MRI in treatment of adult gliomas

Amide proton transfer-weighted imaging and derived radiomics in the classification of adult-type diffuse gliomas

OBJECTIVES

To evaluate the utility of amide proton transfer-weighted (APTw) MRI imaging and its derived radiomics in classifying adult-type diffuse glioma.

MATERIALS AND METHODS

In this prospective study, APTw imaging was performed on 129 patients with adult-type diffuse gliomas. The mean APTw-related metrics (chemical exchange saturation transfer ratio (CESTR), CESTR normalized with the reference value (CESTRnr), and relaxation-compensated inverse magnetization transfer ratio (MTRRex)) and radiomic features within 3D tumor masks were extracted. APTw-radiomics models were developed using a support vector machine (SVM) classifier. Sensitivity analysis with tumor area of interest, different histogram cutoff values, and other classifiers were conducted.

RESULTS

CESTR, CESTRnr, and MTRRex in glioblastomas were all significantly higher (p < 0.0003) than those of oligodendrogliomas and astrocytomas, with no significant difference between oligodendrogliomas and astrocytomas. The APTw-related metrics for IDH-wildtype and high-grade gliomas were significantly higher (p < 0.001) than those for the IDH-mutant and low-grade gliomas, with area under the curve (AUCs) of 0.88 for CESTR. The CESTR-radiomics models demonstrated accuracies of 84% (AUC 0.87), 83% (AUC 0.83), 90% (AUC 0.95), and 84% (AUC 0.86) in predicting the IDH mutation status, differentiating glioblastomas from astrocytomas, distinguishing glioblastomas from oligodendrogliomas, and determining high/low grade prediction, respectively, but showed poor performance in distinguishing oligodendrogliomas from astrocytomas (accuracy 63%, AUC 0.63). The sensitivity analysis affirmed the robustness of the APTw signal and APTw-derived radiomics prediction models.

CONCLUSION

APTw imaging, along with its derived radiomics, presents a promising quantitative approach for prediction IDH mutation and grading adult-type diffuse glioma.

CLINICAL RELEVANCE STATEMENT

Amide proton transfer-weighted imaging, a quantitative imaging biomarker, coupled with its derived radiomics, offers a promising non-invasive approach for predicting IDH mutation status and grading adult-type diffuse gliomas, thereby informing individualized clinical diagnostics and treatment strategies.

KEY POINTS

• This study evaluates the differences of different amide proton transfer-weighted metrics across three molecular subtypes and their efficacy in classifying adult-type diffuse glioma. • Chemical exchange saturation transfer ratio normalized with the reference value and relaxation-compensated inverse magnetization transfer ratio effectively predicts IDH mutation/grading, notably the first one. • Amide proton transfer-weighted imaging and its derived radiomics holds potential to be used as a diagnostic tool in routine clinical characterizing adult-type diffuse glioma.

Evaluation of multimodal MR imaging for differentiating infiltrative versus reactive edema in brain gliomas

OBJECTIVE

To determine the border of glial tumors by diffusion weighted imaging (DWI), apparent diffusion co-efficient (ADC), magnetic resonance spectroscopy (MRS) and perfusion brain MRI.

PATIENTS AND METHODS

Ten patients with brain gliomas were enrolled [mean age: 35.3 ± 13.2, range: 20-62]. Conventional MRI was performed for all patients. Besides, tumor mapping based on Choline (Cho)/Creatine (Cr) color map in MRS, perfusion and diffusion color maps, were gathered. Different tumoral and peritumoral regions [normal tissue, reactive edema, infiltrative edema, and tumor core] were defined. MRI criteria were evaluated in areas targeted for biopsy and histopathologic evaluation was determined.

RESULTS

Tumor cell positive samples [one necrosis, 26 infiltrative and nine tumor cores] composed 36 (75%) of the 48 samples. Seven (19.4%) of the positive samples were interpreted as not tumor on MRI. Five were identified as reactive edema and two as normal tissue] [kappa: .67, p-value < .001]. Mean of ADC, median of N-acetylaspartate (NAA) and NAA/Cho were statistically different between positive and negative samples (p = .02 and p < .001, respectively). Mean ADC and median Cho/NAA were statistically different in missed tumor containing tissue presented as reactive edema compared to normal and correctly diagnosed reactive edema samples together (p-values < .05).

CONCLUSIONS

Multimodal MRI could define infiltrated borders of brain gliomas.

Modulating Nanozyme-Based Nanomachines via Microenvironmental Feedback for Differential Photothermal Therapy of Orthotopic Gliomas

Gliomas are common and refractory primary tumors closely associated with the fine structures of the brain. Photothermal therapy (PTT) has recently shown promise as an effective treatment for gliomas. However, nonspecific accumulation of photothermal agents may affect adjacent normal brain structures, and the inflammatory response induced during PTT may result in an increased risk of brain tumor recurrence or metastasis. Here, the design and fabrication of an intelligent nanomachine is reported based on Gd₂ O₃ @Ir/TMB-RVG29 (G@IT-R) hybrid nanomaterials. These nanomaterials enable tumor-specific PTT and eliminate inflammation to protect normal brain tissue. The mechanism involves the rabies virus glycopeptide-29 peptide (RVG29) passing through the blood-brain barrier (BBB) and targeting gliomas. In the tumor microenvironment, Ir nanozymes can act as logic control systems to trigger chromogenic reaction amplification of 3,3',5,5'-tetramethylbenzidine (TMB) for tumor-specific PTT, whereas in normal brain tissues, they scavenge reactive oxygen species (ROS) generated by poor therapy and function as protective agents. Autophagy inhibition of Gd₂ O₃ enables excellent photothermal therapeutic effects on orthotopic gliomas and protection against inflammation in normal cells. The results of this study may prove useful in developing highly efficient nanomedicines for glioma treatment.

The role of [18F]fluorodopa positron emission tomography in grading of gliomas

PURPOSE

Gliomas are the most commonly occurring brain tumour in adults and there remains no cure for these tumours with treatment strategies being based on tumour grade. All treatment options aim to prolong survival, maintain quality of life and slow the inevitable progression from low-grade to high-grade. Despite imaging advancements, the only reliable method to grade a glioma is to perform a biopsy, and even this is fraught with errors associated with under grading. Positron emission tomography (PET) imaging with amino acid tracers such as [18F]fluorodopa (18F-FDOPA), [11C]methionine (11C-MET), [18F]fluoroethyltyrosine (18F-FET), and 18F-FDOPA are being increasingly used in the diagnosis and management of gliomas.

METHODS

In this review we discuss the literature available on the ability of 18F-FDOPA-PET to distinguish low- from high-grade in newly diagnosed gliomas.

RESULTS

In 2016 the Response Assessment in Neuro-Oncology (RANO) and European Association for Neuro-Oncology (EANO) published recommendations on the clinical use of PET imaging in gliomas. However, since these recommendations there have been a number of studies performed looking at whether 18F-FDOPA-PET can identify areas of high-grade transformation before the typical radiological features of transformation such as contrast enhancement are visible on standard magnetic resonance imaging (MRI).

CONCLUSION

Larger studies are needed to validate 18F-FDOPA-PET as a non-invasive marker of glioma grade and prediction of tumour molecular characteristics which could guide decisions surrounding surgical resection.

