Advanced MRI may complement histological diagnosis of lower grade gliomas and help in predicting survival

Diffusion histogram profiles predict molecular features of grade 4 in histologically lower-grade adult diffuse gliomas following WHO classification 2021

OBJECTIVES

In the latest World Health Organization classification 2021, grade 4 adult diffuse gliomas can be diagnosed with several molecular features even without histological evidence of necrosis or microvascular proliferation. We aimed to explore whole tumor histogram-derived apparent diffusion coefficient (ADC) histogram profiles for differentiating between the presence (Mol-4) and absence (Mol-2/3) of grade 4 molecular features in histologically lower-grade gliomas.

METHODS

Between June 2019 and October 2022, 184 adult patients with diffuse gliomas underwent MRI. After excluding 121 patients, 18 (median age, 64.5 [range, 37-84 years]) Mol-4 and 45 (median 40 [range, 18-73] years) Mol-2/3 patients with histologically lower-grade gliomas were enrolled. Whole tumor volume-of-interest-derived ADC histogram profiles were calculated and compared between the two groups. Stepwise logistic regression analysis with Akaike's information criterion using the ADC histogram profiles with p values < 0.01 and age at diagnosis was used to identify independent variables for predicting the Mol-4 group.

RESULTS

The 90th percentile (p < 0.001), median (p < 0.001), mean (p < 0.001), 10th percentile (p = 0.014), and entropy (p < 0.001) of normalized ADC were lower, and kurtosis (p < 0.001) and skewness (p = 0.046) were higher in the Mol-4 group than in the Mol-2/3 group. Multivariate logistic regression analysis revealed that the entropy of normalized ADC and age at diagnosis were independent predictive parameters for the Mol-4 group with an area under the curve of 0.92.

CONCLUSION

ADC histogram profiles may be promising preoperative imaging biomarkers to predict molecular grade 4 among histologically lower-grade adult diffuse gliomas.

CLINICAL RELEVANCE STATEMENT

This study highlighted the diagnostic usefulness of ADC histogram profiles to differentiate histologically lower grade adult diffuse gliomas with the presence of molecular grade 4 features and those without.

KEY POINTS

• ADC histogram profiles to predict molecular CNS WHO grade 4 status among histologically lower-grade adult diffuse gliomas were evaluated. • Entropy of ADC and age were independent predictive parameters for molecular grade 4 status. • ADC histogram analysis is useful for predicting molecular grade 4 among histologically lower-grade gliomas.

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.

In vivo brain MR spectroscopy in gliomas: clinical and pre-clinical chances

Purpose

Gliomas, the most common primary brain tumours, have recently been re-classified incorporating molecular aspects with important clinical, prognostic, and predictive implications. Concurrently, the reprogramming of metabolism, altering intracellular and extracellular metabolites affecting gene expression, differentiation, and the tumour microenvironment, is increasingly being studied, and alterations in metabolic pathways are becoming hallmarks of cancer. Magnetic resonance spectroscopy (MRS) is a complementary, non-invasive technique capable of quantifying multiple metabolites. The aim of this review focuses on the methodology and analysis techniques in proton MRS (1H MRS), including a brief look at X-nuclei MRS, and on its perspectives for diagnostic and prognostic biomarkers in gliomas in both clinical practice and preclinical research.

Methods

PubMed literature research was performed cross-linking the following key words: glioma, MRS, brain, in-vivo, human, animal model, clinical, pre-clinical, techniques, sequences, 1H, X-nuclei, Artificial Intelligence (AI), hyperpolarization.

Results

We selected clinical works (n = 51), preclinical studies (n = 35) and AI MRS application papers (n = 15) published within the last two decades. The methodological papers (n = 62) were taken into account since the technique first description.

Conclusions

Given the development of treatments targeting specific cancer metabolic pathways, MRS could play a key role in allowing non-invasive assessment for patient diagnosis and stratification, predicting and monitoring treatment responses and prognosis. The characterization of gliomas through MRS will benefit of a wide synergy among scientists and clinicians of different specialties within the context of new translational competences. Head coils, MRI hardware and post-processing analysis progress, advances in research, experts’ consensus recommendations and specific professionalizing programs will make the technique increasingly trustworthy, responsive, accessible.

Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas

Simple Summary

Low-grade gliomas (LGG) with the 1p/19q co-deletion mutation have been proven to have a better survival prognosis and response to treatment than individuals without the mutation. Identifying this mutation has a vital role in managing LGG patients; however, the current diagnostic gold standard, including the brain-tissue biopsy or the surgical resection of the tumor, remains highly invasive and time-consuming. We proposed a model based on the eXtreme Gradient Boosting (XGBoost) classifier to detect 1p/19q co-deletion mutation using non-invasive medical images. The performance of our model achieved 87% and 82.8% accuracy on the training and external test set, respectively. Significantly, the prediction was based on only seven optimal wavelet radiomics features extracted from brain Magnetic Resonance (MR) images. We believe that this model can address clinicians in the rapid diagnosis of clinical 1p/19q co-deletion mutation, thereby improving the treatment prognosis of LGG patients.

