Diffusion-weighted MRI as a biomarker for treatment response in glioma

Exploring the Impact of Irradiation on Glioblastoma Blood-Brain-Barrier Permeability: Insights from Dynamic-Contrast-Enhanced-MRI and Histological Analysis

(1) Background: Glioblastoma (GB) presents a formidable challenge in neuro-oncology due to its aggressive nature, limited treatment options, and poor prognosis. The blood-brain barrier (BBB) complicates treatment by hindering drug delivery to the tumor site, particularly to the infiltrative cells in the margin of the tumor, which are mainly responsible for tumor recurrence. Innovative strategies are therefore needed to enhance drug delivery in the margins of the tumor. This study explores whether irradiation can enhance BBB permeability by assessing hemodynamic changes and the distribution of contrast agents in the core and the margins of GB tumors. (2) Methods: Mice grafted with U-87MG cells were exposed to increasing irradiation doses. The distribution of contrast agents and hemodynamic parameters was evaluated using both non-invasive magnetic resonance imaging (MRI) techniques with gadolinium-DOTA as a contrast agent and invasive histological analysis with Evans blue, a fluorescent vascular leakage marker. Diffusion-MRI was also used to assess cytotoxic effects. (3) Results: The histological study revealed a complex dose-dependent effect of irradiation on BBB integrity, with increased vascular leakage at 5 Gy but reduced leakage at higher doses (10 and 15 Gy). However, there was no significant increase in the diffusion of Gd-DOTA outside the tumor area by MRI. (4) Conclusions: The increase in BBB permeability could be an interesting approach to enhance drug delivery in glioblastoma margins for low irradiation doses. In this model, DCE-MRI analysis was of limited value in assessing the BBB opening in glioblastoma after irradiation.

An update on susceptibility-weighted imaging in brain gliomas

Susceptibility-weighted imaging (SWI) has become a standard component of most brain MRI protocols. While traditionally used for detecting and characterising brain hemorrhages typically associated with stroke or trauma, SWI has also shown promising results in glioma assessment. Numerous studies have highlighted SWI's role in differentiating gliomas from other brain lesions, such as primary central nervous system lymphomas or metastases. Additionally, SWI aids radiologists in non-invasively grading gliomas and predicting their phenotypic profiles. Various researchers have suggested incorporating SWI as an adjunct sequence for predicting treatment response and for post-treatment monitoring. A significant focus of these studies is on the detection of intratumoural susceptibility signals (ITSSs) in gliomas, which are indicative of microhemorrhages and vessels within the tumour. The quantity, distribution, and characteristics of these ITSSs can provide radiologists with more precise information for evaluating and characterising gliomas. Furthermore, the potential benefits and added value of performing SWI after the administration of gadolinium-based contrast agents (GBCAs) have been explored. This review offers a comprehensive, educational, and practical overview of the potential applications and future directions of SWI in the context of glioma assessment. CLINICAL RELEVANCE STATEMENT: SWI has proven effective in evaluating gliomas, especially through assessing intratumoural susceptibility signal changes, and is becoming a promising, easily integrated tool in MRI protocols for both pre- and post-treatment assessments. KEY POINTS: • Susceptibility-weighted imaging is the most sensitive sequence for detecting blood and calcium inside brain lesions. • This sequence, acquired with and without gadolinium, helps with glioma diagnosis, characterisation, and grading through the detection of intratumoural susceptibility signals. • There are ongoing challenges that must be faced to clarify the role of susceptibility-weighted imaging for glioma assessment.

Glioblastoma Margin as a Diffusion Barrier Revealed by Photoactivation of Plasmonic Nanovesicles

Glioblastoma (GBM) is the most complex and lethal primary brain cancer. Adequate drug diffusion and penetration are essential for treating GBM, but how the spatial heterogeneity in GBM impacts drug diffusion and transport is poorly understood. Herein, we report a new method, photoactivation of plasmonic nanovesicles (PANO), to measure molecular diffusion in the extracellular space of GBM. By examining three genetically engineered GBM mouse models that recapitulate key clinical features including the angiogenic core and diffuse infiltration, we found that the tumor margin has the lowest diffusion coefficient (highest tortuosity) compared with the tumor core and surrounding brain tissue. Analysis of the cellular composition shows that tortuosity in the GBM is strongly correlated with neuronal loss and astrocyte activation. Our all-optical measurement reveals the heterogeneous GBM microenvironment and highlights the tumor margin as a diffusion barrier for drug transport in the brain, with implications for therapeutic delivery.

Glioblastoma Margin as a Diffusion Barrier Revealed by Photoactivation of Plasmonic Nanovesicles

Glioblastoma (GBM) is the most complex and lethal adult primary brain cancer. Adequate drug diffusion and penetration are essential for treating GBM, but how the spatial heterogeneity in GBM impacts drug diffusion and transport is poorly understood. Herein, we report a new method, photoactivation of plasmonic nanovesicles (PANO), to measure molecular diffusion in the extracellular space of GBM. By examining three genetically engineered GBM mouse models that recapitulate key clinical features including angiogenic core and diffuse infiltration, we found that the tumor margin has the lowest diffusion coefficient (highest tortuosity) compared with the tumor core and surrounding brain tissue. Analysis of the cellular composition shows that the tortuosity in the GBM is strongly correlated with neuronal loss and astrocyte activation. Our all-optical measurement reveals the heterogeneous GBM microenvironment and highlights the tumor margin as a diffusion barrier for drug transport in the brain, with implications for therapeutic delivery.

Assessment and prediction of glioblastoma therapy response: challenges and opportunities

Glioblastoma is the most aggressive type of primary adult brain tumour. The median survival of patients with glioblastoma remains approximately 15 months, and the 5-year survival rate is <10%. Current treatment options are limited, and the standard of care has remained relatively constant since 2011. Over the last decade, a range of different treatment regimens have been investigated with very limited success. Tumour recurrence is almost inevitable with the current treatment strategies, as glioblastoma tumours are highly heterogeneous and invasive. Additionally, another challenging issue facing patients with glioblastoma is how to distinguish between tumour progression and treatment effects, especially when relying on routine diagnostic imaging techniques in the clinic. The specificity of routine imaging for identifying tumour progression early or in a timely manner is poor due to the appearance similarity of post-treatment effects. Here, we concisely describe the current status and challenges in the assessment and early prediction of therapy response and the early detection of tumour progression or recurrence. We also summarize and discuss studies of advanced approaches such as quantitative imaging, liquid biomarker discovery and machine intelligence that hold exceptional potential to aid in the therapy monitoring of this malignancy and early prediction of therapy response, which may decisively transform the conventional detection methods in the era of precision medicine.