Radiomics Analysis Based on Magnetic Resonance Imaging for Preoperative Overall Survival Prediction in Isocitrate Dehydrogenase Wild-Type Glioblastoma

Purpose

This study aimed to develop a radiomics signature for the preoperative prognosis prediction of isocitrate dehydrogenase (IDH)-wild-type glioblastoma (GBM) patients and to provide personalized assistance in the clinical decision-making for different patients.

Materials and Methods

A total of 142 IDH-wild-type GBM patients classified using the new classification criteria of WHO 2021 from two centers were included in the study and randomly divided into a training set and a test set. Firstly, their clinical characteristics were screened using univariate Cox regression. Then, the radiomics features were extracted from the tumor and peritumoral edema areas on their contrast-enhanced T1-weighted image (CE-T1WI), T2-weighted image (T2WI), and T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) magnetic resonance imaging (MRI) images. Subsequently, inter- and intra-class correlation coefficient (ICC) analysis, Spearman’s correlation analysis, univariate Cox, and the least absolute shrinkage and selection operator (LASSO) Cox regression were used step by step for feature selection and the construction of a radiomics signature. The combined model was established by integrating the selected clinical factors. Kaplan–Meier analysis was performed for the validation of the discrimination ability of the model, and the C-index was used to evaluate consistency in the prediction. Finally, a Radiomics + Clinical nomogram was generated for personalized prognosis analysis and then validated using the calibration curve.

Results

Analysis of the clinical characteristics resulted in the screening of four risk factors. The combination of ICC, Spearman’s correlation, and univariate and LASSO Cox resulted in the selection of eight radiomics features, which made up the radiomics signature. Both the radiomics and combined models can significantly stratify high- and low-risk patients (p &lt; 0.001 and p &lt; 0.05 for the training and test sets, respectively) and obtained good prediction consistency (C-index = 0.74–0.86). The calibration plots exhibited good agreement in both 1- and 2-year survival between the prediction of the model and the actual observation.

Conclusion

Radiomics is an independent preoperative non-invasive prognostic tool for patients who were newly classified as having IDH-wild-type GBM. The constructed nomogram, which combined radiomics features with clinical factors, can predict the overall survival (OS) of IDH-wild-type GBM patients and could be a new supplement to treatment guidelines.

Identification of magnetic resonance imaging features for the prediction of molecular profiles of newly diagnosed glioblastoma

PURPOSE

We predicted molecular profiles in newly diagnosed glioblastoma patients using magnetic resonance (MR) imaging features and explored the associations between imaging features and major molecular alterations.

METHODS

This retrospective study included patients with newly diagnosed glioblastoma and available next-generation sequencing results. From preoperative MR imaging, Visually AcceSAble Rembrandt Images (VASARI) features, volumetric parameters, and apparent diffusion coefficient (ADC) values were obtained. First, univariate random forest was performed to identify gene abnormalities that could be predicted by imaging features with high accuracy and stability. Next, multivariate random forest was trained to predict the selected genes in the discovery cohort and was validated in the external cohort. Univariable logistic regression was performed to further explore the associations between imaging features and genes.

RESULTS

Univariate random forest identified nine genes predicted by imaging features, with high accuracy and stability. The multivariate random forest model showed excellent performance in predicting IDH and PTPN11 mutations in the discovery cohort, which were validated in the external validation cohorts (areas under the receiver operator characteristic curve [AUCs] of 0.855 for IDH and 0.88 for PTPN11). ATRX loss and EGFR mutation were predicted with AUCs of 0.753 and 0.739, respectively, whereas PTEN could not be reliably predicted. Based on univariable logistic regression analyses, IDH, ATRX, and TP53 were clustered according to their shared imaging features, whereas EGFR and CDKN2A/B were clustered in the opposite direction.

CONCLUSIONS

MR imaging features are related to specific molecular alterations and can be used to predict molecular profiles in patients with newly diagnosed glioblastoma.

Clinical Applications of Magnetic Resonance Spectroscopy in Brain Tumors: From Diagnosis to Treatment

Magnetic resonance spectroscopy (MRS) is a valuable tool for imaging brain tumors, primarily as an adjunct to conventional imaging and clinical presentation. MRS is useful in initial diagnosis of brain tumors, helping differentiate tumors from possible mimics such as metastatic disease, lymphoma, demyelination, and infection, as well as in the subsequent follow-up of patients after resection and chemoradiation. Unfortunately, the spectroscopic appearance of many pathologies can overlap, and ultimately follow-up or biopsy may be required to make a definitive diagnosis. Future developments may continue to increase the value of MRS for initial diagnosis, treatment planning, and early detection of recurrence.

Advancing Imaging to Enhance Surgery: From Image to Information Guidance

Conventional magnetic resonance imaging (cMRI) has an established role as a crucial disease parameter in the multidisciplinary management of glioblastoma, guiding diagnosis, treatment planning, assessment, and follow-up. Yet, cMRI cannot provide adequate information regarding tissue heterogeneity and the infiltrative extent beyond the contrast enhancement. Advanced magnetic resonance imaging and PET and newer analytical methods are transforming images into data (radiomics) and providing noninvasive biomarkers of molecular features (radiogenomics), conveying enhanced information for improving decision making in surgery. This review analyzes the shift from image guidance to information guidance that is relevant for the surgical treatment of glioblastoma.

Glioblastoma: Prognostic Factors and Predictive Response to Radio and Chemotherapy

Glioblastoma multiforme (GBM) is characterized by poor prognosis despite an aggressive therapeutic strategy. In recent years, many advances have been achieved in the field of glioblastoma biology. Here we try to summarize the main clinical and biological factors impacting clinical prognostication and therapy of GBM patients. From that standpoint, hopefully, in the near future, personalized therapies will be available.

The Impact of MRI Features and Observer Confidence on the Treatment Decision-Making for Patients with Untreated Glioma

In a blind, dual-center, multi-observer setting, we here identify the pre-treatment radiologic features by Magnetic Resonance Imaging (MRI) associated with subsequent treatment options in patients with glioma. Study included 220 previously untreated adult patients from two institutions (94 + 126 patients) with a histopathologically confirmed diagnosis of glioma after surgery. Using a blind, cross-institutional and randomized setup, four expert neuroradiologists recorded radiologic features, suggested glioma grade and corresponding confidence. The radiologic features were scored using the Visually AcceSAble Rembrandt Images (VASARI) standard. Results were retrospectively compared to patient treatment outcomes. Our findings show that patients receiving a biopsy or a subtotal resection were more likely to have a tumor with pathological MRI-signal (by T2-weighted Fluid-Attenuated Inversion Recovery) crossing the midline (Hazard Ratio; HR = 1.30 [1.21–1.87], P < 0.001), and those receiving a biopsy sampling more often had multifocal lesions (HR = 1.30 [1.16–1.64], P < 0.001). For low-grade gliomas (N = 50), low observer confidence in the radiographic readings was associated with less chance of a total resection (P = 0.002) and correlated with the use of a more comprehensive adjuvant treatment protocol (Spearman = 0.48, P < 0.001). This study may serve as a guide to the treating physician by identifying the key radiologic determinants most likely to influence the treatment decision-making process.

