Exploring MGMT methylation-driven structural connectivity changes in insular gliomas: a tractography and graph theoretical analysis

OBJECTIVES

This study aims to explore the relationship between the methylation levels of the O-6-methylguanine-DNA methyltransferase (MGMT) promoter and the structural connectivity in insular gliomas across hemispheres.

METHODS

We analyzed 32 left and 29 right insular glioma cases and 50 healthy controls, using differential tractography, correlational tractography, and graph theoretical analysis to investigate the correlation between structural connectivity and the methylation level.

RESULTS

The differential tractography results revealed that in left insular glioma, the volume of affected inferior fronto-occipital fasciculus (IFOF, p = 0.019) significantly correlated with methylation levels. Correlational tractography results showed that the quantitative anisotropy (QA) value of peritumoral fiber tracts also exhibited a significant correlation with methylation levels (FDR < 0.05). On the other hand, in right insular glioma, anterior internal part of the reticular tract, IFOF, and thalamic radiation showed a significant correlation with methylation levels but at a different correlation direction from the left side (FDR < 0.05). The graph theoretical analysis showed that in the left insular gliomas, only the radius of graph was significantly lower in methylated MGMT group than unmethylated group (p = 0.047). No significant correlations between global properties and methylation levels were observed in insular gliomas on both sides.

CONCLUSION

Our findings highlight a significant, hemisphere-specific correlation between MGMT promoter methylation and structural connectivity in insular gliomas. This study provides new insights into the genetic influence on glioma pathology, which could inform targeted therapeutic strategies.

Prediction of Ki-67 labeling index, ATRX mutation, and MGMT promoter methylation status in IDH-mutant astrocytoma by morphological MRI, SWI, DWI, and DSC-PWI

OBJECTIVE

Noninvasive detection of molecular status of astrocytoma is of great clinical significance for predicting therapeutic response and prognosis. We aimed to evaluate whether morphological MRI (mMRI), SWI, DWI, and DSC-PWI could predict Ki-67 labeling index (LI), ATRX mutation, and MGMT promoter methylation status in IDH mutant (IDH-mut) astrocytoma.

METHODS

We retrospectively analyzed mMRI, SWI, DWI, and DSC-PWI in 136 patients with IDH-mut astrocytoma.The features of mMRI and intratumoral susceptibility signals (ITSS) were compared using Fisher exact test or chi-square tests. Wilcoxon rank sum test was used to compare the minimum ADC (ADCmin), and minimum relative ADC (rADCmin) of IDH-mut astrocytoma in different molecular markers status. Mann-Whitney U test was used to compare the rCBVmax of IDH-mut astrocytoma with different molecular markers status. Receiver operating characteristic curves was performed to evaluate their diagnostic performances.

RESULTS

ITSS, ADCmin, rADCmin, and rCBVmax were significantly different between high and low Ki-67 LI groups. ITSS, ADCmin, and rADCmin were significantly different between ATRX mutant and wild-type groups. Necrosis, edema, enhancement, and margin pattern were significantly different between low and high Ki-67 LI groups. Peritumoral edema was significantly different between ATRX mutant and wild-type groups. Grade 3 IDH-mut astrocytoma with unmethylated MGMT promoter was more likely to show enhancement compared to the methylated group.

CONCLUSIONS

mMRI, SWI, DWI, and DSC-PWI were shown to have the potential to predict Ki-67 LI and ATRX mutation status in IDH-mut astrocytoma. A combination of mMRI and SWI may improve diagnostic performance for predicting Ki-67 LI and ATRX mutation status.

CLINICAL RELEVANCE STATEMENT

Conventional MRI and functional MRI (SWI, DWI, and DSC-PWI) can predict Ki-67 expression and ATRX mutation status of IDH mutant astrocytoma, which may help clinicians determine personalized treatment plans and predict patient outcomes.

KEY POINTS

• A combination of multimodal MRI may improve the diagnostic performance to predict Ki-67 LI and ATRX mutation status. • Compared with IDH-mutant astrocytoma with low Ki-67 LI, IDH-mutant astrocytoma with high Ki-67 LI was more likely to show necrosis, edema, enhancement, poorly defined margin, higher ITSS levels, lower ADC, and higher rCBV. • ATRX wild-type IDH-mutant astrocytoma was more likely to show edema, higher ITSS levels, and lower ADC compared to ATRX mutant IDH-mutant astrocytoma.

Apparent Diffusion Coefficient as Imaging Biomarker for Identifying IDH Mutation, 1p19q Codeletion, and MGMT Promoter Methylation Status in Patients With Glioma

BACKGROUND

Glioma genotypes are of importance for clinical decision-making. This data can only be acquired through histopathological analysis based on resection or biopsy. Consequently, there is a need for alternative biomarkers that noninvasively provide reliable information for preoperatively identifying molecular characteristics.

PURPOSE

To investigate apparent diffusion coefficient (ADC) as imaging biomarker for preoperatively identifying glioma genotypes based on the 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors.

STUDY TYPE

Retrospective.

SUBJECTS

One hundred and fifty-nine patients (47.6 ± 14.4 years) diagnosed with WHO grade 2-4 glioma including 93 males and 66 females.

FIELD STRENGTH/SEQUENCE

A 3 T/spin echo echo planner imaging.

ASSESSMENT

The ADC measurements were assessed by two neuroradiologists (both with 6 years of experience). Three different lowest portions inside the tumors without overlap were manually drawn on the ADC maps as regions of interest (ROIs). The mean ADC value of the three ROIs was defined as the minimum ADC value (ADCmin ). An ROI was placed in the contralateral normal appearing white matter (NAWM) to obtain the ADC value (ADCNAWM ). The ADCmin to ADCNAWM ratio (ADCratio ) was calculated. Genetics results were retrospectively recorded from pathologic and genetic test reports.

STATISTICAL TESTS

Two-sample independent t-tests, receiver operating characteristic curve analysis, and intraclass correlation coefficient analysis were used. Statistical significance was set at P < 0.05.

RESULTS

Isocitrate dehydrogenase (IDH)-mutated glioma showed higher ADCmin and ADCratio than IDH wild-type glioma. Among IDH-mutated glioma, higher ADCmin and ADCratio were found in 1p19q intact glioma than in 1p19q codeletion glioma. ADC parameters enabled differentiation of IDH mutation status with area under the curve (AUC) of 0.84 and 0.86.

DATA CONCLUSION

ADC has potential discriminative value for IDH mutation and 1p19q codeletion status.

EVIDENCE LEVEL

3.

TECHNICAL EFFICACY

Stage 2.