Abstract

The prognosis and treatment plans for patients diagnosed with low-grade gliomas (LGGs) may significantly be improved if there is evidence of chromosome 1p/19q co-deletion mutation. Many studies proved that the codeletion status of 1p/19q enhances the sensitivity of the tumor to different types of therapeutics. However, the current clinical gold standard of detecting this chromosomal mutation remains invasive and poses implicit risks to patients. Radiomics features derived from medical images have been used as a new approach for non-invasive diagnosis and clinical decisions. This study proposed an eXtreme Gradient Boosting (XGBoost)-based model to predict the 1p/19q codeletion status in a binary classification task. We trained our model on the public database extracted from The Cancer Imaging Archive (TCIA), including 159 LGG patients with 1p/19q co-deletion mutation status. The XGBoost was the baseline algorithm, and we combined the SHapley Additive exPlanations (SHAP) analysis to select the seven most optimal radiomics features to build the final predictive model. Our final model achieved an accuracy of 87% and 82.8% on the training set and external test set, respectively. With seven wavelet radiomics features, our XGBoost-based model can identify the 1p/19q codeletion status in LGG-diagnosed patients for better management and address the drawbacks of invasive gold-standard tests in clinical practice.

Combining 18F-DOPA PET and MRI with perfusion-weighted imaging improves delineation of high-grade subregions in enhancing and non-enhancing gliomas prior treatment: a biopsy-controlled study

PURPOSE

We aimed to compare spatial extent of high-grade subregions detected with combined [¹⁸F]-dihydroxyphenylalanine (¹⁸F-DOPA) PET and MRI to the one provided by advanced multimodal MRI alone including Contrast-enhanced (CE) and Perfusion weighted imaging (PWI). Then, we compared the accuracy between imaging modalities, in a per biopsy analysis.

METHODS

Participants with suspected diffuse glioma were prospectively included between June 2018 and September 2019. Volumes of high-grade subregions were delineated respectively on ¹⁸F-DOPA PET and MRI (CE and PWI). Up to three per-surgical neuronavigation-guided biopsies were performed per patient.

RESULTS

Thirty-eight biopsy samples from sixteen participants were analyzed. Six participants (38%) had grade IV IDH wild-type glioblastoma, six (38%) had grade III IDH-mutated astrocytoma and four (24%) had grade II IDH-mutated gliomas. Three patients had intratumoral heterogeneity with coexisting high- and low-grade tumor subregions. High-grade volumes determined with combined ¹⁸F-DOPA PET/MRI (median of 1.7 [interquartile range (IQR) 0.0, 19.1] mL) were larger than with multimodal MRI alone (median 1.3 [IQR 0.0, 12.8] mL) with low overlap (median Dice's coefficient 0.24 [IQR 0.08, 0.59]). Delineation volumes were substantially increased in five (31%) patients. In a per biopsy analysis, combined ¹⁸F-DOPA PET/MRI detected high-grade subregions with an accuracy of 58% compared to 42% (p = 0.03) with CE MRI alone and 50% (p = 0.25) using multimodal MRI (CE + PWI).

CONCLUSIONS

The addition of ¹⁸F-DOPA PET to multimodal MRI (CE and PWI) enlarged the delineation volumes and enhanced overall accuracy for detection of high-grade subregions. Thus, combining ¹⁸F-DOPA with advanced MRI may improve treatment planning in newly diagnosed gliomas.

Exploring diagnostic performance of T2 mapping in diffuse glioma grading

Background

To evaluate the diagnostic performance of T2 mapping in differentiating WHO grade II glioma from high-grade glioma (HGG).

Methods

We conducted a single-center, retrospective diagnostic study. Confirmed diffuse glioma (WHO grade II-IV) patients who underwent post-contrast T1-weighted imaging, T2-weighted imaging, and T2 mapping were included. All diagnoses were based on histological and molecular tests. Seventy-five percent of cases were subsampled to generate receiver operating characteristic (ROC) curves and areas under the curve (AUC), while the remaining cases were used to test the accuracy of T2 mapping. Subsampling was repeated four times. Age, T2 relaxation time, and contrast-enhancement status were used to generate a multivariable ROC curve. T2 relaxation time was also used to generate ROC curves to predict the isocitrate dehydrogenase (IDH) status.

Results

A total of 159 patients were included in the study. After four repeats of subsampling, the AUCs of the T2 mapping ROC curve were 0.801 (95% CI: 0.724-0.879), 0.795 (95% CI: 0.714-0.875), 0.803 (95% CI: 0.723-0.884), and 0.801 (95% CI: 0.716-0.886), with an average sensitivity of 0.753 and an average specificity of 0.767. When applied to the remaining 25% of cases, the accuracy was 75%, 93.75%, 82.50%, and 71.74%. The AUC of the multivariable ROC was 0.927 (95% CI: 0.882-0.971). IDH-mutant and IDH-wildtype gliomas have significantly different T2 relaxation times (146.28 and 124.10 ms, respectively; P=0.001), and the AUC of IDH-mutant prediction was 0.687 (95% CI: 0.585-0.789).

Conclusions

Quantitative T2 mapping differentiated WHO grade II glioma from HGG with moderate sensitivity and specificity. Given the advantages of short acquisition times and the absence of a contrast agent, our study suggests the application of T2 mapping in pre-operative glioma grading is feasible.

