Diffusion Tensor Imaging in brain tumors: A study on gliomas and metastases

Intraoperative glioblastoma surgery-current challenges and clinical trials: An update

Surgical excision is an important part of the multimodal therapy strategy for patients with glioblastoma, a very aggressive and invasive brain tumor. While major advances in surgical methods and technology have been accomplished, numerous hurdles remain in the field of glioblastoma surgery. The purpose of this literature review is to offer a thorough overview of the current challenges in glioblastoma surgery. We reviewed the difficulties associated with tumor identification and visualization, resection extent, neurological function preservation, tumor margin evaluation, and inclusion of sophisticated imaging and navigation technology. Understanding and resolving these challenges is critical in order to improve surgical results and, ultimately, patient survival.

Diffusion Tensor Imaging (DTI) Biomarker Alterations in Brain Metastases and Comparable Tumors: A Systematic Review of DTI and Tractography Findings

BACKGROUND

Brain metastases (BMs) are the most frequent tumors of the central nervous system. Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that provides insights into brain microstructural alterations and tensor metrics and generates tractography to visualize white matter fiber tracts based on diffusion directionality. This systematic review assessed evidence from DTI biomarker alterations in BMs and comparable tumors such as glioblastoma.

METHODS

PubMed, Scopus, and Web of Science were searched, and published between January 2000 and August 2023. The key inclusion criteria were studies reporting DTI metrics in BMs and comparisons with other tumors. Data on study characteristics, tumor types, sample details, and main DTI findings were extracted.

RESULTS

Fifty-seven studies with 1592 BM patients and 1578 comparable brain tumors were included. Peritumoral fractional anisotropy (FA) consistently differentiates BMs from primary brain tumors, whereas intratumoral FA shows limited discriminatory power. Mean diffusivity increased in BMs versus comparators. Intratumoral metrics were less consistent but revealed differences in BM origin. Axial and radial diffusivity have provided insights into the effects of radiation, tumor origin, and infiltration. Axial diffusivity/radial diffusivity differentiated tumor infiltration from vasogenic edema. Tractography revealed anatomical relationships between white matter tracts and BMs. In addition, tractography-guided BM surgery and radiotherapy planning are required. Machine learning models incorporating DTI biomarkers/metrics accurately classified BMs versus comparators and improved diagnostic classification.

CONCLUSIONS

DTI metrics provide noninvasive biomarkers for distinguishing BMs from other tumors and predicting outcomes. Key metrics included peritumoral FA and mean diffusivity.

Role of regional diffusion tensor imaging (DTI)-derived tensor metrics in the evaluation of intracranial gliomas and its histopathological correlation

Background

The imaging of brain tumours has significantly improved with the use of advanced magnetic resonance (MR) techniques like diffusion tensor imaging (DTI). This study was conducted to analyse the utility of DTI-derived tensor metrics in the evaluation of intracranial gliomas with histopathological correlation and further adoption of these image-data analyses in clinical setting.

Methods

A total of 50 patients with suspected diagnosis of intracranial gliomas underwent DTI along with conventional MR examination. The study correlated various DTI parameters in the enhancing part of the tumour and the peritumoral region with the histopathological grades of the intracranial gliomas.

Results

The study revealed higher values of Cl (linear anisotropy), Cp (planar anisotropy), AD (axial diffusivity), FA (fractional anisotropy) and RA (relative anisotropy) and lower values of Cs (spherical anisotropy), MD (mean diffusivity) and RD (radial diffusivity) in the enhancing part of the tumour in case of high-grade gliomas. However, in the peritumoral region, the values of Cl, Cp, AD, FA and RA were less whereas values of Cs, MD and RD were more in high-grade gliomas than in the low-grade gliomas. The various cutoff values of these DTI-derived tensor metrics were found to be statistically significant.

Conclusion

DTI-derived tensor metrics can be a valuable tool in differentiation between high-grade and low-grade gliomas which might be accepted in clinical practice in near future.