Peritumoral ADC Values Correlate with the MGMT Methylation Status in Patients with Glioblastoma

Different results have been reported concerning the relationship of the apparent diffusion coefficient (ADC) values and the status of methylation as the promoter gene for the enzyme methylguanine-DNA methyltransferase (MGMT) in patients with glioblastomas (GBs). The aim of this study was to investigate if there were correlations between the ADC values of the enhancing tumor and peritumoral areas of GBs and the MGMT methylation status. In this retrospective study, we included 42 patients with newly diagnosed unilocular GB with one MRI study prior to any treatment and histopathological data. After co-registration of ADC maps with T1-weighted sequences after contrast administration and dynamic susceptibility contrast (DSC) perfusion, we manually selected one region-of-interest (ROI) in the enhancing and perfused tumor and one ROI in the peritumoral white matter. Both ROIs were mirrored in the healthy hemisphere for normalization. In the peritumoral white matter, absolute and normalized ADC values were significantly higher in patients with MGMT-unmethylated tumors, as compared to patients with MGMT-methylated tumors (absolute values p = 0.002, normalized p = 0.0007). There were no significant differences in the enhancing tumor parts. The ADC values in the peritumoral region correlated with MGMT methylation status, confirmed by normalized ADC values. In contrast to other studies, we could not find a correlation between the ADC values or the normalized ADC values and the MGMT methylation status in the enhancing tumor parts.

Magnetic Resonance Imaging of Primary Adult Brain Tumors: State of the Art and Future Perspectives

MRI is undoubtedly the cornerstone of brain tumor imaging, playing a key role in all phases of patient management, starting from diagnosis, through therapy planning, to treatment response and/or recurrence assessment. Currently, neuroimaging can describe morphologic and non-morphologic (functional, hemodynamic, metabolic, cellular, microstructural, and sometimes even genetic) characteristics of brain tumors, greatly contributing to diagnosis and follow-up. Knowing the technical aspects, strength and limits of each MR technique is crucial to correctly interpret MR brain studies and to address clinicians to the best treatment strategy. This article aimed to provide an overview of neuroimaging in the assessment of adult primary brain tumors. We started from the basilar role of conventional/morphological MR sequences, then analyzed, one by one, the non-morphological techniques, and finally highlighted future perspectives, such as radiomics and artificial intelligence.

Bosniak classification of cystic renal masses, version 2019: Is it helpful to incorporate the diffusion weighted imaging characteristic of lesions into the guideline?

Objective

To improve understanding of diffusion weighted imaging (DWI) characteristic of MRI and clinical variables, further optimize the Bosniak classification for diagnosis of cystic renal masses (CRMs).

Methods

This study retrospectively analyzed 130 CRMs in 125 patients with CT or MRI, including 87 patients with DWI (b = 600, 1000 s/mm2). Clinical variables and histopathological results were recorded. Two radiologists in consensus analyzed images of each lesion for the size, thickness of wall, number of septum, enhancement of wall/septum, wall nodule, signal intensity on DWI, calcification, and cyst content. Clinical variables, CT and MRI image characteristics were compared with pathology or follow-up results to evaluate the diagnostic performance for CRMs.

Results

Of the 130 lesions in 125 patients, histological analysis reported that 36 were malignant, 38 were benign, and no change was found in 56 followed-up lesions (mean follow-up of 24 months). The incidences of cystic wall thickened, more septa, measurable enhancement of wall/septum, nodule(s) on CT/MRI, and high signal intensity on DWI were significantly higher in malignant than in benign CRMs (CT: p = 0.005, p < 0.001, p < 0.001, p < 0.001, p < 0.001; MRI: p < 0.001, p < 0.001, p < 0.001, p < 0.001, p < 0.001, p < 0.001). Combination of MRI including DWI features with CT findings showed the highest area under ROC curve (0.973) in distinguishing benign and malignant CRMs.

Conclusions

Incorporating DWI characteristic of CRMs into Bosniak classification helps to improve diagnostic efficiency.

Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis

Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main electronic medical databases was undertaken in concordance with the PRISMA guidelines. A total of 30 articles were retrieved from the database and reviewed. Key findings of current AI applications were compiled and summarised. The main clinical applications of AI techniques include image processing, diagnosis, decision support, treatment assistance and prognostic outcomes. In the realm of spinal oncology, artificial intelligence technologies have achieved relatively good performance and hold immense potential to aid clinicians, including enhancing work efficiency and reducing adverse events. Further research is required to validate the clinical performance of the AI tools and facilitate their integration into routine clinical practice.

Non-invasive prediction of p53 and Ki-67 labelling indices and O-6-methylguanine-DNA methyltransferase promoter methylation status in adult patients with isocitrate dehydrogenase wild-type glioblastomas using diffusion-weighted imaging and dynamic susceptibility contrast-enhanced perfusion-weighted imaging combined with conventional MRI

AIM

To assess whether conventional magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), and dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI) could non-invasively predict p53 and Ki-67 labelling index (LI) and O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status in adult isocitrate dehydrogenase (IDH) wild-type glioblastomas.

METHODS

The conventional MRI, DWI, and DSC-PWI results of 120 adult patients with IDH wild-type glioblastomas were reviewed retrospectively and their efficacy was analysed using chi-square tests or Fisher's exact test. Relative minimum apparent diffusion coefficient (rADCmin) and relative maximum cerebral blood volume (rCBVmax) values were compared between glioblastomas with different molecular statuses using the Mann-Whitney U-test. Receiver operating characteristic (ROC) curves and logistic regression were used to evaluate predictive performance.

RESULTS

Glioblastomas with a high p53 LI were more likely to show a well-defined enhancement margin (p=0.047). Glioblastomas in the high-Ki-67-LI group demonstrated significantly lower rADCmin (p<0.001) and higher rCBVmax (p=0.001) values than those in the low-Ki-67-LI group. Tumours without MGMT promoter methylation showed lower rADCmin (p<0.001) and higher rCBVmax (p<0.001) values than those with it. The rCBVmax value exhibited a greater efficacy in predicting the MGMT promoter methylation status of adult IDH wild-type glioblastomas than the rADCmin value (p=0.001).

CONCLUSIONS

The present results suggest that conventional and DWI and DSC-PWI results are influenced by the molecular status of the glioblastoma and indicate that DWI and DSC-PWI may help to identify regions of high invasiveness within heterogeneous glioblastomas.

Multiparametric MRI texture analysis in prediction of glioma biomarker status: added value of MR diffusion

Background

Early identification of glioma molecular phenotypes can lead to understanding of patient prognosis and treatment guidance. We aimed to develop a multiparametric MRI texture analysis model using a combination of conventional and diffusion MRI to predict a wide range of biomarkers in patients with glioma.