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.

Perioperative clinical trials for glioma: Raising the bar

Gliomas are a heterogeneous group of primary brain cancers with poor survival despite multimodality therapy that includes surgery, radiation and chemotherapy. Numerous clinical trials have investigated systemic therapies in glioma, but have largely been negative. Multiple factors have contributed to the lack of progress including tumour heterogeneity, the tumour micro-environment and presence of the blood-brain barrier, as well as extrinsic factors relating to trial design, such as the lack of a contemporaneous biopsy at the time of treatment. A number of strategies have been proposed to progress new agents into the clinic. Here, we review the progress of perioperative, including phase 0 and 'window of opportunity', studies and provide recommendations for trial design in the development of new agents for glioma. The incorporation of pre- and post-treatment biopsies in glioma early phase trials will provide valuable pharmacokinetic and pharmacodynamic data and also determine the target or biomarker effect, which will guide further development of new agents. Perioperative 'window of opportunity' studies must use drugs with a recommended-phase-2-dose, known safety profile and adequate blood-brain barrier penetration. Drugs shown to have on-target effects in perioperative trials can then be evaluated further in a larger cohort of patients in an adaptive trial to increase the efficiency of drug development.

Advanced magnetic resonance imaging to support clinical drug development for malignant glioma

Even though the treatment options and survival of patients with glioblastoma multiforme (GBM), the most common type of malignant glioma, have improved over the past decade, there is still a high unmet medical need to develop novel therapies. Complexity in pathology and therapy require biomarkers to characterize tumors, to define malignant and active areas, to assess disease prognosis, and to quantify and monitor therapy response. While conventional magnetic resonance imaging (MRI) techniques have improved these assessments, limitations remain. In this review, we evaluate the role of various non-invasive biomarkers based on advanced structural and functional MRI techniques in the context of GBM drug development over the past 5 years.

The diagnostic value of quantitative analysis of ASL, DSC-MRI and DKI in the grading of cerebral gliomas: a meta-analysis

OBJECTIVE

To perform quantitative analysis on the efficacy of using relative cerebral blood flow (rCBF) in arterial spin labeling (ASL), relative cerebral blood volume (rCBV) in dynamic magnetic sensitivity contrast-enhanced magnetic resonance imaging (DSC-MRI), and mean kurtosis (MK) in diffusion kurtosis imaging (DKI) to grade cerebral gliomas.

METHODS

Literature regarding ASL, DSC-MRI, or DKI in cerebral gliomas grading in both English and Chinese were searched from PubMed, Embase, Web of Science, CBM, China National Knowledge Infrastructure (CNKI), and Wanfang Database as of 2019. A meta-analysis was performed to evaluate the efficacy of ASL, DSC-MRI, and DKI in the grading of cerebral gliomas.

RESULT

A total of 54 articles (11 in Chinese and 43 in English) were included. Three quantitative parameters in the grading of cerebral gliomas, rCBF in ASL, rCBV in DSC-MRI, and MK in DKI had the pooled sensitivity of 0.88 [95% CI (0.83,0.92)], 0.92 [95% CI (0.83,0.96)], 0.88 [95% CI (0.82,0.92)], and the pooled specificity of 0.91 [95% CI (0.84,0.94)], 0.81 [95% CI (0.73,0.88)], 0.86 [95% CI (0.78,0.91)] respectively. The pooled area under the curve (AUC) were 0.95 [95% CI (0.93,0.97)], 0.91 [95% CI (0.89,0.94)], 0.93 [95% CI (0.91,0.95)] respectively.

CONCLUSION

Quantitative parameters rCBF, rCBV and MK have high diagnostic accuracy for preoperative grading of cerebral gliomas.

The Prognostic Value of MRI Subventricular Zone Involvement and Tumor Genetics in Lower Grade Gliomas

BACKGROUND AND PURPOSE

Glioblastomas (GBMs) that involve the subventricular zone (SVZ) have a poor prognosis, possibly due to recruitment of neural stem cells. The purpose of this study was to evaluate whether SVZ involvement by lower grade gliomas (LGG), WHO grade II and III, similarly predicts poorer outcomes. We further assessed whether tumor genetics and cellularity are associated with SVZ involvement and outcomes.

METHODS

Forty-five consecutive LGG patients with preoperative imaging and next generation sequencing were included in this study. Regional SVZ involvement and whole tumor apparent diffusion coefficient (ADC) values, as a measure of cellularity, were assessed on magnetic resonance imaging. Progression was determined by RANO criteria. Kaplan-Meier curves and Cox regression analyses were used to determine the hazard ratios (HR) for progression and survival.

RESULTS

Frontal, parietal, temporal, and overall SVZ involvement and ADC values were not associated with progression or survival (P ≥ .05). However, occipital SVZ involvement, seen in two patients, was associated with a higher risk of tumor progression (HR = 6.6, P = .016) and death (HR = 31.5, P = .015), CDKN2A/B mutations (P = .03), and lower ADC histogram values at the 5th (P = .026) and 10th percentiles (P = .046). Isocitrate dehydrogenase, phosphatase and tensin homolog, epidermal growth factor receptor, and cyclin-dependent kinase 4 mutations were also prognostic (P ≤ .05).

