Meta-analysis of the diagnostic performance of diffusion magnetic resonance imaging with apparent diffusion coefficient measurements for differentiating glioma recurrence from pseudoprogression

Umbrella review and network meta-analysis of diagnostic imaging test accuracy studies in Differentiating between brain tumor progression versus pseudoprogression and radionecrosis

PURPOSE

In this study we gathered and analyzed the available evidence regarding 17 different imaging modalities and performed network meta-analysis to find the most effective modality for the differentiation between brain tumor recurrence and post-treatment radiation effects.

METHODS

We conducted a comprehensive systematic search on PubMed and Embase. The quality of eligible studies was assessed using the Assessment of Multiple Systematic Reviews-2 (AMSTAR-2) instrument. For each meta-analysis, we recalculated the effect size, sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio from the individual study data provided in the original meta-analysis using a random-effects model. Imaging technique comparisons were then assessed using NMA. Ranking was assessed using the multidimensional scaling approach and by visually assessing surface under the cumulative ranking curves.

RESULTS

We identified 32 eligible studies. High confidence in the results was found in only one of them, with a substantial heterogeneity and small study effect in 21% and 9% of included meta-analysis respectively. Comparisons between MRS Cho/NAA, Cho/Cr, DWI, and DSC were most studied. Our analysis showed MRS (Cho/NAA) and 18F-DOPA PET displayed the highest sensitivity and negative likelihood ratios. 18-FET PET was ranked highest among the 17 studied techniques with statistical significance. APT MRI was the only non-nuclear imaging modality to rank higher than DSC, with statistical insignificance, however.

CONCLUSION

The evidence regarding which imaging modality is best for the differentiation between radiation necrosis and post-treatment radiation effects is still inconclusive. Using NMA, our analysis ranked FET PET to be the best for such a task based on the available evidence. APT MRI showed promising results as a non-nuclear alternative.

Apparent Diffusion Coefficient Metrics to Differentiate between Treatment-Related Abnormalities and Tumor Progression in Post-Treatment Glioblastoma Patients: A Retrospective Study

Distinguishing treatment-related abnormalities (TRA) from tumor progression (TP) in glioblastoma patients is a diagnostic imaging challenge due to the identical morphology of conventional MR imaging sequences. Diffusion-weighted imaging (DWI) and its derived images of the apparent diffusion coefficient (ADC) have been suggested as diagnostic tools for this problem. The aim of this study is to determine the diagnostic accuracy of different cut-off values of the ADC to differentiate between TP and TRA. In total, 76 post-treatment glioblastoma patients with new contrast-enhancing lesions were selected. Lesions were segmented using a T1-weighted, contrast-enhanced scan. The mean ADC values of the segmentations were compared between TRA and TP groups. Diagnostic accuracy was compared by use of the area under the curve (AUC) and the derived sensitivity and specificity values from cutoff points. Although ADC values in TP (mean = 1.32 × 10-3 mm2/s; SD = 0.31 × 10-3 mm2/s) were significantly different compared to TRA (mean = 1.53 × 10-3 mm2/s; SD = 0.28 × 10-3 mm2/s) (p = 0.003), considerable overlap in their distributions exists. The AUC of ADC values to distinguish TP from TRA was 0.71, with a sensitivity and specificity of 65% and 70%, respectively, at an ADC value of 1.47 × 10-3 mm2/s. These findings therefore indicate that ADC maps should not be used in discerning between TP and TRA at a certain timepoint without information on temporal evolution.

Undetected pseudoprogressions in the CeTeG/NOA-09 trial: hints from postprogression survival and MRI analyses

PURPOSE

In the randomized CeTeG/NOA-09 trial, lomustine/temozolomide (CCNU/TMZ) was superior to TMZ therapy regarding overall survival (OS) in MGMT promotor-methylated glioblastoma. Progression-free survival (PFS) and pseudoprogression rates (about 10%) were similar in both arms. Further evaluating this discrepancy, we analyzed patterns of postprogression survival (PPS) and MRI features at first progression according to modified RANO criteria (mRANO).

METHODS

We classified the patients of the CeTeG/NOA-09 trial according to long vs. short PPS employing a cut-off of 18 months and compared baseline characteristics and survival times. In patients with available MRIs and confirmed progression, the increase in T1-enhancing, FLAIR hyperintense lesion volume and the change in ADC mean value of contrast-enhancing tumor upon progression were determined.