Assessing the predictability of IDH mutation and MGMT methylation status in glioma patients using relaxation-compensated multipool CEST MRI at 7.0 T

Background

Early identification of prognostic superior characteristics in glioma patients such as isocitrate dehydrogenase (IDH) mutation and O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation status is of great clinical importance. The study purpose was to investigate the non-invasive predictability of IDH mutation status, MGMT promoter methylation, and differentiation of low-grade versus high-grade glioma (LGG vs HGG) in newly diagnosed patients employing relaxation-compensated multipool chemical exchange saturation transfer (CEST) MRI at 7.0 Tesla.

Methods

Thirty-one patients with newly diagnosed glioma were included in this prospective study. CEST MRI was performed at a 7T whole-body scanner. Nuclear Overhauser effect (NOE) and isolated amide proton transfer (APT; downfield NOE-suppressed APT = dns-APT) CEST signals (mean value and 90th signal percentile) were quantitatively investigated in the whole tumor area with regard to predictability of IDH mutation, MGMT promoter methylation status, and differentiation of LGG versus HGG. Statistics were performed using receiver operating characteristic (ROC) and area under the curve (AUC) analysis. Results were compared with advanced MRI methods (apparent diffusion coefficient and relative cerebral blood volume ROC/AUC analysis) obtained at 3T.

Results

dns-APT CEST yielded highest AUCs in IDH mutation status prediction (dns-APTmean = 91.84%, P < 0.01; dns-APT90 = 97.96%, P < 0.001). Furthermore, dns-APT metrics enabled significant differentiation of LGG versus HGG (AUC: dns-APTmean = 0.78, P < 0.05; dns-APT90 = 0.83, P < 0.05). There was no significant difference regarding MGMT promoter methylation status at any contrast (P > 0.05).

Conclusions

Relaxation-compensated multipool CEST MRI, particularly dns-APT imaging, enabled prediction of IDH mutation status and differentiation of LGG versus HGG and should therefore be considered as a non-invasive MR biomarker in the diagnostic workup.

Relaxation-compensated amide proton transfer (APT) MRI signal intensity is associated with survival and progression in high-grade glioma patients

OBJECTIVES

The purpose of this study was to investigate the association of relaxation-compensated chemical exchange saturation transfer (CEST) MRI with overall survival (OS) and progression-free survival (PFS) in newly diagnosed high-grade glioma (HGG) patients.

METHODS

Twenty-six patients with newly diagnosed high-grade glioma (WHO grades III-IV) were included in this prospective IRB-approved study. CEST MRI was performed on a 7.0-T whole-body scanner. Association of patient OS/PFS with relaxation-compensated CEST MRI (amide proton transfer (APT), relayed nuclear Overhauser effect (rNOE)/NOE, downfield-rNOE-suppressed APT (dns-APT)) and diffusion-weighted imaging (apparent diffusion coefficient) were assessed using the univariate Cox proportional hazards regression model. Hazard ratios (HRs) and corresponding 95% confidence intervals were calculated. Furthermore, OS/PFS association with clinical parameters (age, gender, O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation status, and therapy: biopsy + radio-chemotherapy vs. debulking surgery + radio-chemotherapy) were tested accordingly.

RESULTS

Relaxation-compensated APT MRI was significantly correlated with patient OS (HR = 3.15, p = 0.02) and PFS (HR = 1.83, p = 0.009). The strongest association with PFS was found for the dns-APT metric (HR = 2.61, p = 0.002). These results still stand for the relaxation-compensated APT contrasts in a homogenous subcohort of n = 22 glioblastoma patients with isocitrate dehydrogenase (IDH) wild-type status. Among the tested clinical parameters, patient age (HR = 1.1, p = 0.001) and therapy (HR = 3.68, p = 0.026) were significant for OS; age additionally for PFS (HR = 1.04, p = 0.048).

CONCLUSION

Relaxation-compensated APT MRI signal intensity is associated with overall survival and progression-free survival in newly diagnosed, previously untreated glioma patients and may, therefore, help to customize treatment and response monitoring in the future.

KEY POINTS

• Amide proton transfer (APT) MRI signal intensity is associated with overall survival and progression in glioma patients. • Relaxation compensation enhances the information value of APT MRI in tumors. • Chemical exchange saturation transfer (CEST) MRI may serve as a non-invasive biomarker to predict prognosis and customize treatment.

The surgical perspective in precision treatment of diffuse gliomas

Over the last decade, advances in molecular and imaging-based biomarkers have induced a more versatile diagnostic classification and prognostic evaluation of glioma patients. This, in combination with a growing therapeutic armamentarium, enables increasingly individualized, risk-benefit-optimized treatment strategies. This path to precision medicine in glioma patients requires surgical procedures to be reassessed within multidimensional management considerations. This article attempts to integrate the surgical intervention into a dynamic network of versatile diagnostic characterization, prognostic assessment, and multimodal treatment options in the light of the latest 2016 World Health Organization (WHO) classification of diffuse brain tumors, WHO grade II, III, and IV. Special focus is set on surgical aspects such as resectability, extent of resection, and targeted surgical strategies including minimal invasive stereotactic biopsy procedures, convection enhanced delivery, and photodynamic therapy. Moreover, the influence of recent advances in radiomics/radiogenimics on the process of surgical decision-making will be touched.

Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas

PURPOSE

To perform radiomics analysis for non-invasively predicting chromosome 1p/19q co-deletion in World Health Organization grade II and III (lower-grade) gliomas.

METHODS

This retrospective study included 277 patients histopathologically diagnosed with lower-grade glioma. Clinical parameters were recorded for each patient. We performed a radiomics analysis by extracting 647 MRI-based features and applied the random forest algorithm to generate a radiomics signature for predicting 1p/19q co-deletion in the training cohort (n = 184). The clinical model consisted of pertinent clinical factors, and was built using a logistic regression algorithm. A combined model, incorporating both the radiomics signature and related clinical factors, was also constructed. The receiver operating characteristics curve was used to evaluate the predictive performance. We further validated the predictability of the three developed models using a time-independent validation cohort (n = 93).

RESULTS

The radiomics signature was constructed as an independent predictor for differentiating 1p/19q co-deletion genotypes, which demonstrated superior performance on both the training and validation cohorts with areas under curve (AUCs) of 0.887 and 0.760, respectively. These results outperformed the clinical model (AUCs of 0.580 and 0.627 on training and validation cohorts). The AUCs of the combined model were 0.885 and 0.753 on training and validation cohorts, respectively, which indicated that clinical factors did not present additional improvement for the prediction.

CONCLUSION

Our study highlighted that an MRI-based radiomics signature can effectively identify the 1p/19q co-deletion in histopathologically diagnosed lower-grade gliomas, thereby offering the potential to facilitate non-invasive molecular subtype prediction of gliomas.

A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication

Objectives

Oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation is a significant prognostic biomarker in astrocytomas, especially for temozolomide (TMZ) chemotherapy. This study aimed to preoperatively predict MGMT methylation status based on magnetic resonance imaging (MRI) radiomics and validate its value for evaluation of TMZ chemotherapy effect.

