A prognostic model based on preoperative MRI predicts overall survival in patients with diffuse gliomas

Tumor ADC value predicts outcome and yields refined prognostication in uterine cervical cancer

Pelvic MRI is essential for evaluating local and regional tumor extent in uterine cervical cancer (CC). Tumor microstructure captured by diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) markers may be closely linked to prognosis in CC.Purpose To explore whether primary tumor ADC markers predict survival in CC.Material and methods CC patients (n = 179) diagnosed during 2009-2020 with MRI-assessed primary maximum tumorsize ≥ 2 cm were included in this retrospective single-center study. Two radiologists read all MRIs independently, measuring mean tumor ADC values in manually drawn regions of interest (ROIs) and mean tumor ADC (tumorADCmean) from five measurements for the two readers was used. ADC from ROIs in the myometrium (myometriumADC), cervical stroma (cervixADC), and bladder (bladderADC) were used to calculate ADC ratios. ADC markers were explored in relation to the International Federation of Gynecology and Obstetrics (FIGO) (2018) stage, disease-specific survival (DSS), and recurrence/progression-free survival (RPFS).Results Inter-reader agreement for all ADC measurements was high (ICC:0.59-0.79). Low tumorADCmean predicted advanced FIGO stage (P = 0.04) and reduced DSS (hazard ratio (HR): 0.96, P < 0.001; AIC: 441). MyometriumADC/tumorADCmean yielded the best Cox regression fit (AIC = 430) among all tumor ADC markers. Patients with high myometriumADC/tumorADCmean had significantly reduced 5-year DSS for FIGO stage I, II, and III (P = 0.01, 0.004, and 0.02, respectively) and tended to the same for FIGO IV (P = 0.22).Conclusion Low tumorADCmean predicted reduced DSS in CC. High myometriumADC/tumorADCmean was the strongest ADC predictor of poor DSS and a marker of high-risk phenotype independent of FIGO stage.

Statistical plots in oncologic imaging, a primer for neuroradiologists

The simplest approach to convey the results of scientific analysis, which can include complex comparisons, is typically through the use of visual items, including figures and plots. These statistical plots play a critical role in scientific studies, making data more accessible, engaging, and informative. A growing number of visual representations have been utilized recently to graphically display the results of oncologic imaging, including radiomic and radiogenomic studies. Here, we review the applications, distinct properties, benefits, and drawbacks of various statistical plots. Furthermore, we provide neuroradiologists with a comprehensive understanding of how to use these plots to effectively communicate analytical results based on imaging data.

Role of apparent diffusion map in the evaluation of retinoblastoma

PURPOSE

This study aimed to analyze the association between magnetic resonance imaging apparent diffusion coefficient map value and histopathological differentiation in patients who underwent eye enucleation due to retinoblastomas.

METHODS

An observational chart review study of patients with retinoblastoma that had histopathology of the lesion and orbit magnetic resonance imaging with apparent diffusion coefficient analysis at Hospital de Clínicas de Porto Alegre between November 2013 and November 2016 was performed. The histopathology was reviewed after enucleation. To analyze the difference in apparent diffusion coefficient values between the two major histopathological prognostic groups, Student's t-test was used for the two groups. All statistical analyses were performed using SPSS version 19.0 for Microsoft Windows (SPSS, Inc., Chicago, IL, USA). Our institutional review board approved this retrospective study without obtaining informed consent.

RESULTS

Thirteen children were evaluated, and only eight underwent eye enucleation and were included in the analysis. The others were treated with photocoagulation, embolization, radiotherapy, and chemotherapy and were excluded due to the lack of histopathological results. When compared with histopathology, magnetic resonance imaging demonstrated 100% accuracy in retinoblastoma diagnosis. Optic nerve invasion detection on magnetic resonance imaging showed a 66.6% sensitivity and 80.0% specificity. Positive and negative predictive values were 66.6% and 80.0%, respectively, with an accuracy of 75%. In addition, the mean apparent diffusion coefficient of the eight eyes was 0.615 × 103 mm2/s. The mean apparent diffusion coefficient value of poorly or undifferentiated retinoblastoma and differentiated tumors were 0.520 × 103 mm2/s and 0.774 × 103 mm2/s, respectively.

CONCLUSION

This study revealed that magnetic resonance imaging is useful in the diagnosis of retinoblastoma and detection of optic nerve infiltration, with a sensitivity of 66.6% and specificity of 80%. Our results also showed lower apparent diffusion coefficient values in poorly differentiated retinoblastomas with a mean of 0.520 × 103 mm2/s, whereas in well and moderately differentiated, the mean was 0.774 × 103 mm2/s.

Prognostic value of preoperative diffusion restriction in glioblastoma

INTRODUCTION

Although glioblastoma (GBM) has a very poor prognosis, overall survival (OS) in treated patients shows great difference varying from few days to several months. Identifying factors explaining this difference would improve management of patient treatment.

AIM

To determine the relevance of diffusion restriction in newly diagnosed treatment-naïve GBM patients.

METHODS

Preoperative magnetic resonance scans of 33 patients with GBM were reviewed. Regions of interest including all the T2 hyperintense lesion were drawn on diffusion weighted B0 images and transferred to the apparent diffusion coefficient (ADC) map. For each patient, a histogram displaying the ADC values within in the regions of interest was generated. Volumetric parameters including tumor regions with restricted diffusion, parameters derived from histogram and mean ADC value of the tumor were calculated. Their relationship with OS was analyzed.

RESULTS

Patients with mean ADC value < 1415x10-6 mm2/s had a significantly shorter OS (p=0.021). Among volumetric parameters, the percentage of volume within T2 lesion with a normalized ADC value <1.5 times that in white matter was significantly associated with OS (p=0.0045). Patients with a percentage>23.92% had a shorter OS. Among parameters derived from histogram, the 50th percentile showed a trend towards significance for OS (p=0.055) with patients living longer when having higher values of 50th percentile. A difference in OS was observed between patients according to ADC peak of histogram but this difference did not reach statistical significance (p=0.0959).

CONCLUSION

Diffusion magnetic resonance imaging may provide useful information for predicting GBM prognosis.

