Pseudoprogression in Patients with Glioblastoma: Assessment by Using Volume-weighted Voxel-based Multiparametric Clustering of MR Imaging Data in an Independent Test Set

Assessment of imaging risks for recurrence after stereotactic radiosurgery for brain metastases (IRRaS-BM)

BACKGROUND

The identification of viable tumors and radiation necrosis after stereotactic radiosurgery (SRS) is crucial for patient management. Tumor habitat analysis involving the grouping of similar voxels can identify subregions that share common biology and enable the depiction of areas of tumor recurrence and treatment-induced change. This study aims to validate an imaging biomarker for tumor recurrence after SRS for brain metastasis by conducting tumor habitat analysis using multi-parametric MRI.

METHODS

In this prospective study (NCT05868928), patients with brain metastases will undergo multi-parametric MRI before SRS, and then follow-up MRIs will be conducted every 3 months until 24 months after SRS. The multi-parametric MRI protocol will include T2-weighted and contrast-enhanced T1-weighted imaging, diffusion-weighted imaging, and dynamic susceptibility contrast imaging. Using k-means voxel-wise clustering, this study will define three structural MRI habitats (enhancing, solid low-enhancing, and nonviable) on T1- and T2-weighted images and three physiologic MRI habitats (hypervascular cellular, hypovascular cellular, and nonviable) on apparent diffusion coefficient maps and cerebral blood volume maps. Using RANO-BM criteria as the reference standard, via Cox proportional hazards analysis, the study will prospectively evaluate associations between parameters of the tumor habitats and the time to recurrence. The DICE similarity coefficients between the recurrence site and tumor habitats will be calculated.

DISCUSSION

The tumor habitat analysis will provide an objective and reliable measure for assessing tumor recurrence from brain metastasis following SRS. By identifying subregions for local recurrence, our study could guide the next therapeutic targets for patients after SRS.

TRIAL REGISTRATION

This study is registered at ClinicalTrials.gov (NCT05868928).

Symptomatic pseudoprogression in metastatic colorectal cancer

A man in his 70s with metastatic colorectal cancer presented with worsening clinical symptoms and imaging studies concerning for disease progression. He had received two cycles of pembrolizumab, but due to his symptomatic presentation and significant decline in performance status, there was concern for worsening disease. Transitioning to hospice was briefly considered, given his clinical decline and the notable increase in tumour size. Despite the presence of clinical symptoms and radiographic findings, pseudoprogression-defined as an increase in the size(s) of and/or visual appearance of new lesion(s), followed by a response-was also considered as part of the diagnostic possibilities. Consequently, the decision was made to proceed with a third cycle of pembrolizumab. During his subsequent outpatient follow-up, the patient showed significant symptomatic improvement and reported a decrease in his palpable right flank mass. With further immunotherapy, the patient continued to demonstrate symptomatic and radiological improvement.

Imaging Genomics of Glioma Revisited: Analytic Methods to Understand Spatial and Temporal Heterogeneity

An improved understanding of the cellular and molecular biologic processes responsible for brain tumor development, growth, and resistance to therapy is fundamental to improving clinical outcomes. Imaging genomics is the study of the relationships between microscopic, genetic, and molecular biologic features and macroscopic imaging features. Imaging genomics is beginning to shift clinical paradigms for diagnosing and treating brain tumors. This article provides an overview of imaging genomics in gliomas, in which imaging data including hallmarks such as IDH-mutation, MGMT methylation, and EGFR-mutation status can provide critical insights into the pretreatment and posttreatment stages. This article will accomplish the following: 1) review the methods used in imaging genomics, including visual analysis, quantitative analysis, and radiomics analysis; 2) recommend suitable analytic methods for imaging genomics according to biologic characteristics; 3) discuss the clinical applicability of imaging genomics; and 4) introduce subregional tumor habitat analysis with the goal of guiding future radiogenetics research endeavors toward translation into critically needed clinical applications.

Advances in Neuro-Oncological Imaging: An Update on Diagnostic Approach to Brain Tumors

This study delineates the pivotal role of imaging within the field of neurology, emphasizing its significance in the diagnosis, prognostication, and evaluation of treatment responses for central nervous system (CNS) tumors. A comprehensive understanding of both the capabilities and limitations inherent in emerging imaging technologies is imperative for delivering a heightened level of personalized care to individuals with neuro-oncological conditions. Ongoing research in neuro-oncological imaging endeavors to rectify some limitations of radiological modalities, aiming to augment accuracy and efficacy in the management of brain tumors. This review is dedicated to the comparison and critical examination of the latest advancements in diverse imaging modalities employed in neuro-oncology. The objective is to investigate their respective impacts on diagnosis, cancer staging, prognosis, and post-treatment monitoring. By providing a comprehensive analysis of these modalities, this review aims to contribute to the collective knowledge in the field, fostering an informed approach to neuro-oncological care. In conclusion, the outlook for neuro-oncological imaging appears promising, and sustained exploration in this domain is anticipated to yield further breakthroughs, ultimately enhancing outcomes for individuals grappling with CNS tumors.

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.

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.

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.

Diagnostic performance of DSC perfusion MRI to distinguish tumor progression and treatment-related changes: a systematic review and meta-analysis

Background

In patients with high-grade glioma (HGG), true disease progression and treatment-related changes often appear similar on magnetic resonance imaging (MRI), making it challenging to evaluate therapeutic response. Dynamic susceptibility contrast (DSC) MRI has been extensively studied to differentiate between disease progression and treatment-related changes. This systematic review evaluated and synthesized the evidence for using DSC MRI to distinguish true progression from treatment-related changes.

Methods

We searched Ovid MEDLINE and the Ovid MEDLINE in-process file (January 2005-October 2019) and the reference lists. Studies on test performance of DSC MRI using relative cerebral blood volume in HGG patients were included. One investigator abstracted data, and a second investigator confirmed them; two investigators independently assessed study quality. Meta-analyses were conducted to quantitatively synthesize area under the receiver operating curve (AUROC), sensitivity, and specificity.