Methods

We retrospectively reviewed a cohort of 105 patients with grade II-IV astrocytomas. Radiomic features were extracted from the tumour and peritumoral oedema habitats on contrast-enhanced T1-weighted images, T2-weighted fluid-attenuated inversion recovery images and apparent diffusion coefficient (ADC) maps. The following radiomics analysis was structured in three phases: feature reduction, signature construction and discrimination statistics. A fusion radiomics signature was finally developed using logistic regression modelling. Predictive performance was compared between the radiomics signature, previously reported clinical factors and ADC parameters. Validation was additionally performed on a time-independent cohort (n = 31). The prognostic value of the signature on overall survival for TMZ chemotherapy was explored using Kaplan Meier estimation.

Results

The fusion radiomics signature exhibited supreme power for predicting MGMT promoter methylation, with area under the curve values of 0.925 in the training cohort and 0.902 in the validation cohort. Performance of the radiomics signature surpassed that of clinical factors and ADC parameters. Moreover, the radiomics approach successfully divided patients into high-risk and low-risk groups for overall survival after TMZ chemotherapy (p = 0.03).

Conclusions

The proposed radiomics signature accurately predicted MGMT promoter methylation in patients with astrocytomas, and achieved survival stratification for TMZ chemotherapy, thus providing a preoperative basis for individualised treatment planning.

Key Points

• Radiomics using magnetic resonance imaging can preoperatively perform satisfactory prediction of MGMT methylation in grade II-IV astrocytomas.

• Habitat-based radiomics can improve efficacy in predicting MGMT methylation status.

• Multi-sequence radiomics signature has the power to evaluate TMZ chemotherapy effect.

Electronic supplementary material

The online version of this article (10.1007/s00330-018-5575-z) contains supplementary material, which is available to authorized users.

Evaluation of Glioblastoma Response to Therapy With Chemical Exchange Saturation Transfer

PURPOSE

To monitor cellular and metabolic characteristics of glioblastoma (GBM) over the course of standard 6-week chemoradiation treatment with chemical exchange saturation transfer (CEST)-MRI; and to identify the earliest time point CEST could determine subsequent therapeutic response.

METHODS AND MATERIALS

Nineteen patients with newly diagnosed GBM were recruited, and CEST-MRI was acquired immediately before (Day₀), 2 weeks (Day₁₄) and 4 weeks (Day₂₈) into treatment, and 1 month after the end of treatment (Day₇₀). Several CEST metrics, including magnetization transfer ratio and area under the curve of CEST peaks corresponding to nuclear Overhauser effect (NOE) and amide protons (MTR_NOE, MTR_Amide, CEST_NOE, and CEST_Amide respectively), magnetization transfer (MT), and direct water effect were investigated. Lack of early progression was determined as no increase in tumor size or worsening of clinical symptoms according to routine post-chemoradiation serial structural MRI.

RESULTS

Changes in MTR_NOE (nonprogressors = 1.35 ± 0.18, progressors = 0.97 ± 0.22, P = .006) and MTR_Amide (nonprogressors = 1.25 ± 0.17, progressors = 0.99 ± 0.10, P = .017) between baseline (Day₀) and Day₁₄ resulted in the best separation of nonprogressors from progressors. Moreover, the baseline (Day₀) MTR_NOE (nonprogressors = 6.5% ± 1.6%, progressors = 9.1% ± 2.1%, P = .015), MTR_Amide (nonprogressors = 6.7% ± 1.7%, progressors = 8.9% ± 1.9%, P = .028), MT (nonprogressors = 3.8% ± 0.9%, progressors = 5.4% ± 1.4%, P = .019), and CEST_NOE (nonprogressors = 4.1%ċHz ± 1.7%ċHz, progressors = 6.1%ċHz ± 1.9%ċHz, P = .044) were able to identify progressors even before the start of the treatment.

CONCLUSIONS

Chemical exchange saturation transfer (CEST) provides imaging-based biomarkers of GBM response as early as 2 weeks into the treatment. Certain CEST metrics can characterize tumor aggressiveness and identify early progressors even before beginning the treatment. Such an early biomarker of response may allow for adjusting the GBM treatment plan for adaptive radiation therapy in early progressors and more confidently continuing standard adjuvant treatment for nonprogressors.

A Brownian dynamics tumor progression simulator with application to glioblastoma

Tumor progression modeling offers the potential to predict tumor-spreading behavior to improve prognostic accuracy and guide therapy development. Common simulation methods include continuous reaction-diffusion (RD) approaches that capture mean spatio-temporal tumor spreading behavior and discrete agent-based (AB) approaches which capture individual cell events such as proliferation or migration. The brain cancer glioblastoma (GBM) is especially appropriate for such proliferation-migration modeling approaches because tumor cells seldom metastasize outside of the central nervous system and cells are both highly proliferative and migratory. In glioblastoma research, current RD estimates of proliferation and migration parameters are derived from computed tomography or magnetic resonance images. However, these estimates of glioblastoma cell migration rates, modeled as a diffusion coefficient, are approximately 1-2 orders of magnitude larger than single-cell measurements in animal models of this disease. To identify possible sources for this discrepancy, we evaluated the fundamental RD simulation assumptions that cells are point-like structures that can overlap. To give cells physical size (~10 μm), we used a Brownian dynamics approach that simulates individual single-cell diffusive migration, growth, and proliferation activity via a gridless, off-lattice, AB method where cells can be prohibited from overlapping each other. We found that for realistic single-cell parameter growth and migration rates, a non-overlapping model gives rise to a jammed configuration in the center of the tumor and a biased outward diffusion of cells in the tumor periphery, creating a quasi-ballistic advancing tumor front. The simulations demonstrate that a fast-progressing tumor can result from minimally diffusive cells, but at a rate that is still dependent on single-cell diffusive migration rates. Thus, modeling with the assumption of physically-grounded volume conservation can account for the apparent discrepancy between estimated and measured diffusion of GBM cells and provide a new theoretical framework that naturally links single-cell growth and migration dynamics to tumor-level progression.

Usefulness of perfusion- and diffusion-weighted imaging to differentiate between pilocytic astrocytomas and high-grade gliomas: a multicenter study in Japan

PURPOSE

Imaging findings of pilocytic astrocytoma (PA) vary widely, sometimes resembling those of high-grade glioma (HGG). This study aimed to identify the imaging parameters that can be used to differentiate PA from HGG.

METHODS

Altogether, 60 patients with PAs and 138 patients with HGGs were included in the study. Tumor properties and the presence of hydrocephalus, peritumoral edema, and dissemination were evaluated. We also measured the maximum relative cerebral blood flow (rCBF_max) and volume (rCBV_max) and determined the minimum apparent diffusion coefficient (ADC_min) in the tumor's solid components. The relative T1 (rT1), T2 (rT2), and contrast-enhanced T1 (rCE-T1) intensity values were evaluated. Parameters were compared between PAs and HGGs using the Mann-Whitney U test. Receiver operating characteristic (ROC) curve analysis was also used to evaluate these imaging parameters. A value of P < .05 was considered to indicate significance.

