Differentiating high-grade glioma recurrence from pseudoprogression: Comparing diffusion kurtosis imaging and diffusion tensor imaging

The efficacy of using a multiparametric magnetic resonance imaging-based radiomics model to distinguish glioma recurrence from pseudoprogression

OBJECTIVE

The early differential diagnosis of the postoperative recurrence or pseudoprogression (psPD) of a glioma is of great guiding significance for individualized clinical treatment. This study aimed to evaluate the ability of a multiparametric magnetic resonance imaging (MRI)-based radiomics model to distinguish between the postoperative recurrence and psPD of a glioma early on and in a noninvasive manner.

METHODS

A total of 52 patients with gliomas who attended the Hainan Provincial People's Hospital between 2000 and 2021 and met the inclusion criteria were selected for this study. 1137 and 1137 radiomic features were extracted from T1 enhanced and T2WI/FLAIR sequence images, respectively.After clearing some invalid information and LASSO screening, a total of 9 and 10 characteristic radiological features were extracted and randomly divided into the training set and the test set according to 7:3 ratio. Select-Kbest and minimum Absolute contraction and selection operator (LASSO) were used for feature selection. Support vector machine and logistic regression were used to form a multi-parameter model for training and prediction. The optimal sequence and classifier were selected according to the area under the curve (AUC) and accuracy.

RESULTS

Radiomic models 1, 2 and 3 based on T1WI, T2FLAIR and T1WI + T2T2FLAIR sequences have better performance in the identification of postoperative recurrence and false progression of T1 glioma. The performance of model 2 is more stable, and the performance of support vector machine classifier is more stable. The multiparameter model based on CE-T1 + T2WI/FLAIR sequence showed the best performance (AUC:0.96, sensitivity: 0.87, specificity: 0.94, accuracy: 0.89,95% CI:0.93-1).

CONCLUSION

The use of multiparametric MRI-based radiomics provides a noninvasive, stable, and accurate method for differentiating between the postoperative recurrence and psPD of a glioma, which allows for timely individualized clinical treatment.

Imaging and Liquid Biopsy for Distinguishing True Progression From Pseudoprogression in Gliomas, Current Advances and Challenges

RATIONALE AND OBJECTIVES

Gliomas are aggressive brain tumors with a poor prognosis. Assessing treatment response is challenging because magnetic resonance imaging (MRI) may not distinguish true progression (TP) from pseudoprogression (PsP). This review aims to discuss imaging techniques and liquid biopsies used to distinguish TP from PsP.

MATERIALS AND METHODS

This review synthesizes existing literature to examine advances in imaging techniques, such as magnetic resonance diffusion imaging (MRDI), perfusion-weighted imaging (PWI) MRI, and liquid biopsies, for identifying TP or PsP through tumor markers and tissue characteristics.

RESULTS

Advanced imaging techniques, including MRDI and PWI MRI, have proven effective in delineating tumor tissue properties, offering valuable insights into glioma behavior. Similarly, liquid biopsy has emerged as a potent tool for identifying tumor-derived markers in biofluids, offering a non-invasive glimpse into tumor evolution. Despite their promise, these methodologies grapple with significant challenges. Their sensitivity remains inconsistent, complicating the accurate differentiation between TP and PSP. Furthermore, the absence of standardized protocols across platforms impedes the reliability of comparisons, while inherent biological variability adds complexity to data interpretation.

CONCLUSION

Their potential applications have been highlighted, but gaps remain before routine clinical use. Further research is needed to develop and validate these promising methods for distinguishing TP from PsP in gliomas.