Methods

In this retrospective study, patients were included if they (1) had diagnosis of gliomas with known IDH1, EGFR, MGMT, ATRX, TP53, and PTEN status from surgical pathology and (2) had preoperative MRI including FLAIR, T1c+ and diffusion for radiomic texture analysis. Statistical analysis included logistic regression and receiver-operating characteristic (ROC) curve analysis to determine the optimal model for predicting glioma biomarkers. A comparative analysis between ROCs (conventional only vs conventional + diffusion) was performed.

Results

From a total of 111 patients included, 91 (82%) were categorized to training and 20 (18%) to test datasets. Constructed cross-validated model using a combination of texture features from conventional and diffusion MRI resulted in overall AUC/accuracy of 1/79% for IDH1, 0.99/80% for ATRX, 0.79/67% for MGMT, and 0.77/66% for EGFR. The addition of diffusion data to conventional MRI features significantly (P < .05) increased predictive performance for IDH1, MGMT, and ATRX. The overall accuracy of the final model in predicting biomarkers in the test group was 80% (IDH1), 70% (ATRX), 70% (MGMT), and 75% (EGFR).

Conclusion

Addition of MR diffusion to conventional MRI features provides added diagnostic value in preoperative determination of IDH1, MGMT, and ATRX in patients with glioma.

MGMT Promoter Methylation Status in Initial and Recurrent Glioblastoma: Correlation Study with DWI and DSC PWI Features

BACKGROUND AND PURPOSE

O⁶-methylguanine-DNA methyltransferase (MGMT) promoter methylation status in primary and recurrent glioblastoma may change during treatment. The purpose of this study was to correlate MGMT promoter methylation status changes with DWI and DSC PWI features in patients with recurrent glioblastoma after standard treatment.

MATERIALS AND METHODS

Between January 2008 and November 2016, forty patients with histologically confirmed recurrent glioblastoma were enrolled. Patients were divided into 3 groups according to the MGMT promoter methylation status for the initial and recurrent tumors: 2 groups whose MGMT promoter methylation status remained, group methylated (n = 13) or group unmethylated (n = 18), and 1 group whose MGMT promoter methylation status changed from methylated to unmethylated (n = 9). Normalized ADC and normalized relative CBV values were obtained from both the enhancing and nonenhancing regions, from which histogram parameters were calculated. The ANOVA and the Kruskal-Wallis test followed by post hoc tests were performed to compare histogram parameters among the 3 groups. The t test and Mann-Whitney U test were used to compare parameters between group methylated and group methylated to unmethylated. Receiver operating characteristic curve analysis was used to measure the predictive performance of the normalized relative CBV values between the 2 groups.

RESULTS

Group methylated to unmethylated showed significantly higher means and 90th and 95th percentiles of the cumulative normalized relative CBV values of the nonenhancing region of the initial tumor than group methylated and group unmethylated (all P < .05). The mean normalized relative CBV value of the nonenhancing region of the initial tumor was the best predictor of methylation status change (P < .001), with a sensitivity of 77.78% and specificity of 92.31% at a cutoff value of 2.594.

CONCLUSIONS

MGMT promoter methylation status might change in recurrent glioblastoma after standard treatment. The normalized relative CBV values of the nonenhancing region at the first preoperative MR imaging were higher in the MGMT promoter methylation change group from methylation to unmethylation in recurrent glioblastoma.

Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors

Simple Summary

Advanced neuroimaging is gaining increasing relevance for the characterization and the molecular profiling of brain tumor tissue. On one hand, for some tumor types, the most widespread advanced techniques, investigating diffusion and perfusion features, have been proven clinically feasible and rather robust for diagnosis and prognosis stratification. In addition, 2-hydroxyglutarate spectroscopy, for the first time, offers the possibility to directly measure a crucial molecular marker. On the other hand, numerous innovative approaches have been explored for a refined evaluation of tumor microenvironments, particularly assessing microstructural and microvascular properties, and the potential applications of these techniques are vast and still to be fully explored.

Abstract

In recent years, the clinical assessment of primary brain tumors has been increasingly dependent on advanced magnetic resonance imaging (MRI) techniques in order to infer tumor pathophysiological characteristics, such as hemodynamics, metabolism, and microstructure. Quantitative radiomic data extracted from advanced MRI have risen as potential in vivo noninvasive biomarkers for predicting tumor grades and molecular subtypes, opening the era of “molecular imaging” and radiogenomics. This review presents the most relevant advancements in quantitative neuroimaging of advanced MRI techniques, by means of radiomics analysis, applied to primary brain tumors, including lower-grade glioma and glioblastoma, with a special focus on peculiar oncologic entities of current interest. Novel findings from diffusion MRI (dMRI), perfusion-weighted imaging (PWI), and MR spectroscopy (MRS) are hereby sifted in order to evaluate the role of quantitative imaging in neuro-oncology as a tool for predicting molecular profiles, stratifying prognosis, and characterizing tumor tissue microenvironments. Furthermore, innovative technological approaches are briefly addressed, including artificial intelligence contributions and ultra-high-field imaging new techniques. Lastly, after providing an overview of the advancements, we illustrate current clinical applications and future perspectives.

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.

Congress of neurological surgeons systematic review and evidence-based guidelines update on the role of emerging developments in the management of newly diagnosed glioblastoma

TARGET POPULATION

These recommendations apply to adult patients with newly diagnosed or suspected glioblastoma.

IMAGING

Question What imaging modalities are in development that may be able to provide improvements in diagnosis, and therapeutic guidance for individuals with newly diagnosed glioblastoma?

RECOMMENDATION

Level III: It is suggested that techniques utilizing magnetic resonance imaging for diffusion weighted imaging, and to measure cerebral blood and magnetic spectroscopic resonance imaging of N-acetyl aspartate, choline and the choline to N-acetyl aspartate index to assist in diagnosis and treatment planning in patients with newly diagnosed or suspected glioblastoma.

SURGERY

Question What new surgical techniques can be used to provide improved tumor definition and resectability to yield better tumor control and prognosis for individuals with newly diagnosed glioblastoma?

RECOMMENDATIONS

Level II: The use of 5-aminolevulinic acid is recommended to improve extent of tumor resection in patients with newly diagnosed glioblastoma. Level II: The use of 5-aminolevulinic acid is recommended to improve median survival and 2 year survival in newly diagnosed glioblastoma patients with clinical characteristics suggesting poor prognosis. Level III: It is suggested that, when available, patients be enrolled in properly designed clinical trials assessing the value of diffusion tensor imaging in improving the safety of patients with newly diagnosed glioblastoma undergoing surgery.

