBJECTIVES

We aimed to evaluate the diagnostic performance of diffusion-weighted imaging (DWI) and dynamic susceptibility contrast-enhanced (DSC) magnetic resonance imaging (MRI) parameters in the noninvasive prediction of the isocitrate dehydrogenase (IDH) mutation status in high-grade gliomas (HGGs).

MATERIALS AND METHODS

A total of 58 patients with histopathologically proved HGGs were included in this retrospective study. All patients underwent multiparametric MRI on 3-T, including DSC-MRI and DWI before surgery. The mean apparent diffusion coefficient (ADC), relative maximum cerebral blood volume (rCBV), and percentage signal recovery (PSR) of the tumor core were measured and compared depending on the IDH mutation status and tumor grade. The Mann-Whitney U test was used to detect statistically significant differences in parameters between IDH-mutant-type (IDH-m-type) and IDH-wild-type (IDH-w-type) HGGs. Receiver operating characteristic curve (ROC) analysis was performed to evaluate the diagnostic performance.

RESULTS

The rCBV was significantly higher, and the PSR value was significantly lower in IDH-w-type tumors than in the IDH-m group (p = 0.002 and <0.001, respectively).The ADC value in IDH-w-type tumors was significantly lower compared with the one in IDH-m types (p = 0.023), but remarkable overlaps were found between the groups. The PSR showed the best diagnostic performance with an AUC of 0.938 and with an accuracy rate of 0.87 at the optimal cutoff value of 86.85. The combination of the PSR and the rCBV for the identification of the IDH mutation status increased the discrimination ability at the AUC level of 0.955. In terms of each tumor grade, the PSR and rCBV showed significant differences between the IDH-m and IDH-w groups (p ≤0.001).

CONCLUSION

The rCBV and PSR from DSC-MRI may be feasible noninvasive imaging parameters for predicting the IDH mutation status in HGGs. The standardization of the imaging protocol is indispensable to the utility of DSC perfusion MRI in wider clinical usage.

T2-Fluid-Attenuated Inversion Recovery Mismatch Sign in Grade II and III Gliomas: Is There a Coexisting T2-Diffusion-Weighted Imaging Mismatch?

OBJECTIVE

To determine whether the T2 fluid-attenuated inversion recovery (T2-FLAIR) mismatch sign in diffuse gliomas is associated with an equivalent pattern of disparity in signal intensities when comparing T2- and diffusion-weighted imaging (DWI).

METHODS

The level of correspondence between T2-FLAIR and T2-DWI evaluations in 34 World Health Organization grade II/III gliomas and interreader agreement among 3 neuroradiologists were assessed by calculating intraclass correlation coefficient and κ statistics, respectively. Tumoral apparent diffusion coefficient values were compared using t test.

RESULTS

There was an almost perfect correspondence between the 2 mismatch signs (intraclass correlation coefficient = 0.824 [95% confidence interval, 0.68-0.91]) that were associated with higher mean tumoral apparent diffusion coefficient (P < 0.01). Interreader agreement was substantial for T2-FLAIR (Fleiss κ = 0.724) and moderate for T2-DWI comparisons (Fleiss κ = 0.589) (P < 0.001).

CONCLUSIONS

The T2-FLAIR mismatch sign is usually reflected by a distinct microstructural pattern on DWI. The management of this tumor subtype may benefit from specifically tailored imaging assessments.

Diffusion and perfusion imaging biomarkers of H3 K27M mutation status in diffuse midline gliomas

PURPOSE

H3K27M-mutant diffuse midline gliomas (M-DMGs) exhibit a clinically aggressive course. We studied diffusion-weighted imaging (DWI) and perfusion (PWI) MRI features of DMG with the hypothesis that DWI-PWI metrics can serve as biomarkers for the prediction of the H3K27M mutation status in DMGs.

METHODS

A retrospective review of the institutional database (imaging and histopathology) of patients with DMG (July 2016 to July 2020) was performed. Tumoral apparent diffusion coefficient (ADC) and peritumoral ADC (PT ADC) values and their normalized values (nADC and nPT ADC) were computed. Perfusion data were analyzed with manual arterial input function (AIF) and leakage correction (LC) Boxerman-Weiskoff models. Normalized maximum relative CBV (rCBV) was evaluated. Intergroup analysis of the imaging variables was done between M-DMGs and wild-type (WT-DMGs) groups.

RESULTS

Ninety-four cases (M-DMGs-n = 48 (51%) and WT-DMGs-n = 46(49%)) were included. Significantly lower PT ADC (mutant-1.1 ± 0.33, WT-1.23 ± 0.34; P = 0.033) and nPT ADC (mutant-1.64 ± 0.48, WT-1.83 ± 0.54; P = 0.040) were noted in the M-DMGs. The rCBV (mutant-25.17 ± 27.76, WT-13.73 ± 14.83; P = 0.018) and nrCBV (mutant-3.44 ± 2.16, WT-2.39 ± 1.25; P = 0.049) were significantly higher in the M-DMGs group. Among thalamic DMGs, the min ADC, PT ADC, and nADC and nPT ADC were lower in M-DMGs while nrCBV (corrected and uncorrected) was significantly higher. Receiver operator characteristic curve analysis demonstrated that PT ADC (cut-off-1.245), nPT ADC (cut-off-1.853), and nrCBV (cut-off-1.83) were significant independent predictors of H3K27M mutational status in DMGs.

CONCLUSION

DWI and PWI features hold value in preoperative prediction of H3K27M-mutation status in DMGs.