Results

We screened 1177 citations and included 28 studies with 638 patients with true tumor progression, and 430 patients with treatment-related changes. Nineteen studies reported AUROC and the combined AUROC is 0.85 (95% CI, 0.81-0.90). All studies contributed data for sensitivity and specificity, and the pooled sensitivity and specificity are 0.84 (95% CI, 0.80-0.88), and 0.78 (95% CI, 0.72-0.83). Extensive subgroup analyses based on study, treatment, and imaging characteristics generally showed similar results.

Conclusions

There is moderate strength of evidence that relative cerebral blood volume obtained from DSC imaging demonstrated "excellent" ability to discriminate true tumor progression from treatment-related changes, with robust sensitivity and specificity.

Longitudinal structural and perfusion MRI enhanced by machine learning outperforms standalone modalities and radiological expertise in high-grade glioma surveillance

PURPOSE

Surveillance of patients with high-grade glioma (HGG) and identification of disease progression remain a major challenge in neurooncology. This study aimed to develop a support vector machine (SVM) classifier, employing combined longitudinal structural and perfusion MRI studies, to classify between stable disease, pseudoprogression and progressive disease (3-class problem).

METHODS

Study participants were separated into two groups: group I (total cohort: 64 patients) with a single DSC time point and group II (19 patients) with longitudinal DSC time points (2-3). We retrospectively analysed 269 structural MRI and 92 dynamic susceptibility contrast perfusion (DSC) MRI scans. The SVM classifier was trained using all available MRI studies for each group. Classification accuracy was assessed for different feature dataset and time point combinations and compared to radiologists' classifications.

RESULTS

SVM classification based on combined perfusion and structural features outperformed radiologists' classification across all groups. For the identification of progressive disease, use of combined features and longitudinal DSC time points improved classification performance (lowest error rate 1.6%). Optimal performance was observed in group II (multiple time points) with SVM sensitivity/specificity/accuracy of 100/91.67/94.7% (first time point analysis) and 85.71/100/94.7% (longitudinal analysis), compared to 60/78/68% and 70/90/84.2% for the respective radiologist classifications. In group I (single time point), the SVM classifier also outperformed radiologists' classifications with sensitivity/specificity/accuracy of 86.49/75.00/81.53% (SVM) compared to 75.7/68.9/73.84% (radiologists).

CONCLUSION

Our results indicate that utilisation of a machine learning (SVM) classifier based on analysis of longitudinal perfusion time points and combined structural and perfusion features significantly enhances classification outcome (p value= 0.0001).

Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review. Part 2 - Radiological features and metric markers

After chemoradiotherapy for glioblastoma, pseudoprogression can occur and must be distinguished from true progression to correctly manage glioblastoma treatment and follow-up. Conventional treatment response assessment is evaluated via conventional MRI (contrast-enhanced T1-weighted and T2/FLAIR), which is unreliable. The emergence of advanced MRI techniques, MR spectroscopy, and PET tracers has improved pseudoprogression diagnostic accuracy. This review presents a literature review of the different imaging techniques and potential imaging biomarkers to differentiate pseudoprogression from true progression.

Comparison of Dynamic Contrast-Enhancement Parameters between Gadobutrol and Gadoterate Meglumine in Posttreatment Glioma: A Prospective Intraindividual Study

BACKGROUND AND PURPOSE

Differences in molecular properties between one-molar and half-molar gadolinium-based contrast agents are thought to affect parameters obtained from dynamic contrast-enhanced imaging. The aim of our study was to investigate differences in dynamic contrast-enhanced parameters between one-molar nonionic gadobutrol and half-molar ionic gadoterate meglumine in patients with posttreatment glioma.

MATERIALS AND METHODS

This prospective study enrolled 32 patients who underwent 2 20-minute dynamic contrast-enhanced examinations, one with gadobutrol and one with gadoterate meglumine. The model-free parameter of area under the signal intensity curve from 30 to 1100 seconds and the Tofts model-based pharmacokinetic parameters were calculated and compared intraindividually using paired t tests. Patients were further divided into progression (n = 12) and stable (n = 20) groups, which were compared using Student t tests.

RESULTS

Gadobutrol and gadoterate meglumine did not show any significant differences in the area under the signal intensity curve or pharmacokinetic parameters of K ^trans, V_e, V_p, or K_ep (all P > .05). Gadobutrol showed a significantly higher mean wash-in rate (0.83 ± 0.64 versus 0.29 ± 0.63, P = .013) and a significantly lower mean washout rate (0.001 ± 0.0001 versus 0.002 ± 0.002, P = .02) than gadoterate meglumine. Trends toward higher area under the curve, K ^trans, V_e, V_p, wash-in, and washout rates and lower K_ep were observed in the progression group in comparison with the treatment-related-change group, regardless of the contrast agent used.

CONCLUSIONS

Model-free and pharmacokinetic parameters did not show any significant differences between the 2 gadolinium-based contrast agents, except for a higher wash-in rate with gadobutrol and a higher washout rate with gadoterate meglumine, supporting the interchangeable use of gadolinium-based contrast agents for dynamic contrast-enhanced imaging in patients with posttreatment glioma.

Spatiotemporal Heterogeneity in Multiparametric Physiologic MRI Is Associated with Patient Outcomes in IDH-Wildtype Glioblastoma

PURPOSE

Heterogeneity in glioblastomas is associated with poorer outcomes, and physiologic heterogeneity can be quantified with noninvasive imaging. We developed spatial habitats based on multiparametric physiologic MRI and evaluated associations between temporal changes in these habitats and progression-free survival (PFS) after concurrent chemoradiotherapy (CCRT) in patients with glioblastoma.

EXPERIMENTAL DESIGN

Ninety-seven patients with isocitrate dehydrogenase (IDH)-wildtype glioblastoma were enrolled and two serial MRI examinations after CCRT were analyzed. Cerebral blood volumes and apparent diffusion coefficients were grouped using k-means clustering into three spatial habitats. Associations between temporal changes in spatial habitats and PFS were investigated using Cox proportional hazard modeling. The performance of significant predictors for PFS and overall survival (OS) was measured using a discrete increase of habitat (habitat risk score) in a temporal validation set from a prospective registry (n = 53, ClinicalTrials.gov NCT02619890). The site of progression was matched with the spatiotemporal habitats.