RESULTS

Patients with long PPS in the CCNU/TMZ arm had a particularly short PFS (5.6 months). PFS in this subgroup was shorter than in the long PPS subgroup of the TMZ arm (11.1 months, p = 0.01). At mRANO-defined progression, patients of the CCNU/TMZ long PPS subgroup had a significantly higher increase of mean ADC values (p = 0.015) and a tendency to a stronger volumetric increase in T1-enhancement (p = 0.22) as compared to long PPS patients of the TMZ arm.

CONCLUSION

The combination of survival and MRI analyses identified a subgroup of CCNU/TMZ-treated patients with features that sets them apart from other patients in the trial: short first PFS despite long PPS and significant increase in mean ADC values upon mRANO-defined progression. The observed pattern is compatible with the features commonly observed in pseudoprogression suggesting mRANO-undetected pseudoprogressions in the CCNU/TMZ arm of CeTeG/NOA-09.

Diagnosis of Glioblastoma Multiforme Progression via Interpretable Structure-Constrained Graph Neural Networks

Glioblastoma multiforme (GBM) is the most common type of brain tumors with high recurrence and mortality rates. After chemotherapy treatment, GBM patients still show a high rate of differentiating pseudoprogression (PsP), which is often confused as true tumor progression (TTP) due to high phenotypical similarities. Thus, it is crucial to construct an automated diagnosis model for differentiating between these two types of glioma progression. However, attaining this goal is impeded by the limited data availability and the high demand for interpretability in clinical settings. In this work, we propose an interpretable structure-constrained graph neural network (ISGNN) with enhanced features to automatically discriminate between PsP and TTP. This network employs a metric-based meta-learning strategy to aggregate class-specific graph nodes, focus on meta-tasks associated with various small graphs, thus improving the classification performance on small-scale datasets. Specifically, a node feature enhancement module is proposed to account for the relative importance of node features and enhance their distinguishability through inductive learning. A graph generation constraint module enables learning reasonable graph structures to improve the efficiency of information diffusion while avoiding propagation errors. Furthermore, model interpretability can be naturally enhanced based on the learned node features and graph structures that are closely related to the classification results. Comprehensive experimental evaluation of our method demonstrated excellent interpretable results in the diagnosis of glioma progression. In general, our work provides a novel systematic GNN approach for dealing with data scarcity and enhancing decision interpretability. Our source codes will be released at https://github.com/SJTUBME-QianLab/GBM-GNN.

Liquid Biopsy in Glioblastoma

Simple Summary

Glioblastoma is the most common and malignant primary brain tumor. Despite intensive research for new treatments, the survival of patients has not significantly improved in recent decades. Currently, glioblastoma is mainly diagnosed by neuroimaging techniques followed by histopathological and molecular analysis of the resected or biopsied tissue. Both imaging and tissue-based methods have, despite their advantages, some important limitations highlighting the necessity for alternative techniques such as liquid biopsy. It appears as an attractive and non-invasive alternative to support the diagnosis and the follow-up of patients with glioblastoma and to identify early recurrence. Liquid biopsy, primarily through blood tests, involves the detection and quantification of tumoral content released by tumors into the biofluids. The aim of the present review is to discuss the biological bases, the advantages, and the disadvantages of the most important circulating biomarkers so far proposed for glioblastoma.

Abstract

Glioblastoma (GBM) is the most common and aggressive primary brain tumor. Despite recent advances in therapy modalities, the overall survival of GBM patients remains poor. GBM diagnosis relies on neuroimaging techniques. However, confirmation via histopathological and molecular analysis is necessary. Given the intrinsic limitations of such techniques, liquid biopsy (mainly via blood samples) emerged as a non-invasive and easy-to-implement alternative that could aid in both the diagnosis and the follow-up of GBM patients. Cancer cells release tumoral content into the bloodstream, such as circulating tumor DNA, circulating microRNAs, circulating tumor cells, extracellular vesicles, or circulating nucleosomes: all these could serve as a marker of GBM. In this narrative review, we discuss the current knowledge, the advantages, and the disadvantages of each circulating biomarker so far proposed.