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.

Advanced Imaging Techniques for Newly Diagnosed and Recurrent Gliomas

Management of gliomas following initial diagnosis requires thoughtful presurgical planning followed by regular imaging to monitor treatment response and survey for new tumor growth. Traditional MR imaging modalities such as T1 post-contrast and T2-weighted sequences have long been a staple of tumor diagnosis, surgical planning, and post-treatment surveillance. While these sequences remain integral in the management of gliomas, advances in imaging techniques have allowed for a more detailed characterization of tumor characteristics. Advanced MR sequences such as perfusion, diffusion, and susceptibility weighted imaging, as well as PET scans have emerged as valuable tools to inform clinical decision making and provide a non-invasive way to help distinguish between tumor recurrence and pseudoprogression. Furthermore, these advances in imaging have extended to the operating room and assist in making surgical resections safer. Nevertheless, surgery, chemotherapy, and radiation treatment continue to make the interpretation of MR changes difficult for glioma patients. As analytics and machine learning techniques improve, radiomics offers the potential to be more quantitative and personalized in the interpretation of imaging data for gliomas. In this review, we describe the role of these newer imaging modalities during the different stages of management for patients with gliomas, focusing on the pre-operative, post-operative, and surveillance periods. Finally, we discuss radiomics as a means of promoting personalized patient care in the future.

Effects of Multi-Shell Free Water Correction on Glioma Characterization

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

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

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

Comparison of In Vivo and Ex Vivo Magnetic Resonance Imaging in a Rat Model for Glioblastoma-Associated Epilepsy

Magnetic resonance imaging (MRI) is frequently used for preclinical treatment monitoring in glioblastoma (GB). Discriminating between tumors and tumor-associated changes is challenging on in vivo MRI. In this study, we compared in vivo MRI scans with ex vivo MRI and histology to estimate more precisely the abnormal mass on in vivo MRI. Epileptic seizures are a common symptom in GB. Therefore, we used a recently developed GB-associated epilepsy model from our group with the aim of further characterizing the model and making it useful for dedicated epilepsy research. Ten days after GB inoculation in rat entorhinal cortices, in vivo MRI (T2w and mean diffusivity (MD)), ex vivo MRI (T2w) and histology were performed, and tumor volumes were determined on the different modalities. The estimated abnormal mass on ex vivo T2w images was significantly smaller compared to in vivo T2w images, but was more comparable to histological tumor volumes, and might be used to estimate end-stage tumor volumes. In vivo MD images displayed tumors as an outer rim of hyperintense signal with a core of hypointense signal, probably reflecting peritumoral edema and tumor mass, respectively, and might be used in the future to distinguish the tumor mass from peritumoral edema—associated with reactive astrocytes and activated microglia, as indicated by an increased expression of immunohistochemical markers—in preclinical models. In conclusion, this study shows that combining imaging techniques using different structural scales can improve our understanding of the pathophysiology in GB.

Imaging Glioblastoma Posttreatment: Progression, Pseudoprogression, Pseudoresponse, Radiation Necrosis

Radiographic monitoring of posttreatment glioblastoma is important for clinical trials and determining next steps in management. Evaluation for tumor progression is confounded by the presence of treatment-related radiographic changes, making a definitive determination less straight-forward. The purpose of this article was to describe imaging tools available for assessing treatment response in glioblastoma, as well as to highlight the definitions, pathophysiology, and imaging features typical of true progression, pseudoprogression, pseudoresponse, and radiation necrosis.

GliMR: Cross-Border Collaborations to Promote Advanced MRI Biomarkers for Glioma

Purpose

There is an annual incidence of 50,000 glioma cases in Europe. The optimal treatment strategy is highly personalised, depending on tumour type, grade, spatial localization, and the degree of tissue infiltration. In research settings, advanced magnetic resonance imaging (MRI) has shown great promise as a tool to inform personalised treatment decisions. However, the use of advanced MRI in clinical practice remains scarce due to the downstream effects of siloed glioma imaging research with limited representation of MRI specialists in established consortia; and the associated lack of available tools and expertise in clinical settings. These shortcomings delay the translation of scientific breakthroughs into novel treatment strategy. As a response we have developed the network “Glioma MR Imaging 2.0” (GliMR) which we present in this article.

Methods

GliMR aims to build a pan-European and multidisciplinary network of experts and accelerate the use of advanced MRI in glioma beyond the current “state-of-the-art” in glioma imaging. The Action Glioma MR Imaging 2.0 (GliMR) was granted funding by the European Cooperation in Science and Technology (COST) in June 2019.

Results

GliMR’s first grant period ran from September 2019 to April 2020, during which several meetings were held and projects were initiated, such as reviewing the current knowledge on advanced MRI; developing a General Data Protection Regulation (GDPR) compliant consent form; and setting up the website.

Conclusion

The Action overcomes the pre-existing limitations of glioma research and is funded until September 2023. New members will be accepted during its entire duration.

Predicting Survival in Glioblastoma Patients Using Diffusion MR Imaging Metrics-A Systematic Review

Simple Summary

An accurate survival analysis is crucial for disease management in glioblastoma (GBM) patients. Due to the ability of the diffusion MRI techniques of providing a quantitative assessment of GBM tumours, an ever-growing number of studies aimed at investigating the role of diffusion MRI metrics in survival prediction of GBM patients. Since the role of diffusion MRI in prediction and evaluation of survival outcomes has not been fully addressed and results are often controversial or unsatisfactory, we performed this systematic review in order to collect, summarize and evaluate all studies evaluating the role of diffusion MRI metrics in predicting survival in GBM patients. We found that quantitative diffusion MRI metrics provide useful information for predicting survival outcomes in GBM patients, mainly in combination with other clinical and multimodality imaging parameters.