CFINet: Cross-Modality MRI Feature Interaction Network for Pseudoprogression Prediction of Glioblastoma

Pseudoprogression (PSP) is a related reaction of glioblastoma treatment, and misdiagnosis can lead to unnecessary intervention. Magnetic resonance imaging (MRI) provides cross-modality images for PSP prediction studies. However, how to effectively use the complementary information between the cross-modality MRI to improve PSP prediction is still a challenging task. To address this challenge, we propose a cross-modality feature interaction network for PSP prediction. Firstly, we propose a triple-branch multi-scale module to extract low-order feature representations and a skip-connection multi-scale module to extract high-order feature representations. Then, a cross-modality interaction module based on attention mechanism is designed to make the complementary information between cross-modality MRI fully interact. Finally, the high-order cross-modality interaction information is fed into a multi-layer perceptron to achieve the PSP prediction task. We evaluate the proposed network on a private dataset with 52 subjects from Hunan Cancer Hospital and validate it on a private dataset with 30 subjects from Xiangya Hospital. The accuracy of our proposed network on the datasets is 0.954 and 0.929, respectively, which is better than most typical convolutional neural network and interaction methods.

Detection of recurrent high-grade glioma using microstructure characteristics of distinct metabolic compartments in a multimodal and integrative 18F-FET PET/fast-DKI approach

OBJECTIVES

Differentiation between high-grade glioma (HGG) and post-treatment-related effects (PTRE) is challenging, but advanced imaging techniques were shown to provide benefit. We aim to investigate microstructure characteristics of metabolic compartments identified from amino acid PET and to evaluate the diagnostic potential of this multimodal and integrative O-(2-18F-fluoroethyl)-L-tyrosine-(FET)-PET and fast diffusion kurtosis imaging (DKI) approach for the detection of recurrence and IDH genotyping.

METHODS

Fifty-nine participants with neuropathologically confirmed recurrent HGG (n = 39) or PTRE (n = 20) were investigated using static 18F-FET PET and a fast-DKI variant. PET and advanced diffusion metrics of metabolically defined (80-100% and 60-75% areas of 18F-FET uptake) compartments were assessed. Comparative analysis was performed using Mann-Whitney U tests with Holm-Šídák multiple-comparison test and Wilcoxon signed-rank test. Receiver operating characteristic (ROC) curves, regression, and Spearman's correlation analysis were used for statistical evaluations.

RESULTS

Compared to PTRE, recurrent HGG presented increased 18F-FET uptake and diffusivity (MD60), but lower (relative) mean kurtosis tensor (rMKT60) and fractional anisotropy (FA60) (respectively p < .05). Diffusion metrics determined from the metabolic periphery showed improved diagnostic performance - most pronounced for FA60 (AUC = 0.86, p < .001), which presented similar benefit to 18F-FET PET (AUC = 0.86, p < .001) and was negatively correlated with amino acid uptake (rs =  - 0.46, p < .001). When PET and DKI metrics were evaluated in a multimodal biparametric approach, TBRmax + FA60 showed highest diagnostic accuracy (AUC = 0.93, p < .001), which improved the detection of relapse compared to PET alone (difference in AUC = 0.069, p = .04). FA60 and MD60 distinguished the IDH genotype in the post-treatment setting.

CONCLUSION

Detection of glioma recurrence benefits from a multimodal and integrative PET/DKI approach, which presented significant diagnostic advantage to the assessment based on PET alone.

CLINICAL RELEVANCE STATEMENT

A multimodal and integrative 18F-FET PET/fast-DKI approach for the non-invasive microstructural characterization of metabolic compartments provided improved diagnostic capability for differentiation between recurrent glioma and post-treatment-related changes, suggesting a role for the diagnostic workup of patients in post-treatment settings.

KEY POINTS

• Multimodal PET/MRI with integrative analysis of 18F-FET PET and fast-DKI presents clinical benefit for the assessment of CNS cancer, particularly for the detection of recurrent high-grade glioma. • Microstructure markers of the metabolic periphery yielded biologically pertinent estimates characterising the tumour microenvironment, and, thereby, presented improved diagnostic accuracy with similar accuracy to amino acid PET. • Combined 18F-FET PET/fast-DKI achieved the best diagnostic performance for detection of high-grade glioma relapse with significant benefit to the assessment based on PET alone.

Based on functional and histopathological correlations: is diffusion kurtosis imaging valuable for noninvasive assessment of renal damage in early-stage of chronic kidney disease?

PURPOSE

To evaluate the potential of 3 T magnetic resonance diffusion kurtosis imaging (DKI) in assessing the renal damage in early-stage of chronic kidney disease (CKD) patients with normal or slightly changed functional index, using histopathology as reference standard.