NEUROPATHOLOGY

Question What new pathology techniques and measurement of biomarkers in tumor tissue can be used to provide improved diagnostic ability, and determination of therapeutic responsiveness and prognosis for patients with newly diagnosed glioblastomas?

RECOMMENDATIONS

Level II: Assessment of tumor MGMT promoter methylation status is recommended as a significant predictor of a longer progression free survival and overall survival in patients with newly diagnosed with glioblastoma. Level II: Measurement of tumor expression of neuron-glia-2, neurofilament protein, glutamine synthetase and phosphorylated STAT3 is recommended as a predictor of overall survival in patients with newly diagnosed with glioblastoma. Level III: Assessment of tumor IDH1 mutation status is suggested as a predictor of longer progression free survival and overall survival in patients with newly diagnosed with glioblastoma. Level III: Evaluation of tumor expression of Phosphorylated Mitogen-Activated Protein Kinase protein, EGFR protein, and Insulin-like Growth Factor-Binding Protein-3 is suggested as a predictor of overall survival in patients with newly diagnosed with glioblastoma.

RADIATION

Question What radiation therapy techniques are in development that may be used to provide improved tumor control and prognosis for individuals with newly diagnosed glioblastomas?

RECOMMENDATIONS

Level III: It is suggested that patients with newly diagnosed glioblastoma undergo pretreatment radio-labeled amino acid tracer positron emission tomography to assess areas at risk for tumor recurrence to assist in radiation treatment planning. Level III: It is suggested that, when available, patients be with newly diagnosed glioblastomas be enrolled in properly designed clinical trials of radiation dose escalation, altered fractionation, or new radiation delivery techniques.

CHEMOTHERAPY

Question What emerging chemotherapeutic agents or techniques are available to provide better tumor control and prognosis for patients with newly diagnosed glioblastomas?

RECOMMENDATION

Level III: As no emerging chemotherapeutic agents or techniques were identified in this review that improved tumor control and prognosis it is suggested that, when available, patients with newly diagnosed glioblastomas be enrolled in properly designed clinical trials of chemotherapy.

MOLECULAR AND TARGETED THERAPY

Question What new targeted therapy agents are available to provide better tumor control and prognosis for individuals with newly diagnosed glioblastomas?

RECOMMENDATION

Level III: As no new molecular and targeted therapies have clearly provided better tumor control and prognosis it is suggested that, when available, patients with newly diagnosed glioblastomas be enrolled in properly designed clinical trials of molecular and targeted therapies IMMUNOTHERAPY: Question What emerging immunotherapeutic agents or techniques are available to provide better tumor control and prognosis for patients with newly diagnosed glioblastomas?

RECOMMENDATION

Level III: As no immunotherapeutic agents have clearly provided better tumor control and prognosis it is suggested that, when available, patients with newly diagnosed glioblastomas be enrolled in properly designed clinical trials of immunologically-based therapies.

NOVEL THERAPIES

Question What novel therapies or techniques are in development to provide better tumor control and prognosis for individuals with newly diagnosed glioblastomas?

RECOMMENDATIONS

Level II: The use of tumor-treating fields is recommended for patients with newly diagnosed glioblastoma who have undergone surgical debulking and completed concurrent chemoradiation without progression of disease at the time of tumor-treating field therapy initiation. Level II: It is suggested that, when available, enrollment in properly designed studies of vector containing herpes simplex thymidine kinase gene and prodrug therapies be considered in patients with newly diagnosed glioblastoma.

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

Simple Summary

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

Abstract

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

Grading of Glioma: combined diagnostic value of amide proton transfer weighted, arterial spin labeling and diffusion weighted magnetic resonance imaging

Background

To investigate the ability of amide proton transfer (APT) weighted magnetic resonance imaging (MRI), arterial spin labeling (ASL), diffusion weighted imaging (DWI) and the combination for differentiating high-grade gliomas (HGGs) from low-grade gliomas (LGGs).

Methods

Twenty-seven patients including nine LGGs and eighteen HGGs underwent conventional, APT, ASL and DWI MRI with a 3.0-T MR scanner. Histogram analyses was performed and quantitative parameters including mean apparent diffusion coefficient (ADC mean), 20th-percentile ADC (ADC 20th), mean APT (APT mean), 90th-percentile APT (APT 90th), relative mean cerebral blood flow (rCBF mean) and relative 90th-percentile CBF (rCBF 90th) were compared between HGGs and LGGs. The diagnostic performance was evaluated with receiver operating characteristic (ROC) analysis of each parameter and their combination. Correlations were analyzed among the MRI parameters and Ki-67.

Results

The APT values were significantly higher in the HGGs compared to the LGGs (p <  0.005), whereas ADC values were significantly lower in HGGs than LGGs (P <  0.0001). The ADC 20th and APT mean had higher discrimination abilities compared with other single parameters, with the area under the ROC curve (AUC) of 0.877 and 0.840. Adding ADC parameter, the discrimination ability of APT and rCBF significantly improved. The ADC was negatively correlated with the APT and rCBF value, respectively, while APT value was positively correlated with rCBF value. Significant correlations between ADC values and Ki-67 were also observed.

Conclusions

APT and DWI are valuable in differentiating HGGs from LGGs. The combination of APT, DWI and ASL imaging could improve the ability for discriminating HGGs from LGGs.

Assessment of Early Therapeutic Response to Nitroxoline in Temozolomide-Resistant Glioblastoma by Amide Proton Transfer Imaging: A Preliminary Comparative Study with Diffusion-weighted Imaging

Amide proton transfer (APT) imaging is a novel molecular MRI technique to detect endogenous mobile proteins and peptides through chemical exchange saturation transfer. In this preliminary study, the purpose was to evaluate the feasibility of APT imaging in monitoring the early therapeutic response to nitroxoline (NTX) in a temozolomide (TMZ)-resistant glioblastoma multiforme (GBM) mouse model, which was compared with diffusion-weighted imaging (DWI). Here, we prepared TMZ-resistant GBM mouse model (n = 12), which were treated with 100 mg/kg/day of NTX (n = 4) or TMZ (n = 4), or saline (n = 4) for 7 days for the evaluation of short-term treatment by using APT imaging and DWI sequentially. The APT signal intensities and apparent diffusion coefficient (ADC) values were calculated and compared before and after treatment. Moreover, immunohistological analysis was also employed for the correlation between APT imaging and histopathology. The association between the APT value and Ki-67 labeling index was evaluated by using simple linear regression analysis. The short-term NTX treatment resulted in significant decrease in APT value as compared to untreated and TMZ group, in which APT signals were increased. However, we did not observe significantly increased mean ADC value following short-term NTX treatment. The Ki-67 labeling index shows a correlation with APT value. APT imaging could show the earlier response to NTX treatment as compared to ADC values in a TMZ-resistant mouse model. We believe that APT imaging can be a useful imaging biomarker for the early therapeutic evaluation in GBM patients.