RESULTS

Intratumoral hemorrhage and calcification were observed in 10.0% and 21.7% of PAs, respectively. The rCBF_max and rCBV_max values were significantly lower in PAs (0.50 ± 0.35, 1.82 ± 1.21) than those in HGGs (2.98 ± 1.80, 9.54 ± 6.88) (P < .0001, P = .0002, respectively). The ADC_min values were significantly higher in PAs (1.36 ± 0.56 × 10^-3 mm²/s) than those in HGGs (0.86 ± 0.37 × 10^-3 mm²/s) (P < .0001). ROC analysis showed that the best diagnostic performance was achieved with rCBF_max.

CONCLUSION

The rCBF_max, rCBV_max, and ADC_min can differentiate PAs from HGGs.

Integrative analysis of diffusion-weighted MRI and genomic data to inform treatment of glioblastoma

Gene expression profiling from glioblastoma (GBM) patients enables characterization of cancer into subtypes that can be predictive of response to therapy. An integrative analysis of imaging and gene expression data can potentially be used to obtain novel biomarkers that are closely associated with the genetic subtype and gene signatures and thus provide a noninvasive approach to stratify GBM patients. In this retrospective study, we analyzed the expression of 12,042 genes for 558 patients from The Cancer Genome Atlas (TCGA). Among these patients, 50 patients had magnetic resonance imaging (MRI) studies including diffusion weighted (DW) MRI in The Cancer Imaging Archive (TCIA). We identified the contrast enhancing region of the tumors using the pre- and post-contrast T1-weighted MRI images and computed the apparent diffusion coefficient (ADC) histograms from the DW-MRI images. Using the gene expression data, we classified patients into four molecular subtypes, determined the number and composition of genes modules using the gap statistic, and computed gene signature scores. We used logistic regression to find significant predictors of GBM subtypes. We compared the predictors for different subtypes using Mann-Whitney U tests. We assessed detection power using area under the receiver operating characteristic (ROC) analysis. We computed Spearman correlations to determine the associations between ADC and each of the gene signatures. We performed gene enrichment analysis using Ingenuity Pathway Analysis (IPA). We adjusted all p values using the Benjamini and Hochberg method. The mean ADC was a significant predictor for the neural subtype. Neural tumors had a significantly lower mean ADC compared to non-neural tumors ([Formula: see text]), with mean ADC of [Formula: see text] and [Formula: see text] for neural and non-neural tumors, respectively. Mean ADC showed an area under the ROC of 0.75 for detecting neural tumors. We found eight gene modules in the GBM cohort. The mean ADC was significantly correlated with the gene signature related with dendritic cell maturation ([Formula: see text], [Formula: see text]). Mean ADC could be used as a biomarker of a gene signature associated with dendritic cell maturation and to assist in identifying patients with neural GBMs, known to be resistant to aggressive standard of care.

A systematic pipeline for the objective comparison of whole-brain spectroscopic MRI with histology in biopsy specimens from grade III glioma

The diagnosis, prognosis, and management of patients with gliomas are largely dictated by the pathological analysis of tissue biopsied from a selected region within the lesion. However, the heterogeneous and infiltrative nature of gliomas make it difficult to identify the optimal region for biopsy with conventional magnetic resonance imaging (MRI). This is particularly true for low-grade gliomas, which are often nonenhancing tumors. To improve the management of patients with such tumors, neuro-oncology requires an imaging modality that can specifically identify a tumor's most anaplastic/aggressive region(s) for biopsy targeting. The addition of metabolic mapping using spectroscopic MRI (sMRI) to supplement conventional MRI could improve biopsy targeting and, ultimately, diagnostic accuracy. Here, we describe a pipeline for the integration of state-of-the-art, high-resolution, whole-brain 3-dimensional sMRI maps into a stereotactic neuronavigation system for guiding biopsies in gliomas with nonenhancing components. We also outline a machine-learning method for automated histological analysis that generates normalized, quantitative metrics describing tumor infiltration in immunohistochemically stained tissue specimens. As a proof of concept, we describe the combination of these 2 techniques in a small cohort of patients with grade 3 glioma. With this work, we aim to present a systematic pipeline to stimulate histopathological image validation of advanced MRI techniques, such as sMRI.

Anaplastic astrocytoma

Anaplastic astrocytoma (AA) is a diffusely infiltrating, malignant, astrocytic, primary brain tumor. AA is currently defined by histology although future classification schemes will include molecular alterations. AA can be separated into subgroups, which share similar molecular profiles, age at diagnosis and median survival, based on 1p/19q co-deletion status and IDH mutation status. AA with co-deletion of chromosomes 1p and 19q and IDH mutation have the best prognosis. AA with IDH mutation and no 1p/19q co-deletion have intermediate prognosis and AA with wild-type IDH have the worst prognosis and share many molecular alterations with glioblastoma. Treatment of noncodeleted AA based on preliminary results from the CATNON clinical trial consists of maximal safe resection followed by radiotherapy with post-radiotherapy temozolomide (TMZ) chemotherapy. The role of concurrent TMZ and whether IDH1 subgroups benefit from TMZ is currently being evaluated in the recently completed randomized, prospective Phase III clinical trial, CATNON.

Combining 5-Aminolevulinic Acid Fluorescence and Intraoperative Magnetic Resonance Imaging in Glioblastoma Surgery

BACKGROUND:

Glioblastoma resection guided by 5-aminolevulinic acid (5-ALA) fluorescence and intraoperative magnetic resonance imaging (iMRI) may improve surgical results and prolong survival.

OBJECTIVE:

To evaluate 5-ALA fluorescence combined with subsequent low-field iMRI for resection control in glioblastoma surgery.

METHODS:

Fourteen patients with suspected glioblastoma suitable for complete resection of contrast-enhancing portions were enrolled. The surgery was carried out using 5-ALA–induced fluorescence and frameless navigation. Areas suspicious for tumor underwent biopsy. After complete resection of fluorescent tissue, low-field iMRI was performed. Areas suspicious for tumor remnant underwent biopsy under navigation guidance and were resected. The histological analysis was blinded.

RESULTS:

In 13 of 14 cases, the diagnosis was glioblastoma multiforme. One lymphoma and 1 case without fluorescence were excluded. In 11 of 12 operations, residual contrast enhancement on iMRI was found after complete resection of 5-ALA fluorescent tissue. In 1 case, the iMRI enhancement was in an eloquent area and did not undergo a biopsy. The 28 biopsies of areas suspicious for tumor on iMRI in the remaining 10 cases showed tumor in 39.3%, infiltration zone in 25%, reactive central nervous system tissue in 32.1%, and normal brain in 3.6%. Ninety-three fluorescent and 24 non-fluorescent tissue samples collected before iMRI contained tumor in 95.7% and 87.5%, respectively.

CONCLUSION:

5-ALA fluorescence–guided resection may leave some glioblastoma tissue undetected. MRI might detect areas suspicious for tumor even after complete resection of all fluorescent tissue; however, due to the limited accuracy of iMRI in predicting tumor remnant (64.3%), resection of this tissue has to be considered with caution in eloquent regions.