Abstract

Despite advances in surgical and medical treatment of glioblastoma (GBM), the medium survival is about 15 months and varies significantly, with occasional longer survivors and individuals whose tumours show a significant response to therapy with respect to others. Diffusion MRI can provide a quantitative assessment of the intratumoral heterogeneity of GBM infiltration, which is of clinical significance for targeted surgery and therapy, and aimed at improving GBM patient survival. So, the aim of this systematic review is to assess the role of diffusion MRI metrics in predicting survival of patients with GBM. According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, a systematic literature search was performed to identify original articles since 2010 that evaluated the association of diffusion MRI metrics with overall survival (OS) and progression-free survival (PFS). The quality of the included studies was evaluated using the QUIPS tool. A total of 52 articles were selected. The most examined metrics were associated with the standard Diffusion Weighted Imaging (DWI) (34 studies) and Diffusion Tensor Imaging (DTI) models (17 studies). Our findings showed that quantitative diffusion MRI metrics provide useful information for predicting survival outcomes in GBM patients, mainly in combination with other clinical and multimodality imaging parameters.

Radioresistance in Glioblastoma and the Development of Radiosensitizers

Ionizing radiation is a common and effective therapeutic option for the treatment of glioblastoma (GBM). Unfortunately, some GBMs are relatively radioresistant and patients have worse outcomes after radiation treatment. The mechanisms underlying intrinsic radioresistance in GBM has been rigorously investigated over the past several years, but the complex interaction of the cellular molecules and signaling pathways involved in radioresistance remains incompletely defined. A clinically effective radiosensitizer that overcomes radioresistance has yet to be identified. In this review, we discuss the current status of radiation treatment in GBM, including advances in imaging techniques that have facilitated more accurate diagnosis, and the identified mechanisms of GBM radioresistance. In addition, we provide a summary of the candidate GBM radiosensitizers being investigated, including an update of subjects enrolled in clinical trials. Overall, this review highlights the importance of understanding the mechanisms of GBM radioresistance to facilitate the development of effective radiosensitizers.

TSPO-PET and diffusion-weighted MRI for imaging a mouse model of infiltrative human glioma

BACKGROUND

Glioblastoma (GBM) is the most devastating brain tumor. Despite the use of multimodal treatments, most patients relapse, often due to the highly invasive nature of gliomas. However, the detection of glioma infiltration remains challenging. The aim of this study was to assess advanced PET and MRI techniques for visualizing biological activity and infiltration of the tumor.

METHODS

Using multimodality imaging, we investigated [18F]DPA-714, a radiotracer targeting the 18 kDa translocator protein (TSPO), [18F]FET PET, non-Gaussian diffusion MRI (apparent diffusion coefficient, kurtosis), and the S-index, a composite diffusion metric, to detect tumor infiltration in a human invasive glioma model. In vivo imaging findings were confirmed by autoradiography and immunofluorescence.

RESULTS

Increased tumor-to-contralateral [18F]DPA-714 uptake ratios (1.49 ± 0.11) were found starting 7 weeks after glioma cell implantation. TSPO-PET allowed visualization of glioma infiltration into the contralateral hemisphere 2 weeks earlier compared with the clinically relevant biomarker for biological glioma activity [18F]FET. Diffusion-weighted imaging (DWI), in particular kurtosis, was more sensitive than standard T2-weighted MRI to detect differences between the glioma-bearing and the contralateral hemisphere at 5 weeks. Immunofluorescence data reflect in vivo findings. Interestingly, labeling for tumoral and stromal TSPO indicates a predominant expression of TSPO by tumor cells.

CONCLUSION

These results suggest that advanced PET and MRI methods, such as [18F]DPA-714 and DWI, may be superior to standard imaging methods to visualize glioma growth and infiltration at an early stage.

Mechanism-Based Modeling of Tumor Growth and Treatment Response Constrained by Multiparametric Imaging Data

Multiparametric imaging is a critical tool in the noninvasive study and assessment of cancer. Imaging methods have evolved over the past several decades to provide quantitative measures of tumor and healthy tissue characteristics related to, for example, cell number, blood volume fraction, blood flow, hypoxia, and metabolism. Mechanistic models of tumor growth also have matured to a point where the incorporation of patient-specific measures could provide clinically relevant predictions of tumor growth and response. In this review, we identify and discuss approaches that use multiparametric imaging data, including diffusion-weighted magnetic resonance imaging, dynamic contrast-enhanced magnetic resonance imaging, diffusion tensor imaging, contrast-enhanced computed tomography, [¹⁸F]fluorodeoxyglucose positron emission tomography, and [¹⁸F]fluoromisonidazole positron emission tomography to initialize and calibrate mechanistic models of tumor growth and response. We focus the discussion on brain and breast cancers; however, we also identify three emerging areas of application in kidney, pancreatic, and lung cancers. We conclude with a discussion of the future directions for incorporating multiparametric imaging data and mechanistic modeling into clinical decision making for patients with cancer.

Treatment-related changes in neuroendocrine tumors as assessed by textural features derived from 68Ga-DOTATOC PET/MRI with simultaneous acquisition of apparent diffusion coefficient

Background

Neuroendocrine tumors (NETs) frequently overexpress somatostatin receptors (SSTRs), which is the molecular basis for ⁶⁸Ga-DOTATOC positron-emission tomography (PET) and radiopeptide therapy (PRRT). However, SSTR expression fluctuates and can be subject to treatment-related changes. The aim of this retrospective study was to assess, which changes in PET and apparent diffusion coefficient (ADC) occur for different treatments and if pre-therapeutic ⁶⁸Ga-DOTATOC-PET/MRI was able to predict treatment response to PRRT.

Methods

Patients with histopathologically confirmed NET, at least one liver metastasis > 1 cm and at least two ⁶⁸Ga-DOTATOC-PET/MRI including ADC maps were eligible. ⁶⁸Ga-DOTATOC-PET/MRI of up to 5 liver lesions per patients was subsequently analyzed. Extracted features comprise conventional PET parameters, such as maximum and mean standardized uptake value (SUVmax and SUVmean) and ADC values. Furthermore, textural features (TFs) from both modalities were extracted. In patients with multiple ⁶⁸Ga-DOTATOC-PET/MRI a pair of 2 scans each was analyzed separately and the parameter changes between both scans calculated. The same image analysis was performed in patients with ⁶⁸Ga-DOTATOC-PET/MRI before PRRT. Differences in PET and ADC maps parameters between PRRT-responders and non-responders were compared using Mann-Whitney test to test differences among groups for statistical significance.