Diffusion histogram profiles predict molecular features of grade 4 in histologically lower-grade adult diffuse gliomas following WHO classification 2021

OBJECTIVES

In the latest World Health Organization classification 2021, grade 4 adult diffuse gliomas can be diagnosed with several molecular features even without histological evidence of necrosis or microvascular proliferation. We aimed to explore whole tumor histogram-derived apparent diffusion coefficient (ADC) histogram profiles for differentiating between the presence (Mol-4) and absence (Mol-2/3) of grade 4 molecular features in histologically lower-grade gliomas.

METHODS

Between June 2019 and October 2022, 184 adult patients with diffuse gliomas underwent MRI. After excluding 121 patients, 18 (median age, 64.5 [range, 37-84 years]) Mol-4 and 45 (median 40 [range, 18-73] years) Mol-2/3 patients with histologically lower-grade gliomas were enrolled. Whole tumor volume-of-interest-derived ADC histogram profiles were calculated and compared between the two groups. Stepwise logistic regression analysis with Akaike's information criterion using the ADC histogram profiles with p values < 0.01 and age at diagnosis was used to identify independent variables for predicting the Mol-4 group.

RESULTS

The 90th percentile (p < 0.001), median (p < 0.001), mean (p < 0.001), 10th percentile (p = 0.014), and entropy (p < 0.001) of normalized ADC were lower, and kurtosis (p < 0.001) and skewness (p = 0.046) were higher in the Mol-4 group than in the Mol-2/3 group. Multivariate logistic regression analysis revealed that the entropy of normalized ADC and age at diagnosis were independent predictive parameters for the Mol-4 group with an area under the curve of 0.92.

CONCLUSION

ADC histogram profiles may be promising preoperative imaging biomarkers to predict molecular grade 4 among histologically lower-grade adult diffuse gliomas.

CLINICAL RELEVANCE STATEMENT

This study highlighted the diagnostic usefulness of ADC histogram profiles to differentiate histologically lower grade adult diffuse gliomas with the presence of molecular grade 4 features and those without.

KEY POINTS

• ADC histogram profiles to predict molecular CNS WHO grade 4 status among histologically lower-grade adult diffuse gliomas were evaluated. • Entropy of ADC and age were independent predictive parameters for molecular grade 4 status. • ADC histogram analysis is useful for predicting molecular grade 4 among histologically lower-grade gliomas.

Longitudinal MR spectroscopy to detect progression in patients with lower-grade glioma in the surveillance phase

Background

Monitoring lower-grade gliomas (LrGGs) for disease progression is made difficult by the limits of anatomical MRI to distinguish treatment related tissue changes from tumor progression. MR spectroscopic imaging (MRSI) offers additional metabolic information that can help address these challenges. The goal of this study was to compare longitudinal changes in multiparametric MRI, including diffusion weighted imaging, perfusion imaging, and 3D MRSI, for LrGG patients who progressed at the final time-point and those who remained clinically stable.

Methods

Forty-one patients with LrGG who were clinically stable were longitudinally assessed for progression. Changes in anatomical, diffusion, perfusion and MRSI data were acquired and compared between patients who remained clinically stable and those who progressed.

Results

Thirty-one patients remained stable, and 10 patients progressed. Over the study period, progressed patients had a significantly greater increase in normalized choline, choline-to-N-acetylaspartic acid index (CNI), normalized creatine, and creatine-to-N-acetylaspartic acid index (CRNI), than stable patients. CRNI was significantly associated with progression status and WHO type. Progressed astrocytoma patients had greater increases in CRNI than stable astrocytoma patients.

Conclusions

LrGG patients in surveillance with tumors that progressed had significantly increasing choline and creatine metabolite signals on MRSI, with a trend of increasing T2 FLAIR volumes, compared to LrGG patients who remained stable. These data show that MRSI can be used in conjunction with anatomical imaging studies to gain a clearer picture of LrGG progression, especially in the setting of clinical ambiguity.

Diffusion‐Weighted MR Imaging of the Optic Nerve Can Improve the Detection of Post‐Laminar Optic Nerve Invasion from Retinoblastoma

BACKGROUND

Post-laminar optic nerve invasion (PLONI) is a high-risk factor for the metastasis of retinoblastoma (RB). Unlike conventional MRI, diffusion-weighted imaging (DWI) reflects histopathological features, and may aid the assessment of PLONI.

PURPOSE

To determine the value of conventional MRI plus DWI in detecting PLONI in RB patients.

STUDY TYPE

Retrospective.

POPULATION

Eighty-three RB patients, including 28 with histopathologically proven PLONI and 55 without PLONI.

FIELD STRENGTH/SEQUENCE

3.0 T, precontrast axial T1-weighted and T2-weighted imaging, DWI, and postcontrast axial, coronal, and oblique-sagittal T1-weighted imaging.

ASSESSMENT

PLONI was assessed using post-enucleation histology and preoperative MRI features (optic nerve signal intensity, enlargement, and enhancement on conventional MRI, and apparent diffusion coefficient [ADC] of the optic nerve on DWI) evaluated by three observers.

STATISTICAL TESTS

MRI features suggesting the presence of PLONI were identified using univariable and multivariable analyses. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to analyze diagnostic performance.

RESULTS

Optic nerve enhancement and low ADC of the optic nerve were significant indicators of PLONI. ROC curve analysis showed that the AUC of the combination of these two features for detecting PLONI was 0.87 (95% confidence interval [CI]: 0.78-0.93). The diagnostic performance of this model was significantly superior to that of optic nerve enhancement alone (0.76, 95% CI: 0.65-0.85) and marginally superior to that of the ADC of the affected optic nerve (0.78, 95% CI: 0.68-0.87, P = 0.051).

DATA CONCLUSION

Conventional MRI combined with DWI can improve the detection of PLONI in RB patients over conventional MRI alone.