Clinically Relevant Imaging Features for MGMT Promoter Methylation in Multiple Glioblastoma Studies: A Systematic Review and Meta-Analysis

BACKGROUND

O6-methylguanine methyltransferase (MGMT) promoter methylation status has been reported as a prognostic biomarker in clinical trials.

PURPOSE

Our aim was to systematically evaluate imaging features of MGMT promoter methylated glioblastoma and to determine the diagnostic performance of MR imaging for prediction of MGMT promoter methylation in patients with newly diagnosed glioblastoma.

DATA SOURCES

A computerized search of Ovid MEDLINE and EMBASE up to February 27, 2018, was conducted.

STUDY SELECTION

We selected studies evaluating imaging features of MGMT promoter methylated glioblastoma and the diagnostic performance of MR imaging for prediction of MGMT promoter methylation.

DATA ANALYSIS

Pooled estimates of sensitivity and specificity were calculated using a hierarchic logistic regression model. Meta-regression and sensitivity analysis were performed.

DATA SYNTHESIS

Twenty-two articles including 2199 patients were included. MGMT promoter methylated glioblastoma is likely to show less edema, high ADC, and low perfusion. Ten articles including 753 patients were included in the meta-analysis. The summary sensitivity was 79% (95% CI, 72%-85%), and the summary specificity was 78% (95% CI, 71%-84%). In the meta-regression, MGMT promoter methylation and mean age were associated with heterogeneity. Sensitivity analysis excluding 1 study resolved the heterogeneity.

LIMITATIONS

Included studies used a variety of different MR imaging techniques to predict MGMT promoter methylation.

CONCLUSIONS

MGMT promotor methylated glioblastoma is likely to show less aggressive imaging features than MGMT promotor unmethylated glioblastoma. Despite the variety of different MR imaging techniques used, MR imaging in patients with newly diagnosed glioblastoma was shown to have the potential to predict MGMT promoter methylation noninvasively.

Subtype Differentiation of Small (≤ 4 cm) Solid Renal Mass Using Volumetric Histogram Analysis of DWI at 3-T MRI

OBJECTIVE

The purpose of this article is to evaluate the utility of volumetric histogram analysis of apparent diffusion coefficient (ADC) derived from reduced-FOV DWI for small (≤ 4 cm) solid renal mass subtypes at 3-T MRI.

MATERIALS AND METHODS

This retrospective study included 38 clear cell renal cell carcinomas (RCCs), 16 papillary RCCs, 18 chromophobe RCCs, 13 minimal fat angiomyolipomas (AMLs), and seven oncocytomas evaluated with preoperative MRI. Volumetric ADC maps were generated using all slices of the reduced-FOV DW images to obtain histogram parameters, including mean, median, 10th percentile, 25th percentile, 75th percentile, 90th percentile, and SD ADC values, as well as skewness, kurtosis, and entropy. Comparisons of these parameters were made by one-way ANOVA, t test, and ROC curves analysis.

RESULTS

ADC histogram parameters differentiated eight of 10 pairs of renal tumors. Three subtype pairs (clear cell RCC vs papillary RCC, clear cell RCC vs chromophobe RCC, and clear cell RCC vs minimal fat AML) were differentiated by mean ADC. However, five other subtype pairs (clear cell RCC vs oncocytoma, papillary RCC vs minimal fat AML, papillary RCC vs oncocytoma, chromophobe RCC vs minimal fat AML, and chromophobe RCC vs oncocytoma) were differentiated by histogram distribution parameters exclusively (all p < 0.05). Mean ADC, median ADC, 75th and 90th percentile ADC, SD ADC, and entropy of malignant tumors were significantly higher than those of benign tumors (all p < 0.05). Combination of mean ADC with histogram parameters yielded the highest AUC (0.851; sensitivity, 80.0%; specificity, 86.1%).

CONCLUSION

Quantitative volumetric ADC histogram analysis may help differentiate various subtypes of small solid renal tumors, including benign and malignant lesions.

Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis

With targeted treatments playing an increasing role in oncology, the need arises for fast non-invasive genotyping in clinical practice. Radiogenomics is a rapidly evolving field of research aimed at identifying imaging biomarkers useful for non-invasive genotyping. Radiogenomic genotyping has the advantage that it can capture tumor heterogeneity, can be performed repeatedly for treatment monitoring, and can be performed in malignancies for which biopsy is not available. In this systematic review of 187 included articles, we compiled a database of radiogenomic associations and unraveled networks of imaging groups and gene pathways oncology-wide. Results indicated that ill-defined tumor margins and tumor heterogeneity can potentially be used as imaging biomarkers for 1p/19q codeletion in glioma, relevant for prognosis and disease profiling. In non-small cell lung cancer, FDG-PET uptake and CT-ground-glass-opacity features were associated with treatment-informing traits including EGFR-mutations and ALK-rearrangements. Oncology-wide gene pathway analysis revealed an association between contrast enhancement (imaging) and the targetable VEGF-signalling pathway. Although the need of independent validation remains a concern, radiogenomic biomarkers showed potential for prognosis prediction and targeted treatment selection. Quantitative imaging enhanced the potential of multiparametric radiogenomic models. A wealth of data has been compiled for guiding future research towards robust non-invasive genomic profiling.

Structural and advanced imaging in predicting MGMT promoter methylation of primary glioblastoma: a region of interest based analysis

Background

The methylation status of oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter has been associated with treatment response in glioblastoma(GBM). Using pre-operative MRI techniques to predict MGMT promoter methylation status remains inconclusive. In this study, we investigated the value of features from structural and advanced imagings in predicting the methylation of MGMT promoter in primary glioblastoma patients.

Methods

Ninety-two pathologically confirmed primary glioblastoma patients underwent preoperative structural MR imagings and the efficacy of structural image features were qualitatively analyzed using Fisher’s exact test. In addition, 77 of the 92 patients underwent additional advanced MRI scans including diffusion-weighted (DWI) and 3-diminsional pseudo-continuous arterial spin labeling (3D pCASL) imaging. Apparent diffusion coefficient (ADC) and relative cerebral blood flow (rCBF) values within the manually drawn region-of-interest (ROI) were calculated and compared using independent sample t test for their efficacies in predicting MGMT promoter methylation. Receiver operating characteristic curve (ROC) analysis was used to investigate the predicting efficacy with the area under the curve (AUC) and cross validations. Multiple-variable logistic regression model was employed to evaluate the predicting performance of multiple variables.