Results

29 pairs of ⁶⁸Ga-DOTATOC-PET/MRI scans of 18 patients were eligible for the assessment of treatment-related changes. In 12 cases patients were treated with somatostatin analogues between scans, in 9 cases with PRRT and in 2 cases each patients received local treatment, chemotherapy and sunitinib. Treatment responders showed a statistically significant decrease in lesion volume and a borderline significant decrease in entropy on ADC maps when compared to non-responders. Patients treated with standalone SSA showed a borderline significant decrease in mean and maximum ADC, compared to patients treated with PRRT. No parameters were able to predict treatment response to PRRT on pre-therapeutic ⁶⁸Ga-DOTATOC-PET/MRI.

Conclusions

Patients responding to current treatment showed a statistically significant decrease in lesion volume on ADC maps and a borderline significant decrease in entropy. No statistically significant changes in PET parameters were observed. No PET or ADC maps parameters predicted treatment response to PRRT. However, the sample size of this preliminary study is small and further research needed.

ADC quantification to classify patients candidate to receive bevacizumab treatment for recurrent glioblastoma

Background

Recurrent high-grade gliomas progressing after surgery and temozolomide plus radiation therapy have traditionally been treated using antiangiogenic drugs as the first-line therapy. Since the phase 3 EORTC 26101 trial showed no significant benefit of administering antiangiogenic drugs, the need to identify a biomarker to classify subgroups of potential responders has increased.

Purpose

To investigate the feasibility of using apparent diffusion coefficient as a predictor of the response of recurrent high-grade gliomas to bevacizumab or classifier for patients showing better response.

Material and Methods

This retrospective study analyzed magnetic resonance images obtained from 39 patients at the time of high-grade glioma progression who were treated using bevacizumab. The apparent diffusion coefficient maps were quantified and modelled as a mixture of Gaussian functions. The correlation of their descriptors and the time to second progression was studied. Log-rank tests were performed to determine the power of these descriptors as the classifiers for patients exhibiting better survival.

Results

None of the descriptors showed correlation with time to second progression (r < 0.35) but several of them stratified subgroups showing a better time to second progression passing log-rank tests ( P <  0.02).

Conclusion

Apparent diffusion coefficient cannot be used to predict the time to second progression of recurrent high-grade gliomas treated with bevacizumab, but it can stratify groups with better time to second progression distributions.

Heterogeneity Diffusion Imaging of gliomas: Initial experience and validation

Objectives

Primary brain tumors are composed of tumor cells, neural/glial tissues, edema, and vasculature tissue. Conventional MRI has a limited ability to evaluate heterogeneous tumor pathologies. We developed a novel diffusion MRI-based method—Heterogeneity Diffusion Imaging (HDI)—to simultaneously detect and characterize multiple tumor pathologies and capillary blood perfusion using a single diffusion MRI scan.

Methods

Seven adult patients with primary brain tumors underwent standard-of-care MRI protocols and HDI protocol before planned surgical resection and/or stereotactic biopsy. Twelve tumor sampling sites were identified using a neuronavigational system and recorded for imaging data quantification. Metrics from both protocols were compared between World Health Organization (WHO) II and III tumor groups. Cerebral blood volume (CBV) derived from dynamic susceptibility contrast (DSC) perfusion imaging was also compared with the HDI-derived perfusion fraction.

Results

The conventional apparent diffusion coefficient did not identify differences between WHO II and III tumor groups. HDI-derived slow hindered diffusion fraction was significantly elevated in the WHO III group as compared with the WHO II group. There was a non-significantly increasing trend of HDI-derived tumor cellularity fraction in the WHO III group, and both HDI-derived perfusion fraction and DSC-derived CBV were found to be significantly higher in the WHO III group. Both HDI-derived perfusion fraction and slow hindered diffusion fraction strongly correlated with DSC-derived CBV. Neither HDI-derived cellularity fraction nor HDI-derived fast hindered diffusion fraction correlated with DSC-derived CBV.

Conclusions

Conventional apparent diffusion coefficient, which measures averaged pathology properties of brain tumors, has compromised accuracy and specificity. HDI holds great promise to accurately separate and quantify the tumor cell fraction, the tumor cell packing density, edema, and capillary blood perfusion, thereby leading to an improved microenvironment characterization of primary brain tumors. Larger studies will further establish HDI’s clinical value and use for facilitating biopsy planning, treatment evaluation, and noninvasive tumor grading.

Imaging Glioblastoma Posttreatment: Progression, Pseudoprogression, Pseudoresponse, Radiation Necrosis

Radiographic monitoring of posttreatment glioblastoma is important for clinical trials and determining next steps in management. Evaluation for tumor progression is confounded by the presence of treatment-related radiographic changes, making a definitive determination less straight-forward. The purpose of this article was to describe imaging tools available for assessing treatment response in glioblastoma, as well as to highlight the definitions, pathophysiology, and imaging features typical of true progression, pseudoprogression, pseudoresponse, and radiation necrosis.

DWI of Pancreatic Ductal Adenocarcinoma: A Pilot Study to Estimate the Correlation With Metastatic Disease Potential and Overall Survival

OBJECTIVE

The purpose of this study is to analyze the relationship between the apparent diffusion coefficient (ADC) of pancreatic ductal adenocarcinoma (PDAC) and the presence or development of metastasis and overall survival (OS).

MATERIALS AND METHODS

Of 290 consecutive patients with histopathologically proven PDAC from January 2013 to December 2014, staging DWI was performed for 124 patients. Image quality was adequate in 112 studies. Sixty-five patients were treatment naïve, but 17 of the 65 were excluded because of the presence of other associated pancreatic pathologic abnormalities. Data for the remaining 48 patients (24 men and 24 women; median age, 65.5 years; interquartile range, 56-77 years) were obtained during a 4-year follow-up period (mean [± SD], 397 ± 415.1 days). The correlation between ADC and the presence or development of metastasis was assessed using descriptive statistics. OS was determined and mortality analysis was performed using Pearson correlation and Kaplan-Meier curves.

RESULTS

Of 48 patients, 10 had metastases at staging MRI, and 12 later developed metastatic disease. Among the latter, the mean time from staging MRI to metastasis was 258 ± 274.1 days. Most (86%) metastases were hepatic (n = 19). During the follow-up period, the remaining 26 patients (54%) never developed metastases. Patients with metastatic disease (n = 22) had significantly lower mean ADCs than did those without metastases (1.27 × 10^-3 vs 1.43 × 10^-3 mm²/s; p = 0.047). The ADC of PDAC had a positive correlation with survival: patients with PDAC with lower ADCs (< 1.36 × 10^-3 mm²/s) had significantly worse 4-year OS rates than did patients with higher ADC values (p = 0.036).