RESULTS

Three spatial habitats of hypervascular cellular, hypovascular cellular, and nonviable tissue were identified. A short-term increase in the hypervascular cellular habitat (HR, 40.0; P = 0.001) and hypovascular cellular habitat was significantly associated with shorter PFS (HR, 3.78; P < 0.001) after CCRT. Combined with clinical predictors, the habitat risk score showed a C-index of 0.79 for PFS and 0.74 for OS and stratified patients with short, intermediate, and long PFS (P = 0.016). An increase in the hypovascular cellular habitat predicted tumor progression sites.

CONCLUSIONS

Hypovascular cellular habitats derived from multiparametric physiologic MRIs may be useful predictors of clinical outcomes in patients with posttreatment glioblastoma.

Radiomics and Deep Learning from Research to Clinical Workflow: Neuro-Oncologic Imaging

Imaging plays a key role in the management of brain tumors, including the diagnosis, prognosis, and treatment response assessment. Radiomics and deep learning approaches, along with various advanced physiologic imaging parameters, hold great potential for aiding radiological assessments in neuro-oncology. The ongoing development of new technology needs to be validated in clinical trials and incorporated into the clinical workflow. However, none of the potential neuro-oncological applications for radiomics and deep learning has yet been realized in clinical practice. In this review, we summarize the current applications of radiomics and deep learning in neuro-oncology and discuss challenges in relation to evidence-based medicine and reporting guidelines, as well as potential applications in clinical workflows and routine clinical practice.

Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients

BACKGROUND

Pseudoprogression is a diagnostic challenge in early posttreatment glioblastoma. We therefore developed and validated a radiomics model using multiparametric MRI to differentiate pseudoprogression from early tumor progression in patients with glioblastoma.

METHODS

The model was developed from the enlarging contrast-enhancing portions of 61 glioblastomas within 3 months after standard treatment with 6472 radiomic features being obtained from contrast-enhanced T1-weighted imaging, fluid-attenuated inversion recovery imaging, and apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) maps. Imaging features were selected using a LASSO (least absolute shrinkage and selection operator) logistic regression model with 10-fold cross-validation. Diagnostic performance for pseudoprogression was compared with that for single parameters (mean and minimum ADC and mean and maximum CBV) and single imaging radiomics models using the area under the receiver operating characteristics curve (AUC). The model was validated with an external cohort (n = 34) imaged on a different scanner and internal prospective registry data (n = 23).

RESULTS

Twelve significant radiomic features (3 from conventional, 2 from diffusion, and 7 from perfusion MRI) were selected for model construction. The multiparametric radiomics model (AUC, 0.90) showed significantly better performance than any single ADC or CBV parameter (AUC, 0.57-0.79, P < 0.05), and better than a single radiomics model using conventional MRI (AUC, 0.76, P = 0.012), ADC (AUC, 0.78, P = 0.014), or CBV (AUC, 0.80, P = 0.43). The multiparametric radiomics showed higher performance in the external validation (AUC, 0.85) and internal validation (AUC, 0.96) than any single approach, thus demonstrating robustness.

CONCLUSIONS

Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improved diagnostic performance for identifying pseudoprogression and showed robustness in a multicenter setting.

Machine learning and glioma imaging biomarkers

AIM

To review how machine learning (ML) is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis, and treatment response monitoring.

MATERIALS AND METHODS

The PubMed and MEDLINE databases were searched for articles published before September 2018 using relevant search terms. The search strategy focused on articles applying ML to high-grade glioma biomarkers for treatment response monitoring, prognosis, and prediction.

RESULTS

Magnetic resonance imaging (MRI) is typically used throughout the patient pathway because routine structural imaging provides detailed anatomical and pathological information and advanced techniques provide additional physiological detail. Using carefully chosen image features, ML is frequently used to allow accurate classification in a variety of scenarios. Rather than being chosen by human selection, ML also enables image features to be identified by an algorithm. Much research is applied to determining molecular profiles, histological tumour grade, and prognosis using MRI images acquired at the time that patients first present with a brain tumour. Differentiating a treatment response from a post-treatment-related effect using imaging is clinically important and also an area of active study (described here in one of two Special Issue publications dedicated to the application of ML in glioma imaging).

CONCLUSION

Although pioneering, most of the evidence is of a low level, having been obtained retrospectively and in single centres. Studies applying ML to build neuro-oncology monitoring biomarker models have yet to show an overall advantage over those using traditional statistical methods. Development and validation of ML models applied to neuro-oncology require large, well-annotated datasets, and therefore multidisciplinary and multi-centre collaborations are necessary.

Radiomics in peritumoral non-enhancing regions: fractional anisotropy and cerebral blood volume improve prediction of local progression and overall survival in patients with glioblastoma

PURPOSE

The peritumoral non-enhancing region (NER) is frequently not removed during the surgical resection of glioblastoma, with most recurrences occurring within the original treatment field. This study determined whether radiomics analysis of the NER can predict local recurrence and overall survival in patients with glioblastoma.

METHODS

Preoperative magnetic resonance imaging (MRI) scans from 83 consecutive patients with glioblastoma were retrospectively reviewed and grouped into training (n = 59) and test sets (n = 24). A total of 6472 radiomic features were extracted from contrast-enhanced T1-weighted and fluid-attenuated inversion recovery images and from fractional anisotropy (FA) and normalized cerebral blood volume (CBV) maps. A diagnostic model to predict 6-month progression was tested using the area under the receiver operating characteristics curve (AUC) and compared with the single parameters of FA and CBV. A survival model was tested using Harrell's C-index and compared with clinical models that included age, sex, Karnofsky performance score, and extent of surgical resection.