Results

MGMT promoter methylation was associated with tumor location and necrosis (P <  0.05). Significantly increased ADC value (P <  0.001) and decreased rCBF (P <  0.001) were associated with MGMT promoter methylation in primary glioblastoma. The ADC achieved the better predicting efficacy than rCBF (ADC: AUC, 0.860; sensitivity, 81.1%; specificity, 82.5%; vs rCBF: AUC, 0.835; sensitivity, 75.0%; specificity, 78.4%; P = 0.032). The combination of tumor location, necrosis, ADC and rCBF resulted in the highest AUC of 0.914.

Conclusion

ADC and rCBF are promising imaging biomarkers in clinical routine to predict the MGMT promoter methylation in primary glioblastoma patients.

Associations between apparent diffusion coefficient (ADC) and KI 67 in different tumors: a meta-analysis. Part 1: ADCmean

Diffusion weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique based on measure of water diffusion in tissues. This diffusion can be quantified by apparent diffusion coefficient (ADC). Some reports indicated that ADC can reflect tumor proliferation potential. The purpose of this meta-analysis was to provide evident data regarding associations between ADC and KI 67 in different tumors. Studies investigating the relationship between ADC and KI 67 in different tumors were identified. MEDLINE library was screened for associations between ADC and KI 67 in different tumors up to April 2017. Overall, 42 studies with 2026 patients were identified. The following data were extracted from the literature: authors, year of publication, number of patients, tumor type, and correlation coefficients. Associations between ADC and KI 67 were analyzed by Spearman's correlation coefficient. The reported Pearson correlation coefficients in some studies were converted into Spearman correlation coefficients. The pooled correlation coefficient between ADC_mean and KI 67 for all included tumors was ρ = -0.44. Furthermore, correlation coefficient for every tumor entity was calculated. The calculated correlation coefficients were as follows: ovarian cancer: ρ = -0.62, urothelial carcinomas: ρ = -0.56, cerebral lymphoma: ρ = -0.55, neuroendocrine tumors: ρ = -0.52, glioma: ρ = -0.51, lung cancer: ρ = -0.50, prostatic cancer: ρ = -0.43, rectal cancer: ρ = -0.42, pituitary adenoma:ρ = -0.44, meningioma, ρ = -0.43, hepatocellular carcinoma: ρ = -0.37, breast cancer: ρ = -0.22.

From K-space to Nucleotide: Insights Into the Radiogenomics of Brain Tumors

Radiogenomics is a relatively new and exciting field within radiology that links different imaging features with diverse genomic events. Genomics advances provided by the Cancer Genome Atlas and the Human Genome Project have enabled us to harness and integrate this information with noninvasive imaging phenotypes to create a better 3-dimensional understanding of tumor behavior and biology. Beyond imaging-histopathology, imaging genomic linkages provide an important layer of complexity that can help in evaluating and stratifying patients into clinical trials, monitoring treatment response, and enhancing patient outcomes. This article reviews some of the important radiogenomic literatures in brain tumors.

Imaging Correlates of Adult Glioma Genotypes

Primary brain tumors, most commonly gliomas, are histopathologically typed and graded as World Health Organization (WHO) grades I-IV according to increasing degrees of malignancy. These grades provide prognostic information and guidance on treatment such as radiation therapy and chemotherapy after surgery. Despite the confirmed value of the WHO grading system, results of a multitude of studies and prospective interventional trials now indicate that tumors with identical morphologic criteria can have highly different outcomes. Molecular markers can allow subtypes of tumors of the same morphologic type and WHO grade to be distinguished and are, therefore, of great interest in personalization of brain tumor treatment. Recent genomic-wide studies have resulted in a far more comprehensive understanding of the genomic alterations in gliomas and provide suggestions for a new molecularly based classification. Magnetic resonance (MR) imaging phenotypes can serve as noninvasive surrogates for tumor genotypes and can provide important information for diagnosis, prognosis, and, eventually, personalized treatment. The newly emerged field of radiogenomics allows specific MR imaging phenotypes to be linked with gene expression profiles. In this article, the authors review the conventional and advanced imaging features of three tumoral genotypes with prognostic and therapeutic consequences: (a) isocitrate dehydrogenase mutation; (b) the combined loss of the short arm of chromosome 1 and the long arm of chromosome 19, or 1p19q codeletion; and (c) methylguanine methyltransferase promoter methylation. ^© RSNA, 2017.

Apparent diffusion coefficient maps obtained from high b value diffusion-weighted imaging in the preoperative evaluation of gliomas at 3T: comparison with standard b value diffusion-weighted imaging

OBJECTIVE

To assess whether ADC maps obtained from high b value DWI were more valuable in preoperatively evaluating the grade, Ki-67 index and outcome of gliomas.

METHODS

Sixty-three patients with gliomas, who underwent preoperative multi b value DWI at 3 T, were enrolled. The ADC₁₀₀₀, ADC₂₀₀₀ and ADC₃₀₀₀ maps were generated. Receiver operating characteristic analyses were conducted to determine the area under the curve (AUC) in differentiating high-grade gliomas (HGG) from low-grade gliomas (LGG). Pearson correlation coefficients (R value) were calculated to investigate the correlation between parameters with the Ki-67 proliferation index. Survival analysis was conducted by using Cox regression.

RESULTS

The AUC of the mean ADC₁₀₀₀ value (0.820) was lower than that of the mean ADC₂₀₀₀ value (0.847) and mean ADC₃₀₀₀ value (0.875) in differentiating HGG from LGG. The R value of the mean ADC₁₀₀₀ value (-0.499) was less negative than that of the mean ADC₂₀₀₀ value (-0.530) and mean ADC₃₀₀₀ value (-0.567). The mean ADC₃₀₀₀ value was an independent prognosis factor for gliomas (p = 0.008), while the mean ADC₁₀₀₀ and ADC₂₀₀₀ values were not.

CONCLUSION

ADC maps obtained from high b value DWI might be a better imaging biomarker in the preoperative evaluation of gliomas.

KEY POINTS

• ADC ₃₀₀₀ maps could improve the differentiation between HGG and LGG. • The mean ADC ₃₀₀₀ value had a closer correlation with the Ki-67 index. • The mean ADC ₃₀₀₀ value was an independent prognosis factor for gliomas.