Using of Laplacian Re-decomposition image fusion algorithm for glioma grading with SWI, ADC, and FLAIR images

Introduction: Based on the tumor’s growth potential and aggressiveness, glioma is most often classified into low or high-grade groups. Traditionally, tissue sampling is used to determine the glioma grade. The aim of this study is to evaluate the efficiency of the Laplacian Re-decomposition (LRD) medical image fusion algorithm for glioma grading by advanced magnetic resonance imaging (MRI) images and introduce the best image combination for glioma grading.

Material and methods: Sixty-one patients (17 low-grade and 44 high-grade) underwent Susceptibility-weighted image (SWI), apparent diffusion coefficient (ADC) map, and Fluid attenuated inversion recovery (FLAIR) MRI imaging. To fuse different MRI image, LRD medical image fusion algorithm was used. To evaluate the effectiveness of LRD in the classification of glioma grade, we compared the parameters of the receiver operating characteristic curve (ROC).

Results: The average Relative Signal Contrast (RSC) of SWI and ADC maps in high-grade glioma are significantly lower than RSCs in low-grade glioma. No significant difference was detected between low and high-grade glioma on FLAIR images. In our study, the area under the curve (AUC) for low and high-grade glioma differentiation on SWI and ADC maps were calculated at 0.871 and 0.833, respectively.

Conclusions: By fusing SWI and ADC map with LRD medical image fusion algorithm, we can increase AUC for low and high-grade glioma separation to 0.978. Our work has led us to conclude that, by fusing SWI and ADC map with LRD medical image fusion algorithm, we reach the highest diagnostic accuracy for low and high-grade glioma differentiation and we can use LRD medical fusion algorithm for glioma grading.

Whole tumor volumetric ADC analysis: relationships with histopathological differentiation of hepatocellular carcinoma

PURPOSE

The aim of this study was to investigate the relationships between values obtained from whole tumor volumetric apparent diffusion coefficient (ADC) measurements and histopathological grade in patients with hepatocellular carcinoma (HCC).

METHODS

Fifty-one naïve patients with HCC were included in the study. The tumors were classified according to the Edmondson-Steiner grade and separated as well-differentiated and non-well-differentiated (moderately and poorly differentiated). The ADC parameters of groups were compared by applying Mann-Whitney U test. The correlation between tumors' histopathological stage and whole tumor ADC parameters was investigated using Spearman's Rank Correlation Coefficient. The receiver operating characteristic curve analysis (ROC) was applied to calculate the area under curve (AUC) with intersection point of ADC parameters and curve.

RESULTS

Mean and percentile ADC values of well-differentiated tumors were significantly higher than those of non-well-differentiated tumors (p < 0.05). The strongest correlation between histopathological grade and ADC parameters was 75th percentile ADC (r = - 0.501), 50th percentile ADC (r = - 0.476) and mean ADC (r = - 0.465). Mean, 75th and 50th percentile ADC values used for the distinction of groups gave the highest AUC at ROC analysis (0.781, 0.781, 0.767, respectively). When threshold values of mean, 75th and 50th percentile ADC values were applied (1516 mm²/s, 1194 mm²/s, and 1035 mm²/s) sensitivity was calculated as 0.73, 0.91, 0.83, respectively, and specificity was calculated as 0.82, 0.61, and 0.68, respectively.

CONCLUSIONS

A correlation between whole tumor volumetric ADC values and HCCs' histopathological grade was detected in this study. 75th percentile, 50th percentile and mean ADC values are determined as highly sensitive and specific tests when the threshold values are applied for distinguishing between well-differentiated tumors and moderately/poorly differentiated tumors. When all these findings are evaluated together, HCCs' volumetric ADC values might be a useful noninvasive predictive parameters for histopathological grade in patients with HCC.

Factors Associated With Dysfunction of Glymphatic System in Patients With Glioma

Objectives

Rodent experiments have provided some insights into the changes of glymphatic function associated with glioma growth. The diffusion tensor image analysis along the perivascular space (DTI-ALPS) method offers an opportunity for the noninvasive investigation of the glymphatic system in patients with glioma. We aimed to investigate the factors associated with glymphatic function changes in patients with glioma.

Materials and Methods

A total of 201 glioma patients (mean age = 47.4 years, 116 men; 86 grade II, 52 grade III, and 63 grade IV) who had preoperative diffusion tensor imaging for calculation of the ALPS index were retrospectively included. Information collected from each patient included sex, age, tumor grade, isocitrate dehydrogenase 1 (IDH1) mutation status, peritumoral brain edema volume, tumor volume, and ALPS index. Group differences in the ALPS index according to sex, tumor grade, and IDH1 mutation status were assessed using analysis of covariance with age adjustment. Linear regression analyses were performed to identify the factors associated with the ALPS index.

Results

Group comparisons revealed that the ALPS index of grade II/III gliomas was significantly higher than that of grade IV gliomas (p < 0.001). The ALPS index of IDH1 mutant gliomas was significantly higher than that of IDH1 wild-type gliomas (p < 0.001). On multivariable linear regression analysis, IDH1 mutation (β = 0.308, p < 0.001) and peritumoral brain edema volume (β = −0.353, p < 0.001) were the two independent factors associated with the ALPS index.

Conclusion

IDH1 wild-type gliomas and gliomas with larger peritumoral brain edema volumes were associated with a lower ALPS index, which may reflect impaired glymphatic function.