RESULTS

Four FA features and six CBV features were selected for the diagnostic model; no features were extracted from conventional MRI. Combined FA and CBV radiomics showed better predictive value for local progression (AUC, 0.79; 95% CI, 0.67-0.90) than single imaging radiomics (AUC, 0.70-0.76) or single imaging parameters (AUC, 0.51-0.54). The combined model (C-index, 0.87) improved prognostication when added to clinical models (C-index, 0.72).

CONCLUSION

Radiomics features using FA and CBV in the NER have the potential to improve prediction of local progression and overall survival in patients with glioblastoma.

Prospective comparative diagnostic accuracy evaluation of dynamic contrast-enhanced (DCE) vs. dynamic susceptibility contrast (DSC) MR perfusion in differentiating tumor recurrence from radiation necrosis in treated high-grade gliomas

BACKGROUND

The appearance of a new enhancing lesion after surgery and chemoradiation for high-grade glioma (HGG) presents a common diagnostic dilemma. Histopathological analysis remains the reference standard in this situation.

PURPOSE

To prospectively compare the diagnostic accuracy of dynamic contrast-enhanced (DCE) vs. dynamic susceptibility contrast (DSC) in differentiating tumor recurrence (TR) from radiation necrosis (RN).

STUDY TYPE

Prospective diagnostic accuracy study.

POPULATION

In all, 98 consecutive treated HGG patients with new enhancing lesion. We excluded 32 patients due to inadequate follow-up or technical limitation.

FIELD STRENGTH/SEQUENCE

3 T DCE and DSC MR.

ASSESSMENT

Histogram and hot-spot analysis of cerebral blood volume (CBV), corrected CBV, K^trans , area under the curve (AUC), and plasma volume (Vp). The reference standard of TR and/or RN was determined by histopathology in 43 surgically resected lesions or by clinical/imaging follow-up in the rest.

STATISTICAL TESTS

Mann-Whitney U-tests, receiver operating characteristic (ROC) curve, and logistic regression analysis.

RESULTS

A total of 68 lesions were included. There were 37 TR, 28 RN, and three lesions with equal proportions of TR and RN. TR had significantly higher CBV, corrected CBV, CBV ratio, corrected CBV ratio, AUC ratio, and Vp ratio (P < 0.05) than RN on hot-spot analysis. CBV had the highest diagnostic accuracy (AUROC 0.71). On histogram analysis, TR had higher CBV and corrected CBV maximal value compared with RN (P = 0.006, AUROC = 0.70). Only CBV on hot-spot analysis remained significant after correction for multiple comparison, with no significant improvement in diagnostic accuracy when using a combination of parameters (AUROC 0.71 vs. 0.76, P = 0.24).

DATA CONCLUSION

DSC-derived CBV is the most accurate perfusion parameter in differentiating TR and RN. DSC and DCE-derived parameters reflecting the blood volume in an enhancing lesion are more accurate than the DCE-derived parameter K^trans . Clinical practice may be best guided by blood volume measurements, rather than permeability assessment for differentiation of TR from RN.

LEVEL OF EVIDENCE

1 Technical Efficacy Stage: 4 J. Magn. Reson. Imaging 2019;50:573-582.

Current concepts and challenges in the radiologic assessment of brain tumors in children: part 2

Assessing tumor response is a large part of everyday clinical work in neuroradiology. However in the setting of tumor treatment, distinguishing tumor progression from treatment-related changes is difficult on conventional MRI sequences. This is made even more challenging in children where mainstay advanced imaging techniques that are often used to decipher progression versus treatment-related changes have technical limitations. In this review, we highlight the challenges in pediatric neuro-oncologic tumor assessment with discussion of pseudophenomenon including pseudoresponse and pseudoprogression. Additionally, we discuss the advanced imaging techniques often employed in neuroradiology to distinguish between pseudophenomenon and true progressive disease.

Current State of Image Guidance in Radiation Oncology: Implications for PTV Margin Expansion and Adaptive Therapy

Image guidance technology has evolved and seen widespread application in the past several decades. Advancements in the diagnostic imaging field have found new applications in radiation oncology and promoted the development of therapeutic devices with advanced imaging capabilities. A recent example is the development of linear accelerators that offer magnetic resonance imaging for real-time imaging and online adaptive planning. Volumetric imaging, in particular, offers more precise localization of soft tissue targets and critical organs which reduces setup uncertainty and permit the use of smaller setup margins. We present a review of the status of current imaging modalities available for radiation oncology and its impact on target margins and use for adaptive therapy.

Simultaneous perfusion and permeability assessments using multiband multi-echo EPI (M2-EPI) in brain tumors

PURPOSE

To study a multiband multi-echo EPI (M2-EPI) sequence for dynamic susceptibility contrast (DSC) perfusion imaging with leakage correction and vascular permeability measurements, and to evaluate the benefits of increased temporal resolution provided by this acquisition strategy on the accuracy of perfusion and permeability estimations.

METHODS

A novel M2-EPI sequence was developed, and a pharmacokinetic model accounting for contrast agent extravasation was used to produce perfusion maps and additional vascular permeability maps. The advantage of M2-EPI for DSC perfusion imaging was demonstrated in vivo in 5 patients with brain tumors, and numerical simulations were performed to evaluate the advantage of improved temporal resolution afforded by the technique.

RESULTS

In contrast to underestimations of cerebral blood volume (CBV) in tumors using the single-echo acquisition strategy, M2-EPI provided more plausible estimates of CBV. A quantitative evaluation showed higher estimated values of CBV and mean transit time in tumor tissues using M2-EPI (CBV: 3.08 ± 0.78 mL/100 g versus 1.56 ± 1.38 mL/100 g [P = .006]; mean transit time: 4.94 ± 1.17 seconds versus 1.83 ± 2.06 seconds [P = 0.033]). Numerical simulations showed that higher temporal resolution provided by M2-EPI was associated with more accurate estimates of cerebral blood flow, CBV, and permeability parameters.