Diffusion tensor imaging derived metrics in high grade glioma and brain metastasis differentiation

Background: Pretreatment differentiation between glioblastoma and metastasis is a frequently encountered dilemma in neurosurgical practice. Distinction is required for precise planning of resection or radiotherapy, and also for defining further diagnostic procedures. Morphology and spectroscopy imaging features are not specific and frequently overlap. This limitation of magnetic resonance imaging and magnetic resonance spectroscopy was the reason to initiate this study. The aim of the present study was to determine whether the dataset of diffusion tensor imaging metrics contains information which may be used for the distinction between primary and secondary intra-axial neoplasms. Methods: Two diffusion tensor imaging parameters were measured in 81 patients with an expansive, ring-enhancing, intra-axial lesion on standard magnetic resonance imaging (1.5 T system). All tumors were histologically verified glioblastoma or secondary deposit. For qualitative analysis, two regions of interest were defined: intratumoral and immediate peritumoral region (locations 1 and 2, respectively). Fractional anisotropy and mean difusivity values of both groups were compared. Additional test was performed to determine if there was a significant difference in mean values between two locations. Results: A statistically significant difference was found in fractional anisotropy values among two locations, with decreasing values in the direction of neoplastic infiltration, although such difference was not observed in fractional anisotropy values in the group with secondary tumors. Mean difusivity values did not appear helpful in differentiation between these two entities. In both groups there was no significant difference in mean difusivity values, neither in intratumoral nor in peritumoral location. Conclusion: The results of our study justify associating the diffusion tensor imaging technique to conventional morphologic magnetic resonance imaging as an additional diagnostic tool for the distinction between primary and secondary intra-axial lesions. Quantitative analysis of diffusion tensor imaging metric, in particular measurement of fractional anisotropy in peritumoral edema facilitates accurate diagnosis.

Brain metastases: An update on the multi-disciplinary approach of clinical management

IMPORTANCE

Brain metastasis (BM) is the most common malignant intracranial neoplasm in adults with over 100,000 new cases annually in the United States and outnumbering primary brain tumors 10:1.

OBSERVATIONS

The incidence of BM in adult cancer patients ranges from 10-40%, and is increasing with improved surveillance, effective systemic therapy, and an aging population. The overall prognosis of cancer patients is largely dependent on the presence or absence of brain metastasis, and therefore, a timely and accurate diagnosis is crucial for improving long-term outcomes, especially in the current era of significantly improved systemic therapy for many common cancers. BM should be suspected in any cancer patient who develops new neurological deficits or behavioral abnormalities. Gadolinium enhanced MRI is the preferred imaging technique and BM must be distinguished from other pathologies. Large, symptomatic lesion(s) in patients with good functional status are best treated with surgery and stereotactic radiosurgery (SRS). Due to neurocognitive side effects and improved overall survival of cancer patients, whole brain radiotherapy (WBRT) is reserved as salvage therapy for patients with multiple lesions or as palliation. Newer approaches including multi-lesion stereotactic surgery, targeted therapy, and immunotherapy are also being investigated to improve outcomes while preserving quality of life.

CONCLUSION

With the significant advancements in the systemic treatment for cancer patients, addressing BM effectively is critical for overall survival. In addition to patient's performance status, therapeutic approach should be based on the type of primary tumor and associated molecular profile as well as the size, number, and location of metastatic lesion(s).

An Automated Framework to Segment and Classify Gliomas Using Efficient Shuffled Complex Evolution Convolutional Neural Network

Detection of Glioma and its segmentation can be a very challenging task for clinicians and radiologists. Accuracy in classifying glioma is required where brain tumorsgrow from the star-shaped glial cells among adults. Magnetic Resonance Imaging (MRI) indicates the human soft tissue and its anatomical structure away from displaying the location, histological traits, and location of the lesions used to diagnose glioma clinically. An automated framework for the identification of gliomas is presented. Feature extraction will present much higher imaging features such as texture, color, contrast, and shape. The Gabor filters can carry out multi-resolution decomposition due to localization with regard to spatial frequency. The Shuffle Complex Evolution (SCE) algorithm will combine Controlled random search, a complex mix, competition, evolution, and the adaptation of the world’s population Nelder-Mead Simplex for all the benefits of optimal solutions. The CNN process is in an input texture that collects statistics within the spatial domain. The CNNs are normally capable of capturing spatial features, and spectral analysis can capture all scale-invariant features. This work implements an automated method for classifying the Gliomas with an optimized shuffled complex evolution CNN.

The Potential Role of Peritumoral Apparent Diffusion Coefficient Evaluation in Differentiating Glioblastoma and Solitary Metastatic Lesions of the Brain

OBJECTIVE

Differentiating glioblastoma (GBM) and solitary metastasis is not always possible using conventional magnetic resonance imaging (MRI) techniques. In conventional brain MRI, GBM and brain metastases are lesions with mostly similar imaging findings. In this study, we investigated whether apparent diffusion coefficient (ADC) ratios, ADC gradients, and minimum ADC values in the peritumoral edema tissue can be used to discriminate between these two tumors.