CONCLUSIONS

Unlike in GBM, overall SVZ involvement was not found to strongly predict poor prognosis in LGGs. However, occipital SVZ involvement, though uncommon, was prognostic and found to be associated with CDKN2A/B mutations and tumor hypercellularity. Further investigation into these molecular mechanisms underlying occipital SVZ involvement in larger cohorts is warranted.

Association of Tumor [18F]FDG Activity and Diffusion Restriction with Clinical Outcomes of Rhabdomyosarcomas

PURPOSE

To evaluate whether the extent of restricted diffusion and 2-deoxy-2-[¹⁸F] fluoro-D-glucose ([¹⁸F]FDG) uptake of pediatric rhabdomyosarcomas (RMS) on positron emission tomography (PET)/magnetic resonance (MR) images provides prognostic information.

PROCEDURE

In a retrospective, IRB-approved study, we evaluated [¹⁸F]FDG PET/CT and diffusion-weighted (DW) MR imaging studies of 21 children and adolescents (age 1-20 years) with RMS of the head and neck. [¹⁸F]FDG PET and DW MR scans at the time of the initial tumor diagnosis were fused using MIM software. Quantitative measures of the tumor mass with restricted diffusion, [¹⁸F]FDG hypermetabolism, or both were dichotomized at the median and tested for significance using Gray's test. Data were analyzed using a survival analysis and competing risk model with death as the competing risk.

RESULTS

[¹⁸F]FDG PET/MR images demonstrated a mismatch between tumor areas with increased [¹⁸F]FDG uptake and restricted diffusion. The DWI, PET, and DWI + PET tumor volumes were dichotomized at their median values, 23.7, 16.4, and 9.5 cm³, respectively, and were used to estimate survival. DWI, PET, and DWI + PET overlap tumor volumes above the cutoff values were associated with tumor recurrence, regardless of post therapy COG stage (p = 0.007, p = 0.04, and p = 0.07, respectively).

CONCLUSION

The extent of restricted diffusion within RMS and overlap of hypermetabolism plus restricted diffusion predict unfavorable clinical outcomes.

Correction to: Advanced imaging in adult diffusely infiltrating low-grade gliomas

The original article [1] contains errors in Table 1 in rows ktrans and Ve; the correct version of Table 1 can be viewed in this Correction article.

Role of imaging biomarkers for prognostic prediction in patients with pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive tumours. PDAC has a poor prognosis; therefore, it is necessary to perform further risk stratification. Identifying prognostic factors before treatment might help to implement suitable and personalised treatment for individuals and avoid side effects. Conventional staging systems and tumour biomarkers are fundamental to establish prognosis; however, they have obvious limitations. Novel imaging biomarkers extracted from advanced imaging techniques offer opportunities to evaluate underlying tumour physiological characteristics, such as mutational status, cellular composition, local microenvironment, tumour metabolism, and biological behaviour. Thus, imaging biomarkers might help the decision making of oncologists and surgeons. The present review discusses the functions of imaging biomarkers for prognostic prediction in patients with PDAC and their potential value for further translation in clinical practice.

Advanced imaging in adult diffusely infiltrating low-grade gliomas

The adult diffusely infiltrating low-grade gliomas (LGGs) are typically IDH mutant and slow-growing gliomas having moderately increased cellularity generally without mitosis, necrosis, and microvascular proliferation. Supra-total resection of LGG significantly increases the overall survival by delaying malignant transformation compared with a simple debulking so accurate MR diagnosis is crucial for treatment planning. Data from meta-analysis support the addition of diffusion and perfusion-weighted MR imaging and MR spectroscopy in the diagnosis of suspected LGG. Typically, LGG has lower cellularity (ADCmin), angiogenesis (rCBVmax), capillary permeability (Ktrans), and mitotic activity (Cho/Cr ratio) compared to high-grade glioma. The identification of 2-hydroxyglutarate by MR spectroscopy can reflect the IDH status of the tumor. The initial low ADCmin, high rCBVmax, and Ktrans values are consistent with the poor prognosis. The gradual increase in intratumoral Cho/Cr ratio and rCBVmax values are well correlated with tumor progression. Besides MR-based technical artifacts, which are minimized by the voxel-based assessment of data obtained by histogram analysis, the problems derived from the diversity and the analysis of imaging data should be solved by using artificial intelligence techniques. The quantitative multiparametric MR imaging of LGG can either improve the diagnostic accuracy of their differential diagnosis or assess their prognosis.

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.

Survival Associations Using Perfusion and Diffusion Magnetic Resonance Imaging in Patients With Histologic and Genetic Defined Diffuse Glioma World Health Organization Grades II and III

OBJECTIVE

According to the new World Health Organization 2016 classification for tumors of the central nervous system, 1p/19q codeletion defines the genetic hallmark that differentiates oligodendrogliomas from diffuse astrocytomas. The aim of our study was to evaluate whether relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC) histogram analysis can stratify survival in adult patients with genetic defined diffuse glioma grades II and III.