EVIDENCE LEVEL

3 Technical Efficacy: Stage 2.

Treatment Response and Prognosis Evaluation in High-Grade Glioma: An Imaging Review Based on MRI

In recent years, the development of advanced magnetic resonance imaging (MRI) technology and machine learning (ML) have created new tools for evaluating treatment response and prognosis of patients with high-grade gliomas (HGG); however, patient prognosis has not improved significantly. This is mainly due to the heterogeneity between and within HGG tumors, resulting in standard treatment methods not benefitting all patients. Moreover, the survival of patients with HGG is not only related to tumor cells, but also to noncancer cells in the tumor microenvironment (TME). Therefore, during preoperative diagnosis and follow-up treatment of patients with HGG, noninvasive imaging markers are needed to characterize intratumoral heterogeneity, and then to evaluate treatment response and predict prognosis, timeously adjust treatment strategies, and achieve individualized diagnosis and treatment. In this review, we summarize the research progress of conventional MRI, advanced MRI technology, and ML in evaluation of treatment response and prognosis of patients with HGG. We further discuss the significance of the TME in the prognosis of HGG patients, associate imaging features with the TME, indirectly reflecting the heterogeneity within the tumor, and shifting treatment strategies from tumor cells alone to systemic therapy of the TME, which may be a major development direction in the future. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 4.

Imaging and treatment of brain tumors through molecular targeting: Recent clinical advances

Molecular imaging techniques have rapidly progressed over recent decades providing unprecedented in vivo characterization of metabolic pathways and molecular biomarkers. Many of these new techniques have been successfully applied in the field of neuro-oncological imaging to probe tumor biology. Targeting specific signaling or metabolic pathways could help to address several unmet clinical needs that hamper the management of patients with brain tumors. This review aims to provide an overview of the recent advances in brain tumor imaging using molecular targeting with positron emission tomography and magnetic resonance imaging, as well as the role in patient management and possible therapeutic implications.

The value of intravoxel incoherent motion imaging in predicting the survival of patients with astrocytoma

BACKGROUND

The evaluation of the prognosis of gliomas may have great value in individualized treatment.

PURPOSE

To evaluate the value of intravoxel incoherent motion (IVIM) in predicting the survival of patients with astrocytoma and comparing it to apparent diffusion coefficients (ADC).

MATERIAL AND METHODS

Sixty patients with pathologically confirmed cerebral astrocytomas underwent IVIM scans before any treatment was performed. Patients were divided into death group and survival group according to a two-year follow-up. ADC and quantitative parameters of IVIM including D, D*, and f were measured. Independent sample t test was used to compare the two groups of parameters. The accuracy of each parameter for two-year survival rate was analyzed by receiver operating characteristic (ROC) curve and Kaplan-Meier survival curves. The correlation between quantitative parameters and survival days was analyzed by Pearson correlation analysis.

RESULTS

The ADC, D*, and f values were statistically significant different between the death and the survival groups (P < 0.05). The AUC of the ADC, D*, and f were 0.811, 0.858, and 0.892, respectively. The ADC cut-off value of 0.668 × 10^-3 mm²/s corresponded to 82.6% sensitivity and 73% specificity. The D* cut-off value of 3.913 × 10^-3 mm²/s corresponded to 78.4% sensitivity and 87% specificity. The f cut-off value of 0.487 corresponded to 83.8% sensitivity and 87% specificity. Significant log rank test was performed for each parameter to predict overall survival (P < 0.05). There was a correlation between ADC (r = 0.625, P = 0.023), D* (r = -0.655, P = 0.012), f (r = -0.725, P = 0.000) and survival days.

CONCLUSION

The D* and f values demonstrated great potential in predicting the two-year survival rate for patients with astrocytoma.

Histogram-based analysis of cerebral blood flow using arterial spin labeling MRI in de novo brain gliomas: relationship to histopathologic grade and molecular markers

PURPOSE

We developed multiple histogram-based CBF indices and evaluated their association with histopathologic grade in de novo brain tumor patients. Furthermore, the associations between these advanced CBF indices and molecular markers, including IDH1 mutation, ATRX loss, and 1p/19q co-deletion were also investigated.

METHODS

Thirteen de novo brain tumor patients (age 21-68 years, 9 M/4F) who were enrolled in our prospective study were scanned on 3 T MRI using a pCASL perfusion sequence following IRB-approved written informed consent. All patients have since undergone surgical intervention with tissue sampling for histopathologic tumor grading and molecular marker assessment. Tumor region of interest (ROI) were manually delineated on FLAIR images including the full extent of the tumor and peritumoral edema. Fourteen rCBF indices were derived from the histogram of the voxels with the ROI. Multi-linear regression was then used to compare rCBF indices with histopathologic tumor grade and molecular markers.

RESULTS

Averaged rCBF in top 10 and top 20 voxels (p < 0.004), but not the entire tumor ROI, was positively associated with WHO tumor grade. After accounting for tumor grade, the presence of 1p/19q co-deletion was associated with higher rCBF in top voxels, as well as with standard deviation of rCBF in the tumor ROI (p < 0.001). ATRX retention was related to higher rCBF, and this effect appears to be present in both higher-perfusion (p < 0.004) and low-perfusion (p < 0.05) voxels. IDH mutation was not significantly associated with any of the CBF indices investigated.

CONCLUSION

ASL MRI may provide useful supplemental noninvasive imaging assessment of brain tumor grade and molecular marker status.

Development and validation of a novel prognostic model for predicting AMD progression using longitudinal fundus images

Objective

To develop a prognostic tool to predict the progression of age-related eye disease progression using longitudinal colour fundus imaging.

Methods and analysis

Previous prognostic models using deep learning with imaging data require annotation during training or only use a single time point. We propose a novel deep learning method to predict the progression of diseases using longitudinal imaging data with uneven time intervals, which requires no prior feature extraction. Given previous images from a patient, our method aims to predict whether the patient will progress onto the next stage of the disease. The proposed method uses InceptionV3 to produce feature vectors for each image. In order to account for uneven intervals, a novel interval scaling is proposed. Finally, a recurrent neural network is used to prognosticate the disease. We demonstrate our method on a longitudinal dataset of colour fundus images from 4903 eyes with age-related macular degeneration (AMD), taken from the Age-Related Eye Disease Study, to predict progression to late AMD.

Results

Our method attains a testing sensitivity of 0.878, a specificity of 0.887 and an area under the receiver operating characteristic of 0.950. We compare our method to previous methods, displaying superior performance in our model. Class activation maps display how the network reaches the final decision.