Imaging Advances for Central Nervous System Tumors

This article reviews recent advances in the use of standard and advanced imaging techniques for diagnosis and treatment of central nervous system (CNS) tumors, including glioma and brain metastasis. Following the recent transition from a histology-based approach in classifying CNS tumors to one that integrates histology with the molecular information of tumor, the approaches for imaging CNS tumors have also been adapted to this new framework. Some challenges related to the diagnosis and treatment of CNS tumors, such as differentiating tumor from treatment-related imaging changes, require further progress to implement advanced imaging for clinical use.

7-T Magnetic Resonance Imaging in the Management of Brain Tumors

This article provides an overview of the current status of ultrahigh-field 7-T magnetic resonance (MR) imaging in neuro-oncology, specifically for the management of patients with brain tumors. It includes a discussion of areas across the pretherapeutic, peritherapeutic, and posttherapeutic stages of patient care where 7-T MR imaging is currently being exploited and holds promise. This discussion includes existing technical challenges, barriers to clinical integration, as well as our impression of the future role of 7-T MR imaging as a clinical tool in neuro-oncology.

Joint Modeling of RNAseq and Radiomics Data for Glioma Molecular Characterization and Prediction

RNA sequencing (RNAseq) is a recent technology that profiles gene expression by measuring the relative frequency of the RNAseq reads. RNAseq read counts data is increasingly used in oncologic care and while radiology features (radiomics) have also been gaining utility in radiology practice such as disease diagnosis, monitoring, and treatment planning. However, contemporary literature lacks appropriate RNA-radiomics (henceforth, radiogenomics ) joint modeling where RNAseq distribution is adaptive and also preserves the nature of RNAseq read counts data for glioma grading and prediction. The Negative Binomial (NB) distribution may be useful to model RNAseq read counts data that addresses potential shortcomings. In this study, we propose a novel radiogenomics-NB model for glioma grading and prediction. Our radiogenomics-NB model is developed based on differentially expressed RNAseq and selected radiomics/volumetric features which characterize tumor volume and sub-regions. The NB distribution is fitted to RNAseq counts data, and a log-linear regression model is assumed to link between the estimated NB mean and radiomics. Three radiogenomics-NB molecular mutation models (e.g., IDH mutation, 1p/19q codeletion, and ATRX mutation) are investigated. Additionally, we explore gender-specific effects on the radiogenomics-NB models. Finally, we compare the performance of the proposed three mutation prediction radiogenomics-NB models with different well-known methods in the literature: Negative Binomial Linear Discriminant Analysis (NBLDA), differentially expressed RNAseq with Random Forest (RF-genomics), radiomics and differentially expressed RNAseq with Random Forest (RF-radiogenomics), and Voom-based count transformation combined with the nearest shrinkage classifier (VoomNSC). Our analysis shows that the proposed radiogenomics-NB model significantly outperforms (ANOVA test, p < 0.05) for prediction of IDH and ATRX mutations and offers similar performance for prediction of 1p/19q codeletion, when compared to the competing models in the literature, respectively.

BOLD Asynchrony Elucidates Tumor Burden in IDH-Mutated Gliomas

BACKGROUND

Gliomas comprise the most common type of primary brain tumor, are highly invasive, and often fatal. IDH-mutated gliomas are particularly challenging to image and there is currently no clinically accepted method for identifying the extent of tumor burden in these neoplasms. This uncertainty poses a challenge to clinicians who must balance the need to treat the tumor while sparing healthy brain from iatrogenic damage. The purpose of this study was to investigate the feasibility of using resting-state blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) to detect glioma-related asynchrony in vascular dynamics for distinguishing tumor from healthy brain.

METHODS

Twenty-four stereotactically localized biopsies were obtained during open surgical resection from ten treatment-naïve patients with IDH-mutated gliomas who received standard of care preoperative imaging as well as echo-planar resting-state BOLD fMRI. Signal intensity for BOLD asynchrony and standard of care imaging was compared to cell counts of total cellularity (H&E), tumor density (IDH1 & Sox2), cellular proliferation (Ki67), and neuronal density (NeuN), for each corresponding sample.

RESULTS

BOLD asynchrony was directly related to total cellularity (H&E, p = 4 x 10 -5), tumor density (IDH1, p = 4 x 10 -5; Sox2, p = 3 x 10 -5), cellular proliferation (Ki67, p = 0.002), and as well as inversely related to neuronal density (NeuN, p = 1 x 10 -4).

CONCLUSIONS

Asynchrony in vascular dynamics, as measured by resting-state BOLD fMRI, correlates with tumor burden and provides a radiographic delineation of tumor boundaries in IDH-mutated gliomas.

Predictive Role of the Apparent Diffusion Coefficient and MRI Morphologic Features on IDH Status in Patients With Diffuse Glioma: A Retrospective Cross-Sectional Study

Purpose

To evaluate isocitrate dehydrogenase (IDH) status in clinically diagnosed grade II~IV glioma patients using the 2016 World Health Organization (WHO) classification based on MRI parameters.

Materials and Methods

One hundred and seventy-six patients with confirmed WHO grade II~IV glioma were retrospectively investigated as the study set, including lower-grade glioma (WHO grade II, n = 64; WHO grade III, n = 38) and glioblastoma (WHO grade IV, n = 74). The minimum apparent diffusion coefficient (ADCmin) in the tumor and the contralateral normal-appearing white matter (ADCn) and the rADC (ADCmin to ADCn ratio) were defined and calculated. Intraclass correlation coefficient (ICC) analysis was carried out to evaluate interobserver and intraobserver agreement for the ADC measurements. Interobserver agreement for the morphologic categories was evaluated by Cohen’s kappa analysis. The nonparametric Kruskal-Wallis test was used to determine whether the ADC measurements and glioma subtypes were related. By univariable analysis, if the differences in a variable were significant (P<0.05) or an image feature had high consistency (ICC >0.8; κ >0.6), then it was chosen as a predictor variable. The performance of the area under the receiver operating characteristic curve (AUC) was evaluated using several machine learning models, including logistic regression, support vector machine, Naive Bayes and Ensemble. Five evaluation indicators were adopted to compare the models. The optimal model was developed as the final model to predict IDH status in 40 patients with glioma as the subsequent test set. DeLong analysis was used to compare significant differences in the AUCs.