CONCLUSION

Pretreatment ADC values of PDAC may be significantly lower in patients who have or will develop metastatic disease and may correlate with worse OS.

Adeno-associated virus 2 mediated gene transfer of vascular endothelial growth factor Trap: a new treatment option for glioma

Background: Adeno-associated virus 2 mediated gene transfer of vascular endothelial growth factor Trap (AAV2-VEGF-Trap) has been reported to inhibit the growth of primary tumor as well as distant metastasis in 4T1 metastatic breast cancer models. The aim of this study was to investigate the inhibiting efficacy of AAV2-VEGF-Trap for glioma.

Methods: The intracranial transplanted model of glioma in rats was established. They were treated with AAV2-VEGF-Trap, bevacizumab (BEV), temozolomide (TMZ), TMZ combined with AAV2-VEGF-Trap, TMZ combined with BEV and the control group, respectively. A 7.0 Tesla magnetic resonance (MR) was used to assess the tumor volumes and obtain the apparent diffusion coefficient (ADC) values. Immunohistochemical and terminal dexynucleotidyl transferase (TdT)-mediated dUTP nick end labeling (TUNEL) staining were used to evaluate the effects on tumor angiogenesis, proliferation and apoptosis.

Results: The combination of TMZ with AAV2-VEGF-Trap or BEV showed greater tumor growth inhibition than the other groups, and the ADC values in these two groups were larger than that of the control group. The decreased microvessel density in treatment groups which contain AAV2-VEGF-Trap or BEV was observed. The reduced proliferation activity in groups containing TMZ and increased apoptotic tumor cells in TMZ combined with AAV2-VEGF-Trap group and TMZ combined with BEV group were detected. In addition, there were no differences in antitumor effect, ADC values, Ki-67 and CD31 staining and apoptosis analysis between the two combined therapy groups.

Conclusion: AAV2-VEGF-Trap has an obvious anti-angiogenic effect and inhibits the growth of glioma just by a single intravenous injection, which is similar to BEV. Moreover, there is a synergistic antitumor effect between AAV2-VEGF-Trap and TMZ.

Stretched-exponential model diffusion-weighted imaging as a potential imaging marker in preoperative grading and assessment of proliferative activity of gliomas

PURPOSE

To assess the feasibility of using diffusion-weighted imaging (DWI) with the stretched-exponential model (SEM) for glioma grading and determining the correlations among parameters and proliferating cell nuclear antigen and Ki-67 expression.

MATERIALS AND METHODS

Mono-exponential model-DWI (MEM-DWI) and SEM-DWI were performed in 104 patients with pathologically proven gliomas. The patients were divided into the training set (n = 72) and test set (n = 32). Apparent diffusion coefficient (ADC), solid tumor distributed diffusion coefficient (DDC), and whole tumor α values were measured. These parameters were applied as cut-off values to determine the predictive accuracy. Proliferating cell nuclear antigen and Ki-67 expression correlated with all parameters.

RESULTS

Significant differences between low-grade gliomas (LGG) and high-grade gliomas (HGG) were observed for all parameters (P < 0.05), and significant differences in the ability of DDC to distinguish between any two glioma grades (P < 0.05) were also evident. DDC showed the highest sensitivity and specificity for glioma grading and was negatively correlated with Ki-67 and proliferating cell nuclear antigen expression. DDC also showed greater predictive accuracy than ADC and α.

CONCLUSION

SEM-DWI offers a better approach for glioma grading than MEM-DWI, and DDC may be a better imaging biomarker for grading and evaluating the proliferative activity of brain gliomas.

Early Changes in Tumor Perfusion from T1-Weighted Dynamic Contrast-Enhanced MRI following Neural Stem Cell-Mediated Therapy of Recurrent High-Grade Glioma Correlate with Overall Survival

Background

The aim of this study was to correlate T1-weighted dynamic contrast-enhanced MRI- (DCE-MRI-) derived perfusion parameters with overall survival of recurrent high-grade glioma patients who received neural stem cell- (NSC-) mediated enzyme/prodrug gene therapy.

Methods

A total of 12 patients were included in this retrospective study. All patients were enrolled in a first-in-human study (NCT01172964) of NSC-mediated therapy for recurrent high-grade glioma. DCE-MRI data from all patients were collected and analyzed at three time points: MRI#1—day 1 postsurgery/treatment, MRI#2— day 7 ± 3 posttreatment, and MRI#3—one-month follow-up. Plasma volume (V_p), permeability (K^tr), and leakage (λ^tr) perfusion parameters were calculated by fitting a pharmacokinetic model to the DCE-MRI data. The contrast-enhancing (CE) volume was measured from the last dynamic phase acquired in the DCE sequence. Perfusion parameters and CE at each MRI time point were recorded along with their relative change between MRI#2 and MRI#3 (Δ₃₂). Cox regression was used to analyze patient survival.

Results

At MRI#1 and at MRI#3, none of the parameters showed a significant correlation with overall survival (OS). However, at MRI#2, CE and λ^tr were significantly associated with OS (p < 0.05). The relative λ^tr and V_p from timepoint 2 to timepoint 3 (Δ₃₂λ^tr and Δ₃₂V_p) were each associated with a higher hazard ratio (p < 0.05). All parameters were highly correlated, resulting in a multivariate model for OS including only CE at MRI#2 and Δ₃₂V_p, with an R² of 0.89.

Conclusion

The change in perfusion parameter values from 1 week to 1 month following NSC-mediated therapy combined with contrast-enhancing volume may be a useful biomarker to predict overall survival in patients with recurrent high-grade glioma.

Noninvasive Glioblastoma Testing: Multimodal Approach to Monitoring and Predicting Treatment Response

Glioblastoma is the most aggressive adult primary brain tumor which is incurable despite intensive multimodal treatment. Inter- and intratumoral heterogeneity poses one of the biggest barriers in the diagnosis and treatment of glioblastoma, causing differences in treatment response and outcome. Noninvasive prognostic and predictive tests are highly needed to complement the current armamentarium. Noninvasive testing of glioblastoma uses multiple techniques that can capture the heterogeneity of glioblastoma. This set of diagnostic approaches comprises advanced MRI techniques, nuclear imaging, liquid biopsy, and new integrated approaches including radiogenomics and radiomics. New treatment options such as agents targeted at driver oncogenes and immunotherapy are currently being developed, but benefit for glioblastoma patients still has to be demonstrated. Understanding and unraveling tumor heterogeneity and microenvironment can help to create a treatment regime that is patient-tailored to these specific tumor characteristics. Improved noninvasive tests are crucial to this success. This review discusses multiple diagnostic approaches and their effect on predicting and monitoring treatment response in glioblastoma.