Conclusion

The proposed method can be used to predict progression to advanced AMD at some future visit. Using multiple images at different time points improves predictive performance.

A Neural Network Approach to Identify the Peritumoral Invasive Areas in Glioblastoma Patients by Using MR Radiomics

The challenge in the treatment of glioblastoma is the failure to identify the cancer invasive area outside the contrast-enhancing tumour which leads to the high local progression rate. Our study aims to identify its progression from the preoperative MR radiomics. 57 newly diagnosed cerebral glioblastoma patients were included. All patients received 5-aminolevulinic acid (5-ALA) fluorescence guidance surgery and postoperative temozolomide concomitant chemoradiotherapy. Preoperative 3 T MRI data including structure MR, perfusion MR, and DTI were obtained. Voxel-based radiomics features extracted from 37 patients were used in the convolutional neural network to train and as internal validation. Another 20 patients of the cohort were tested blindly as external validation. Our results showed that the peritumoural progression areas had higher signal intensity in FLAIR (p = 0.02), rCBV (p = 0.038), and T1C (p = 0.0004), and lower intensity in ADC (p = 0.029) and DTI-p (p = 0.001) compared to non-progression area. The identification of the peritumoural progression area was done by using a supervised convolutional neural network. There was an overall accuracy of 92.6% in the training set and 78.5% in the validation set. Multimodal MR radiomics can demonstrate distinct characteristics in areas of potential progression on preoperative MRI.

Tumor grade-related language and control network reorganization in patients with left cerebral glioma

Language processing relies on both a functionally specialized language network and a domain-general cognitive control network. Yet, how the two networks reorganize after damage resulting from diffuse and progressive glioma remains largely unknown. To address this issue, 130 patients with left cerebral gliomas, including 77 patients with low-grade glioma (LGG, WHO grade Ⅰ/II), 53 patients with high-grade glioma (HGG, WHO grade III/IV) and 38 healthy controls (HC) were adopted. The changes in resting-state functional connectivity (rsFC) of the language network and the cingulo-opercular/fronto-parietal (CO-FP) network were examined using network-based statistics. We found that tumor grade negatively correlated with language scores and language network integrity. Compared with HCs, patients with LGGs exhibited slight language deficits, both decreased and increased changes in rsFC of language network, and nearly normal CO-FP network. Patients with HGGs had significantly lower language scores than those with LGG and exhibited more severe language and CO-FP network disruptions than HCs or patients with LGGs. Moreover, we found that in patients with HGGs, the decreased rsFCs of language network were positively correlated with language scores. Together, our findings suggest tumor grade-related network reorganization of both language and control networks underlie the different levels of language impairments observed in patients with gliomas.

Correction to: Advanced imaging in adult diffusely infiltrating low-grade gliomas

The original article [1] contains errors in Table 1 in rows ktrans and Ve; the correct version of Table 1 can be viewed in this Correction article.

An Online Calculator for the Prediction of Survival in Glioblastoma Patients Using Classical Statistics and Machine Learning

BACKGROUND

Although survival statistics in patients with glioblastoma multiforme (GBM) are well-defined at the group level, predicting individual patient survival remains challenging because of significant variation within strata.

OBJECTIVE

To compare statistical and machine learning algorithms in their ability to predict survival in GBM patients and deploy the best performing model as an online survival calculator.

METHODS

Patients undergoing an operation for a histopathologically confirmed GBM were extracted from the Surveillance Epidemiology and End Results (SEER) database (2005-2015) and split into a training and hold-out test set in an 80/20 ratio. Fifteen statistical and machine learning algorithms were trained based on 13 demographic, socioeconomic, clinical, and radiographic features to predict overall survival, 1-yr survival status, and compute personalized survival curves.

RESULTS

In total, 20 821 patients met our inclusion criteria. The accelerated failure time model demonstrated superior performance in terms of discrimination (concordance index = 0.70), calibration, interpretability, predictive applicability, and computational efficiency compared to Cox proportional hazards regression and other machine learning algorithms. This model was deployed through a free, publicly available software interface (https://cnoc-bwh.shinyapps.io/gbmsurvivalpredictor/).

CONCLUSION

The development and deployment of survival prediction tools require a multimodal assessment rather than a single metric comparison. This study provides a framework for the development of prediction tools in cancer patients, as well as an online survival calculator for patients with GBM. Future efforts should improve the interpretability, predictive applicability, and computational efficiency of existing machine learning algorithms, increase the granularity of population-based registries, and externally validate the proposed prediction tool.

Advanced imaging in adult diffusely infiltrating low-grade gliomas

The adult diffusely infiltrating low-grade gliomas (LGGs) are typically IDH mutant and slow-growing gliomas having moderately increased cellularity generally without mitosis, necrosis, and microvascular proliferation. Supra-total resection of LGG significantly increases the overall survival by delaying malignant transformation compared with a simple debulking so accurate MR diagnosis is crucial for treatment planning. Data from meta-analysis support the addition of diffusion and perfusion-weighted MR imaging and MR spectroscopy in the diagnosis of suspected LGG. Typically, LGG has lower cellularity (ADCmin), angiogenesis (rCBVmax), capillary permeability (Ktrans), and mitotic activity (Cho/Cr ratio) compared to high-grade glioma. The identification of 2-hydroxyglutarate by MR spectroscopy can reflect the IDH status of the tumor. The initial low ADCmin, high rCBVmax, and Ktrans values are consistent with the poor prognosis. The gradual increase in intratumoral Cho/Cr ratio and rCBVmax values are well correlated with tumor progression. Besides MR-based technical artifacts, which are minimized by the voxel-based assessment of data obtained by histogram analysis, the problems derived from the diversity and the analysis of imaging data should be solved by using artificial intelligence techniques. The quantitative multiparametric MR imaging of LGG can either improve the diagnostic accuracy of their differential diagnosis or assess their prognosis.

Non-invasive assessment of glioma microstructure using VERDICT MRI: correlation with histology

PURPOSE

This prospective study evaluated the use of vascular, extracellular and restricted diffusion for cytometry in tumours (VERDICT) MRI to investigate the tissue microstructure in glioma. VERDICT-derived parameters were correlated with both histological features and tumour subtype and were also used to explore the peritumoural region.