Results

In the study set, six measured variables (rADC, age, enhancement, calcification, hemorrhage, and cystic change) were selected for the machine learning model. Logistic regression had better performance than other models. Two predictive models, model 1 (including all predictor variables) and model 2 (excluding calcification), correctly classified IDH status with an AUC of 0.897 and 0.890, respectively. The test set performed equally well in prediction, indicating the effectiveness of the trained classifier. The subgroup analysis revealed that the model predicted IDH status of LGG and GBM with accuracy of 84.3% (AUC = 0.873) and 85.1% (AUC = 0.862) in the study set, and with the accuracy of 70.0% (AUC = 0.762) and 70.0% (AUC = 0.833) in the test set, respectively.

Conclusion

Through the use of machine-learning algorithms, the accurate prediction of IDH-mutant versus IDH-wildtype was achieved for adult diffuse gliomas via noninvasive MR imaging characteristics, including ADC values and tumor morphologic features, which are considered widely available in most clinical workstations.

R2* value derived from multi-echo Dixon technique can aid discrimination between benign and malignant focal liver lesions

BACKGROUND

R2* estimation reflects the paramagnetism of the tumor tissue, which may be used to differentiate between benign and malignant liver lesions when contrast agents are contraindicated.

AIM

To investigate whether R2* derived from multi-echo Dixon imaging can aid differentiating benign from malignant focal liver lesions (FLLs) and the impact of 2D region of interest (2D-ROI) and volume of interest (VOI) on the outcomes.

METHODS

We retrospectively enrolled 73 patients with 108 benign or malignant FLLs. All patients underwent conventional abdominal magnetic resonance imaging and multi-echo Dixon imaging. Two radiologists independently measured the mean R2* values of lesions using 2D-ROI and VOI approaches. The Bland-Altman plot was used to determine the interobserver agreement between R2* measurements. Intraclass correlation coefficient (ICC) was used to determine the reliability between the two readers. Mean R2* values were compared between benign and malignant FFLs using the nonparametric Mann-Whitney test. Receiver operating characteristic curve analysis was used to determine the diagnostic performance of R2* in differentiation between benign and malignant FFLs. We compared the diagnostic performance of R2* measured by 2D-ROI and VOI approaches.

RESULTS

This study included 30 benign and 78 malignant FLLs. The interobserver reproducibility of R2* measurements was excellent for the 2D-ROI (ICC = 0.994) and VOI (ICC = 0.998) methods. Bland-Altman analysis also demonstrated excellent agreement. Mean R2* was significantly higher for malignant than benign FFLs as measured by 2D-ROI (P < 0.001) and VOI (P < 0.001). The area under the curve (AUC) of R2* measured by 2D-ROI was 0.884 at a cut-off of 25.2/s, with a sensitivity of 84.6% and specificity of 80.0% for differentiating benign from malignant FFLs. R2* measured by VOI yielded an AUC of 0.875 at a cut-off of 26.7/s in distinguishing benign from malignant FFLs, with a sensitivity of 85.9% and specificity of 76.7%. The AUCs of R2* were not significantly different between the 2D-ROI and VOI methods.

CONCLUSION

R2* derived from multi-echo Dixon imaging whether by 2D-ROI or VOI can aid in differentiation between benign and malignant FLLs.

The role of 11C-methionine PET in patients with negative diffusion-weighted magnetic resonance imaging: correlation with histology and molecular biomarkers in operated gliomas

OBJECTIVE

To compare 11C-methionine (11C-METH) PET with diffusion-weighted MRI (DWI-MRI) diagnostic accuracy and prognostic value in patients with glioma candidate to neurosurgery.

METHODS

We collected and analyzed data from 124 consecutive patients (n = 124) investigated during preoperative work-up. Both visual and semiquantitative parameters were utilized for image analysis. The reference standard was based on histopathology. The median follow-up was 14.3 months.

RESULTS

Overall, 47 high-grade gliomas (HGG) and 77 low-grade gliomas (LGG) were diagnosed. On visual assessment, sensitivity and specificity for differentiating HGG from LGG were 80.8 and 59.7% for DWI-MRI, versus 95.7 and 41.5% for 11C-METH PET, respectively. On semiquantitative analysis, the sensitivity, specificity, and area under the curve were 78.7, 71.4, and 80.4% for SUVmax, 78.7, 70.1, and 81.1% for SUVratio, and 74.5, 61, and 76.7% for MTB (metabolic tumor burden), respectively. In patients with negative DWI-MRI and IDH-wild type, SUVmax and SUVratio were higher compared to IDH-mutated (P = 0.025 and P = 0.01, respectively). In LGG, patients with 1p/19q codeletion showed higher SUVmax (P = 0.044). In all patients with negative DWI-MRI, median PFS was longer for SUVmax <3.9 (median not reached vs 34.2 months, P = 0.004), SUVratio <2.3 (median not reached vs 21.5 months, P < 0.001), and MTB <3.1 (median not reached vs 45.7 months, P = 0.05). In LGG patients with negative DWI-MRI, only SUVratio <2.3 and MTB <3.1 were associated with longer PFS (P = 0.016 and P = 0.024, respectively).