METHODS

Sixty-seven patients with untreated diffuse gliomas World Health Organization grades II and III and known 1p/19q codeletion status were included retrospectively and analyzed using ADC and rCBV maps based on whole-tumor volume histograms. Overall survival and progression-free survival (PFS) were analyzed by using Kaplan-Meier and Cox survival analyses adjusted for known survival predictors.

RESULTS

Significant longer PFS was associated with homogeneous rCBV distribution-higher rCBVpeak (median, 37 vs 26 months; hazard ratio [HR], 3.2; P = 0.02) in patients with astrocytomas, and heterogeneous rCBV distribution-lower rCBVpeak (median, 46 vs 37 months; HR, 5.3; P < 0.001) and higher rCBVmean (median, 44 vs 39 months; HR, 7.9; P = 0.003) in patients with oligodendrogliomas. Apparent diffusion coefficient parameters (ADCpeak, ADCmean) did not stratify PFS and overall survival.

CONCLUSIONS

Tumors with heterogeneous perfusion signatures and high average values were associated with longer PFS in patients with oligodendrogliomas. On the contrary, heterogeneous perfusion distribution was associated with poor outcome in patients with diffuse astrocytomas.

Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics

BACKGROUND

Molecular markers of WHO grade II/III glioma are known to have important prognostic and predictive implications and may be associated with unique imaging phenotypes. The purpose of this study is to determine whether three clinically relevant molecular markers identified in gliomas-IDH, 1p/19q, and MGMT status-show distinct quantitative MRI characteristics on FLAIR imaging.

METHODS

Sixty-one patients with grade II/III gliomas who had molecular data and MRI available prior to radiation were included. Quantitative MRI features were extracted that measured tissue heterogeneity (homogeneity and pixel correlation) and FLAIR border distinctiveness (edge contrast; EC). T-tests were conducted to determine whether patients with different genotypes differ across the features. Logistic regression with LASSO regularization was used to determine the optimal combination of MRI and clinical features for predicting molecular subtypes.

RESULTS

Patients with IDH wildtype tumors showed greater signal heterogeneity (p = 0.001) and lower EC (p = 0.008) within the FLAIR region compared to IDH mutant tumors. Among patients with IDH mutant tumors, 1p/19q co-deleted tumors had greater signal heterogeneity (p = 0.002) and lower EC (p = 0.005) compared to 1p/19q intact tumors. MGMT methylated tumors showed lower EC (p = 0.03) compared to the unmethylated group. The combination of FLAIR border distinctness, heterogeneity, and pixel correlation optimally classified tumors by IDH status.

CONCLUSION

Quantitative imaging characteristics of FLAIR heterogeneity and border pattern in grade II/III gliomas may provide unique information for determining molecular status at time of initial diagnostic imaging, which may then guide subsequent surgical and medical management.

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

Objectives

To investigate if quantitative apparent diffusion coefficient (ADC) measurements can predict genetic subtypes of non-gadolinium-enhancing gliomas, comparing whole tumour against single slice analysis.

Methods

Volumetric T2-derived masks of 44 gliomas were co-registered to ADC maps with ADC mean (ADC_mean) calculated. For the slice analysis, two observers placed regions of interest in the largest tumour cross-section. The ratio (ADC_ratio) between ADC_mean in the tumour and normal appearing white matter was calculated for both methods.

Results

Isocitrate dehydrogenase (IDH) wild-type gliomas showed the lowest ADC values throughout (p < 0.001). ADC_mean in the IDH-mutant 1p19q intact group was significantly higher than in the IDH-mutant 1p19q co-deleted group (p < 0.01). A volumetric ADC_mean threshold of 1201 × 10⁻⁶ mm²/s identified IDH wild-type with a sensitivity of 83% and a specificity of 86%; a volumetric ADC_ratio cut-off value of 1.65 provided a sensitivity of 80% and a specificity of 92% (area under the curve (AUC) 0.9–0.94). A slice ADC_ratio threshold for observer 1 (observer 2) of 1.76 (1.83) provided a sensitivity of 80% (86%), specificity of 91% (100%) and AUC of 0.95 (0.96). The intraclass correlation coefficient was excellent (0.98).

Conclusions

ADC measurements can support the distinction of glioma subtypes. Volumetric and two-dimensional measurements yielded similar results in this study.

Key Points

• Diffusion-weighted MRI aids the identification of non-gadolinium-enhancing malignant gliomas

• ADC measurements may permit non-gadolinium-enhancing glioma molecular subtyping

• IDH wild-type gliomas have lower ADC values than IDH-mutant tumours

• Single cross-section and volumetric ADC measurements yielded comparable results in this study

The value of multi ultra high-b-value DWI in grading cerebral astrocytomas and its association with aquaporin-4

OBJECTIVE

To investigate the value of multi-ultrahigh-b-value diffusion-weighted imaging (UHBV-DWI) in differentiating high-grade astrocytomas (HGAs) from low-grade astrocytomas (LGAs), analyze its association with aquaporin (AQP) expression.