Conventional and Advanced Imaging Techniques in Post-treatment Glioma Imaging

Despite decades of advancement in the diagnosis and therapy of gliomas, the most malignant primary brain tumors, the overall survival rate is still dismal, and their post-treatment imaging appearance remains very challenging to interpret. Since the limitations of conventional magnetic resonance imaging (MRI) in the distinction between recurrence and treatment effect have been recognized, a variety of advanced MR and functional imaging techniques including diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion-weighted imaging (PWI), MR spectroscopy (MRS), as well as a variety of radiotracers for single photon emission computed tomography (SPECT) and positron emission tomography (PET) have been investigated for this indication along with voxel-based and more quantitative analytical methods in recent years. Machine learning and radiomics approaches in recent years have shown promise in distinguishing between recurrence and treatment effect as well as improving prognostication in a malignancy with a very short life expectancy. This review provides a comprehensive overview of the conventional and advanced imaging techniques with the potential to differentiate recurrence from treatment effect and includes updates in the state-of-the-art in advanced imaging with a brief overview of emerging experimental techniques. A series of representative cases are provided to illustrate the synthesis of conventional and advanced imaging with the clinical context which informs the radiologic evaluation of gliomas in the post-treatment setting.

High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 1: Perfusion and Diffusion Techniques

Objective

Summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and highlight the latest bench-to-bedside developments.

Methods

Experts in advanced MRI techniques applied to high-grade glioma treatment response assessment convened through a European framework. Current evidence regarding the potential for monitoring biomarkers in adult high-grade glioma is reviewed, and individual modalities of perfusion, permeability, and microstructure imaging are discussed (in Part 1 of two). In Part 2, we discuss modalities related to metabolism and/or chemical composition, appraise the clinic readiness of the individual modalities, and consider post-processing methodologies involving the combination of MRI approaches (multiparametric imaging) or machine learning (radiomics).

Results

High-grade glioma vasculature exhibits increased perfusion, blood volume, and permeability compared with normal brain tissue. Measures of cerebral blood volume derived from dynamic susceptibility contrast-enhanced MRI have consistently provided information about brain tumor growth and response to treatment; it is the most clinically validated advanced technique. Clinical studies have proven the potential of dynamic contrast-enhanced MRI for distinguishing post-treatment related effects from recurrence, but the optimal acquisition protocol, mode of analysis, parameter of highest diagnostic value, and optimal cut-off points remain to be established. Arterial spin labeling techniques do not require the injection of a contrast agent, and repeated measurements of cerebral blood flow can be performed. The absence of potential gadolinium deposition effects allows widespread use in pediatric patients and those with impaired renal function. More data are necessary to establish clinical validity as monitoring biomarkers. Diffusion-weighted imaging, apparent diffusion coefficient analysis, diffusion tensor or kurtosis imaging, intravoxel incoherent motion, and other microstructural modeling approaches also allow treatment response assessment; more robust data are required to validate these alone or when applied to post-processing methodologies.

Conclusion

Considerable progress has been made in the development of these monitoring biomarkers. Many techniques are in their infancy, whereas others have generated a larger body of evidence for clinical application.

High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 2: Spectroscopy, Chemical Exchange Saturation, Multiparametric Imaging, and Radiomics

Objective

To summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and to highlight the latest bench-to-bedside developments.

Methods

The current evidence regarding the potential for monitoring biomarkers was reviewed and individual modalities of metabolism and/or chemical composition imaging discussed. Perfusion, permeability, and microstructure imaging were similarly analyzed in Part 1 of this two-part review article and are valuable reading as background to this article. We appraise the clinic readiness of all the individual modalities and consider methodologies involving machine learning (radiomics) and the combination of MRI approaches (multiparametric imaging).

Results

The biochemical composition of high-grade gliomas is markedly different from healthy brain tissue. Magnetic resonance spectroscopy allows the simultaneous acquisition of an array of metabolic alterations, with choline-based ratios appearing to be consistently discriminatory in treatment response assessment, although challenges remain despite this being a mature technique. Promising directions relate to ultra-high field strengths, 2-hydroxyglutarate analysis, and the use of non-proton nuclei. Labile protons on endogenous proteins can be selectively targeted with chemical exchange saturation transfer to give high resolution images. The body of evidence for clinical application of amide proton transfer imaging has been building for a decade, but more evidence is required to confirm chemical exchange saturation transfer use as a monitoring biomarker. Multiparametric methodologies, including the incorporation of nuclear medicine techniques, combine probes measuring different tumor properties. Although potentially synergistic, the limitations of each individual modality also can be compounded, particularly in the absence of standardization. Machine learning requires large datasets with high-quality annotation; there is currently low-level evidence for monitoring biomarker clinical application.