METHODS

49 CKD patients and 18 healthy volunteers were recruited in this study. CKD patients were divided into two groups based on estimated glomerular filtration rate (eGFR): Study group I (eGFR ≥ 90 ml/min/1.73 m2 [n = 20]) and Study group II (eGFR < 90 ml/min/1.73 m2 [n = 29]). DKI was performed in all participants. The DKI parameters (mean kurtosis [MK], mean diffusivity [MD], fractional anisotropy [FA]) of renal cortex and medulla were measured. The differences of parenchymal MD, MK and FA values among the different groups were compared. The correlations between DKI parameters and clinicopathological characteristics were assessed. Diagnostic performance of DKI to assess renal damage in early-stage of CKD was analyzed.

RESULTS

The cortex MD and MK showed significant difference among three groups (P < 0.05): trend of cortex MD: Study group II < Study group I < control group; trend of cortex MK: control group < Study group I < Study group II. The cortex MD and MK and medulla FA were correlated with eGFR and Interstitial fibrosis/Tubular atrophy score (0.3 < r < 0.5). Cortex MD and MK yielded an AUC of 0.752 for differentiating healthy volunteers from CKD patients with eGFR ≥ 90 ml/min/1.73 m2.

CONCLUSION

DKI shows potential in non-invasive and multi-parameter quantitative assessment of renal damage in early-stage of CKD patients and provide additional information for changes in renal function and histopathology.

Integrated analysis reveals the potential of cluster of differentiation 86 as a key biomarker in high-grade glioma

This study aimed to evaluate the potential of cluster of differentiation 86 (CD86) as a biomarker in high-grade glioma (HGG). The TCGA and TCIA databases were used to obtain the CD86 expression value, clinical data, and MRI images of HGG patients. Prognostic values were assessed by the Kaplan-Meier method, Receiver operating characteristic curve (ROC), Cox regression, logistic regression, and nomogram analyses. CD86-associated pathways were also explored. We found that CD86 was significantly upregulated in HGG compared with the normal group. Survival analysis showed a significant association between CD86 high expression and shorter overall survival time. Its independent prognostic value was also confirmed. These results suggested the possibility of CD86 as a biomarker in HGG. We also innovatively established 2 radiomics models with Support Vector Machine (SVM) and Logistic regression (LR) algorithms to predict the CD86 expression. The 2 models containing 5 optimal features by SVM and LR methods showed similar favorable performance in predicting CD86 expression in the training set, and their performance were also confirmed in validation set. These results indicated the successful construction of a radiomics model for non-invasively predicting biomarker in HGG. Finally, pathway analysis indicated that CD86 might be involved in the natural killer cell-mediated cytotoxicity in HGG progression.

Use of Radiomics Models in Preoperative Grading of Cerebral Gliomas and Comparison with Three-dimensional Arterial Spin Labelling

AIMS

To build machine learning-based radiomics models to discriminate between high- (HGGs) and low-grade gliomas (LGGs) and to compare the effectiveness of three-dimensional arterial spin labelling (3D-ASL) to evaluate which is a better method.

MATERIALS AND METHODS

We retrospectively analysed the magnetic resonance imaging T1WI-enhanced images of 105 patients with gliomas that were pathologically confirmed in our hospital. We divided the patients into a training group and a verification group at a ratio of 8:2; 200 patients from the Brain Tumour Segmentation Challenge 2020 were selected as the test group for image segmentation, feature extraction and screening. We constructed models using multilayer perceptron (MLP), support vector machine, random forest and logistic regression and evaluated their predictive performance. We obtained the mean maximum relative cerebral blood flow (rCBFmax) value from 3D-ASL of 105 patients from the hospital to evaluate its efficacy in discriminating between HGGs and LGGs.

RESULTS

In machine learning, the MLP classifier model exhibited the best performance in discriminating between HGGs and LGGs; the areas under the curve obtained by MLP and rCBFmax were 0.968 versus 0.815 (verification group) and 0.981 versus 0.815 (test group), respectively. The machine learning-based MLP classifier model performed better in discriminating between HGGs and LGGs than 3D-ASL.