METHODS

Fourteen consecutive treatment-naïve patients (43.5 years ± 15.1 years, six males, eight females) with suspected glioma underwent diffusion-weighted imaging including VERDICT modelling. Tumour cell radius and intracellular and combined extracellular/vascular volumes were estimated using a framework based on linearisation and convex optimisation. An experienced neuroradiologist outlined the peritumoural oedema, enhancing tumour and necrosis on T2-weighted imaging and contrast-enhanced T1-weighted imaging. The same regions of interest were applied to the co-registered VERDICT maps to calculate the microstructure parameters. Pathology sections were analysed with semi-automated software to measure cellularity and cell size.

RESULTS

VERDICT parameters were successfully calculated in all patients. The imaging-derived results showed a larger intracellular volume fraction in high-grade glioma compared to low-grade glioma (0.13 ± 0.07 vs. 0.08 ± 0.02, respectively; p = 0.05) and a trend towards a smaller extracellular/vascular volume fraction (0.88 ± 0.07 vs. 0.92 ± 0.04, respectively; p = 0.10). The conventional apparent diffusion coefficient was higher in low-grade gliomas compared to high-grade gliomas, but this difference was not statistically significant (1.22 ± 0.13 × 10-3 mm2/s vs. 0.98 ± 0.38 × 10-3 mm2/s, respectively; p = 0.18).

CONCLUSION

This feasibility study demonstrated that VERDICT MRI can be used to explore the tissue microstructure of glioma using an abbreviated protocol. The VERDICT parameters of tissue structure correlated with those derived on histology. The method shows promise as a potential test for diagnostic stratification and treatment response monitoring in the future.

KEY POINTS

• VERDICT MRI is an advanced diffusion technique which has been correlated with histopathological findings obtained at surgery from patients with glioma in this study. • The intracellular volume fraction measured with VERDICT was larger in high-grade tumours compared to that in low-grade tumours. • The results were complementary to measurements from conventional diffusion-weighted imaging, and the technique could be performed in a clinically feasible timescale.

Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma

OBJECTIVES

Integrating multiple imaging modalities is crucial for MRI data interpretation. The purpose of this study is to determine whether a previously proposed multi-view approach can effectively integrate the histogram features from multi-parametric MRI and whether the selected features can offer incremental prognostic values over clinical variables.

METHODS

Eighty newly-diagnosed glioblastoma patients underwent surgery and chemoradiotherapy. Histogram features of diffusion and perfusion imaging were extracted from contrast-enhancing (CE) and non-enhancing (NE) regions independently. An unsupervised patient clustering was performed by the multi-view approach. Kaplan-Meier and Cox proportional hazards regression analyses were performed to evaluate the relevance of patient clustering to survival. The metabolic signatures of patient clusters were compared using multi-voxel spectroscopy analysis. The prognostic values of histogram features were evaluated by survival and ROC curve analyses.

RESULTS

Two patient clusters were generated, consisting of 53 and 27 patients respectively. Cluster 2 demonstrated better overall survival (OS) (p = 0.007) and progression-free survival (PFS) (p < 0.001) than Cluster 1. Cluster 2 displayed lower N-acetylaspartate/creatine ratio in NE region (p = 0.040). A higher mean value of anisotropic diffusion in NE region was associated with worse OS (hazard ratio [HR] = 1.40, p = 0.020) and PFS (HR = 1.36, p = 0.031). The seven features selected by this approach showed significantly incremental value in predicting 12-month OS (p = 0.020) and PFS (p = 0.022).

CONCLUSIONS

The multi-view clustering method can provide an effective integration of multi-parametric MRI. The histogram features selected may be used as potential prognostic markers.

KEY POINTS

• Multi-parametric magnetic resonance imaging captures multi-faceted tumor physiology. • Contrast-enhancing and non-enhancing tumor regions represent different tumor components with distinct clinical relevance. • Multi-view data analysis offers a method which can effectively select and integrate multi-parametric and multi-regional imaging features.

Multiparametric and multiregional diffusion features help predict molecule information, grade and survival in lower-grade gliomas: a feasibility study

OBJECTIVE

This study was to investigate the relationship of diffusion features with molecule information, and then predict grade and survival in lower-grade gliomas.

METHODS

65 patients with primary lower-grade gliomas (WHO Grade II & III) who underwent conventional MRI and diffusion tensor imaging were retrospectively studied. The tumor region was automatically segmented into contrast-enhancing tumor, non-enhancing tumor, edematous and necrotic volumes. Diffusion features, including fractional anisotropy (FA), axial diffusivity, radial diffusivity and apparent diffusion coefficient (ADC), were extracted from each volume using histogram analysis. To estimate molecule biomarkers and predict clinical characteristics of grade and survival, support vector machine, generalized linear model, logistic regression and Cox regression were performed on the related features.

RESULTS

The diffusion features in non-enhancing tumor volume showed differences between isocitrate dehydrogenase mutant and wild-type gliomas. And the mean accuracy of support vector machine classifiers was 0.79. Ki-67 labeling index was correlated with these features, which were combined to significantly estimate Ki-67 expression level (r = 0.657, p < 0.001). These features also showed differences between Grade II and III gliomas. A combination of them for grade classification resulted in an area under the curve of 0.914 (0.857-0.971). Mean FA and fifth percentile of ADC were independently associated with overall survival, with lower FA and higher ADC showing better survival outcome.

CONCLUSION

In lower-grade gliomas, multiparametric and multiregional diffusion features could help predict molecule information, histological grade and survival.

ADVANCES IN KNOWLEDGE

The multi parametric diffusion features in non-enhancing tumor were associated with molecule information, grade and survival in lower-grade gliomas.