METHODS

This retrospective study was approved by the local institutional review board with a waiver of written informed consent. Prior to surgical and medical treatment, conventional brain MRI and diffusion-weighted MRI (b = 0 and b = 1000) images were taken from 43 patients (12 GBM and 31 solitary metastasis cases). Quantitative ADC measurements were performed on the peritumoral tissue from the nearest segment to the tumor (ADC1), the middle segment (ADC2), and the most distant segment (ADC3). The ratios of these three values were determined proportionally to calculate the peritumoral ADC ratios. In addition, these three values were subtracted from each other to obtain the peritumoral ADC gradients. Lastly, the minimum peritumoral and tumoral ADC values, and the quantitative ADC values from the normal appearing ipsilateral white matter, contralateral white matter and ADC values from cerebrospinal fluid (CSF) were recorded.

RESULTS

For the differentiation of GBM and solitary metastasis, ADC3 / ADC1 was the most powerful parameter with a sensitivity of 91.7% and specificity of 87.1% at the cut-off value of 1.105 (p < 0.001), followed by ADC3 / ADC2 with a cut-off value of 1.025 (p = 0.001), sensitivity of 91.7%, and specificity of 74.2%. The cut-off, sensitivity and specificity of ADC2 / ADC1 were 1.055 (p = 0.002), 83.3%, and 67.7%, respectively. For ADC3 - ADC1, the cut-off value, sensitivity and specificity were calculated as 150 (p < 0.001), 91.7% and 83.9%, respectively. ADC3 - ADC2 had a cut-off value of 55 (p = 0.001), sensitivity of 91.7%, and specificity of 77.4 whereas ADC2 - ADC1 had a cut-off value of 75 (p = 0.003), sensitivity of 91.7%, and specificity of 61.3%. Among the remaining parameters, only the ADC3 value successfully differentiated between GBM and metastasis (GBM 1802.50 ± 189.74 vs. metastasis 1634.52 ± 212.65, p = 0.022).

CONCLUSION

The integration of the evaluation of peritumoral ADC ratio and ADC gradient into conventional MR imaging may provide valuable information for differentiating GBM from solitary metastatic lesions.

From Neurosurgical Planning to Histopathological Brain Tumor Characterization: Potentialities of Arcuate Fasciculus Along-Tract Diffusion Tensor Imaging Tractography Measures

Background: Tractography has been widely adopted to improve brain gliomas' surgical planning and guide their resection. This study aimed to evaluate state-of-the-art of arcuate fasciculus (AF) tractography for surgical planning and explore the role of along-tract analyses in vivo for characterizing tumor histopathology.

Methods: High angular resolution diffusion imaging (HARDI) images were acquired for nine patients with tumors located in or near language areas (age: 41 ± 14 years, mean ± standard deviation; five males) and 32 healthy volunteers (age: 39 ± 16 years; 16 males). Phonemic fluency task fMRI was acquired preoperatively for patients. AF tractography was performed using constrained spherical deconvolution diffusivity modeling and probabilistic fiber tracking. Along-tract analyses were performed, dividing the AF into 15 segments along the length of the tract defined using the Laplacian operator. For each AF segment, diffusion tensor imaging (DTI) measures were compared with those obtained in healthy controls (HCs). The hemispheric laterality index (LI) was calculated from language task fMRI activations in the frontal, parietal, and temporal lobe parcellations. Tumors were grouped into low/high grade (LG/HG).

Results: Four tumors were LG gliomas (one dysembryoplastic neuroepithelial tumor and three glioma grade II) and five HG gliomas (two grade III and three grade IV). For LG tumors, gross total removal was achieved in all but one case, for HG in two patients. Tractography identified the AF trajectory in all cases. Four along-tract DTI measures potentially discriminated LG and HG tumor patients (false discovery rate < 0.1): the number of abnormal MD and RD segments, median AD, and MD measures. Both a higher number of abnormal AF segments and a higher AD and MD measures were associated with HG tumor patients. Moreover, correlations (unadjusted p < 0.05) were found between the parietal lobe LI and the DTI measures, which discriminated between LG and HG tumor patients. In particular, a more rightward parietal lobe activation (LI < 0) correlated with a higher number of abnormal MD segments (R = −0.732) and RD segments (R = −0.724).