METHODS

40 astrocytomas divided into LGAs (N = 15) and HGAs (N = 25) were studied. Apparent diffusion coefficient (ADC) and UHBV-ADC values in solid parts and peritumoral edema were compared between LGAs and HGAs groups by the t-test. Using receiver operating characteristic curves to identify the better parameter. Using real time polymerase chain reaction to assess AQP messenger ribonucleic acid (mRNA). Using spearman correlation analysis to assess the correlation of AQP mRNA with each parameter.

RESULTS

ADC values in solid parts of HGAs were significantly lower than LGAs (p = 0.02), while UHBV-ADC values of HGAs were significantly higher than LGAs (p < 0.01). Area under the curve (AUC) of UHBV-ADC (0.810) was larger than ADC (0.713), and the area under the curve of UHBV-ADC was significantly higher than that of ADC (p = 0.041). AQP4 mRNA was significantly higher in HGAs than that in LGAs (p < 0.01); there was less AQP9 mRNA and no AQP1 mRNA in LGAs and HGAs groups (p > 0.05); ADC value showed a negative correlation with AQP4 mRNA (r = -0.357; p = 0.024). UHBV-ADC value positively correlated with the AQP4 mRNA (r = 0.646; p < 0.01).

CONCLUSION

UHBV-DWI allowed for a more accurate grading of cerebral astrocytoma than DWI, and UHBV-ADC value may be related with the AQP4 mRNA levels. UHBV-DWI could be of value in the assessment of astrocytoma. Advances in knowledge: UHBV-DWI generated by multi UHBV could have particular value for astrocytoma grading, and the level of AQP4 mRNA might be potentially linked to the change of UHBV-DWI parameter, and we might find the exact reason for the difference of UHBV-ADC between the LGAs and HGAs.

Intravoxel incoherent motion (IVIM) in diffusion-weighted imaging (DWI) for Hepatocellular carcinoma: correlation with histologic grade

Purpose

To assess the correlation between DWI diffusion parameters obtained using Intravoxel Incoherent Motion Method (IVIM) and histological grade of Hepatocellular carcinoma (HCC).

Results

According to Edmondson-Steiner grade lesions were classified with grade 1 (14), grade 2 (30), grade 3 (18), and grade 4 (0). Apparent Diffusion Coefficient (ADC), perfusion fraction (fp), tissue diffusion coefficient (Dt) median values were statistically different in HCC groups with 1, 2, 3 histological grade (p<0.001). A significant correlation was reported between ADC, fp, Dt and histologic grade respectively of 0.687, 0.737 and 0.674. Receiver operating characteristic (ROC) analysis demonstrated that an ADC of 2.11×10-3 mm2/sec, an fp of 47.33% and an Dt of 0.94×10-3 mm²/sec were the optimal cutoff values to differentiate high histological grade (3) versus low histological grade (1-2), with a sensitivity and specificity for ADC of 100% and 100%, for fp of 100% and 89%, for Dt of 100% and 74%, respectively.

Material and Methods

A retrospective approved study was performed including 34 patients with 62 HCCs. IVIM was performed to obtain ADC, fp, pseudo-diffusion coefficient (Dp), Dt coefficients. Kruskal Wallis, Spearman Correlation Coefficient, ROC analysis were performed.

Conclusions

ADC and IVIM-derived fp showed significantly better diagnostic performance in differentiating high-grade from low-grade HCC, and significant correlation was observed between ADC, fp, Dt and histological grade.

Magnetic resonance perfusion for differentiating low-grade from high-grade gliomas at first presentation

BACKGROUND

Gliomas are the most common primary brain tumour. They are graded using the WHO classification system, with Grade II-IV astrocytomas, oligodendrogliomas and oligoastrocytomas. Low-grade gliomas (LGGs) are WHO Grade II infiltrative brain tumours that typically appear solid and non-enhancing on magnetic resonance imaging (MRI) scans. People with LGG often have little or no neurologic deficit, so may opt for a watch-and-wait-approach over surgical resection, radiotherapy or both, as surgery can result in early neurologic disability. Occasionally, high-grade gliomas (HGGs, WHO Grade III and IV) may have the same MRI appearance as LGGs. Taking a watch-and-wait approach could be detrimental for the patient if the tumour progresses quickly. Advanced imaging techniques are increasingly used in clinical practice to predict the grade of the tumour and to aid clinical decision of when to intervene surgically. One such advanced imaging technique is magnetic resonance (MR) perfusion, which detects abnormal haemodynamic changes related to increased angiogenesis and vascular permeability, or "leakiness" that occur with aggressive tumour histology. These are reflected by changes in cerebral blood volume (CBV) expressed as rCBV (ratio of tumoural CBV to normal appearing white matter CBV) and permeability, measured by K^trans.

OBJECTIVES

To determine the diagnostic test accuracy of MR perfusion for identifying patients with primary solid and non-enhancing LGGs (WHO Grade II) at first presentation in children and adults. In performing the quantitative analysis for this review, patients with LGGs were considered disease positive while patients with HGGs were considered disease negative.To determine what clinical features and methodological features affect the accuracy of MR perfusion.