MR Imaging-Based Analysis of Glioblastoma Multiforme: Estimation of IDH1 Mutation Status

BACKGROUND AND PURPOSE

Glioblastoma multiforme is highly aggressive and the most common type of primary malignant brain tumor in adults. Imaging biomarkers may provide prognostic information for patients with this condition. Patients with glioma with isocitrate dehydrogenase 1 (IDH1) mutations have a better clinical outcome than those without such mutations. Our purpose was to investigate whether the IDH1 mutation status in glioblastoma multiforme can be predicted by using MR imaging.

MATERIALS AND METHODS

We retrospectively studied 55 patients with glioblastoma multiforme with wild type IDH1 and 11 patients with mutant IDH1. Absolute tumor blood flow and relative tumor blood flow within the enhancing portion of each tumor were measured by using arterial spin-labeling data. In addition, the maximum necrosis area, the percentage of cross-sectional necrosis area inside the enhancing lesions, and the minimum and mean apparent diffusion coefficients were obtained from contrast-enhanced T1-weighted images and diffusion-weighted imaging data. Each of the 6 parameters was compared between patients with wild type IDH1 and mutant IDH1 by using the Mann-Whitney U test. The performance in discriminating between the 2 entities was evaluated by using receiver operating characteristic analysis.

RESULTS

Absolute tumor blood flow, relative tumor blood flow, necrosis area, and percentage of cross-sectional necrosis area inside the enhancing lesion were significantly higher in patients with wild type IDH1 than in those with mutant IDH1 (P < .05 each). In contrast, no significant difference was found in the ADC(minimum) and ADC(mean). The area under the curve for absolute tumor blood flow, relative tumor blood flow, percentage of cross-sectional necrosis area inside the enhancing lesion, and necrosis area were 0.850, 0.873, 0.739, and 0.772, respectively.

CONCLUSIONS

Tumor blood flow and necrosis area calculated from MR imaging are useful for predicting the IDH1 mutation status.

A Type-2 Fuzzy Image Processing Expert System for Diagnosing Brain Tumors

The focus of this paper is diagnosing and differentiating Astrocytomas in MRI scans by developing an interval Type-2 fuzzy automated tumor detection system. This system consists of three modules: working memory, knowledge base, and inference engine. An image processing method with three steps of preprocessing, segmentation and feature extraction, and approximate reasoning is used in inference engine module to enhance the quality of MRI scans, segment them into desired regions, extract the required features, and finally diagnose and differentiate Astrocytomas. However, brain tumors have different characteristics in different planes, so considering one plane of patient's MRI scan may cause inaccurate results. Therefore, in the developed system, several consecutive planes are processed. The performance of this system is evaluated using 95 MRI scans and the results show good improvement in diagnosing and differentiating Astrocytomas.

Low-grade glioma management: a contemporary surgical approach

PURPOSE OF REVIEW

The primary treatment of low-grade gliomas is still claimed to lack robust supporting evidence. Yet, several investigations were performed in the last 2 decades. To critically review these studies could help in further clarifying the role of surgery aimed at maximal resection.

RECENT FINDINGS

Despite the lack of randomized clinical trials hampering the performance of appropriate meta-analyses, the increasing amount of evidence pointed toward an aggressive surgical strategy to low-grade glioma. Low-grade glioma surgery has to be performed with the appropriate armamentarium, which is the availability of intraoperative stimulation mapping, especially for those lesions occurring in cortical and subcortical eloquent sites.

SUMMARY

According to the recently published guidelines, surgical treatment has been increasingly recognized as the initial therapeutic act of choice for patients diagnosed with a presumed low-grade glioma, given that total resection can improve seizure control, progression-free survival and overall survival, while reducing the risk of malignant transformation and preserving patients' functional status.

Clinical value of O-(2-[(18)F]-fluoroethyl)-L-tyrosine positron emission tomography in patients with low-grade glioma

Progress in morphological imaging has facilitated the diagnosis of low-grade glioma (LGG) and plays a decisive role in therapeutic decisions. To date, the method of choice is contrast-enhanced MRI including T1-/T2-weighted and FLAIR sequences. However, tumor delineation and the differentiation between neoplastic and normal brain tissue can be difficult when using morphological MRI and may complicate the identification of anaplastic foci for biopsy and further treatment planning. Furthermore, therapy monitoring and the differentiation of tumor recurrence from unspecific post-therapeutic changes in the tissue are challenging. Additional information about tumor metabolism may be very helpful for the diagnostic assessment of LGG and can be provided by PET. In recent years, the PET amino acid tracer O-(2-[(18)F]-fluoroethyl)-L-tyrosine ((18)F-FET) has been clinically validated for brain tumor diagnosis. This tracer has logistical advantages over the widely used PET tracer (11)C-methyl-L-methionine due to the longer half-life of the (18)F-label (109 vs 20 minutes, respectively). Additionally, it has been demonstrated that both tracers provide comparable diagnostic information. The authors provide an overview of the recent literature regarding the value of various clinical applications of (18)F-FET PET in patients with LGG.

Multiparametric magnetic resonance in the assessment of the gender differences in a high-grade glioma rat model

Background

Glioblastoma, the most frequent and aggressive of all astrocytomas, presents a clear predominance in male humans, but the assessment of sexual differences in its tumourigenesis and growth has received little attention so far. In this study, we aim to identify gender-dependent surrogate markers in an animal model of this cancer by means of magnetic resonance (MR) imaging and biochemical and behavioural studies.

Methods

A high-grade glioma model developed in male and female rats was used. Multiparametric magnetic resonance images and localized spectra were acquired. The MR parameters linked to tumoural features were quantified. Motor and metabolic activity was also assessed. Postmortem analyses were carried out to measure indicators of malignancy, tumoural metabolism and viability of the blood-brain barrier (BBB).

Results

Statistically significant differences dependent on the animal sex were found in the study of pathological indicators like oedema, inflammation, cellularity and microvasculature. Results suggest higher cell proliferative rate, inflammation and vasogenic oedema and or necrosis in glioma-bearing male rats. Haemodynamic parameters measured indicated a major disruption of the BBB, postmortem confirmed, in this sex. Metabolomic and energetic metabolism activity data are in agreement with a major malignancy and aggressiveness of this cancer model on males.

Conclusions

Gender differences should be taken into account in preclinical studies of glioblastoma models, in the characterization of the tumoural behaviour and consequently in the development and validation of new therapeutic approaches. MR imaging and spectroscopy allow to non-invasively monitor this sexual dimorphism in the diagnosis and prognosis of brain cancer.

Treatment of adults with recurrent malignant glioma

Virtually all malignant gliomas recur. Treatment options at recurrence have relied upon surgical intervention, radiation therapy or cytotoxic chemotherapy. Unfortunately, none are associated with significant improvements in survival. Advances in treatment options at recurrence have been dependent upon the combination of surgical resection, focal radiation and chemotherapy. Despite aggressive interventions, few patients have meaningful improvements. Current research focuses on novel targeted molecular therapy that will hopeful be able to take advantage of advances in our understanding of the biology of glial neoplasms.