CONCLUSION

In our study, we found that machine learning-based radiomics models and 3D-ASL were valuable in discriminating between HGGs and LGGs and between them, the machine learning-based MLP model had better diagnostic performance.

Exploring neural activity in inflammatory bowel diseases using functional connectivity and DKI-fMRI fusion

Although MRI has made considerable progress in Inflammatory bowel disease (IBD), most studies have concentrated on data information from a single modality, and a better understanding of the interplay between brain function and structure, as well as appropriate clinical aids to diagnosis, is required. We calculated functional connectivity through fMRI time series using resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion kurtosis imaging (DKI) data from 27 IBD patients and 29 healthy controls. Through the DKI data of each subject, its unique structure map is obtained, and the relevant indicators are projected onto the structure map corresponding to each subject by using the graph Fourier transform in the grasp signal processing (GSP) technology. After the features are optimized, a classical support vector machine is used to classify the features. IBD patients have altered functional connectivity in the default mode network (DMN) and subcortical network (SCN). At the same time, compared with the traditional brain network analysis, in the test of some indicators, the average classification accuracy produced by the framework method is 12.73% higher than that of the traditional analysis method. This paper found that the brain network structure of IBD patients in DMN and SCN has changed. Simultaneously, the application of GSP technology to fuse functional information and structural information is superior to the traditional framework in classification, providing a new perspective for subsequent clinical auxiliary diagnosis.

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.

Apatinib combined with temozolomide treatment for pseudoprogression in glioblastoma: A case report

RATIONALE

Glioblastoma is the most common malignant tumor of the central nervous system, which originates from glial cells and corresponding precursors. Due to its strong invasion and rapid growth, the prognosis of patients after treatment is very poor and easy to relapse.

PATIENT CONCERNS

In August 2015, a 48 years old man with a relapse of glioblastoma.

DIAGNOSES

The patient was diagnosed by computed tomography, magnetic resonance imaging, and pathological biopsy in this case report.

INTERVENTIONS

The patient underwent 2 surgeries, radiotherapy, and multiple regular chemotherapy sessions over the next 6 years. Apatinib, an inhibitor of vascular endothelial growth factor receptor 2 was given to treat recurrent glioma.

OUTCOMES

It was found that radiotherapy combined with temozolomide administration often increased the size of the original lesion or produced a new glioblastoma lesion. The lesion development was similar to tumor progression, which was called pseudoprogression. And it significantly prolonged the survival of this patient.

LESSONS

Surgery, radiotherapy and chemotherapy with apatinib and temozolomide are effective to treat the patients with pseudoprogression in glioblastoma.

Tumor Progression and Treatment-Related Changes: Radiological Diagnosis Challenges for the Evaluation of Post Treated Glioma

Simple Summary

Glioma is the most common primary malignant tumor of the adult central nervous system. Despite aggressive multimodal treatment, its prognosis remains poor. During follow-up, it remains challenging to distinguish treatment-related changes from tumor progression in treated patients with gliomas due to both share clinical symptoms and morphological imaging characteristics (with new and/or increasing enhancing mass lesions). The early effective identification of tumor progression and treatment-related changes is of great significance for the prognosis and treatment of gliomas. We believe that advanced neuroimaging techniques can provide additional information for distinguishing both at an early stage. In this article, we focus on the research of magnetic resonance imaging technology and artificial intelligence in tumor progression and treatment-related changes. Finally, it provides new ideas and insights for clinical diagnosis.