CONCLUSION

The novel M2-EPI acquisition strategy for DSC imaging facilitates leakage-corrected perfusion measurements with additional permeability assessments and more accurate estimates of perfusion/permeability parameters, and may be used as a quantitative tool for the diagnosis, prognosis, and treatment monitoring of brain tumors.

Earliest radiological progression in glioblastoma by multidisciplinary consensus review

BACKGROUND

Detection of glioblastoma progression is important for clinical decision-making on cessation or initiation of therapy, for enrollment in clinical trials, and for response measurement in time and location. The RANO-criteria are considered standard for the timing of progression. To evaluate local treatment, we aim to find the most accurate progression location. We determined the differences in progression free survival (PFS) and in tumor volumes at progression (Vprog) by three definitions of progression.

METHODS

In a consecutive cohort of 73 patients with newly-diagnosed glioblastoma between 1/1/2012 and 31/12/2013, progression was established according to three definitions. We determined (1) earliest radiological progression (ERP) by retrospective multidisciplinary consensus review using all available imaging and follow-up, (2) clinical practice progression (CPP) from multidisciplinary tumor board conclusions, and (3) progression by the RANO-criteria.

RESULTS

ERP was established in 63 (86%), CPP in 64 (88%), RANO progression in 42 (58%). Of the 63 patients who had died, 37 (59%) did with prior RANO-progression, compared to 57 (90%) for both ERP and CPP. The median overall survival was 15.3 months. The median PFS was 8.8 months for ERP, 9.5 months for CPP, and 11.8 months for RANO. The PFS by ERP was shorter than CPP (HR 0.57, 95% CI 0.38-0.84, p = 0.004) and RANO-progression (HR 0.29, 95% CI 0.19-0.43, p < 0.001). The Vprog were significantly smaller for ERP (median 8.8 mL), than for CPP (17 mL) and RANO (22 mL).

CONCLUSION

PFS and Vprog vary considerably between progression definitions. Earliest radiological progression by retrospective consensus review should be considered to accurately localize progression and to address confounding of lead time bias in clinical trial enrollment.

Prediction of Pseudoprogression versus Progression using Machine Learning Algorithm in Glioblastoma

We aimed to investigate the feasibility of machine learning (ML) algorithm to distinguish pseudoprogression (PsPD) from progression (PD) in patients with glioblastoma (GBM). We recruited the patients diagnosed as primary GBM who received gross total resection (GTR) and concurrent chemoradiotherapy in two institutions from April 2010 to April 2017 and presented suspicious contrast-enhanced lesion on brain magnetic resonance imaging (MRI) during follow-up. Patients from two institutions were allocated to training (N = 59) and testing (N = 19) datasets, respectively. We developed a convolutional neural network combined with a long short-term memory ML structure. MRI data, which was 9 axial post-contrast T1-weighted images in our study, and clinical features were incorporated (Model 1). In the testing set, the trained Model 1 resulted in AUC of 0.83, AUPRC of 0.87, and F1-score of 0.74 using optimal threshold. The performance was superior to that of Model 2 (CNN-LSTM model with MRI data alone) and Model 3 (random forest model with clinical feature alone). The developed algorithm involving MRI data and clinical features could help making decision during follow-up of patients with GBM treated with GTR and concurrent CCRT.

Differentiating between Glioblastoma and Primary CNS Lymphoma Using Combined Whole-tumor Histogram Analysis of the Normalized Cerebral Blood Volume and the Apparent Diffusion Coefficient

Purpose:

This study aimed to determine whether whole-tumor histogram analysis of normalized cerebral blood volume (nCBV) and apparent diffusion coefficient (ADC) for contrast-enhancing lesions can be used to differentiate between glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL).

Methods:

From 20 patients, 9 with PCNSL and 11 with GBM without any hemorrhagic lesions, underwent MRI, including diffusion-weighted imaging and dynamic susceptibility contrast perfusion-weighted imaging before surgery. Histogram analysis of nCBV and ADC from whole-tumor voxels in contrast-enhancing lesions was performed. An unpaired t-test was used to compare the mean values for each type of tumor. A multivariate logistic regression model (LRM) was performed to classify GBM and PCNSL using the best parameters of ADC and nCBV.

Results:

All nCBV histogram parameters of GBMs were larger than those of PCNSLs, but only average nCBV was statistically significant after Bonferroni correction. Meanwhile, ADC histogram parameters were also larger in GBM compared to those in PCNSL, but these differences were not statistically significant. According to receiver operating characteristic curve analysis, the nCBV average and ADC 25th percentile demonstrated the largest area under the curve with values of 0.869 and 0.838, respectively. The LRM combining these two parameters differentiated between GBM and PCNSL with a higher area under the curve value (Logit (P) = −21.12 + 10.00 × ADC 25th percentile (10⁻³ mm²/s) + 5.420 × nCBV mean, P < 0.001).

Conclusion:

Our results suggest that whole-tumor histogram analysis of nCBV and ADC combined can be a valuable objective diagnostic method for differentiating between GBM and PCNSL.

Pseudoprogression of brain tumors

This review describes the definition, incidence, clinical implications, and magnetic resonance imaging (MRI) findings of pseudoprogression of brain tumors, in particular, but not limited to, high-grade glioma. Pseudoprogression is an important clinical problem after brain tumor treatment, interfering not only with day-to-day patient care but also the execution and interpretation of clinical trials. Radiologically, pseudoprogression is defined as a new or enlarging area(s) of contrast agent enhancement, in the absence of true tumor growth, which subsides or stabilizes without a change in therapy. The clinical definitions of pseudoprogression have been quite variable, which may explain some of the differences in reported incidences, which range from 9-30%. Conventional structural MRI is insufficient for distinguishing pseudoprogression from true progressive disease, and advanced imaging is needed to obtain higher levels of diagnostic certainty. Perfusion MRI is the most widely used imaging technique to diagnose pseudoprogression and has high reported diagnostic accuracy. Diagnostic performance of MR spectroscopy (MRS) appears to be somewhat higher, but MRS is less suitable for the routine and universal application in brain tumor follow-up. The combination of MRS and diffusion-weighted imaging and/or perfusion MRI seems to be particularly powerful, with diagnostic accuracy reaching up to or even greater than 90%. While diagnostic performance can be high with appropriate implementation and interpretation, even a combination of techniques, however, does not provide 100% accuracy. It should also be noted that most studies to date are small, heterogeneous, and retrospective in nature. Future improvements in diagnostic accuracy can be expected with harmonization of acquisition and postprocessing, quantitative MRI and computer-aided diagnostic technology, and meticulous evaluation with clinical and pathological data.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Multiparametric MRI as a potential surrogate endpoint for decision-making in early treatment response following concurrent chemoradiotherapy in patients with newly diagnosed glioblastoma: a systematic review and meta-analysis

OBJECTIVE

To evaluate the value of multiparametric MRI for determination of early treatment response following concurrent chemoradiotherapy in patients with newly diagnosed glioblastoma.