Overexpression of human MX2 gene suppresses cell proliferation, migration, and invasion via ERK/P38/NF-κB pathway in glioblastoma cells

In human, there are two myxovirus resistance genes-MX1 and MX2, which respectively encode MXA and MXB protein. For MXB, it was traditionally deemed to work in the progression of cell cycle and adjustment of nuclear import. Thus, we speculated that it might play important roles in tumor progression. The purpose of this study was to preliminarily explore the underlying functions and mechanism of the MX2 gene on glioblastoma multiforme. Quantitative reverse transcription polymerase chain reaction, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazoliumbromide (MTT), and transwell experiments were to detect the relative MX2 mRNA level and its biological functions on glioma cells, respectively. The data displayed that MX2 was obviously downregulated both in glioblastoma (GBM) and GBM cell lines, meanwhile, its overexpression could markedly reduce cell proliferation, migration, and invasion of glioma cells, implying that it was related with glioblastoma progression. In addition, the overall survival of patient with glioblastoma had a negative correlation with the MX2 expression. Then, Western blot indicated the potential mechanism of MX2 in glioblastoma. We found that MX2 overexpression could decrease the relative levels of phosphorylated-ERK1/2 (p-ERK1/2), p-p38, and nuclear factor-κB (NF-κB), while have no effects on extracellular signal-regulated kinase (ERK), p38, and lamin B1. Moreover, the influences of MX2 overexpression on cell proliferation, migration, and invasion could be weakened by the three inhibitors (PD98059, SB203580, and (pyridin-2-ylmethyl) dithiocarbamate [PDTC]). These results implied that MX2 might suppress the proliferation and metastasis of glioma cells by manipulating the ERK/P38/NF-κB signaling pathway. In conclusion, MX2 is potential to be a new marker used for glioblastoma prognosis or a new target for glioblastoma treatments.

Correlation of radiological and immunochemical parameters with clinical outcome in patients with recurrent glioblastoma treated with Bevacizumab

BACKGROUND

Some phase 2 trials had reported encouraging progression-free survival with Bevacizumab in monotherapy or combined with chemotherapy in glioblastoma. However, phase 3 trials showed a significant improvement in progression free survival without a benefit in overall survival. To date, there are no predictive biomarker of response for Bevacizumab in glioblastoma.

METHODS

We used Immunochemical analysis on tumor samples and pretreatment and post-treatment perfusion-MRI to try to identify possible predictive angiogenesis-related biomarkers of response and survival in patients with glioblastoma treated with bevacizumab in the first recurrence. We analyzed histological parameters: vascular proliferation, mitotic number and Ki-67 index; molecular factors: MGMT promoter methylation, EGFR amplification and EGFR variant III; immunohistochemical: MET, Midkine, HIF1, VEGFA, VEGF-R2, CD44, Olig2, microvascular area and microvascular density; and radiological: rCBV.

RESULTS

In the statistical analysis, no significant correlation of any histological, molecular, microvascular or radiological parameters could be demonstrated with the response rate, PFS or OS with bevacizumab treatment.

CONCLUSION

Unfortunately, in this histopathological, molecular, immunohistochemical and neuroradiological study we did not find any predictive biomarker of response or survival benefit for Bevacizumab in glioblastoma.

Diffusion kurtosis imaging histogram parameter metrics predicting survival in integrated molecular subtypes of diffuse glioma: An observational cohort study

PURPOSE

The aim of the study was to assess the predictive value of preoperatively assessed diffusion kurtosis imaging (DKI) metrics as prognostic factors in the 2016 World Health Organization Classification of Tumors of the Central Nervous System integrated glioma groups.

MATERIAL AND METHODS

Seventy-seven patients with histopathologically confirmed treatment-naïve glioma were retrospectively assessed between 08/2013 and 10/2017 using mean kurtosis (MK) and mean diffusivity (MD) histogram parameters from DKI, overall and progression-free survival, and relevant prognostic molecular data (isocitrate dehydrogenase, [IDH]; alpha-thalassemia/mental retardation syndrome X-linked, [ATRX]; chromosome 1p/19q loss of heterozygosity). Receiver operating characteristic (ROC) analysis was performed on metric variables to determine the optimal cutoff-values. The Kaplan-Meier method was used to assess univariate survival data. A multivariate Cox proportional hazards model was performed on significant results from the univariate analysis.

RESULTS

There were significant differences in overall and progression-free survival between patient age (p = 0.001), resection statuses (p = 0.002), WHO glioma grades (p < 0.0001), and integrated molecular profiles (p < 0.0001). Survival was significantly better in patients with lower MK and higher MD values globally (p = 0.009), in gliomas without chromosome 1p/19q LOH (p < 0.0001), and those with retained ATRX expression (p = 0.008).

CONCLUSIONS

Patient age and MK from DKI from DKI are relevant factors for preoperatively predicting overall and progression-free survival. Regarding the molecular subgroups, they seem to be predictive in gliomas with ATRX retention, representing a feature of IDH wild-type gliomas.

Radiomic MRI Phenotyping of Glioblastoma: Improving Survival Prediction

Purpose To investigate whether radiomic features at MRI improve survival prediction in patients with glioblastoma multiforme (GBM) when they are integrated with clinical and genetic profiles. Materials and Methods Data in patients with a diagnosis of GBM between December 2009 and January 2017 (217 patients) were retrospectively reviewed up to May 2017 and allocated to training and test sets (3:1 ratio). Radiomic features (n = 796) were extracted from multiparametric MRI. A random survival forest (RSF) model was trained with the radiomic features along with clinical and genetic profiles (O-6-methylguanine-DNA-methyltransferase promoter methylation and isocitrate dehydrogenase 1 mutation statuses) to predict overall survival (OS) and progression-free survival (PFS). The RSF models were validated on the test set. The incremental values of radiomic features were evaluated by using the integrated area under the receiver operating characteristic curve (iAUC). Results The 217 patients had a mean age of 57.9 years, and there were 87 female patients (age range, 22-81 years) and 130 male patients (age range, 17-85 years). The median OS and PFS of patients were 352 days (range, 20-1809 days) and 264 days (range, 21-1809 days), respectively. The RSF radiomics models were successfully validated on the test set (iAUC, 0.652 [95% confidence interval {CI}, 0.524, 0.769] and 0.590 [95% CI: 0.502, 0.689] for OS and PFS, respectively). The addition of a radiomics model to clinical and genetic profiles improved survival prediction when compared with models containing clinical and genetic profiles alone (P = .04 and .03 for OS and PFS, respectively). Conclusion Radiomic MRI phenotyping can improve survival prediction when integrated with clinical and genetic profiles and thus has potential as a practical imaging biomarker. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Jain and Lui in this issue.