Abstract

As the most common neuro-epithelial tumors of the central nervous system in adults, gliomas are highly malignant and easy to recurrence, with a dismal prognosis. Imaging studies are indispensable for tracking tumor progression (TP) or treatment-related changes (TRCs). During follow-up, distinguishing TRCs from TP in treated patients with gliomas remains challenging as both share similar clinical symptoms and morphological imaging characteristics (with new and/or increasing enhancing mass lesions) and fulfill criteria for progression. Thus, the early identification of TP and TRCs is of great significance for determining the prognosis and treatment. Histopathological biopsy is currently the gold standard for TP and TRC diagnosis. However, the invasive nature of this technique limits its clinical application. Advanced imaging methods (e.g., diffusion magnetic resonance imaging (MRI), perfusion MRI, magnetic resonance spectroscopy (MRS), positron emission tomography (PET), amide proton transfer (APT) and artificial intelligence (AI)) provide a non-invasive and feasible technical means for identifying of TP and TRCs at an early stage, which have recently become research hotspots. This paper reviews the current research on using the abovementioned advanced imaging methods to identify TP and TRCs of gliomas. First, the review focuses on the pathological changes of the two entities to establish a theoretical basis for imaging identification. Then, it elaborates on the application of different imaging techniques and AI in identifying the two entities. Finally, the current challenges and future prospects of these techniques and methods are discussed.

Discriminators of pseudoprogression and true progression in high-grade gliomas: A systematic review and meta-analysis

High-grade gliomas remain the most common primary brain tumour with limited treatments options and early recurrence rates following adjuvant treatments. However, differentiating true tumour progression (TTP) from treatment-related effects or pseudoprogression (PsP), may critically influence subsequent management options. Structural MRI is routinely employed to evaluate treatment responses, but misdiagnosis of TTP or PsP may lead to continuation of ineffective or premature cessation of effective treatments, respectively. A systematic review and meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses method. Embase, MEDLINE, Web of Science and Google Scholar were searched for methods applied to differentiate PsP and TTP, and studies were selected using pre-specified eligibility criteria. The sensitivity and specificity of included studies were summarised. Three of the identified methods were compared in a separate subgroup meta-analysis. Thirty studies assessing seven distinct neuroimaging methods in 1372 patients were included in the systematic review. The highest performing methods in the subgroup analysis were DWI (AUC = 0.93 [0.91-0.95]) and DSC-MRI (AUC = 0.93 [0.90-0.95]), compared to DCE-MRI (AUC = 0.90 [0.87-0.93]). 18F-fluoroethyltyrosine PET (18F-FET PET) and amide proton transfer-weighted MRI (APTw-MRI) also showed high diagnostic accuracy, but results were based on few low-powered studies. Both DWI and DSC-MRI performed with high sensitivity and specificity for differentiating PsP from TTP. Considering the technical parameters and feasibility of each identified method, the authors suggested that, at present, DSC-MRI technique holds the most clinical potential.

The Value of Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) in the Differentiation of Pseudoprogression and Recurrence of Intracranial Gliomas

Objective

The objective of this study was to determine the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in assessing postoperative changes in intracranial gliomas.

Method

A total of fifty-one patients who had new enhanced lesions after surgical resection followed by standard radiotherapy and chemotherapy were collected retrospectively from October 2014 to June 2021. The patients were divided into a pseudoprogression group (15 cases) and a recurrence group (36 cases) according to the pathological results of the second operation or a follow-up of more than six months. The follow-up data of all patients were complete, and DCE-MRI was performed. The images were processed to obtain the quantitative parameters K^trans, Ve, and Kep and the semiquantitative parameter iAUC, which were analysed with relevant statistical software.

Results

First, the difference in K^trans and iAUC values between the two groups was statistically significant (P < 0.05), and the difference in Ve and Kep values was not statistically significant (P > 0.05). Second, by comparing the area under the curve, threshold, sensitivity and specificity of K^trans, and iAUC, it was found that the iAUC threshold value was slightly higher than that of K^trans, and the specificity of K^trans was equal to that of iAUC, while the area under the curve and sensitivity of K^trans were higher than those of iAUC. Third, K^trans and iAUC had high accuracy in diagnosing recurrence and pseudoprogression, and K^trans had higher accuracy than iAUC.

Conclusion

In this study, DCE-MRI has a certain diagnostic value in the early differentiation of recurrence and pseudoprogression, offering a new method for the diagnosis and assessment of gliomas after surgery.