Conclusions: AF tractography allows to detect the course of the tract, favoring the safer-as-possible tumor resection. Our preliminary study shows that along-tract DTI metrics can provide useful information for differentiating LG and HG tumors during pre-surgical tumor characterization.

Current landscape and future perspectives in preclinical MR and PET imaging of brain metastasis

Brain metastasis (BM) is a major cause of cancer patient morbidity. Clinical magnetic resonance imaging (MRI) and positron emission tomography (PET) represent important resources to assess tumor progression and treatment responses. In preclinical research, anatomical MRI and to some extent functional MRI have frequently been used to assess tumor progression. In contrast, PET has only to a limited extent been used in animal BM research. A considerable culprit is that results from most preclinical studies have shown little impact on the implementation of new treatment strategies in the clinic. This emphasizes the need for the development of robust, high-quality preclinical imaging strategies with potential for clinical translation. This review focuses on advanced preclinical MRI and PET imaging methods for BM, describing their applications in the context of what has been done in the clinic. The strengths and shortcomings of each technology are presented, and recommendations for future directions in the development of the individual imaging modalities are suggested. Finally, we highlight recent developments in quantitative MRI and PET, the use of radiomics and multimodal imaging, and the need for a standardization of imaging technologies and protocols between preclinical centers.

Research progress on radiotherapy technology and dose fraction scheme for advanced gliomas

Glioma is the most common central malignant tumor. High-grade glioma (HGG) has high malignancy and a short median survival. Complete surgical resection and comprehensive treatment with postoperative radiotherapy and chemotherapy is the recommended treatment for HGGs at present in clinic. Postoperative radiotherapy can reduce the local recurrence rate and prolong the survival time of patients. In recent years, researchers have made some progress on different radiotherapy technologies and dose fraction schemes. With the continuous development of medical technology, different groups of people should choose different dose fraction schemes, in order to realize the individualization of treatment schemes, and provide more benefits to patients. At present, the optimal radiotherapy dose, the fraction model, and how to achieve individualized radiotherapy remains unclear. In view of the poor prognosis of this disease, patients should be encouraged to participate in properly conducted experimental studies.

Current Applications of Diffusion Tensor Imaging and Tractography in Intracranial Tumor Resection

In the treatment of brain tumors, surgical intervention remains a common and effective therapeutic option. Recent advances in neuroimaging have provided neurosurgeons with new tools to overcome the challenge of differentiating healthy tissue from tumor-infiltrated tissue, with the aim of increasing the likelihood of maximizing the extent of resection volume while minimizing injury to functionally important regions. Novel applications of diffusion tensor imaging (DTI), and DTI-derived tractography (DDT) have demonstrated that preoperative, non-invasive mapping of eloquent cortical regions and functionally relevant white matter tracts (WMT) is critical during surgical planning to reduce postoperative deficits, which can decrease quality of life and overall survival. In this review, we summarize the latest developments of applying DTI and tractography in the context of resective surgery and highlight its utility within each stage of the neurosurgical workflow: preoperative planning and intraoperative management to improve postoperative outcomes.

Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme

PURPOSE

While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between various tumors based on their imaging characteristics might be challenging due to overlapping imaging features. The purpose of this study was to apply a machine learning scheme using basic and advanced MR sequences for distinguishing different types of brain tumors.

METHODS

The study cohort included 141 patients (41 glioblastoma, 38 metastasis, 50 meningioma, and 12 primary central nervous system lymphoma). A computer-assisted classification scheme, combining morphologic MRI, perfusion MRI, and DTI metrics, was developed and used for tumor classification. The proposed multistep scheme consists of pre-processing, ROI definition, features extraction, feature selection, and classification. Feature subset selection was performed using support vector machines (SVMs). Classification performance was assessed by leave-one-out cross-validation. Given an ROI, the entire classification process was done automatically via computer and without any human intervention.

RESULTS

A binary hierarchical classification tree was chosen. In the first step, selected features were chosen for distinguishing glioblastoma from the remaining three classes, followed by separation of meningioma from metastasis and PCNSL, and then to discriminate PCNSL from metastasis. The binary SVM classification accuracy, sensitivity and specificity for glioblastoma, metastasis, meningiomas, and primary central nervous system lymphoma were 95.7, 81.6, and 91.2%; 92.7, 95.1, and 93.6%; 97, 90.8, and 58.3%; and 91.5, 90, and 96.9%, respectively.