Perfusion MRI grading diffuse gliomas: Impact of permeability parameters on molecular biomarkers and survival

BACKGROUND AND PURPOSE

Our objectives were: (1) compare dynamic susceptibility-weighted (DSC) and dynamic contrast-enhanced (DCE) permeability parameters, (2) evaluate diagnostic accuracy of DSC and DCE discriminating high- and low-grade tumors, (3) analyze relationship of permeability parameters with overall (OS) and progression-free survival (PFS) and (4) assess differences in high-grade tumors classified according to molecular biomarkers.

MATERIALS AND METHODS

49 patients with histologically proved diffuse gliomas underwent DSC and DCE imaging. Parametric maps of cerebral blood volume (CBV), CBV-leakage corrected, volume transfer coefficient (Ktrans), fractional volume of the extravascular extracellular space (EES) (Ve), fractional blood plasma volume (Vp) and rate constant between EES and blood plasma (Kep) were calculated. High-grade gliomas were also classified according to isocitrate dehydrogenase (IDH), alpha-thalassemia/mental retardation syndrome X-linked (ATRX) and O6-methylguanine-dna-methyltransferase promoter methylation (MGMT) status.

RESULTS

There is correlation between parameters leakage, Ktrans and Vp. ROC curve analysis showed significance in both Ktrans and Ve for glioma grading. Threshold value of 0.075 for Ve generated the best combination of sensitivity (80%) and specificity (75%) in tumor gradation. Leakage was the only permeability parameter related to OS (P=0.006) and PFS (0.012); with prolonged survival for leakage values lower than 1.2. IDH-mutated high-grade tumors showed lower leakage and Ktrans values. High-grade tumors with loss of ATRX presented lower leakage and Vp values.

CONCLUSIONS

Both DSC and DCE permeability parameters serve as non-invasive method for glioma grading. Leakage was the unique permeability parameter related to survival and the best discriminating high-grade gliomas classified according to IDH and ATRX status.

Prognostic Implications of Gadolinium Enhancement of Skull Base Chordomas

BACKGROUND AND PURPOSE

Skull base chordomas often demonstrate variable MR imaging characteristics, and there has been limited prior research investigating the potential clinical relevance of this variability. The purpose of this retrospective study was to assess the prognostic implications of signal intensity on standard imaging techniques for the biologic behavior of skull base chordomas.

MATERIALS AND METHODS

Medical records were retrospectively reviewed for 22 patients with pathologically confirmed skull base chordomas. Clinical data were recorded, including the degree of surgical resection, the presence or absence of radiation therapy, and time to progression/recurrence of the tumor or time without progression/recurrence of the tumor following initial treatment. Pretreatment imaging was reviewed for the presence or absence of enhancement and the T2 signal characteristics. Tumor-to-brain stem signal intensity ratios on T2, precontrast T1, and postcontrast T1 spin-echo sequences were also calculated. Statistical analysis was then performed to assess correlations between imaging characteristics and tumor progression/recurrence.

RESULTS

Progression/recurrence of skull base chordomas was seen following surgical resection in 11 of 14 (78.6%) patients with enhancing tumors and in zero of 8 patients with nonenhancing tumors. There was a statistically significant correlation between skull base chordoma enhancement and subsequent tumor progression/recurrence (P < .001), which remained significant after controlling for differences in treatment strategy (P < .001). There was also a correlation between postcontrast T1 signal intensity (as measured by postcontrast T1 tumor-to-brain stem signal intensity ratios) and recurrence/progression (P = .02). While T2 signal intensity was higher in patients without tumor progression (median tumor-to-brain stem signal intensity ratios on T2 = 2.27) than in those with progression (median tumor-to-brain stem signal intensity ratios on T2 = 1.78), this association was not significant (P = .12).

CONCLUSIONS

Enhancement of skull base chordomas is a risk factor for tumor progression/recurrence following surgical resection.

The Role of Advanced Brain Tumor Imaging in the Care of Patients with Central Nervous System Malignancies

OPINION STATEMENT

T1-weighted post-contrast and T2-weighted fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) constitute the gold standard for diagnosis and response assessment in neuro-oncologic patients but are limited in their ability to accurately reflect tumor biology and metabolism, particularly over the course of a patient's treatment. Advanced MR imaging methods are sensitized to different biophysical processes in tissue, including blood perfusion, tumor metabolism, and chemical composition of tissue, and provide more specific information on tissue physiology than standard MRI. This review provides an overview of the most common and emerging advanced imaging modalities in the field of brain tumor imaging and their applications in the care of neuro-oncologic patients.

Relationship of In Vivo MR Parameters to Histopathological and Molecular Characteristics of Newly Diagnosed, Nonenhancing Lower-Grade Gliomas

The goal of this research was to elucidate the relationship between WHO 2016 molecular classifications of newly diagnosed, nonenhancing lower grade gliomas (LrGG), tissue sample histopathology, and magnetic resonance (MR) parameters derived from diffusion, perfusion, and ¹H spectroscopic imaging from the tissue sample locations and the entire tumor. A total of 135 patients were scanned prior to initial surgery, with tumor cellularity scores obtained from 88 image-guided tissue samples. MR parameters were obtained from corresponding sample locations, and histograms of normalized MR parameters within the T2 fluid-attenuated inversion recovery lesion were analyzed in order to evaluate differences between subgroups. For tissue samples, higher tumor scores were related to increased normalized apparent diffusion coefficient (nADC), lower fractional anisotropy (nFA), lower cerebral blood volume (nCBV), higher choline (nCho), and lower N-acetylaspartate (nNAA). Within the T2 lesion, higher tumor grade was associated with higher nADC, lower nFA, and higher Cho to NAA index. Pathological analysis confirmed that diffusion and metabolic parameters increased and perfusion decreased with tumor cellularity. This information can be used to select targets for tissue sampling and to aid in making decisions about treating residual disease.