Integrative Diffusion-Weighted Imaging and Radiogenomic Network Analysis of Glioblastoma multiforme

In the past, changes of the Apparent Diffusion Coefficient in glioblastoma multiforme have been shown to be related to specific genes and described as being associated with survival. The purpose of this study was to investigate diffusion imaging parameters in combination with genome-wide expression data in order to obtain a comprehensive characterisation of the transcriptomic changes indicated by diffusion imaging parameters. Diffusion-weighted imaging, molecular and clinical data were collected prospectively in 21 patients. Before surgery, MRI diffusion metrics such as axial (AD), radial (RD), mean diffusivity (MD) and fractional anisotropy (FA) were assessed from the contrast enhancing tumour regions. Intraoperatively, tissue was sampled from the same areas using neuronavigation. Transcriptional data of the tissue samples was analysed by Weighted Gene Co-Expression Network Analysis (WGCNA) thus classifying genes into modules based on their network-based affiliations. Subsequent Gene Set Enrichment Analysis (GSEA) identified biological functions or pathways of the expression modules. Network analysis showed a strong association between FA and epithelial-to-mesenchymal-transition (EMT) pathway activation. Also, patients with high FA had a worse clinical outcome. MD correlated with neural function related genes and patients with high MD values had longer overall survival. In conclusion, FA and MD are associated with distinct molecular patterns and opposed clinical outcomes.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

KEY POINTS

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

Amide proton transfer imaging to discriminate between low- and high-grade gliomas: added value to apparent diffusion coefficient and relative cerebral blood volume

OBJECTIVES

To evaluate the added value of amide proton transfer (APT) imaging to the apparent diffusion coefficient (ADC) from diffusion tensor imaging (DTI) and the relative cerebral blood volume (rCBV) from perfusion magnetic resonance imaging (MRI) for discriminating between high- and low-grade gliomas.

METHODS

Forty-six consecutive adult patients with diffuse gliomas who underwent preoperative APT imaging, DTI and perfusion MRI were enrolled. APT signals were compared according to the World Health Organization grade. The diagnostic ability and added value of the APT signal to the ADC and rCBV for discriminating between low- and high-grade gliomas were evaluated using receiver operating characteristic (ROC) analyses and integrated discrimination improvement.

RESULTS

The APT signal increased as the glioma grade increased. The discrimination abilities of the APT, ADC and rCBV values were not significantly different. Using both the APT signal and ADC significantly improved discrimination vs. the ADC alone (area under the ROC curve [AUC], 0.888 vs. 0.910; P = 0.007), whereas using both the APT signal and rCBV did not improve discrimination vs. the rCBV alone (AUC, 0.927 vs. 0.923; P = 0.222).

CONCLUSIONS

APT imaging may be a useful imaging biomarker that adds value to the ADC for discriminating between low- and high-grade gliomas.

KEY POINTS

• Higher APT values were correlated with higher glioma grades. • Adding the APT signal to the ADC improved glioma grading. • Adding the APT signal to rCBV did not improve glioma grading. • APT is a useful adjunct to the ADC for glioma grading.

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.

Incremental Prognostic Value of ADC Histogram Analysis over MGMT Promoter Methylation Status in Patients with Glioblastoma

Purpose To investigate the incremental prognostic value of apparent diffusion coefficient (ADC) histogram analysis over oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status in patients with glioblastoma and the correlation between ADC parameters and MGMT status. Materials and Methods This retrospective study was approved by institutional review board, and informed consent was waived. A total of 112 patients with glioblastoma were divided into training (74 patients) and test (38 patients) sets. Overall survival (OS) and progression-free survival (PFS) was analyzed with ADC parameters, MGMT status, and other clinical factors. Multivariate Cox regression models with and without ADC parameters were constructed. Model performance was assessed with c index and receiver operating characteristic curve analyses for 12- and 16-month OS and 12-month PFS in the training set and validated in the test set. ADC parameters were compared according to MGMT status for the entire cohort. Results By using ADC parameters, the c indices and diagnostic accuracies for 12- and 16-month OS and 12-month PFS in the models showed significant improvement, with the exception of c indices in the models for PFS (P < .05 for all) in the training set. In the test set, the diagnostic accuracy was improved by using ADC parameters and was significant, with the 25th and 50th percentiles of ADC for 16-month OS (P = .040 and P = .047) and the 25th percentile of ADC for 12-month PFS (P = .026). No significant correlation was found between ADC parameters and MGMT status. Conclusion ADC histogram analysis had incremental prognostic value over MGMT promoter methylation status in patients with glioblastoma. (©) RSNA, 2016 Online supplemental material is available for this article.

Imaging genomics in cancer research: limitations and promises

Recently, radiogenomics or imaging genomics has emerged as a novel high-throughput method of associating imaging features with genomic data. Radiogenomics has the potential to provide comprehensive intratumour, intertumour and peritumour information non-invasively. This review article summarizes the current state of radiogenomic research in tumour characterization, discusses some of its limitations and promises and projects its future directions. Semi-radiogenomic studies that relate specific gene expressions to imaging features will also be briefly reviewed.

Squamous Cell Carcinoma of the Head and Neck: Comparison of Diffusion-weighted MRI at b-values of 1,000 and 2,000 s/mm(2) to Predict Response to Induction Chemotherapy

PURPOSE

Recent publications have reported contradictory results of pretreatment diffusion-weighted magnetic resonance imaging (DWI) for the prediction of chemoradiotherapeutic response in primary squamous cell carcinomas of the head and neck (HNSCC). Therefore, we evaluated the diagnostic performance of DWI obtained with both standard (b = 0 and 1,000 s/mm²) and high (b = 0 and 2,000 s/mm²) b-values for predicting response to induction chemotherapy in HNSCCs.

METHODS

For 25 patients with primary HNSCC who underwent DWI with both standard and high b-values prior to treatment, we calculated corresponding apparent diffusion coefficient (ADC) maps. Regions of interest containing the tumor were drawn on every section of ADC maps and summated to make volume-based data of the entire tumor. Histogram parameters (mean ADC, kurtosis, and skewness) were correlated with treatment response using unpaired Student t test. Univariate and multivariate analysis of the ADC parameters, patient age, sex, whole tumor volume, and T stage were also performed to predict tumor response to induction chemotherapy.

RESULTS

Response to induction chemotherapy was good in 13 of the 25 patients and poor in 12. The mean ADC values of good responders at standard b-value (ADC1000), 1.23 ± 0.34 (× 10⁻³ mm²/s), and high b-value (ADC2000), 0.62 ± 0.14 (× 10⁻³ mm²/s), were lower than those of poor responders (ADC1000, 1.32 ± 0.28 [× 10⁻³ mm²/s]; ADC2000, 0.76 ± 0.15 [× 10⁻³ mm²/s]), but significant difference was achieved only at the ADC2000 map (P = 0.02). In addition, mean tumor volume prior to treatment of good responders was smaller than that of poor responders. However, at multiple logistic regression analysis, only the mean ADC2000 value remained as a significant predictor of response to induction chemotherapy.