Conclusion

Advanced MRI techniques show huge promise in treatment response assessment. The clinical readiness analysis highlights that most monitoring biomarkers require standardized international consensus guidelines, with more facilitation regarding technique implementation and reporting in the clinic.

Effects of Multi-Shell Free Water Correction on Glioma Characterization

Diffusion MRI is a useful tool to investigate the microstructure of brain tumors. However, the presence of fast diffusing isotropic signals originating from non-restricted edematous fluids, within and surrounding tumors, may obscure estimation of the underlying tissue characteristics, complicating the radiological interpretation and quantitative evaluation of diffusion MRI. A multi-shell regularized free water (FW) elimination model was therefore applied to separate free water from tissue-related diffusion components from the diffusion MRI of 26 treatment-naïve glioma patients. We then investigated the diagnostic value of the derived measures of FW maps as well as FW-corrected tensor-derived maps of fractional anisotropy (FA). Presumed necrotic tumor regions display greater mean and variance of FW content than other parts of the tumor. On average, the area under the receiver operating characteristic (ROC) for the classification of necrotic and enhancing tumor volumes increased by 5% in corrected data compared to non-corrected data. FW elimination shifts the FA distribution in non-enhancing tumor parts toward higher values and significantly increases its entropy (p ≤ 0.003), whereas skewness is decreased (p ≤ 0.004). Kurtosis is significantly decreased (p < 0.001) in high-grade tumors. In conclusion, eliminating FW contributions improved quantitative estimations of FA, which helps to disentangle the cancer heterogeneity.

Multiparametric radiomic tissue signature and machine learning for distinguishing radiation necrosis from tumor progression after stereotactic radiosurgery

Background

Stereotactic radiosurgery (SRS) may cause radiation necrosis (RN) that is difficult to distinguish from tumor progression (TP) by conventional MRI. We hypothesize that MRI-based multiparametric radiomics (mpRad) and machine learning (ML) can differentiate TP from RN in a multi-institutional cohort.

Methods

Patients with growing brain metastases after SRS at 2 institutions underwent surgery, and RN or TP were confirmed by histopathology. A radiomic tissue signature (RTS) was selected from mpRad, as well as single T₁ post-contrast (T₁c) and T₂ fluid-attenuated inversion recovery (T₂-FLAIR) radiomic features. Feature selection and supervised ML were performed in a randomly selected training cohort (N = 95) and validated in the remaining cases (N = 40) using surgical pathology as the gold standard.

Results

One hundred and thirty-five discrete lesions (37 RN, 98 TP) from 109 patients were included. Radiographic diagnoses by an experienced neuroradiologist were concordant with histopathology in 67% of cases (sensitivity 69%, specificity 59% for TP). Radiomic analysis indicated institutional origin as a significant confounding factor for diagnosis. A random forest model incorporating 1 mpRad, 4 T₁c, and 4 T₂-FLAIR features had an AUC of 0.77 (95% confidence interval [CI]: 0.66–0.88), sensitivity of 67% and specificity of 86% in the training cohort, and AUC of 0.71 (95% CI: 0.51–0.91), sensitivity of 52% and specificity of 90% in the validation cohort.

Conclusions

MRI-based mpRad and ML can distinguish TP from RN with high specificity, which may facilitate the triage of patients with growing brain metastases after SRS for repeat radiation versus surgical intervention.

Advanced Imaging Techniques for Differentiating Pseudoprogression and Tumor Recurrence After Immunotherapy for Glioblastoma

Glioblastoma (GBM) is the most common malignant tumor of the central nervous system with poor prognosis. Although the field of immunotherapy in glioma is developing rapidly, glioblastoma is still prone to recurrence under strong immune intervention. The major challenges in the process of immunotherapy are evaluating the curative effect, accurately distinguishing between treatment-related reactions and tumor recurrence, and providing guidance for clinical decision-making. Since the conventional magnetic resonance imaging (MRI) is usually difficult to distinguish between pseudoprogression and the true tumor progression, many studies have used various advanced imaging techniques to evaluate treatment-related responses. Meanwhile, criteria for efficacy evaluation of immunotherapy are constantly updated and improved. A standard imaging scheme to evaluate immunotherapeutic response will benefit patients finally. This review mainly summarizes the application status and future trend of several advanced imaging techniques in evaluating the efficacy of GBM immunotherapy.