SEARCH METHODS

Our search strategy used two concepts: (1) glioma and the various histologies of interest, and (2) MR perfusion. We used structured search strategies appropriate for each database searched, which included: MEDLINE (Ovid SP), Embase (Ovid SP), and Web of Science Core Collection (Science Citation Index Expanded and Conference Proceedings Citation Index). The most recent search for this review was run on 9 November 2016.We also identified 'grey literature' from online records of conference proceedings from the American College of Radiology, European Society of Radiology, American Society of Neuroradiology and European Society of Neuroradiology in the last 20 years.

SELECTION CRITERIA

The titles and abstracts from the search results were screened to obtain full-text articles for inclusion or exclusion. We contacted authors to clarify or obtain missing/unpublished data.We included cross-sectional studies that performed dynamic susceptibility (DSC) or dynamic contrast-enhanced (DCE) MR perfusion or both of untreated LGGs and HGGs, and where rCBV and/or K^trans values were reported. We selected participants with solid and non-enhancing gliomas who underwent MR perfusion within two months prior to histological confirmation. We excluded studies on participants who received radiation or chemotherapy before MR perfusion, or those without histologic confirmation.

DATA COLLECTION AND ANALYSIS

Two review authors extracted information on study characteristics and data, and assessed the methodological quality using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. We present a summary of the study characteristics and QUADAS-2 results, and rate studies as good quality when they have low risk of bias in the domains of reference standard of tissue diagnosis and flow and timing between MR perfusion and tissue diagnosis.In the quantitative analysis, LGGs were considered disease positive, while HGGs were disease negative. The sensitivity refers to the proportion of LGGs detected by MR perfusion, and specificity as the proportion of detected HGGs. We constructed two-by-two tables with true positives and false negatives as the number of correctly and incorrectly diagnosed LGG, respectively, while true negatives and false positives are the number of correctly and incorrectly diagnosed HGG, respectively.Meta-analysis was performed on studies with two-by-two tables, with further sensitivity analysis using good quality studies. Limited data precluded regression analysis to explore heterogeneity but subgroup analysis was performed on tumour histology groups.

MAIN RESULTS

Seven studies with small sample sizes (4 to 48) met our inclusion criteria. These were mostly conducted in university hospitals and mostly recruited adult patients. All studies performed DSC MR perfusion and described heterogeneous acquisition and post-processing methods. Only one study performed DCE MR perfusion, precluding quantitative analysis.Using patient-level data allowed selection of individual participants relevant to the review, with generally low risks of bias for the participant selection, reference standard and flow and timing domains. Most studies did not use a pre-specified threshold, which was considered a significant source of bias, however this did not affect quantitative analysis as we adopted a common rCBV threshold of 1.75 for the review. Concerns regarding applicability were low.From published and unpublished data, 115 participants were selected and included in the meta-analysis. Average rCBV (range) of 83 LGGs and 32 HGGs were 1.29 (0.01 to 5.10) and 1.89 (0.30 to 6.51), respectively. Using the widely accepted rCBV threshold of <1.75 to differentiate LGG from HGG, the summary sensitivity/specificity estimates were 0.83 (95% CI 0.66 to 0.93)/0.48 (95% CI 0.09 to 0.90). Sensitivity analysis using five good quality studies yielded sensitivity/specificity of 0.80 (95% CI 0.61 to 0.91)/0.67 (95% CI 0.07 to 0.98). Subgroup analysis for tumour histology showed sensitivity/specificity of 0.92 (95% CI 0.55 to 0.99)/0.42 (95% CI 0.02 to 0.95) in astrocytomas (6 studies, 55 participants) and 0.77 (95% CI 0.46 to 0.93)/0.53 (95% CI 0.14 to 0.88) in oligodendrogliomas+oligoastrocytomas (6 studies, 56 participants). Data were too sparse to investigate any differences across subgroups.

AUTHORS' CONCLUSIONS

The limited available evidence precludes reliable estimation of the performance of DSC MR perfusion-derived rCBV for the identification of grade in untreated solid and non-enhancing LGG from that of HGG. Pooled data yielded a wide range of estimates for both sensitivity (range 66% to 93% for detection of LGGs) and specificity (range 9% to 90% for detection of HGGs). Other clinical and methodological features affecting accuracy of the technique could not be determined from the limited data. A larger sample size of both LGG and HGG, preferably using a standardised scanning approach and with an updated reference standard incorporating molecular profiles, is required for a definite conclusion.

Discrimination between Glioma Grades II and III Using Dynamic Susceptibility Perfusion MRI: A Meta-Analysis

BACKGROUND

DSC perfusion has been evaluated in the discrimination between low-grade and high-grade glioma but the diagnostic potential to discriminate beween glioma grades II and III remains unclear.

PURPOSE

Our aim was to evaluate the diagnostic accuracy of relative maximal CBV from DSC perfusion MR imaging to discriminate glioma grades II and III.

DATA SOURCES

A systematic literature search was performed in PubMed/MEDLINE, Embase, Web of Science, and ClinicalTrials.gov.

STUDY SELECTION

Eligible studies reported on patients evaluated with relative maximal CBV derived from DSC with a confirmed neuropathologic diagnosis of glioma World Health Organization grades II and III. Studies reporting on mean or individual patient data were considered for inclusion.