CONCLUSION

A machine learning scheme using data from anatomical and advanced MRI sequences resulted in high-performance automatic tumor classification algorithm. Such a scheme can be integrated into clinical decision support systems to optimize tumor classification.

Neonatal brain: Fabrication of a tissue-mimicking phantom and optimization of clinical Τ1w and T2w MRI sequences at 1.5 T

PURPOSE

Tο fabricate a tissue-mimicking phantom simulating the MR relaxation times of neonatal gray and white matter at 1.5 T, for the optimization of clinical Τ1 weighted (T1w) and T2 weighted (T2w) sequences.

METHODS

Numerous agarose gel solutions, doped with paramagnetic Gadopentetic acid (Gd-DTPA) ions, underwent quantitative relaxometry with a Turbo-Inversion-Recovery Spin-Echo (TIRSE) sequence and a Car-Purcell-Meiboom-Gill (CPMG) sequence for T1 and T2 measurements, respectively. Twenty samples which simulated the spectrum of relaxation times of neonatal brain parenchyma were selected. Reproducibility was tested by refabrication and relaxometry of the relevant samples while stability was tested by six sets of quantitative relaxometry scans during a 12-month period.

RESULTS

"Neonatal gray matter equivalent"(0.6%w/v agarose-0.10 mM Gd-DTPA), accurately mimicked relaxation times of neonatal gray matter: T1 = (1134 ± 7)ms, T2 = (200 ± 7)ms. "Neonatal white matter equivalent"(0.3%w/v agarose-0.03 mM Gd-DTPA), accurately mimicked relaxation times of neonatal white matter: T1 = (1654 ± 9)ms, T2 = (376 ± 4)ms. Coefficient of variation of T1 and T2 relaxation times measurements remained less than 5% during 12 months. Sequences were modified according to maximum relative contrast (RC) between neonatal gray and white matter equivalents. Optimized T2wTSE and T1wTSE parameters were TR/TE = 9500 ms/280 ms and TR/TE = 1200 ms/10 ms, respectively for a MAGNETOM Vision/Sonata Hybrid 1.5 T system. Quantitative relaxometry at different 1.5 T MR systems resulted in inter-system T1, T2 measurement deviations of 12% and 3%, respectively.

CONCLUSION

A precise, stable and reproducible phantom for the neonatal brain was fabricated. Subsequent optimization of clinical T1w and T2w sequences based on maximum RC between neonatal gray and white matter equivalents was scientifically supported with robust relaxometry. The procedure was applicable in different 1.5 T systems.

HIGHLIGHT

TR & TE optimization of neonatal brain at 1.5 T was based on relaxometry of a stable, reproducible phantom.

Dependence of apparent diffusion coefficient measurement on diffusion gradient direction and spatial position - A quality assurance intercomparison study of forty-four scanners for quantitative diffusion-weighted imaging

PURPOSE

To propose an MRI quality assurance procedure that can be used for routine controls and multi-centre comparison of different MR-scanners for quantitative diffusion-weighted imaging (DWI).

MATERIALS AND METHODS

44 MR-scanners with different field strengths (1 T, 1.5 T and 3 T) were included in the study. DWI acquisitions (b-value range 0-1000 s/mm²), with three different orthogonal diffusion gradient directions, were performed for each MR-scanner. All DWI acquisitions were performed by using a standard spherical plastic doped water phantom. Phantom solution ADC value and its dependence with temperature was measured using a DOSY sequence on a 600 MHz NMR spectrometer. Apparent diffusion coefficient (ADC) along each diffusion gradient direction and mean ADC were estimated, both at magnet isocentre and in six different position 50 mm away from isocentre, along positive and negative AP, RL and HF directions.

RESULTS

A good agreement was found between the nominal and measured mean ADC at isocentre: more than 90% of mean ADC measurements were within 5% from the nominal value, and the highest deviation was 11.3%. Away from isocentre, the effect of the diffusion gradient direction on ADC estimation was larger than 5% in 47% of included scanners and a spatial non uniformity larger than 5% was reported in 13% of centres.

CONCLUSION

ADC accuracy and spatial uniformity can vary appreciably depending on MR scanner model, sequence implementation (i.e. gradient diffusion direction) and hardware characteristics. The DWI quality assurance protocol proposed in this study can be employed in order to assess the accuracy and spatial uniformity of estimated ADC values, in single- as well as multi-centre studies.