ADC-Based Stratification of Molecular Glioma Subtypes Using High b-Value Diffusion-Weighted Imaging

PURPOSE

To investigate the diagnostic performance of in vivo ADC-based stratification of integrated molecular glioma grades.

MATERIALS AND METHODS

Ninety-seven patients with histopathologically confirmed glioma were evaluated retrospectively. All patients underwent pre-interventional MRI-examination including diffusion-weighted imaging (DWI) with implemented b-values of 500, 1000, 1500, 2000, and 2500 s/mm2. Apparent Diffusion Coefficient (ADC), Mean Kurtosis (MK), and Mean Diffusivity (MD) maps were generated. The average values were compared among the molecular glioma subgroups of IDH-mutant and IDH-wildtype astrocytoma, and 1p/19q-codeleted oligodendroglioma. One-way ANOVA with post-hoc Games-Howell correction compared average ADC, MD, and MK values between molecular glioma groups. A Receiver Operating Characteristic (ROC) analysis determined the area under the curve (AUC).

RESULTS

Two b-value-dependent ADC-based evaluations presented statistically significant differences between the three molecular glioma sub-groups (p < 0.001, respectively).

CONCLUSIONS

High-b-value ADC from preoperative DWI may be used to stratify integrated molecular glioma subgroups and save time compared to diffusion kurtosis imaging. Higher b-values of up to 2500 s/mm2 may present an important step towards increasing diagnostic accuracy compared to standard DWI protocol.

MRI biomarkers in neuro-oncology

The central role of MRI in neuro-oncology is undisputed. The technique is used, both in clinical practice and in clinical trials, to diagnose and monitor disease activity, support treatment decision-making, guide the use of focused treatments and determine response to treatment. Despite recent substantial advances in imaging technology and image analysis techniques, clinical MRI is still primarily used for the qualitative subjective interpretation of macrostructural features, as opposed to quantitative analyses that take into consideration multiple pathophysiological features. However, the field of quantitative imaging and imaging biomarker development is maturing. The European Imaging Biomarkers Alliance (EIBALL) and Quantitative Imaging Biomarkers Alliance (QIBA) are setting standards for biomarker development, validation and implementation, as well as promoting the use of quantitative imaging and imaging biomarkers by demonstrating their clinical value. In parallel, advanced imaging techniques are reaching the clinical arena, providing quantitative, commonly physiological imaging parameters that are driving the discovery, validation and implementation of quantitative imaging and imaging biomarkers in the clinical routine. Additionally, computational analysis techniques are increasingly being used in the research setting to convert medical images into objective high-dimensional data and define radiomic signatures of disease states. Here, I review the definition and current state of MRI biomarkers in neuro-oncology, and discuss the clinical potential of quantitative image analysis techniques.

Neuroimaging in the Era of the Evolving WHO Classification of Brain Tumors, From the AJR Special Series on Cancer Staging

The inclusion of molecular and genetic information with histopathologic information defines the framework for brain tumor classification and grading. This framework is reflected in the major restructuring of the WHO brain tumor classification system in 2016 and in numerous subsequent proposed updates reflecting ongoing developments in understanding the impact of tumor genotype on classification and grading. This incorporation of molecular and genetic features improves tumor diagnosis and prediction of tumor behavior and response to treatment. Neuroimaging is essential for the noninvasive assessment of pretreatment tumor grading and for identification and determination of therapeutic efficacy. Use of conventional neuroimaging and physiologic imaging techniques, such as diffusion- and perfusion-weighted MRI, can increase diagnostic confidence before and after treatment. Although the use of neuroimaging to consistently determine tumor genetics is not yet robust, promising developments are on the horizon. Given the complexity of the brain tumor microenvironment, the development and implementation of a standardized reporting system can aid in conveying to radiologists, referring providers, and patients important information about brain tumor response to treatment. The purpose of this article is to review the current state and role of neuroimaging in this continuously evolving field.