METHODS

A computerized search of Ovid-MEDLINE and EMBASE up to 1 October 2017 was performed to find studies on the diagnostic performance of multiparametric MRI for differentiating true progression from pseudoprogression. The beginning search date was not specified. Pooled estimates of sensitivity and specificity were obtained using hierarchical logistic regression modeling. We performed meta-regression and sensitivity analyses to explain the effects of the study heterogeneity.

RESULTS

Nine studies including 456 patients were included. Pooled sensitivity and specificity were 84 % (95 % CI 74-91) and 95 % (95 % CI 83-99), respectively. Area under the hierarchical summary receiver operating characteristic curve was 0.95 (95 % CI 0.92-0.96). Meta-regression showed true progression in the study population, the mean age and the reference standard were significant factors affecting heterogeneity.

CONCLUSION

Multiparametric MRI may be used as a potential surrogate endpoint for assessment of early treatment response, especially in the differentiation of true progression from pseudoprogression. However, based on the current evidence, monoparametric and multiparametric MRI perform equally in the clinical context. Further evaluation will be needed.

KEY POINTS

• Multiparametric MRI shows high diagnostic performance for early treatment response in glioblastoma. • Multiparametric MRI could differentiate true progression from pseudoprogression in newly diagnosed glioblastoma. • The normalized rCBV derived from DSC was the most commonly used parameter.

Diffusion Weighted/Tensor Imaging, Functional MRI and Perfusion Weighted Imaging in Glioblastoma-Foundations and Future

In this article, we review the basics of diffusion tensor imaging and functional MRI, their current utility in preoperative neurosurgical mapping, and their limitations. We also discuss potential future applications, including implementation of resting state functional MRI. We then discuss perfusion and diffusion-weighted imaging and their application in advanced neuro-oncologic practice. We explain how these modalities can be helpful in guiding surgical biopsies and differentiating recurrent tumor from treatment related changes.

MR-guided radiation therapy: transformative technology and its role in the central nervous system

This review article describes advancement of magnetic resonance imaging technologies in radiation therapy planning, guidance, and adaptation of brain tumors. The potential for MR-guided radiation therapy to improve outcomes and the challenges in its adoption are discussed.

Differentiation of Recurrent Glioblastoma from Delayed Radiation Necrosis by Using Voxel-based Multiparametric Analysis of MR Imaging Data

Purpose To assess a volume-weighted voxel-based multiparametric (MP) clustering method as an imaging biomarker to differentiate recurrent glioblastoma from delayed radiation necrosis. Materials and Methods The institutional review board approved this retrospective study and waived the informed consent requirement. Seventy-five patients with pathologic analysis-confirmed recurrent glioblastoma (n = 42) or radiation necrosis (n = 33) who presented with enlarged contrast material-enhanced lesions at magnetic resonance (MR) imaging after they completed concurrent chemotherapy and radiation therapy were enrolled. The diagnostic performance of the total MP cluster score was determined by using the area under the receiver operating characteristic curve (AUC) with cross-validation and compared with those of single parameter measurements (10% histogram cutoffs of apparent diffusion coefficient [ADC10] or 90% histogram cutoffs of normalized cerebral blood volume and initial time-signal intensity AUC). Results Receiver operating characteristic curve analysis showed that an AUC for differentiating recurrent glioblastoma from delayed radiation necrosis was highest in the total MP cluster score and lowest for ADC10 for both readers. The total MP cluster score had significantly better diagnostic accuracy than any single parameter (corrected P = .001-.039 for reader 1; corrected P = .005-.041 for reader 2). The total MP cluster score was the best predictor of recurrent glioblastoma (cross-validated AUCs, 0.942-0.946 for both readers), with a sensitivity of 95.2% for reader 1 and 97.6% for reader 2. Conclusion Quantitative analysis with volume-weighted voxel-based MP clustering appears to be superior to the use of single imaging parameters to differentiate recurrent glioblastoma from delayed radiation necrosis. ^© RSNA, 2017 Online supplemental material is available for this article.

Volume of high-risk intratumoral subregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma

OBJECTIVE

To develop and validate a volume-based, quantitative imaging marker by integrating multi-parametric MR images for predicting glioblastoma survival, and to investigate its relationship and synergy with molecular characteristics.

METHODS

We retrospectively analysed 108 patients with primary glioblastoma. The discovery cohort consisted of 62 patients from the cancer genome atlas (TCGA). Another 46 patients comprising 30 from TCGA and 16 internally were used for independent validation. Based on integrated analyses of T1-weighted contrast-enhanced (T1-c) and diffusion-weighted MR images, we identified an intratumoral subregion with both high T1-c and low ADC, and accordingly defined a high-risk volume (HRV). We evaluated its prognostic value and biological significance with genomic data.

RESULTS

On both discovery and validation cohorts, HRV predicted overall survival (OS) (concordance index: 0.642 and 0.653, P < 0.001 and P = 0.038, respectively). HRV stratified patients within the proneural molecular subtype (log-rank P = 0.040, hazard ratio = 2.787). We observed different OS among patients depending on their MGMT methylation status and HRV (log-rank P = 0.011). Patients with unmethylated MGMT and high HRV had significantly shorter survival (median survival: 9.3 vs. 18.4 months, log-rank P = 0.002).