Metabolic and physiologic magnetic resonance imaging in distinguishing true progression from pseudoprogression in patients with glioblastoma

Pseudoprogression (PsP) refers to treatment-related clinico-radiologic changes mimicking true progression (TP) that occurs in patients with glioblastoma (GBM), predominantly within the first 6 months after the completion of surgery and concurrent chemoradiation therapy (CCRT) with temozolomide. Accurate differentiation of TP from PsP is essential for making informed decisions on appropriate therapeutic intervention as well as for prognostication of these patients. Conventional neuroimaging findings are often equivocal in distinguishing between TP and PsP and present a considerable diagnostic dilemma to oncologists and radiologists. These challenges have emphasized the need for developing alternative imaging techniques that may aid in the accurate diagnosis of TP and PsP. In this review, we encapsulate the current state of knowledge in the clinical applications of commonly used metabolic and physiologic magnetic resonance (MR) imaging techniques such as diffusion and perfusion imaging and proton spectroscopy in distinguishing TP from PsP. We also showcase the potential of promising imaging techniques, such as amide proton transfer and amino acid-based positron emission tomography, in providing useful information about the treatment response. Additionally, we highlight the role of "radiomics", which is an emerging field of radiology that has the potential to change the way in which advanced MR techniques are utilized in assessing treatment response in GBM patients. Finally, we present our institutional experiences and discuss future perspectives on the role of multiparametric MR imaging in identifying PsP in GBM patients treated with "standard-of-care" CCRT as well as novel/targeted therapies.

Nomogram for predicting early recurrence in patients with high-grade gliomas

OBJECTIVE

To develop a nomogram to predict early recurrence of high-grade glioma (HGG) based on clinical pathology, genetic factors and MRI parameters.

METHODS

154 patients with HGG were classified into recurrence and non-recurrence groups based on the pathological diagnosis and RANO criteria. Clinical pathology information included age, sex, preoperative Karnofsky performance status (KPS) scores,grade, and cell proliferation index (Ki-67). Gene information included P53, IDH1, MGMT, and TERT expression status. All patients underwent baseline MRIs before treatment, including T1WI, T2WI, T1C, Flair, and DWI examinations. Tumor location, single/multiple tumors, tumor diameter, peritumoral edema, necrotic cyst, hemorrhage, average apparent diffusion coefficient(ADC) value, and minimum ADC values were evaluated. Univariate and multivariate logistic regression analyses were used to determine the predictors of early recurrence and build nomogram.

RESULTS

Univariate analysis showed that the number of tumors (OR, 0.258; 95% CI: 0.104, 0.639; P = 0.003) and peritumoral edema (OR, 0.965; 95% CI 0.942, 0.988; P = 0.003; mean in the recurrence group 22.04±17.21 mm; mean in the non-recurrence group 14.22±12.84 mm) were statistically significantly different in patients with early recurrence. Genetic factors associated with early recurrence included IDH1 (OR, 4.405; 95% CI 1.874, 10.353; P= 0.001), and MGMT (OR, 2.389; 95% CI 1.234, 4.628; P= 0.010). Multivariate logistic regression analysis revealed that the number of tumors (OR, 0.227; 95% CI 0.084, 0.616; P = 0.004), peritumoral edema (OR, 0.969; 95% CI 0.945, 0.993; P = 0.013), and IDH1 (OR, 4.200; 95% CI 1.602, 10.013; P= 0.004) were independent risk factors for early recurrence. The nomogram showed the highest net benefit when the threshold probability was less than 60%.

CONCLUSION

A nomogram prediction model can effectively aid in clinical treatment decisions for patients with newly diagnosed HGG .

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.

Treatment Response and Prognosis Evaluation in High-Grade Glioma: An Imaging Review Based on MRI

In recent years, the development of advanced magnetic resonance imaging (MRI) technology and machine learning (ML) have created new tools for evaluating treatment response and prognosis of patients with high-grade gliomas (HGG); however, patient prognosis has not improved significantly. This is mainly due to the heterogeneity between and within HGG tumors, resulting in standard treatment methods not benefitting all patients. Moreover, the survival of patients with HGG is not only related to tumor cells, but also to noncancer cells in the tumor microenvironment (TME). Therefore, during preoperative diagnosis and follow-up treatment of patients with HGG, noninvasive imaging markers are needed to characterize intratumoral heterogeneity, and then to evaluate treatment response and predict prognosis, timeously adjust treatment strategies, and achieve individualized diagnosis and treatment. In this review, we summarize the research progress of conventional MRI, advanced MRI technology, and ML in evaluation of treatment response and prognosis of patients with HGG. We further discuss the significance of the TME in the prognosis of HGG patients, associate imaging features with the TME, indirectly reflecting the heterogeneity within the tumor, and shifting treatment strategies from tumor cells alone to systemic therapy of the TME, which may be a major development direction in the future. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 4.