CONCLUSION

C-METH PET was found highly sensitive for glioma differentiation and molecular characterization. In DWI-negative patients, PET parameters correlated with molecular profile were associated with clinical outcome.

Regional and Volumetric Parameters for Diffusion-Weighted WHO Grade II and III Glioma Genotyping: A Method Comparison

BACKGROUND AND PURPOSE

Studies consistently report lower ADC values in isocitrate dehydrogenase (IDH) wild-type gliomas than in IDH mutant tumors, but their methods and thresholds vary. This research aimed to compare volumetric and regional ADC measurement techniques for glioma genotyping, with a focus on IDH status prediction.

MATERIALS AND METHODS

Treatment-naïve World Health Organization grade II and III gliomas were analyzed by 3 neuroradiologist readers blinded to tissue results. ADC minimum and mean ROIs were defined in tumor and in normal-appearing white matter to calculate normalized values. T2-weighted tumor VOIs were registered to ADC maps with histogram parameters (mean, 2nd and 5th percentiles) extracted. Nonparametric testing (eta² and ANOVA) was performed to identify associations between ADC metrics and glioma genotypes. Logistic regression was used to probe the ability of VOI and ROI metrics to predict IDH status.

RESULTS

The study included 283 patients with 79 IDH wild-type and 204 IDH mutant gliomas. Across the study population, IDH status was most accurately predicted by ROI mean normalized ADC and VOI mean normalized ADC, with areas under the curve of 0.83 and 0.82, respectively_. The results for ROI-based genotyping of nonenhancing and solid-patchy enhancing gliomas were comparable with volumetric parameters (area under the curve = 0.81-0.84). In rim-enhancing, centrally necrotic tumors (n = 23), only volumetric measurements were predictive (0.90).

CONCLUSIONS

Regional normalized mean ADC measurements are noninferior to volumetric segmentation for defining solid glioma IDH status. Partially necrotic, rim-enhancing tumors are unsuitable for ROI assessment and may benefit from volumetric ADC quantification.

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.

Imaging characteristics of H3 K27M histone-mutant diffuse midline glioma in teenagers and adults

Background

To assess anatomical and quantitative diffusion-weighted MR imaging features in a recently classified lethal neoplasm, H3 K27M histone-mutant diffuse midline glioma [World Health Organization (WHO) IV].

Methods

Fifteen untreated gliomas in teenagers and adults (median age 19, range, 14-64) with confirmed H3 K27M histone-mutant genotype were analysed at a national referral centre. Morphological characteristics including tumour epicentre(s), T2/FLAIR and Gadolinium enhancement patterns, calcification, haemorrhage and cyst formation were recorded. Multiple apparent diffusion coefficient (ADC_min, ADC_mean) regions of interest were sited in solid tumour and normal appearing white matter (ADC_NAWM) using post-processing software (Olea Sphere v2.3, Olea Medical). ADC histogram data (2^nd, 5^th, 10^th percentile, median, mean, kurtosis, skewness) were calculated from volumetric tumour segmentations and tested against the regions of interest (ROI) data (Wilcoxon signed rank test).

Results

The median interval from imaging to tissue diagnosis was 9 (range, 0-74) days. The structural MR imaging findings varied between individuals and within tumours, often featuring signal heterogeneity on all MR sequences. All gliomas demonstrated contact with the brain midline, and 67% exhibited rim-enhancing necrosis. The mean ROI ADC_min value was 0.84 (±0.15 standard deviation, SD) ×10^-3 mm²/s. In the largest tumour cross-section (excluding necrosis), an average ADC_mean value of 1.12 (±0.25)×10^-3 mm²/s was observed. The mean ADC_min/NAWM ratio was 1.097 (±0.149), and the mean ADC_mean/NAWM ratio measured 1.466 (±0.299). With the exception of the 2^nd centile, no statistical difference was observed between the regional and histogram derived ADC results.

Conclusions

H3 K27M-mutant gliomas demonstrate variable morphology and diffusivity, commonly featuring moderately low ADC values in solid tumour. Regional ADC measurements appeared representative of volumetric histogram data in this study.

Combined T2 SPAIR, Dynamic Enhancement and DW Imaging Reliably Detect T Staging and Grading of Bladder Cancer With 3.0T MRI

Objectives

To evaluate bladder cancer by integrating multiple imaging features acquired using multimodal 3.0T magnetic resonance imaging (MRI).

Methods

We prospectively enrolled 163 consecutive patients including 142 men (mean age, 65.2 years) and 21 women (mean age, 65.8 years). We evaluated the efficiency and reliability of the multiple imaging modalities including T2-weighted spectral attenuated inversion recovery (SPAIR) imaging, dynamic contrast-enhanced (DCE) imaging and diffusion-weighted (DW) imaging, and the imaging feature, apparent diffusion coefficient (ADC) in the identification of the T staging and grading. We compared our imaging findings with the results of histological examination using McNemar’s test. We reported the results under the significance of p < 0.05. Approval for the study was obtained from the local institutional review board.

Results

The sensitivity and specificity using T2 SPAIR plus DW imaging (sensitivity: 85.2%; specificity: 93.2%), DCE plus DW imaging (sensitivity: 92.4%; specificity: 96.8%), and all the three imaging modalities combined, i.e., T2 SPAIR plus DCE plus DW imaging (sensitivity: 92.5%; specificity: 97.4%), were significantly greater than using T2 SPAIR imaging alone (sensitivity: 74.1%; specificity: 72.2%). One hundred six (93.0%) lesions showed a thin, pedicle arch-like shape and thus primarily demonstrated to be in Ta stage; by contrast, a large number of lesions (137 [85.6%]) were sessile and were found to be in T1 stage. The differences in the ADC were significant between low-grade (877.57 ± 24.15) and high-grade (699.54 ± 23.82) lesions (P < .01).