Mass spectrometry imaging as a tool for surgical decision-making

Despite significant advances in image-guided therapy, surgeons are still too often left with uncertainty when deciding to remove tissue. This binary decision between removing and leaving tissue during surgery implies that the surgeon should be able to distinguish tumor from healthy tissue. In neurosurgery, current image-guidance approaches such as magnetic resonance imaging (MRI) combined with neuronavigation offer a map as to where the tumor should be, but the only definitive method to characterize the tissue at stake is histopathology. Although extremely valuable information is derived from this gold standard approach, it is limited to very few samples during surgery and is not practically used for the delineation of tumor margins. The development and implementation of faster, comprehensive, and complementary approaches for tissue characterization are required to support surgical decision-making--an incremental and iterative process with tumor removed in multiple and often minute biopsies. The development of atmospheric pressure ionization sources makes it possible to analyze tissue specimens with little to no sample preparation. Here, we highlight the value of desorption electrospray ionization as one of many available approaches for the analysis of surgical tissue. Twelve surgical samples resected from a patient during surgery were analyzed and diagnosed as glioblastoma tumor or necrotic tissue by standard histopathology, and mass spectrometry results were further correlated to histopathology for critical validation of the approach. The use of a robust statistical approach reiterated results from the qualitative detection of potential biomarkers of these tissue types. The correlation of the mass spectrometry and histopathology results to MRI brings significant insight into tumor presentation that could not only serve to guide tumor resection, but that is also worthy of more detailed studies on our understanding of tumor presentation on MRI.

In vivo metabolic profiling of glioma-initiating cells using proton magnetic resonance spectroscopy at 14.1 Tesla

In the last decade, evidence has emerged indicating that the growth of a vast majority of tumors including gliomas is sustained by a subpopulation of cancer cells with stem cell properties called cancer initiating cells. These cells are able to initiate and propagate tumors and constitute only a fraction of all tumor cells. In the present study, we showed that intracerebral injection of cultured glioma-initiating cells into nude mice produced fast growing tumors showing necrosis and gadolinium enhancement in MR images, whereas gliomas produced by injecting freshly purified glioma-initiating cells grew slowly and showed no necrosis and very little gadolinium enhancement. Using proton localized spectroscopy at 14.1 Tesla, decreasing trends of N-acetylaspartate, glutamate and glucose concentrations and an increasing trend of glycine concentration were observed near the injection site after injecting cultured glioma-initiating cells. In contrast to the spectra of tumors grown from fresh cells, those from cultured cells showed intense peaks of lipids, increased absolute concentrations of glycine and choline-containing compounds, and decreased concentrations of glutamine, taurine and total creatine, when compared with a contralateral non-tumor-bearing brain tissue. A decrease in concentrations of N-acetylaspartate and γ-aminobutyrate was found in both tumor phenotypes after solid tumor formation. Further investigation is needed to determine the cause of the dissimilarities between the tumors grown from cultured glioma-initiating cells and those from freshly purified glioma-initiating cells, both derived from human glioblastomas.

Treatment monitoring in gliomas: comparison of dynamic susceptibility-weighted contrast-enhanced and spectroscopic MRI techniques for identifying treatment failure

OBJECTIVE

To evaluate whether dynamic susceptibility-weighted contrast-enhanced (DSC), dynamic contrast-enhanced (DCE), and proton spectroscopic imaging ((1)H-MRSI) can identify progression and predict treatment failure during follow-up before tumor size changes, contrast agent uptake, or when new lesions become obvious. The aim was also to find out which of the aforementioned techniques had the best diagnostic performance compared with each other and standard magnetic resonance imaging (MRI).

MATERIALS AND METHODS

Thirty-seven patients with gliomas (21 women, 16 men; mean age at inclusion, 48 ± 14 years [standard deviation]) were assessed prospectively by (1)H-MRSI (point-resolved spectroscopy), DCE, and DSC perfusion MRI, each after a single dose of gadobenate dimeglumine during follow-up. Histology was available in all cases (resection, N = 18; biopsy, N = 19). All patients with low-grade gliomas (n = 20) did not receive any radio- or chemotherapy after partial resection (n = 7) or biopsy (n = 13), whereas 17 patients with high-grade gliomas had received adjuvant radiotherapy immediately after surgery. Tumor progression (progressive disease, PD) was defined as increase in longest glioma diameter by at least 20% (Response Evaluation Criteria in Solid Tumors), appearance of new lesions, or new contrast-enhancement. DSC, DCE, and MRSI image analyses comprised a detailed semiquantitative region of interest (ROI) analysis of the different parameters. Wilcoxon signed-rank test, Wilcoxon rank sum test, and Cox regression were used for statistical analysis.

RESULTS

The median follow-up time was 607 days. Twenty patients showed PD (54%), 8 of 20 with low-grade (40%) and 12 of 17 with high-grade gliomas (71%). In PD, significant positive differences between log2-transformed ROI ratios at the last measurement in comparison to the first measurement (baseline) could be detected for tumor blood flow (P < 0.006) and volume (P < 0.001) derived from DSC and for maximum choline within tumor tissue (P = 0.0029) and Cho/Cr (P = 0.032) but not choline/N-acetyl-aspartate (P = 0.37) derived from MRSI. In contrast, these parameters were not significantly higher at last measurement in stable disease. Also, the differences between last value and baseline were significantly different between PD and stable disease for tumor blood flow (P < 0.004) and volume (P < 0.002) as well as for maximum choline within tumor tissue (P = 0.0011). The best prognostic parameter for PD at Cox analysis was time-dependent difference to baseline of log2 of relative regional cerebral blood flow normalized on gray matter (hazard ratio, 2.67; 95% confidence interval, 1.25-6.08; P = 0.01), while a prognostic value of MRS parameters could not be demonstrated.

CONCLUSION

DSC perfusion imaging can identify progression and can predict treatment failure during follow-up of gliomas with the best diagnostic performance.

Low-grade gliomas in adults

Making treatment decisions for patients with infiltrating low-grade gliomas (LGGs) is challenging. Patients frequently present with seizures and usually have little or no neurologic deficit. In this younger and relatively well patient population, despite the potential for significant morbidity, we believe that surgical resection, radiation therapy, and chemotherapy each play an important role in the optimal management of these tumors. Randomized clinical trials have begun to address some of the many questions about prognosis, natural history, and treatment, but most questions have yet to be answered. We believe that, when possible, a maximal surgical resection consistent with preservation of neurologic function should be performed, even though it is likely that no randomized clinical trial will ever be done to demonstrate a survival advantage for this approach. External beam radiation therapy is most often given to a total dose of 50.4 or 54 Gy in 1.8-Gy fractions. The role of chemotherapy is less certain, but a growing body of evidence suggests that temozolomide, a generally well-tolerated drug, is active in the treatment of LGGs. In recent years, loss of heterozygosity of chromosome 1p and 19q, as well as silencing of the MGMT gene, have been identified as promising predictors of response to adjuvant therapy in gliomas. Although randomized trials have not yet shown a survival benefit for early radiation therapy or chemotherapy, one study by the European Organisation for Research and Treatment of Cancer did show an improvement in time to tumor progression with the earlier use of radiation therapy. In addition, a trial by the Radiation Therapy Oncology Group (soon to be analyzed and reported) is comparing radiation alone with radiation followed by a year (six cycles) of standard-dose PCV chemotherapy (procarbazine, CCNU, and vincristine); this trial may shed light on the use of chemotherapy in conjunction with radiation therapy for the initial treatment of LGGs. Because patients remain at risk for tumor progression for the remainder of their lives, we recommend lifelong follow-up with MRI scans, even for patients without documented tumor regrowth over long intervals. To give clinicians a more solid basis for guiding therapy recommendations, we encourage participation in large cooperative group clinical trials.