CONCLUSION

DWI with high b-values (b = 0 and 2,000 s/mm²) as an assessment of ADC values may help predict tumor response to neoadjuvant chemotherapy for primary HNSCCs.

Chronic arterial hypertension impedes glioma growth: a multiparametric MRI study in the rat

Glioblastoma is the most aggressive brain tumor and is almost always fatal. These tumors are highly vascularized and angiogenesis is one of the pre-eminent mechanisms underlying their growth. Chronic arterial hypertension (CAH) is a common and worldwide pathology that markedlly alters the structure and function of the vasculature. Yet, essential hypertension is associated in the brain with potential locally impaired vasoreactivity, disturbed perfusion supply and hypoxia phenomena. Even though CAH is a global burden and has an important impact on brain function, nothing is known about the way this frequent pathology would interact with the evolution of glioma. We sought to determine if arterial hypertension influences gliobastoma growth. In the present study, rat glioma C6 tumor cells were implanted in the caudate-putamen of spontaneously hypertensive rats (SHR) or their normotensive controls, the Wistar-Kyoto (WKY) rats. The evolution of the tumor was sequentially analyzed by multiparametric magnetic resonance imaging and the inflammatory response was examined by histochemistry. We found that CAH significantly attenuates the growth of the tumor as, at 21 days, the volume of the tumor was 85.4±34.7 and 126.1±28.8 mm(3), respectively, in hypertensive and normotensive rats (P<0.02). Moreover, cerebral blood volume and cerebral blood flow were greater in the tumors of hypertensive rats (P<0.05). The lesser growth of the tumor observed in normotensive animals was not due to an enhanced rejection of the tumor cells in WKY rats, the inflammatory response being similar in both groups. For the first time, these results show that CAH impedes the growth of glioblastoma and illustrate the need to further study the impact of hypertension on the evolution of brain tumors.

Using the apparent diffusion coefficient to identifying MGMT promoter methylation status early in glioblastoma: importance of analytical method

INTRODUCTION

Accurate knowledge of O(6)-methylguanine methyltransferase (MGMT) gene promoter subtype in patients with glioblastoma (GBM) is important for treatment. However, this test is not always available. Pre-operative diffusion MRI (dMRI) can be used to probe tumour biology using the apparent diffusion coefficient (ADC); however, its ability to act as a surrogate to predict MGMT status has shown mixed results. We investigated whether this was due to variations in the method used to analyse ADC.

METHODS

We undertook a retrospective study of 32 patients with GBM who had MGMT status measured. Matching pre-operative MRI data were used to calculate the ADC within contrast enhancing regions of tumour. The relationship between ADC and MGMT was examined using two published ADC methods.

RESULTS

A strong trend between a measure of 'minimum ADC' and methylation status was seen. An elevated minimum ADC was more likely in the methylated compared to the unmethylated MGMT group (U = 56, P = 0.0561). In contrast, utilising a two-mixture model histogram approach, a significant reduction in mean measure of the 'low ADC' component within the histogram was associated with an MGMT promoter methylation subtype (P < 0.0246).

CONCLUSION

This study shows that within the same patient cohort, the method selected to analyse ADC measures has a significant bearing on the use of that metric as a surrogate marker of MGMT status. Thus for dMRI data to be clinically useful, consistent methods of data analysis need to be established prior to establishing any relationship with genetic or epigenetic profiling.

Glioma: application of whole-tumor texture analysis of diffusion-weighted imaging for the evaluation of tumor heterogeneity

BACKGROUND AND PURPOSE

To apply a texture analysis of apparent diffusion coefficient (ADC) maps to evaluate glioma heterogeneity, which was correlated with tumor grade.

MATERIALS AND METHODS

Forty patients with glioma (WHO grade II (n = 8), grade III (n = 10) and grade IV (n = 22)) underwent diffusion-weighted imaging (DWI), and the corresponding ADC maps were obtained. Regions of interest containing the lesions were drawn on every section of the ADC map containing the tumor, and volume-based data of the entire tumor were constructed. Texture and first order features including entropy, skewness and kurtosis were derived from the ADC map using in-house software. A histogram analysis of the ADC map was also performed. The texture and histogram parameters were compared between low-grade and high-grade gliomas using an unpaired student's t-test. Additionally, a one-way analysis of variance analysis with a post-hoc test was performed to compare the parameters of each grade.

RESULTS

Entropy was observed to be significantly higher in high-grade gliomas than low-grade tumors (6.861±0.539 vs. 6.261±0.412, P  = 0.006). The fifth percentiles of the ADC cumulative histogram also showed a significant difference between high and low grade gliomas (836±235 vs. 1030±185, P = 0.037). Only entropy proved to be significantly different between grades III and IV (6.295±0.4963 vs. 7.119±0.3165, P<0.001). The diagnostic accuracy of ADC entropy was significantly higher than that of the fifth percentile of the ADC histogram (P = 0.0034) in distinguishing high- from low-grade glioma.

CONCLUSION

A texture analysis of the ADC map based on the entire tumor volume can be useful for evaluating glioma grade, which provides tumor heterogeneity.

Quantified ADC histogram analysis: a new method for differentiating mass-forming focal pancreatitis from pancreatic cancer

BACKGROUND

As their prognosis and management are different, differentiation of mass-forming focal pancreatitis (FP) from pancreatic adenocarcinoma (PC) is important. However, the similar clinical presentations and imaging features of these conditions, along with inconclusive biopsy results can make such differentiation difficult.

PURPOSE

To determine whether apparent diffusion coefficient (ADC) histogram analysis can discriminate between a normal pancreas, FP, and PC.

MATERIAL AND METHODS

In a retrospective study, 25 PC patients, 14 FP patients, and 25 subjects with a normal pancreas underwent breath-hold diffusion-weighted imaging (DWI) on a 3.0 T magnetic resonance (MR) scanner. Regions of interest (ROIs) were drawn on the normal pancreases and on the entire focal lesions of both PC and FP. The ADC value was averaged from the lowest to 10th, 30th, 50th, and 100th percentile of the histogram (i.e. ADC10, ADC30, ADC50, and ADC100, respectively), and the results were analyzed statistically.

RESULTS

There were no significant differences among the head, body, and tail of normal pancreases for any of the mean ADC values (P > 0.05). ADC10, ADC30, and ADC50 values demonstrated significant differences between lesion and non-lesion areas of both PC (P < 0.05) and FP (P < 0.05). Differences in lesion areas between PC and FP were found with ADC50 and ADC100 values (P < 0.05), and helped differentiate a normal pancreas from FP and PC, and FP from PC.

CONCLUSION

Quantified ADC histogram can specifically reflect tissue heterogeneity and help differentiate a normal pancreas from FP and PC.