Conclusions

T2 SPAIR and DCE plus DW imaging provided useful information for evaluating T staging and grading in bladder cancer. Those imaging features to distinguish Ta stage from T1 stage were presented.

Neuro-Oncology and Radiogenomics: Time to Integrate?

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

Thin-Slice Magnetic Resonance Imaging-Based Radiomics Signature Predicts Chromosomal 1p/19q Co-deletion Status in Grade II and III Gliomas

Objective: Chromosomal 1p/19q co-deletion is recognized as a diagnostic, prognostic, and predictive biomarker in lower grade glioma (LGG). This study aims to construct a radiomics signature to non-invasively predict the 1p/19q co-deletion status in LGG.

Methods: Ninety-six patients with pathology-confirmed LGG were retrospectively included and randomly assigned into training (n = 78) and validation (n = 18) dataset. Three-dimensional contrast-enhanced T1 (3D-CE-T1)-weighted magnetic resonance (MR) images and T2-weighted MR images were acquired, and simulated-conventional contrast-enhanced T1 (SC-CE-T1)-weighted images were generated. One hundred and seven shape, first-order, and texture radiomics features were extracted from each imaging modality and selected using the least absolute shrinkage and selection operator on the training dataset. A 3D-radiomics signature based on 3D-CE-T1 and T2-weighted features and a simulated-conventional (SC) radiomics signature based on SC-CE-T1 and T2-weighted features were established using random forest. The radiomics signatures were validated independently and evaluated using receiver operating characteristic (ROC) curves. Tumors with IDH mutations were also separately assessed.

Results: Four radiomics features were selected to construct the 3D-radiomics signature and displayed accuracies of 0.897 and 0.833, areas under the ROC curves (AUCs) of 0.940 and 0.889 in the training and validation datasets, respectively. The SC-radiomics signature was constructed with 4 features, but the AUC values were lower than that of the 3D signature. In the IDH-mutated subgroup, the 3D-radiomics signature presented AUCs of 0.950–1.000.

Conclusions: The MRI-based radiomics signature can differentiate 1p/19q co-deletion status in LGG with or without predetermined IDH status. 3D-CE-T1-weighted radiomics features are more favorable than SC-CE-T1-weighted features in the establishment of radiomics signatures.

A Comparative Study of 2 Different Segmentation Methods of ADC Histogram for Differentiation Genetic Subtypes in Lower-Grade Diffuse Gliomas

Background

To evaluate the diagnostic performance of apparent diffusion coefficient (ADC) histogram parameters for differentiating the genetic subtypes in lower-grade diffuse gliomas and explore which segmentation method (ROI-1, the entire tumor ROI; ROI2, the tumor ROI excluding cystic and necrotic portions) performs better.

Materials and Methods

We retrospectively evaluated 56 lower-grade diffuse gliomas and divided them into three categories: IDH-wild group (IDH^wt, 16cases); IDH mutant with the intact 1p or 19q group (IDH^mut/1p19q⁺, 18cases); and IDH mutant with the 1p/19q codeleted group (IDH^mut/1p19q^-, 22cases). Histogram parameters of ADC maps calculated with the two different ROI methods: ADCmean, min, max, mode, P5, P10, P25, P75, P90, P95, kurtosis, skewness, entropy, StDev, and inhomogenity were compared between these categories using the independent t test or Mann-Whitney U test. For statistically significant results, a receiver operating characteristic (ROC) curves were constructed, and the optimal cutoff value was determined by maximizing Youden's index. Area under the curve (AUC) results were compared using the method of Delong et al.

Results

The inhomogenity from the two different ROI methods for distinguishing IDH^wt gliomas from IDH^mut gliomas both showed the biggest AUC (0.788, 0.930), the optimal cutoff value was 0.229 (sensitivity, 81.3%; specificity, 75.0%) for the ROI-1 and 0.186 (sensitivity, 93.8%; specificity, 82.5%) for the ROI-2, and the AUC of the inhomogenity from the ROI-2 was significantly larger than that from another segmentation, but no significant differences were identified between the AUCs of other same parameters from the two different ROI methods. For the differentiaiton of IDH^mut/1p19q^- tumors and IDH^mut/1p19q⁺ tumors, with the ROI-1, the ADCmode showed the biggest AUC (AUC: 0.784; sensitivity, 61.1%; specificity, 90.9%), with the ROI-2, and the skewness performed best (AUC, 0.821; sensitivity, 81.8%; specificity, 77.8%), but no significant differences were identified between the AUCs of the same parameters from the two different ROI methods.

Conclusion

ADC values analyzed by the histogram method could help to classify the genetic subtypes in lower-grade diffuse gliomas, no matter which ROI method was used. Extracting cystic and necrotic portions from the entire tumor lesions is preferable for evaluating the difference of the intratumoral heterogeneity and classifying IDH-wild tumors, but not significantly beneficial to predicting the 1p19q genotype in the lower-grade gliomas.