Predicting survival in glioblastomas using diffusion tensor imaging metrics

PURPOSE

To retrospectively correlate various diffusion tensor imaging (DTI) metrics in patients with glioblastoma multiforme (GBM) with patient survival analysis and also degree of tumor proliferation index determined histologically.

MATERIALS AND METHODS

Thirty-four patients with histologically confirmed treatment naive GBMs underwent DTI on a 3.0 Tesla (T) scanner. Region-of-interest was placed on the whole lesion including the enhancing as well as nonenhancing component of the lesion to determine the various DTI metrics. Kaplan-Meier estimates and Cox proportional hazards regression methods were used to assess the relationship of DTI metrics (minimum and mean values) and Ki-67 with progression free survival (PFS). To study the relationship between DTI metrics and Ki-67, Pearson's correlation coefficient was computed.

RESULTS

Univariate analysis showed that patients with fractional anisotropy (FA)(mean) ≤ 0.2, apparent diffusion coefficient (ADC)(min) ≤ 0.6, planar anisotropy (CP)(min) ≤ 0.002, spherical anisotropy (CS)(mean) > 0.68 and Ki-67 > 0.3 had lower PFS rate. The multivariate analysis demonstrated that only CP(min) was the best predictor of survival in these patients, after adjusting for age, Karnofsky performance scale and extent of resection. No significant correlation between DTI metrics and Ki-67 were observed.

CONCLUSION

DTI metrics can be used as a sensitive and early indicator for PFS in patients with glioblastomas. This could be useful for treatment planning as high-grade gliomas with lower ADC(min), FA(mean), CP(min), and higher CS(mean) values may be treated more aggressively.

The biology underlying molecular imaging in oncology: from genome to anatome and back again

Cancers are complex, evolving, multiscale ecosystems that are characterized by profound spatial and temporal heterogeneity. The interactions in cancer are non-linear in that small changes in one variable can have large changes on another. These multiple interacting phenotypes and spatial scales can best be understood with appropriate mathematical and computational models. Imaging is central to this investigation because it can non-destructively and longitudinally characterize spatial variations in the tumour phenotype and environment so that the system dynamics over time can be captured quantitatively.

Ex vivo MR spectroscopic measure differentiates tumor from treatment effects in GBM

The motivation of this study was to address the urgent clinical problem related to the inability of magnetic resonance (MR) imaging measures to differentiate tumor progression from treatment effects in patients with glioblastoma multiforme (GBM). While contrast enhancement on MR imaging (MRI) is routinely used for assessment of tumor burden, therapy response, and progression-free survival in GBM, it is well known that changes in enhancement following treatment are nonspecific to tumor. To address this issue, the objective of this study was to investigate whether MR spectroscopy can provide improved biomarker surrogates for tumor following treatment. High-resolution metabolic profiles of tissue samples obtained from patients with GBM were directly correlated with their pathological assessment to determine metabolic markers that correspond to pathological indications of tumor or treatment effects. Acquisition of tissue samples with image guidance enabled the association of ex vivo biochemical and pathological properties of the tissue samples with in vivo MR anatomical and structural properties derived from presurgical MR images. Using this approach, we found that metabolic concentration levels of [Myo-inositol/total choline (MCI)] in tissue samples are able to differentiate tumor from nontumor and treatment-induced reactive astrocytosis with high significance (P < .001) in newly diagnosed and recurrent GBM. The MCI index has a sensitivity of 93% to tumor in recurrent GBM and delineates the contribution of cellularity that originates from tumor and astrocytic proliferation following treatment. Low levels of MCI for tumor were associated with a reduced apparent diffusion coefficient and elevated choline-N-acetyl-aspartate index derived from in vivo MR images.

FDA drug approval summary: bevacizumab (Avastin) as treatment of recurrent glioblastoma multiforme

On May 5, 2009, the U.S. Food and Drug Administration granted accelerated approval to bevacizumab injection (Avastin; Genentech, Inc., South San Francisco, CA) as a single agent for patients with glioblastoma multiforme (GBM) with progressive disease following prior therapy. The approval was based on durable objective responses (independent radiologic review with stable or decreasing corticosteroid use). Two trials evaluating bevacizumab, 10 mg/kg by i.v. infusion every 2 weeks, were submitted. One trial also randomized patients to bevacizumab plus irinotecan treatment. All patients had received prior surgery, radiotherapy, and temozolomide. Patients with active brain hemorrhage were excluded. One trial enrolled 78 independently confirmed GBM patients. Partial responses were observed in 25.9% (95% confidence interval [CI], 17.0%-36.1%) of the patients. The median response duration was 4.2 months (95% CI, 3.0-5.7 months). The second trial enrolled 56 GBM patients. Partial responses were observed in 19.6% (95% CI, 10.9%-31.3%) of the patients. The median response duration was 3.9 months (95% CI, 2.4-17.4 months). Safety data were provided for the first study. The most frequently reported bevacizumab adverse events of any grade were infection, fatigue, headache, hypertension, epistaxis, and diarrhea. Grade 3-5 bevacizumab-related adverse events included bleeding/hemorrhage, central nervous system (CNS) hemorrhage, hypertension, venous and arterial thromboembolic events, wound-healing complications, proteinuria, gastrointestinal perforation, and reversible posterior leukoencephalopathy. The attribution of certain adverse events (e.g., CNS hemorrhage, wound-healing complications, and thromboembolic events) to either bevacizumab, underlying disease, or both could not be determined because of the single-arm, noncomparative study design.

Endogenous tenascin-C enhances glioblastoma invasion with reactive change of surrounding brain tissue

Tenascin-C is an extracellular matrix glycoprotein implicated in embryogenesis, wound healing and tumor progression. We previously revealed that tenascin-C expression is correlated with the prognosis of patients with glioblastoma. However, the exact role of endogenous tenascin-C in regulation of glioblastoma proliferation and invasion remains to be established. We show here that endogenous tenascin-C facilitates glioblastoma invasion, followed by reactive change of the surrounding brain tissue. Although shRNA-mediated knockdown of endogenous tenascin-C does not affect proliferation of glioblastoma cells, it abolishes cell migration on a two-dimensional substrate and tumor invasion with brain tissue changes in a xenograft model. The tyrosine phosphorylation of focal adhesion kinase, a cytoplasmic tyrosine kinase that associates with integrins, was decreased in tenascin-C-knockdown cells. In the analysis of clinical samples, tenascin-C expression correlates with the volume of peritumoral reactive change detected by magnetic resonance imaging. Interestingly, glioblastoma cells with high tenascin-C expression infiltrate brain tissue in an autocrine manner. Our results suggest that endogenous tenascin-C contributes the invasive nature of glioblastoma and the compositional change of brain tissue, which renders tenascin-C as a prime candidate for anti-invasion therapy for glioblastoma.