CONCLUSION

Volume of the high-risk intratumoral subregion identified on multi-parametric MRI predicts glioblastoma survival, and may provide complementary value to genomic information.

KEY POINTS

• High-risk volume (HRV) defined on multi-parametric MRI predicted GBM survival. • The proneural molecular subtype tended to harbour smaller HRV than other subtypes. • Patients with unmethylated MGMT and high HRV had significantly shorter survival. • HRV complements genomic information in predicting GBM survival.

Emerging Techniques in Brain Tumor Imaging: What Radiologists Need to Know

Among the currently available brain tumor imaging, advanced MR imaging techniques, such as diffusion-weighted MR imaging and perfusion MR imaging, have been used for solving diagnostic challenges associated with conventional imaging and for monitoring the brain tumor treatment response. Further development of advanced MR imaging techniques and postprocessing methods may contribute to predicting the treatment response to a specific therapeutic regimen, particularly using multi-modality and multiparametric imaging. Over the next few years, new imaging techniques, such as amide proton transfer imaging, will be studied regarding their potential use in quantitative brain tumor imaging. In this review, the pathophysiologic considerations and clinical validations of these promising techniques are discussed in the context of brain tumor characterization and treatment response.

High-grade glioma management and response assessment-recent advances and current challenges

The management of high-grade gliomas (hggs) is complex and ever-evolving. The standard of care for the treatment of hggs consists of surgery, chemotherapy, and radiotherapy. However, treatment options are influenced by multiple factors such as patient age and performance status, extent of tumour resection, biomarker profile, and tumour histology and grade. Follow-up cranial magnetic resonance imaging (mri) to differentiate treatment response from treatment effect can be challenging and affects clinical decision-making. An assortment of advanced radiologic techniques-including perfusion imaging with dynamic susceptibility contrast mri, dynamic contrast-enhanced mri, diffusion-weighted imaging, proton spectroscopy, mri subtraction imaging, and amino acid radiotracer imaging-can now incorporate novel physiologic data, providing new methods to help characterize tumour progression, pseudoprogression, and pseudoresponse. In the present review, we provide an overview of current treatment options for hgg and summarize recent advances and challenges in imaging technology.

MR perfusion-weighted imaging in the evaluation of high-grade gliomas after treatment: a systematic review and meta-analysis

BACKGROUND

Distinction between tumor and treatment related changes is crucial for clinical management of patients with high-grade gliomas. Our purpose was to evaluate whether dynamic susceptibility contrast-enhanced (DSC) and dynamic contrast enhanced (DCE) perfusion-weighted imaging (PWI) metrics can effectively differentiate between recurrent tumor and posttreatment changes within the enhancing signal abnormality on conventional MRI.

METHODS

A comprehensive literature search was performed for studies evaluating PWI-based differentiation of recurrent tumor and posttreatment changes in patients with high-grade gliomas (World Health Organization grades III and IV). Only studies published in the "temozolomide era" beginning in 2005 were included. Summary estimates of diagnostic accuracy were obtained by using a random-effects model.

RESULTS

Of 1581 abstracts screened, 28 articles were included. The pooled sensitivities and specificities of each study's best performing parameter were 90% and 88% (95% CI: 0.85-0.94; 0.83-0.92) and 89% and 85% (95% CI: 0.78-0.96; 0.77-0.91) for DSC and DCE, respectively. The pooled sensitivities and specificities for detecting tumor recurrence using the 2 most commonly evaluated parameters, mean relative cerebral blood volume (rCBV) (threshold range, 0.9-2.15) and maximum rCBV (threshold range, 1.49-3.1), were 88% and 88% (95% CI: 0.81-0.94; 0.78-0.95) and 93% and 76% (95% CI: 0.86-0.98; 0.66-0.85), respectively.

CONCLUSIONS

PWI-derived thresholds separating viable tumor from treatment changes demonstrate relatively good accuracy in individual studies. However, because of significant variability in optimal reported thresholds and other limitations in the existing body of literature, further investigation and standardization is needed before implementing any particular quantitative PWI strategy across institutions.

Different diagnostic values of imaging parameters to predict pseudoprogression in glioblastoma subgroups stratified by MGMT promoter methylation

OBJECTIVES

The aim of this study was to determine whether diffusion and perfusion imaging parameters demonstrate different diagnostic values for predicting pseudoprogression between glioblastoma subgroups stratified by O⁶-mythylguanine-DNA methyltransferase (MGMT) promoter methylation status.

METHODS

We enrolled seventy-five glioblastoma patients that had presented with enlarged contrast-enhanced lesions on magnetic resonance imaging (MRI) one month after completing concurrent chemoradiotherapy and undergoing MGMT promoter methylation testing. The imaging parameters included 10 or 90 % histogram cutoffs of apparent diffusion coefficient (ADC10), normalized cerebral blood volume (nCBV90), and initial area under the time signal-intensity curve (IAUC90). The results of the areas under the receiver operating characteristic curve (AUCs) with cross-validation were compared between MGMT methylation and unmethylation groups.

RESULTS

MR imaging parameters demonstrated a trend toward higher accuracy in the MGMT promoter methylation group than in the unmethylation group (cross-validated AUCs = 0.70-0.95 and 0.56-0.87, respectively). The combination of MGMT methylation status with imaging parameters improved the AUCs from 0.70 to 0.75-0.90 for both readers in comparison with MGMT methylation status alone. The probability of pseudoprogression was highest (95.7 %) when nCBV90 was below 4.02 in the MGMT promoter methylation group.

CONCLUSIONS

MR imaging parameters could be stronger predictors of pseudoprogression in glioblastoma patients with the methylated MGMT promoter than in patients with the unmethylated MGMT promoter.