Tumor Cell Load and Heterogeneity Estimation From Diffusion-Weighted MRI Calibrated With Histological Data: an Example From Lung Cancer

Diffusion-weighted magnetic resonance imaging (DWI) is a key non-invasive imaging technique for cancer diagnosis and tumor treatment assessment, reflecting Brownian movement of water molecules in tissues. Since densely packed cells restrict molecule mobility, tumor tissues produce usually higher signal (a.k.a. less attenuated signal) on isotropic maps compared with normal tissues. However, no general quantitative relation between DWI data and the cell density has been established. In order to link low-resolution clinical cross-sectional data with high-resolution histological information, we developed an image processing and analysis chain, which was used to study the correlation between the diffusion coefficient (D value) estimated from DWI and tumor cellularity from serial histological slides of a resected non-small cell lung cancer tumor. Color deconvolution followed by cell nuclei segmentation was performed on digitized histological images to determine local and cell-type specific 2d (two-dimensional) densities. From these, the 3d cell density was inferred by a model-based sampling technique, which is necessary for the calculation of local and global 3d tumor cell count. Next, DWI sequence information was overlaid with high-resolution CT data and the resected histology using prominent anatomical hallmarks for co-registration of histology tissue blocks and non-invasive imaging modalities' data. The integration of cell numbers information and DWI data derived from different tumor areas revealed a clear negative correlation between cell density and D value. Importantly, spatial tumor cell density can be calculated based on DWI data. In summary, our results demonstrate that tumor cell count and heterogeneity can be predicted from DWI data, which may open new opportunities for personalized diagnosis and therapy optimization.

Apparent diffusion coefficient histogram analysis for prediction of prognosis in glioblastoma

BACKGROUND

To investigate the potential to predict prognosis of glioblastoma (GBM) patients by analysis of the broader and lower values in the lower distribution of apparent diffusion coefficient (ADC_L) (B&L-ADC_L) values in the ADC histogram.

BACKGROUND

Presurgical publicly available diffusion-weighted images (DWI) and contrast-enhanced T1-weighted images from 76 GBM patients were analyzed. With applied 2-mixture normal distribution in the ADC histogram of enhanced lesions on T1-weighted images, the mean and width of ADC_L were analyzed. We dichotomized the lower mean of ADC_L (L-ADC_L) and the broader width of ADC_L (B-ADC_L) at their own average. B&L-ADC_L was defined as B-ADC_L with L-ADC_L. Progression-free survival (PFS) and overall survival (OS) were determined by using Cox proportional hazards analysis and the Kaplan-Meier method with the log-rank test. The difference between PFS and OS was calculated.

RESULTS

Six (7.89%) patients had B&L-ADC_L values. B&L-ADC_L was strongly associated with poor PFS (hazard risk: 5.747; P=0.002) and OS (hazard risk: 3.331; P=0.018). There were significant differences in PFS (median, 77 vs. 302 days; P<0.001) and OS (median, 199 vs. 472 days; P=0.004) between the patents with and without B&L-ADC_L. There was no significant difference in the OS-PFS duration difference between the patients with (median, 79 days) and without B&L-ADC_L (median, 132 days) (P=0.348).

CONCLUSION

In this study, B&L-ADC_L from pretreatment ADC analysis predicted poor PFS. B&L-ADC_L may indicate higher cellularity and heterogeneity in GBM tumor tissue.

An improved model for prostate diffusion incorporating the results of Monte Carlo simulations of diffusion in the cellular compartment

The purpose of this work was to refine a previously published model of prostate diffusion by incorporating improved estimates of cellular diffusivity obtained by Monte Carlo simulation. Stromal and epithelial cell size and intracellular volume fraction in different grades of cancer were determined from histological images. Diffusion in different mixtures of cells, corresponding to different tumor grades, was simulated and cellular apparent diffusion coefficient and kurtosis values determined. These values were incorporated into the previously published model of prostate diffusion and model predictions compared with values found in the literature. Stromal cell radius and intracellular volume fraction were 3.74 ± 0.96 μm and 13 ± 3% respectively in normal peripheral zone (PZ), and were similar in all grades of cancer. Epithelial cell radius and intracellular volume fraction were 3.40 ± 0.15 μm and 45 ± 5% respectively in normal PZ, rising to 4.75 ± 0.20 μm and 70 ± 8% in high grade cancer. Cellular apparent diffusion coefficient and kurtosis were 1.02 μm² ms^-1 and 0.58 respectively in normal PZ, and 0.61 μm² ms^-1 and 1.15 in high grade cancer (variation in simulation values are less than 0.1%). Agreement between model predictions and measurements were good, with a mean square error of 0.22 μm² ms^-1 . Incorporation of cellular diffusion coefficient and kurtosis values obtained by Monte Carlo simulation into a model of prostate diffusion gives good agreement with published results.

Value of Minimum Apparent Diffusion Coefficient on Magnetic Resonance Imaging as a Biomarker for Predicting Progression of Disease Following Surgery and Radiotherapy in Glial Tumors from a Tertiary Care Center in Northern India

Purpose:

Studies have shown that cellularity of glial tumors are inversely correlated to minimum apparent diffusion coefficient (ADC) values derived on diffusion-weighted imaging (DWI). The purpose of this prospective exploratory study was to evaluate whether temporal change in “minimum ADC” values during follow-up predict progressive disease in glial tumors post radiotherapy and surgery.

Materials and Methods:

Adult patients of glial tumors, subjected to surgery followed by Radiotherapy (RT), were included in the study. Serial conventional magnetic resonance imaging with DWI at the following time points – presurgery, pre-RT, post-RT imaging at 3, 7, and 15 months were done. For “minimum ADC” values, multiple regions of interest (ROI) were identified on ADC maps derived from DWI. A mean of 5 minimum ADC values was chosen as “minimum ADC” value. The correlation was drawn between histology and minimum ADC values and time trends were studied.