Diffusion kurtosis imaging combined with dynamic susceptibility contrast-enhanced MRI in differentiating high-grade glioma recurrence from pseudoprogression

OBJECTIVES

To compare the added value of diffusion kurtosis imaging (DKI) with the combination of dynamic susceptibility contrast-enhanced (DSC) MRI in differentiating glioma recurrence from pseudoprogression.

METHODS

Thirty-four patients with high-grade gliomas developing new and/or increasing enhanced lesions within six months after surgery and chemoradiotherapy were retrospectively analyzed. All patients were pathologically confirmed to have recurrent glioma (n = 22) or pseudoprogression (n = 12). The DKI and DSC MRI parameters were calculated based on the enhanced lesions on contrast-enhanced T1WI. ROC analysis was performed on significant variables to determine their diagnostic performance. Multivariate logistic regression was used to determine the best prediction model for discrimination.

RESULTS

The relative mean kurtosis (rMK), relative axial kurtosis (rKa), relative cerebral blood volume (rCBV), and relative mean transit time (rMTT) of glioma recurrence were higher than those of pseudoprogression (all, P < 0.05). The AUCs and diagnostic accuracy were 0.879 and 82.35% for rMK, 0.723 and 70.59% for rKa, 0.890 and 82.35% for rCBV, 0.765 and 73.53% for rMTT, respectively. A multivariate logistic regression model showed a significant contribution of rMK (P = 0.006) and rCBV (P = 0.009) as independent imaging classifiers for discrimination. The combined use of rMK and rCBV improved the AUC to 0.924 (P < 0.001) and the diagnostic accuracy to 88.24%.

CONCLUSION

DKI may be a valuable non-invasive tool in differentiating glioma recurrence from pseudoprogression, and its use in combination with DSC MRI can improve diagnostic performance in assessing treatment response compared with either technique alone.

Diffusion-Weighted Imaging: Recent Advances and Applications

Quantitative diffusion imaging techniques enable the characterization of tissue microstructural properties of the human brain "in vivo", and are widely used in neuroscientific and clinical contexts. In this review, we present the basic physical principles behind diffusion imaging and provide an overview of the current diffusion techniques, including standard and advanced techniques as well as their main clinical applications. Standard diffusion tensor imaging (DTI) offers sensitivity to changes in microstructure due to diseases and enables the characterization of single fiber distributions within a voxel as well as diffusion anisotropy. Nonetheless, its inability to represent complex intravoxel fiber topologies and the limited biological specificity of its metrics motivated the development of several advanced diffusion MRI techniques. For example, high-angular resolution diffusion imaging (HARDI) techniques enabled the characterization of fiber crossing areas and other complex fiber topologies in a single voxel and supported the development of higher-order signal representations aiming to decompose the diffusion MRI signal into distinct microstructure compartments. Biophysical models, often known by their acronym (e.g., CHARMED, WMTI, NODDI, DBSI, DIAMOND) contributed to capture the diffusion properties from each of such tissue compartments, enabling the computation of voxel-wise maps of axonal density and/or morphology that hold promise as clinically viable biomarkers in several neurological and neuroscientific applications; for example, to quantify tissue alterations due to disease or healthy processes. Current challenges and limitations of state-of-the-art models are discussed, including validation efforts. Finally, novel diffusion encoding approaches (e.g., b-tensor or double diffusion encoding) may increase the biological specificity of diffusion metrics towards intra-voxel diffusion heterogeneity in clinical settings, holding promise in neurological applications.