KEY POINTS

• The glioblastoma subgroup was stratified according to MGMT promoter methylation status. • Diagnostic values of diffusion and perfusion parameters for predicting pseudoprogression were compared. • Imaging parameters showed higher diagnostic accuracy in the MGMT promoter methylation group. • Imaging parameters were independent to MGMT promoter methylation status for predicting pseudoprogression. • Imaging biomarkers might demonstrate different diagnostic values according to MGMT promoter methylation.

Recent Application of Advanced MR Imaging to Predict Pseudoprogression in High-grade Glioma Patients

Pseudoprogression is regarded as a subacute form of treatment-related change with a reported incidence of 20-30%, occurring predominantly within the first three months after the completion of concurrent chemoradiotherapy (CCRT) in glioblastoma multiforme (GBM) patients. Occurrence of progressive lesions on conventional contrast-enhanced MR imaging may also accompany clinical deterioration, posing considerable diagnostic challenges to clinicians and radiologists. False interpretation of treatment-related change as true progression may lead to the cessation of effective first-line therapy (i.e., adjuvant temozolomide) and unnecessary surgery. Increasing awareness of the diagnostic challenge of the phenomenon has underscored the need for better imaging techniques that may aid in differentiating the treatment-related change from true progression. In this review, we discuss the recent applications of advanced MR imaging such as diffusion-weighted and perfusion-weighted imaging in the evaluation of treatment response in high-grade glioma patients and highlight their potential role in differentiating pseudoprogression from true progression.

Semi-automated quantitative intravoxel incoherent motion analysis and its implementation in breast diffusion-weighted imaging

BACKGROUND

To optimize and validate intravoxel incoherent motion (IVIM) modeled diffusion-weighted imaging (DWI) compared with the apparent diffusion coefficient (ADC) for semi-automated analysis of breast lesions using a multi-reader setup.

MATERIALS AND METHODS

Patients (n = 176) with breast lesions (≥1 cm) and known pathology were prospectively examined (1.5 Tesla) with DWI (b = 0, 50, 200, 500, 800, 1000 s/mm(2) ) between November 2008 and July 2014 and grouped into a training and test set. Three independent readers applied a semi-automated procedure for setting regions-of-interest for each lesion and recorded ADC and IVIM parameters: molecular diffusion (Dslow ), microperfusion (Dfast ), and the fraction of Dfast (ffast ). In the training set (24 lesions, 12 benign), a semi-automated method was optimized to yield maximum true negatives (TN) with minimal false negatives (FN): only the optimal fraction (Fo) of voxels in the lesions was used and optimal thresholds were determined. The optimal Fo and thresholds were then applied to a consecutive test set (139 lesions, 23 benign) to obtain specificity and sensitivity.

RESULTS

In the training set, optimal thresholds were 1.44 × 10(-3) mm(2) /s (Dslow ), 18.55 × 10(-3) mm(2) /s (Dfast ), 0.247 (ffast ) and 2.00 × 10(-3) mm(2) /s (ADC) with Fo set to 0.61, 0.85, 1.0, and 1.0, respectively, this resulted in TN = 5 (IVIM) and TN = 1 (ADC), with FN = 0. In the test set, sensitivity and specificity among the readers were 90.5-93.1% and 43.5-52.2%, respectively, for IVIM, and 94.8-95.7% and 13.0-21.7% for ADC (P ≤ 0.0034) without inter-reader differences (P = 1.000).

CONCLUSION

The presented semi-automated method for breast lesion evaluation is reader independent and yields significantly higher specificity for IVIM compared with the ADC.

Physiologic MRI for assessment of response to therapy and prognosis in glioblastoma

Aside from bidimensional measurements from conventional contrast-enhanced MRI, there are no validated or FDA-qualified imaging biomarkers for high-grade gliomas. However, advanced functional MRI techniques, including perfusion- and diffusion-weighted MRI, have demonstrated much potential for determining prognosis, predicting therapeutic response, and assessing early treatment response. They may also prove useful for differentiating pseudoprogression from true progression after temozolomide chemoradiation and pseudoresponse from true response after anti-angiogenic therapy. This review will highlight recent developments using these techniques and emphasize the need for technical standardization and validation in prospective studies in order for these methods to become incorporated into standard-of-care imaging for brain tumor patients.

Intratumoral and peritumoral post-irradiation changes, but not viable tumor tissue, may respond to bevacizumab in previously irradiated meningiomas

The efficacy of bevacizumab has not been determined for treatment-refractory meningiomas. We treated meningiomas with low-dose bevacizumab and compared the radiological responses of non-irradiated meningiomas with previously irradiated meningiomas. In addition, we assessed intraparenchymal radiation necrosis following bevacizumab treatment.

Six patients with meningiomas (three anaplastic, one atypical, and two grade I) who were previously treated with multiple sessions of radiotherapy and subsequently developed perilesional edema were treated with bevacizumab. Of six patients, two patients with anaplastic meningiomas developed three tumors following radiotherapy, which were defined as non-irradiated tumors. There were 12 pre-existing extra-axial tumors that were previously irradiated. Some of these tumors demonstrated adjacent intraparenchymal contrast enhancement. These tumors were defined as post-irradiated tumors. Four patients had intraparenchymal radiation necrosis. Low-dose bevacizumab was administered biweekly over 3–6 cycles to all patients.

Four tumors decreased in contrast-enhanced volume, nine tumors were unchanged, and two tumors progressed. Of the three non-irradiated tumors, two tumors increased in volume (126 % and 198 %) and one tumor was stable (−5 %). The median reduction rates determined by contrast volume were −31 % and −71 % in post-irradiated tumors and radiation necrosis, respectively. Non-irradiated tumors had a significantly poorer response to bevacizumab than post-irradiated tumors and radiation necrosis (p = 0.0013 and p = 0.0005, respectively, Tukey-Kramer test).

Low-dose bevacizumab did not demonstrate efficacy in the treatment of non-irradiated meningiomas. Responses to low-dose bevacizumab could be related to its effect on post-irradiation changes, rather than its effect on biologically active tumor tissue in post-irradiated meningiomas. Radiological responses to low-dose bevacizumab may distinguish biologically active tumors from post-irradiation changes in progressive meningiomas following radiotherapy.