Clinical Management of Diffuse Low-Grade Gliomas

Diffuse low-grade gliomas (LGG) represent a heterogeneous group of primary brain tumors arising from supporting glial cells and usually affecting young adults. Advances in the knowledge of molecular profile of these tumors, including mutations in the isocitrate dehydrogenase genes, or 1p/19q codeletion, and in neuroradiological techniques have contributed to the diagnosis, prognostic stratification, and follow-up of these tumors. Optimal post-operative management of LGG is still controversial, though radiation therapy and chemotherapy remain the optimal treatments after surgical resection in selected patients. In this review, we report the most important and recent research on clinical and molecular features, new neuroradiological techniques, the different therapeutic modalities, and new opportunities for personalized targeted therapy and supportive care.

Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign

PURPOSE

The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG.

METHODS

Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × 10^-3mm²/s (V_ADC>1.5), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach.

RESULTS

V_ADC>1.5 classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with V_ADC>1.5 ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign-all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited V_ADC>1.5 ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign.

CONCLUSION

Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with V_ADC>1.5 achieving > 90% classification specificity in both internal and validation cohorts. V_ADC>1.5 exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG.

Conventional MRI features of adult diffuse glioma molecular subtypes: a systematic review

PURPOSE

Molecular parameters have become integral to glioma diagnosis. Much of radiogenomics research has focused on the use of advanced MRI techniques, but conventional MRI sequences remain the mainstay of clinical assessments. The aim of this research was to synthesize the current published data on the accuracy of standard clinical MRI for diffuse glioma genotyping, specifically targeting IDH and 1p19q status.

METHODS

A systematic search was performed in September 2019 using PubMed and the Cochrane Library, identifying studies on the diagnostic value of T1 pre-/post-contrast, T2, FLAIR, T2*/SWI and/or 3-directional diffusion-weighted imaging sequences for the prediction of IDH and/or 1p19q status in WHO grade II-IV diffuse astrocytic and oligodendroglial tumours as defined in the WHO 2016 Classification of CNS Tumours.

RESULTS

Forty-four studies including a total of 5286 patients fulfilled the inclusion criteria. Correlations between key glioma molecular markers, namely IDH and 1p19q, and distinctive MRI findings have been established, including tumour location, signal composition (including the T2-FLAIR mismatch sign) and apparent diffusion coefficient values.

CONCLUSION

Consistent trends have emerged indicating that conventional MRI is valuable for glioma genotyping, particularly in presumed lower grade glioma. However, due to limited interobserver testing, the reproducibility of qualitatively assessed visual features remains an area of uncertainty.

Italian consensus and recommendations on diagnosis and treatment of low-grade gliomas. An intersociety (SINch/AINO/SIN) document

In 2018, the SINch (Italian Society of Neurosurgery) Neuro-Oncology Section, AINO (Italian Association of Neuro-Oncology) and SIN (Italian Association of Neurology) Neuro-Oncology Section formed a collaborative Task Force to look at the diagnosis and treatment of low-grade gliomas (LGGs). The Task Force included neurologists, neurosurgeons, neuro-oncologists, pathologists, radiologists, radiation oncologists, medical oncologists, a neuropsychologist and a methodologist. For operational purposes, the Task Force was divided into five Working Groups: diagnosis, surgical treatment, adjuvant treatments, supportive therapies, and follow-up. The resulting guidance document is based on the available evidence and provides recommendations on diagnosis and treatment of LGG patients, considering all aspects of patient care along their disease trajectory.

World Health Organization Grade II/III Glioma Molecular Status: Prediction by MRI Morphologic Features and Apparent Diffusion Coefficient

Background A readily implemented MRI biomarker for glioma genotyping is currently lacking. Purpose To evaluate clinically available MRI parameters for predicting isocitrate dehydrogenase (IDH) status in patients with glioma. Materials and Methods In this retrospective study of patients studied from July 2008 to February 2019, untreated World Health Organization (WHO) grade II/III gliomas were analyzed by three neuroradiologists blinded to tissue results. Apparent diffusion coefficient (ADC) minimum (ADC_min) and mean (ADC_mean) regions of interest were defined in tumor and normal appearing white matter (ADC_NAWM). A visual rating of anatomic features (T1 weighted, T1 weighted with contrast enhancement, T2 weighted, and fluid-attenuated inversion recovery) was performed. Interobserver comparison (intraclass correlation coefficient and Cohen κ) was followed by nonparametric (Kruskal-Wallis analysis of variance) testing of associations between ADC metrics and glioma genotypes, including Bonferroni correction for multiple testing. Descriptors with sufficient concordance (intraclass correlation coefficient, >0.8; κ > 0.6) underwent univariable analysis. Predictive variables (P < .05) were entered into a multivariable logistic regression and tested in an additional test sample of patients with glioma. Results The study included 290 patients (median age, 40 years; interquartile range, 33-52 years; 169 male patients) with 82 IDH wild-type, 107 IDH mutant/1p19q intact, and 101 IDH mutant/1p19q codeleted gliomas. Two predictive models incorporating ADC_mean-to-ADC_NAWM ratio, age, and morphologic characteristics, with model A mandating calcification result and model B recording cyst formation, classified tumor type with areas under the receiver operating characteristic curve of 0.94 (95% confidence interval [CI]: 0.91, 0.97) and 0.96 (95% CI: 0.93, 0.98), respectively. In the test sample of 49 gliomas (nine IDH wild type, 21 IDH mutant/1p19q intact, and 19 IDH mutant/1p19q codeleted), the classification accuracy was 40 of 49 gliomas (82%; 95% CI: 71%, 92%) for model A and 42 of 49 gliomas (86%; 95% CI: 76%, 96%) for model B. Conclusion Two algorithms that incorporated apparent diffusion coefficient values, age, and tumor morphologic characteristics predicted isocitrate dehydrogenase status in World Health Organization grade II/III gliomas on the basis of standard clinical MRI sequences alone. © RSNA, 2020 Online supplemental material is available for this article.