Differentiation of High-Grade Glioma and Intracranial Metastasis Using Volumetric Diffusion Tensor Imaging Tractography

OBJECTIVE

A reliable, noninvasive method to differentiate high-grade glioma (HGG) and intracranial metastasis (IM) has remained elusive. The aim of this study was to differentiate between HGG and IM using tumoral and peritumoral diffusion tensor imaging characteristics.

METHODS

A semiautomated script generated volumetric regions of interest (ROIs) for the tumor and a peritumoral shell at a predetermined voxel thickness. ROI differences in diffusion tensor imaging-related metrics between HGG and IM groups were estimated, including fractional anisotropy, mean diffusivity, total fiber tract counts, and tract density.

RESULTS

The HGG group (n = 46) had a significantly higher tumor-to-brain volume ratio than the IM group (n = 35) (P < 0.001). The HGG group exhibited significantly higher mean fractional anisotropy and significantly lower mean diffusivity within peritumoral ROI than the IM group (P < 0.05). The HGG group exhibited significantly higher total tract count and higher tract density in tumoral and peritumoral ROIs than the IM group (P < 0.05). Tumoral tract count and peritumoral tract density were the most optimal metrics to differentiate the groups based on receiver operating characteristic curve analysis. Predictive analysis using receiver operating characteristic curve thresholds was performed on 13 additional participants. Compared with correct clinical diagnoses, the 2 thresholds exhibited equal specificities (66.7%), but the tumoral tract count (85.7%) seemed more sensitive in differentiating the 2 groups.

CONCLUSIONS

Tract count and tract density were significantly different in tumoral and peritumoral regions between HGG and IM. Differences in microenvironmental interactions between the tumor types may cause these tract differences.

Diffusion-Weighted Imaging and Diffusion Tensor Imaging for Differentiating High-Grade Glioma from Solitary Brain Metastasis: A Systematic Review and Meta-Analysis

BACKGROUND

Accurate diagnosis of high-grade glioma and solitary brain metastasis is clinically important because it affects the patient's outcome and alters patient management.

PURPOSE

To evaluate the diagnostic performance of DWI and DTI for differentiating high-grade glioma from solitary brain metastasis.

DATA SOURCES

A literature search of Ovid MEDLINE and EMBASE was conducted up to November 10, 2017.

STUDY SELECTION

Studies evaluating the diagnostic performance of DWI and DTI for differentiating high-grade glioma from solitary brain metastasis were selected.

DATA ANALYSIS

Summary sensitivity and specificity were established by hierarchic logistic regression modeling. Multiple subgroup analyses were also performed.

DATA SYNTHESIS

Fourteen studies with 1143 patients were included. The individual sensitivities and specificities of the 14 included studies showed a wide variation, ranging from 46.2% to 96.0% for sensitivity and 40.0% to 100.0% for specificity. The pooled sensitivity of both DWI and DTI was 79.8% (95% CI, 70.9%-86.4%), and the pooled specificity was 80.9% (95% CI, 75.1%-85.5%). The area under the hierarchical summary receiver operating characteristic curve was 0.87 (95% CI, 0.84-0.89). The multiple subgroup analyses also demonstrated similar diagnostic performances (sensitivities of 76.8%-84.7% and specificities of 79.7%-84.0%). There was some level of heterogeneity across the included studies (I² = 36%); however, it did not reach a level of concern.

LIMITATIONS

The included studies used various DWI and DTI parameters.

CONCLUSIONS

DWI and DTI demonstrated a moderate diagnostic performance for differentiation of high-grade glioma from solitary brain metastasis.