Results:

Fourteen patients were included in this study. Histologies were low-grade glioma (LGG) – 5, anaplastic oligodendroglioma (ODG) -5, and glioblastoma multiforme (GBM) – 4. Minimum ADC values were significantly higher in LGG and GBM than ODG. Presurgery, the values were 0.812, 0.633, and 0.787 × 10⁻³ mm²/s for LGG, ODG, and GBM, respectively. DWI done at the time of RT planning showed values of 0.786, 0.636, 0.869 × 10⁻³ mm²/s, respectively. During follow-up, the increasing trend of minimum ADC was observed in LGG (P = 0.02). All these patients were clinically and radiologically stable. Anaplastic ODGs, however, showed an initial increase followed by the fall of minimum ADC in all the 5 cases (P = 0.00). Four of the five cases developed progressive disease subsequently. In all the 4 GBM cases, a consistent fall of minimum ADC values was observed (P = 0.00), and they all progressed in spite of RT.

Conclusions:

The DWI-derived minimum ADC values are an important yet simple quantitative tool to assess the treatment response and disease progression before they are evident on conventional imaging during the follow-up of glial tumors.

MRI in Glioma Immunotherapy: Evidence, Pitfalls, and Perspectives

Pseudophenomena, that is, imaging alterations due to therapy rather than tumor evolution, have an important impact on the management of glioma patients and the results of clinical trials. RANO (response assessment in neurooncology) criteria, including conventional MRI (cMRI), addressed the issues of pseudoprogression after radiotherapy and concomitant chemotherapy and pseudoresponse during antiangiogenic therapy of glioblastomas (GBM) and other gliomas. The development of cancer immunotherapy forced the identification of further relevant response criteria, summarized by the iRANO working group in 2015. In spite of this, the unequivocal definition of glioma progression by cMRI remains difficult particularly in the setting of immunotherapy approaches provided by checkpoint inhibitors and dendritic cells. Advanced MRI (aMRI) may in principle address this unmet clinical need. Here, we discuss the potential contribution of different aMRI techniques and their indications and pitfalls in relation to biological and imaging features of glioma and immune system interactions.

Clinical PET/MRI in neurooncology: opportunities and challenges from a single-institution perspective

Purpose

Magnetic resonance imaging (MRI) plays a key role in neurooncology, i.e., for diagnosis, treatment evaluation and detection of recurrence. However, standard MRI cannot always separate malignant tissue from other pathologies or treatment-induced changes. Advanced MRI techniques such as diffusion-weighted imaging, perfusion imaging and spectroscopy show promising results in discriminating malignant from benign lesions. Further, supplemental imaging with amino acid positron emission tomography (PET) has been shown to increase accuracy significantly and is used routinely at an increasing number of sites. Several centers are now implementing hybrid PET/MRI systems allowing for multiparametric imaging, combining conventional MRI with advanced MRI and amino acid PET imaging. Neurooncology is an obvious focus area for PET/MR imaging.

Methods

Based on the literature and our experience from more than 300 PET/MRI examinations of brain tumors with ¹⁸F-fluoro-ethyl-tyrosine, the clinical use of PET/MRI in adult and pediatric neurooncology is critically reviewed.

Results

Although the results are increasingly promising, the added value and range of indications for multiparametric imaging with PET/MRI are yet to be established. Robust solutions to overcome the number of issues when using a PET/MRI scanner are being developed, which is promising for a more routine use in the future.

Conclusions

In a clinical setting, a PET/MRI scan may increase accuracy in discriminating recurrence from treatment changes, although sequential same-day imaging on separate systems will often constitute a reliable and cost-effective alternative. Pediatric patients who require general anesthesia will benefit the most from simultaneous PET and MR imaging.

Analytical performance bounds for multi-tensor diffusion-MRI

PURPOSE

To examine the effects of MR acquisition parameters on brain white matter fiber orientation estimation and parameter of clinical interest in crossing fiber areas based on the Multi-Tensor Model (MTM).

MATERIAL AND METHODS

We compute the Cramér-Rao Bound (CRB) for the MTM and the parameter of clinical interest such as the Fractional Anisotropy (FA) and the dominant fiber orientations, assuming that the diffusion MRI data are recorded by a multi-coil, multi-shell acquisition system. Considering the sum-of-squares method for the reconstructed magnitude image, we introduce an approximate closed-form formula for Fisher Information Matrix that has the simplicity and easy interpretation advantages. In addition, we propose to generalize the FA and the mean diffusivity to the multi-tensor model.

RESULTS

We show the application of the CRB to reduce the scan time while preserving a good estimation precision. We provide results showing how the increase of the number of acquisition coils compensates the decrease of the number of diffusion gradient directions. We analyze the impact of the b-value and the Signal-to-Noise Ratio (SNR). The analysis shows that the estimation error variance decreases with a quadratic rate with the SNR, and that the optimum b-values are not unique but depend on the target parameter, the context, and eventually the target cost function.

CONCLUSION

In this study we highlight the importance of choosing the appropriate acquisition parameters especially when dealing with crossing fiber areas. We also provide a methodology for the optimal tuning of these parameters using the CRB.

A longitudinal magnetic resonance elastography study of murine brain tumors following radiation therapy

An accurate and noninvasive method for assessing treatment response following radiotherapy is needed for both treatment monitoring and planning. Measurement of solid tumor volume alone is not sufficient for reliable early detection of therapeutic response, since changes in physiological and/or biomechanical properties can precede tumor volume change following therapy. In this study, we use magnetic resonance elastography to evaluate the treatment effect after radiotherapy in a murine brain tumor model. Shear modulus was calculated and compared between the delineated tumor region of interest (ROI) and its contralateral, mirrored counterpart. We also compared the shear modulus from both the irradiated and non-irradiated tumor and mirror ROIs longitudinally, sampling four time points spanning 9-19 d post tumor implant. Results showed that the tumor ROI had a lower shear modulus than that of the mirror ROI, independent of radiation. The shear modulus of the tumor ROI decreased over time for both the treated and untreated groups. By contrast, the shear modulus of the mirror ROI appeared to be relatively constant for the treated group, while an increasing trend was observed for the untreated group. The results provide insights into the tumor properties after radiation treatment and demonstrate the potential of using the mechanical properties of the tumor as a biomarker. In future studies, more closely spaced time points will be employed for detailed analysis of the radiation effect.

A model describing diffusion in prostate cancer

PURPOSE

Quantitative diffusion MRI has frequently been studied as a means of grading prostate cancer. Interpretation of results is complicated by the nature of prostate tissue, which consists of four distinct compartments: vascular, ductal lumen, epithelium, and stroma. Current diffusion measurements are an ill-defined weighted average of these compartments. In this study, prostate diffusion is analyzed in terms of a model that takes explicit account of tissue compartmentalization, exchange effects, and the non-Gaussian behavior of tissue diffusion.