DATA ANALYSIS

Data were analyzed by using inverse variance with the random-effects model and receiver operating characteristic curves describing optimal cutoffs and areas under the curve. Bivariate diagnostic random-effects meta-analysis was used to calculate diagnostic accuracy.

DATA SYNTHESIS

Twenty-eight studies evaluating 727 individuals were included in the meta-analysis. Individual data were available from 10 studies comprising 190 individuals. The mean difference of relative maximal CBV between glioma grades II and III (n = 727) was 1.76 (95% CI, 1.27-2.24; P < .001). Individual patient data (n = 190) had an area under the curve of 0.77 for discriminating glioma grades II and III at an optimal cutoff of 2.02. When we analyzed astrocytomas separately, the area under the curve increased to 0.86 but decreased to 0.61 when we analyzed oligodendrogliomas.

LIMITATIONS

A substantial heterogeneity was found among included studies.

CONCLUSIONS

Glioma grade III had higher relative maximal CBV compared with glioma grade II. A high diagnostic accuracy was found for all patients and astrocytomas; however, the diagnostic accuracy was substantially reduced when discriminating oligodendroglioma grades II and III.

Noninvasive Assessment of IDH Mutational Status in World Health Organization Grade II and III Astrocytomas Using DWI and DSC-PWI Combined with Conventional MR Imaging

BACKGROUND AND PURPOSE

Isocitrate dehydrogenase (IDH) has been shown to have both diagnostic and prognostic implications in gliomas. The purpose of this study was to examine whether DWI and DSC-PWI combined with conventional MR imaging could noninvasively predict IDH mutational status in World Health Organization grade II and III astrocytomas.

MATERIALS AND METHODS

We retrospectively reviewed DWI, DSC-PWI, and conventional MR imaging in 42 patients with World Health Organization grade II and III astrocytomas. Minimum ADC, relative ADC, and relative maximum CBV values were compared between IDH-mutant and wild-type tumors by using the Mann-Whitney U test. Receiver operating characteristic curve and logistic regression were used to assess their diagnostic performances.

RESULTS

Minimum ADC and relative ADC were significantly higher in IDH-mutated grade II and III astrocytomas than in IDH wild-type tumors (P < .05). Minimum ADC with the cutoff value of ≥1.01 × 10^-3 mm²/s could differentiate the mutational status with a sensitivity, specificity, positive predictive value, and negative predictive value of 76.9%, 82.6%, 91.2%, and 60.5%, respectively. The threshold value of <2.35 for relative maximum CBV in the prediction of IDH mutation provided a sensitivity, specificity, positive predictive value, and negative predictive value of 100.0%, 60.9%, 85.6%, and 100.0%, respectively. A combination of DWI, DSC-PWI, and conventional MR imaging for the identification of IDH mutations resulted in a sensitivity, specificity, positive predictive value, and negative predictive value of 92.3%, 91.3%, 96.1%, and 83.6%.

CONCLUSIONS

A combination of conventional MR imaging, DWI, and DSC-PWI techniques produces a high sensitivity, specificity, positive predictive value, and negative predictive value for predicting IDH mutations in grade II and III astrocytomas. The strategy of using advanced, semiquantitative MR imaging techniques may provide an important, noninvasive, surrogate marker that should be studied further in larger, prospective trials.

How PET/MR Can Add Value For Children With Cancer

PURPOSE

To review how PET/MR technology could add value for pediatric cancer patients.

RECENT FINDINGS

Since many primary tumors in children are evaluated with MRI and metastases are detected with PET/CT, integrated PET/MR can be a time-efficient and convenient solution for pediatric cancer staging. 18F-FDG PET/MR can assess primary tumors and the whole body in one imaging session, avoid repetitive anesthesia and reduce radiation exposure compared to 18F-FDG PET/CT. This article lists 10 action points, which might improve the clinical value of PET/MR for children with cancer. However, even if PET/MR proves valuable, it cannot enter mainstream applications if it is not accessible to the majority of pediatric cancer patients. Therefore, innovations are needed to make PET/MR scanners affordable and increase patient throughput.

SUMMARY

PET/MR offers opportunities for more efficient, accurate and safe diagnoses of pediatric cancer patients. The impact on patient management and outcomes has to be substantiated by large-scale prospective clinical trials.

Quantitative multi-modal MR imaging as a non-invasive prognostic tool for patients with recurrent low-grade glioma

Low-grade gliomas can vary widely in disease course and therefore patient outcome. While current characterization relies on both histological and molecular analysis of tissue resected during surgery, there remains high variability within glioma subtypes in terms of response to treatment and outcome. In this study we hypothesized that parameters obtained from magnetic resonance data would be associated with progression-free survival for patients with recurrent low-grade glioma. The values considered were derived from the analysis of anatomic imaging, diffusion weighted imaging, and ¹H magnetic resonance spectroscopic imaging data. Metrics obtained from diffusion and spectroscopic imaging presented strong prognostic capability within the entire population as well as when restricted to astrocytomas, but demonstrated more limited efficacy in the oligodendrogliomas. The results indicate that multi-parametric imaging data may be applied as a non-invasive means of assessing prognosis and may contribute to developing personalized treatment plans for patients with recurrent low-grade glioma.