Differentiation of glioma malignancy grade using diffusion MRI

Modern diffusion MR protocols allow one to acquire the multi-shell diffusion data with high diffusion weightings in a clinically feasible time. In the present work we assessed three diffusion approaches based on diffusion and kurtosis tensor imaging (DTI, DKI), and neurite orientation dispersion and density imaging (NODDI) as possible biomarkers for human brain glioma grade differentiation based on the one diffusion protocol. We used three diffusion weightings (so called b-values) equal to 0, 1000, and 2500s/mm² with 60 non-coplanar diffusion directions in the case of non-zero b-values. The patient groups of the glioma grades II, III, and IV consist of 8 subjects per group. We found that DKI, and NODDI scalar metrics can be effectively used as glioma grade biomarkers with a significant difference (p<0.05) for grading between low- and high-grade gliomas, in particular, for glioma II versus glioma III grades, and glioma III versus glioma IV grades. The use of mean/axial kurtosis and intra-axonal fraction/orientation dispersion index metrics allowed us to obtain the most feasible and reliable differentiation criteria. For example, in the case of glioma grades II, III, and IV the mean kurtosis is equal to 0.31, 0.51, and 0.90, and the orientation dispersion index is equal to 0.14, 0.30, and 0.59, respectively. The limitations and perspectives of the biophysical diffusion models based on intra-/extra-axonal compartmentalisation for glioma differentiation are discussed.

Cerebral Microstructural Alterations after Radiation Therapy in High-Grade Glioma: A Diffusion Tensor Imaging-Based Study

Objective

To investigate radiation therapy-induced microstructural damage of white matter in patients with high-grade glioma by diffusion tensor imaging (DTI).

Methods

DTI was performed in 18 patients with high-grade glioma (WHO grades III and IV) and 13 healthy controls. DTI images were cross-sectionally aligned for the calculation of baseline fractional anisotropy (FA). Interhemispheric FA values in patients with high-grade glioma before or without brain radiation therapy were compared with the interhemispheric FA values in patients after radiation therapy and in healthy controls. In a subgroup without any clinical or diagnostic evidence of tumor progression, serial DTI data (5–11 scans) before and after radiation therapy were collected and longitudinal interhemispheric FA changes were assessed and compared to longitudinal data from the control group.In addition, interhemispheric axial, mean, and radial diffusivity was assessed.

Results

Global interhemispheric FA reductions could be detected cross-sectionally in patients after radiation therapy; these were significantly different from global interhemispheric FA differences both in patients without radiation and in healthy controls. Longitudinal scans in patients with radiation therapy confirmed these findings and revealed progressive microstructural white matter damage after partial brain radiotherapy. The additional DTI metrics axial diffusion, mean diffusivity, and radial diffusion confirmed interhemispheric differences in patients without or before radiation therapy, which were lower than the differences in patients after radiation therapy, although not reaching significance.

Conclusion

Interhemispheric global FA differences could potentially serve as a biological marker for irradiation-induced microstructural white matter damage.

High-grade Gliomas Exhibit Higher Peritumoral Fractional Anisotropy and Lower Mean Diffusivity than Intracranial Metastases

Differentiating high-grade gliomas and intracranial metastases through non-invasive imaging has been challenging. Here, we retrospectively compared both intratumoral and peritumoral fractional anisotropy (FA), mean diffusivity (MD), and fluid-attenuated inversion recovery (FLAIR) measurements between high-grade gliomas and metastases. Two methods were utilized to select peritumoral region of interest (ROI). The first method utilized the manual placement of four ROIs adjacent to the lesion. The second method utilized a semiautomated and proprietary MATLAB script to generate an ROI encompassing the entire tumor. The average peritumoral FA, MD, and FLAIR values were determined within the ROIs for both methods. Forty patients with high-grade gliomas and 44 with metastases were enrolled in this study. Thirty-five patients with high-grade glioma and 30 patients with metastases had FLAIR images. There was no significant difference in age, gender, or race between the two patient groups. The high-grade gliomas had a significantly higher tumor-to-brain area ratio compared to the metastases. There were no differences in average intratumoral FA, MD, and FLAIR values between the two groups. Both the manual sample method and the semiautomated peritumoral ring method resulted in significantly higher peritumoral FA and significantly lower peritumoral MD in high-grade gliomas compared to metastases (p < 0.05). No significant difference was found in FLAIR values between the two groups peritumorally. Receiver operating curve analysis revealed FA to be a more sensitive and specific metric to differentiate high-grade gliomas and metastases than MD. The differences in the peritumoral FA and MD values between high-grade gliomas and metastases seemed due to the infiltration of glioma to the surrounding brain parenchyma.

The utility of diffusion MRI with quantitative ADC measurements for differentiating high-grade from low-grade cerebral gliomas: Evidence from a meta-analysis

OBJECTIVE

The aim of this meta-analysis was to predict the grades of cerebral gliomas using quantitative apparent diffusion coefficient (ADC) values.