METHOD

The model assumes that exchange between the cellular (ie, stromal plus epithelial) and the vascular and ductal compartments is slow. Ductal and cellular diffusion characteristics are estimated by Monte Carlo simulation and a two-compartment exchange model, respectively. Vascular pseudodiffusion is represented by an additional signal at b = 0. Most model parameters are obtained either from published data or by comparing model predictions with the published results from 41 studies. Model prediction error is estimated using 10-fold cross-validation.

RESULTS

Agreement between model predictions and published results is good. The model satisfactorily explains the variability of ADC estimates found in the literature.

CONCLUSION

A reliable model that predicts the diffusion behavior of benign and cancerous prostate tissue of different Gleason scores has been developed. Magn Reson Med 78:316-326, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Relation of apparent diffusion coefficient with Ki-67 proliferation index in meningiomas

OBJECTIVE

The purpose of this study was to investigate the relationship between Ki-67 proliferation indexes and apparent diffusion coefficient (ADC) values of low-grade and atypical/anaplastic (high-grade) meningiomas.

METHODS

Pre-operative diffusion-weighted imaging and histopathological evaluation of 44 patients with meningiomas were performed retrospectively. Regions of interest (ROIs) were manually drawn on the ADC images. In total six ROI measurements were taken in three consecutive slices, and the average of the mean ADC value was used. The relationship between the ADC and Ki-67 values was investigated, and the ADC values of the low-grade and high-grade meningiomas were compared.

RESULTS

31 (70%) patients had low-grade the meningiomas. 10 (23%) patients had atypical and 3 (7%) had anaplastic meningiomas. ADC values of the low-grade and high-grade meningiomas were 0.81 ± 0.12 × 10(-3) and 0.66 ± 0.08 × 10(-3) mm(2) s(-1), respectively. Ki-67 proliferation indexes were 2.19% ± 1.14% for low-grade and 11.20% ± 9.80% for high-grade meningiomas. A statistically significant negative correlation between Ki-67 proliferation index and ADC values of the low-grade and high-grade meningiomas was detected (r(2) = 0.326, p < 0.001). High-grade meningiomas had lower ADC values than that of low-grade meningiomas. There was statistically significant difference between the ADC values of the low-grade and high-grade meningiomas (p < 0.001).

CONCLUSION

Our data provide an inverse correlation between the ADC and Ki-67 proliferation index values of meningiomas. ADC values can be used for histopathological characterization of the meningiomas and pre-surgical planning.

ADVANCES IN KNOWLEDGE

The purpose of this study was to investigate the relationship between Ki-67 proliferation indexes and ADC values of low-grade and atypical/anaplastic (high-grade) meningiomas. In addition, we compared the ADC and Ki-67 proliferative index values of the low-grade and atypical/anaplastic (high-grade) meningiomas. We concluded that there was an inverse correlation between the ADC and Ki-67 proliferation index values in meningiomas, and we have found statistically significant difference between the ADC values of the low-grade and high-grade meningiomas. ADC values can be used for histopathological characterization of the meningiomas and pre-surgical planning.

Diffusion-weighted imaging in cancer: physical foundations and applications of restriction spectrum imaging

Diffusion-weighted imaging (DWI) has been at the forefront of cancer imaging since the early 2000s. Before its application in clinical oncology, this powerful technique had already achieved widespread recognition due to its utility in the diagnosis of cerebral infarction. Following this initial success, the ability of DWI to detect inherent tissue contrast began to be exploited in the field of oncology. Although the initial oncologic applications for tumor detection and characterization, assessing treatment response, and predicting survival were primarily in the field of neurooncology, the scope of DWI has since broadened to include oncologic imaging of the prostate gland, breast, and liver. Despite its growing success and application, misconceptions about the underlying physical basis of the DWI signal exist among researchers and clinicians alike. In this review, we provide a detailed explanation of the biophysical basis of diffusion contrast, emphasizing the difference between hindered and restricted diffusion, and elucidating how diffusion parameters in tissue are derived from the measurements via the diffusion model. We describe one advanced DWI modeling technique, called restriction spectrum imaging (RSI). This technique offers a more direct in vivo measure of tumor cells, due to its ability to distinguish separable pools of water within tissue based on their intrinsic diffusion characteristics. Using RSI as an example, we then highlight the ability of advanced DWI techniques to address key clinical challenges in neurooncology, including improved tumor conspicuity, distinguishing actual response to therapy from pseudoresponse, and delineation of white matter tracts in regions of peritumoral edema. We also discuss how RSI, combined with new methods for correction of spatial distortions inherent in diffusion MRI scans, may enable more precise spatial targeting of lesions, with implications for radiation oncology and surgical planning. See all articles in this Cancer Research section, "Physics in Cancer Research."

Comparison of perfusion- and diffusion-weighted imaging parameters in brain tumor studies processed using different software platforms

RATIONALE AND OBJECTIVES

To compare quantitative imaging parameter measures from diffusion- and perfusion-weighted imaging magnetic resonance imaging (MRI) sequences in subjects with brain tumors that have been processed with different software platforms.

MATERIALS AND METHODS

Scans from 20 subjects with primary brain tumors were selected from the Comprehensive Neuro-oncology Data Repository at Washington University School of Medicine (WUSM) and the Swedish Neuroscience Institute. MR images were coregistered, and each subject's data set was processed by three software packages: 1) vendor-specific scanner software, 2) research software developed at WUSM, and 3) a commercially available, Food and Drug Administration-approved, processing platform (Nordic Ice). Regions of interest (ROIs) were chosen within the brain tumor and normal nontumor tissue. The results obtained using these methods were compared.

RESULTS

For diffusion parameters, including mean diffusivity and fractional anisotropy, concordance was high when comparing different processing methods. For perfusion-imaging parameters, a significant variance in cerebral blood volume, cerebral blood flow, and mean transit time (MTT) values was seen when comparing the same raw data processed using different software platforms. Correlation was better with larger ROIs (radii ≥ 5 mm). Greatest variance was observed in MTT.

CONCLUSIONS

Diffusion parameter values were consistent across different software processing platforms. Perfusion parameter values were more variable and were influenced by the software used. Variation in the MTT was especially large suggesting that MTT estimation may be unreliable in tumor tissues using current MRI perfusion methods.