Simultaneous FET-PET and contrast-enhanced MRI based on hybrid PET/MR improves delineation of tumor spatial biodistribution in gliomas: a biopsy validation study

Purpose

Glioma treatment planning requires precise tumor delineation, which is typically performed with contrast-enhanced (CE) MRI. However, CE MRI fails to reflect the entire extent of glioma. O-(2-¹⁸F-fluoroethyl)-L-tyrosine (¹⁸F-FET) PET may detect tumor volumes missed by CE MRI. We investigated the clinical value of simultaneous FET-PET and CE MRI in delineating tumor extent before treatment planning. Guided stereotactic biopsy was used to validate the findings.

Methods

Conventional MRI and ¹⁸F-FET PET were performed simultaneously on a hybrid PET/MR in 33 patients with histopathologically confirmed glioma. Tumor volumes were quantified using a tumor-to-brain ratio ≥ 1.6 (V_PET) and a visual threshold (V_CE). We visually assessed abnormal areas on FLAIR images and calculated Dice’s coefficient (DSC), overlap volume (OV), discrepancy-PET, and discrepancy-CE. Additionally, several stereotactic biopsy samples were taken from “matched” or “mismatched” FET-PET and CE MRI regions.

Results

Among 31 patients (93.94%), FET-PET delineated significantly larger tumor volumes than CE MRI (77.84 ± 51.74 cm³ vs. 34.59 ± 27.07 cm³, P < 0.05). Of the 21 biopsy samples obtained from regions with increased FET uptake, all were histopathologically confirmed as glioma tissue or tumor infiltration, whereas only 13 showed enhancement on CE MRI. Among all patients, the spatial similarity between V_PET and V_CE was low (average DSC 0.56 ± 0.22), while the overlap was high (average OV 0.95 ± 0.08). The discrepancy-CE and discrepancy-PET were lower than 10% in 28 and 0 patients, respectively. Eleven patients showed V_PET partially beyond abnormal signal areas on FLAIR images.

Conclusion

The metabolically active biodistribution of gliomas delineated with FET-PET significantly exceeds tumor volume on CE MRI, and histopathology confirms these findings. Our preliminary results indicate that combining the anatomic and molecular information obtained from conventional MRI and FET-PET would reveal a more accurate glioma extent, which is critical for individualized treatment planning.

Electronic supplementary material

The online version of this article (10.1007/s00259-019-04656-2) contains supplementary material, which is available to authorized users.

Overview of established and emerging immunohistochemical biomarkers and their role in correlative studies in MRI

Clinical practice in radiology and pathology requires professional expertise and many years of training to visually evaluate and interpret abnormal phenotypic features in medical images and tissue sections to generate diagnoses that guide patient management and treatment. Recent advances in digital image analysis methods and machine learning have led to significant interest in extracting additional information from medical and digital whole-slide images in radiology and pathology, respectively. This has led to significant interest and research in radiomics and pathomics to correlate phenotypic features of disease with image analytics in order to identify image-based biomarkers. The expanding role of big data in radiology and pathology parallels the development and role of immunohistochemistry (IHC) in the daily practice of pathology. IHC methods were initially developed to provide additional information to help classify tumors and then transformed into an indispensable tool to guide treatment in many types of cancer. IHC markers are used in daily practice to identify specific types of cells and highlight their distributions in tissues in order to distinguish benign from neoplastic cells, determine tumor origin, subclassify neoplasms, and support and confirm diagnoses. In this regard, radiomics, pathomics, and IHC methods are very similar since they enable the extraction of image-based features to characterize various properties of diseases. Due to the dramatic advancements in recent radiomics research, we provide a brief overview of the role of established and emerging IHC biomarkers in various tumor types that have been correlated with radiologic biomarkers to improve diagnostic accuracy, predict prognosis, guide patient management, and select treatment strategies. Level of Evidence: 5 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:341-354.

Filtration-histogram based magnetic resonance texture analysis (MRTA) for glioma IDH and 1p19q genotyping

BACKGROUND

To determine if filtration-histogram based texture analysis (MRTA) of clinical MR imaging can non-invasively identify molecular subtypes of untreated gliomas.

METHODS

Post Gadolinium T1-weighted (T1+Gad) images, T2-weighted (T2) images and apparent diffusion coefficient (ADC) maps of 97 gliomas (54 = WHO II, 20 = WHO III, 23 = WHO IV) between 2010 and 2016 were studied. Whole-tumor segmentations were performed on a proprietary texture analysis research platform (TexRAD, Cambridge, UK) using the software's freehand drawing tool. MRTA commences with a filtration step, followed by quantification of texture using histogram texture parameters. Results were correlated using non-parametric statistics with a logistic regression model generated.

RESULTS

T1+Gad performed best for IDH typing of glioblastoma (sensitivity 91.9%, specificity 100%, AUC 0.945) and ADC for non-Gadolinium-enhancing gliomas (sensitivity 85.7%, specificity 78.4%, AUC 0.877). T2 was moderately precise (sensitivity 83.1%, specificity 78.9%, AUC 0.821). Excellent results for IDH typing were achieved from a combination of the three sequences (sensitivity 90.5%, specificity 94.5%, AUC = 0.98). For discriminating 1p19q genotypes, ADC produced the best results using unfiltered textures (sensitivity 80.6%, specificity 89.3%, AUC 0.811).

CONCLUSION

Preoperative glioma genotyping with MRTA appears valuable with potential for clinical translation. The optimal choice of texture parameters is influenced by sequence choice, tumour morphology and segmentation method.