MATERIALS AND METHODS

A comprehensive search of the PubMed, EMBASE, Web of Science, and Cochrane Library databases was performed up to 8, 2016. The quality assessment of diagnostic accuracy studies (QUADAS 2) was used to evaluate the quality of studies. Statistical analyses included pooling of sensitivity and specificity, positive likelihood ratio (PLR), negative likelihood ratio' (NLR), diagnostic odds ratio (DOR), and diagnostic accuracy values of the included studies using the summary receiver operating characteristic (SROC). All analyses were conducted using STATA (version 12.0), RevMan (version 5.3), and Meta-Disc 1.4 software programs.

RESULTS

Fifteen studies were analyzed and included a total of 821 patients and 821 lesions. In regards to the diagnostic accuracy of ADC maps, the pooled SEN, SPE, PLR, NLR, and DOR with 95%CIs were 0.82 [95%CI: 0.76, 0.87] and 0.75 [95%CI: 0.67, 0.81], 3.24 [95%CI: 2.48, 4.24], 0.24 [95%CI: 0.17, 0.33], and 13.60 [95%CI: 8.37, 22.07], respectively. The SROC curve showed an AUC of 0.85. Deeks testing confirmed no significant publication bias in all studies.

CONCLUSION

Our findings indicate that quantitative ADC values have high accuracy in separating high-grade from low-grade cerebral gliomas. Further studies using a standardized methodology may help guide the use of ADC values for clinical decision-making.

Assessment of diffusion tensor imaging metrics in differentiating low-grade from high-grade gliomas

AIM

The aim of this article is to assess diffusion tensor imaging (DTI) metrics in differentiating low-grade from high-grade gliomas.

PATIENTS AND METHODS

A prospective study was conducted on 35 patients with gliomas who underwent DTI. Gliomas were classified into low-grade and high-grade gliomas. The fractional anisotropy (FA), mean diffusivity (MD), linear coefficient (CL), planar coefficient (CP) and spherical coefficient (CS) of the solid tumoral part and peri-tumoral regions were calculated.

RESULTS

There was significant difference (p = 0.001) in MD of the solid tumoral part of low-grade (1.78 ± 0.33 × 10(-3 )mm(2)/s) and high-grade (1.16 ± 0.22 × 10(-3 )mm(2)/s) gliomas. The selection of 1.42 × 10(-3 )mm(2)/s as a cutoff value of MD of the tumoral part was used to differentiate low-grade and high-grade gliomas; the best results were obtained with area under the curve (AUC) of 0.957 and accuracy of 91.4%. There was a significant difference in FA, MD, CP and CS of peri-tumoral regions of both groups with p values of 0.006, 0.042, 0.030 and 0.037, respectively. The cutoff values of MD, FA, CS and CP of the peri-tumoral region used to differentiate low-grade from high-grade gliomas were 1.24, 0.315, 0.726 and 0.321 with AUC of 0.694, 0.773, 0.734 and 0.724 and accuracy of 68.6%, 80.0%, 74.3% and 74.3%, respectively. The combined MD of the solid tumoral part and FA of the peri-tumoral region used to differentiate low-grade from high-grade gliomas revealed AUC of 0.974 and accuracy of 88.6%.

CONCLUSION

We conclude that the combination of MD of the solid tumoral part and FA of the peri-tumoral region is a noninvasive method to differentiate low-grade from high-grade gliomas.

Glioblastoma multiforme: emerging treatments and stratification markers beyond new drugs

Glioblastoma multiforme (GBM) is the most common primary brain tumour in adults. The standard therapy for GBM is maximal surgical resection followed by radiotherapy with concurrent and adjuvant temozolomide (TMZ). In spite of the extensive treatment, the disease is associated with poor clinical outcome. Further intensification of the standard treatment is limited by the infiltrating growth of the GBM in normal brain areas, the expected neurological toxicities with radiation doses >60 Gy and the dose-limiting toxicities induced by systemic therapy. To improve the outcome of patients with GBM, alternative treatment modalities which add low or no additional toxicities to the standard treatment are needed. Many Phase II trials on new chemotherapeutics or targeted drugs have indicated potential efficacy but failed to improve the overall or progression-free survival in Phase III clinical trials. In this review, we will discuss contemporary issues related to recent technical developments and new metabolic strategies for patients with GBM including MR (spectroscopy) imaging, (amino acid) positron emission tomography (PET), amino acid PET, surgery, radiogenomics, particle therapy, radioimmunotherapy and diets.