Radiomic MRI signature reveals three distinct subtypes of glioblastoma with different clinical and molecular characteristics, offering prognostic value beyond IDH1

The remarkable heterogeneity of glioblastoma, across patients and over time, is one of the main challenges in precision diagnostics and treatment planning. Non-invasive in vivo characterization of this heterogeneity using imaging could assist in understanding disease subtypes, as well as in risk-stratification and treatment planning of glioblastoma. The current study leveraged advanced imaging analytics and radiomic approaches applied to multi-parametric MRI of de novo glioblastoma patients (n = 208 discovery, n = 53 replication), and discovered three distinct and reproducible imaging subtypes of glioblastoma, with differential clinical outcome and underlying molecular characteristics, including isocitrate dehydrogenase-1 (IDH1), O⁶-methylguanine–DNA methyltransferase, epidermal growth factor receptor variant III (EGFRvIII), and transcriptomic subtype composition. The subtypes provided risk-stratification substantially beyond that provided by WHO classifications. Within IDH1-wildtype tumors, our subtypes revealed different survival (p < 0.001), thereby highlighting the synergistic consideration of molecular and imaging measures for prognostication. Moreover, the imaging characteristics suggest that subtype-specific treatment of peritumoral infiltrated brain tissue might be more effective than current uniform standard-of-care. Finally, our analysis found subtype-specific radiogenomic signatures of EGFRvIII-mutated tumors. The identified subtypes and their clinical and molecular correlates provide an in vivo portrait of phenotypic heterogeneity in glioblastoma, which points to the need for precision diagnostics and personalized treatment.

Correlation between conventional MR imaging combined with diffusion-weighted imaging and histopathologic findings in eyes primarily enucleated for advanced retinoblastoma: a retrospective study

OBJECTIVES

To determine the diagnostic accuracy of conventional MRI in detecting tumour invasion of advanced intraocular retinoblastoma and to correlate ADC values with high-risk prognostic parameters.

METHOD

The sensitivities, specificities, positive predictive values (PPV), negative predictive values (NPV) and accuracies of MRI in detecting tumour-extent parameters of 63 retinoblastomas were determined. Furthermore, ADC values were correlated with high-risk prognostic parameters.

RESULTS

MRI detected postlaminar optic nerve with a sensitivity of 73.3% (95% CI 44.9-92.2%) and a specificity of 89.6% (77.3-96.5%), while the specificity for choroidal invasion was only 31.8% (13.9-54.9%). Likewise, MRI failed to predicted early optic nerve invasion in terms of low sensitivity and PPV. In contrast, scleral and ciliary body invasion could be correctly excluded with high NPV. ADC values were significantly lower in patients with undifferentiated tumours, large tumour size, as with optic nerve and scleral invasion (all p < 0.05). However, no correlation was found between ADC values and the degree of choroidal or ciliary body infiltration. Additionally, ADC values were negatively correlated with Ki-67 index (r = -0.62, P = 0.002).

CONCLUSIONS

Conventional MRI has some limitations in reliably predicting microscopic infiltration, with the diagnostic efficiency showing room for improvement, whereas ADC values correlated well with certain high-risk prognostic parameters for retinoblastoma.

KEY POINTS

• Conventional MRI failed to predicted microscopic infiltration of the retinoblastoma. • Scleral and ciliary body invasion could be excluded with high NPV. • ADC values correlated well with some high-risk pathological prognostic parameters.

Quantitative multi-modal MR imaging as a non-invasive prognostic tool for patients with recurrent low-grade glioma

Low-grade gliomas can vary widely in disease course and therefore patient outcome. While current characterization relies on both histological and molecular analysis of tissue resected during surgery, there remains high variability within glioma subtypes in terms of response to treatment and outcome. In this study we hypothesized that parameters obtained from magnetic resonance data would be associated with progression-free survival for patients with recurrent low-grade glioma. The values considered were derived from the analysis of anatomic imaging, diffusion weighted imaging, and ¹H magnetic resonance spectroscopic imaging data. Metrics obtained from diffusion and spectroscopic imaging presented strong prognostic capability within the entire population as well as when restricted to astrocytomas, but demonstrated more limited efficacy in the oligodendrogliomas. The results indicate that multi-parametric imaging data may be applied as a non-invasive means of assessing prognosis and may contribute to developing personalized treatment plans for patients with recurrent low-grade glioma.

Advanced MRI assessment to predict benefit of anti-programmed cell death 1 protein immunotherapy response in patients with recurrent glioblastoma

INTRODUCTION

We describe the imaging findings encountered in GBM patients receiving immune checkpoint blockade and assess the potential of quantitative MRI biomarkers to differentiate patients who derive therapeutic benefit from those who do not.

METHODS

A retrospective analysis was performed on longitudinal MRIs obtained on recurrent GBM patients enrolled on clinical trials. Among 10 patients with analyzable data, bidirectional diameters were measured on contrast enhanced T1 (pGd-T1WI) and volumes of interest (VOI) representing measurable abnormality suggestive of tumor were selected on pGdT1WI (pGdT1 VOI), FLAIR-T2WI (FLAIR VOI), and ADC maps. Intermediate ADC (IADC) VOI represented voxels within the FLAIR VOI having ADC in the range of highly cellular tumor (0.7-1.1 × 10^-3 mm²/s) (IADC VOI). Therapeutic benefit was determined by tissue pathology and survival on trial. IADC VOI, pGdT1 VOI, FLAIR VOI, and RANO assessment results were correlated with patient benefit.

RESULTS

Five patients were deemed to have received therapeutic benefit and the other five patients did not. The average time on trial for the benefit group was 194 days, as compared to 81 days for the no benefit group. IADC VOI correlated well with the presence or absence of clinical benefit in 10 patients. Furthermore, pGd VOI, FLAIR VOI, and RANO assessment correlated less well with response.

CONCLUSION

MRI reveals an initial increase in volumes of abnormal tissue with contrast enhancement, edema, and intermediate ADC suggesting hypercellularity within the first 0-6 months of immunotherapy. Subsequent stabilization and improvement in IADC VOI appear to better predict ultimate therapeutic benefit from these agents than conventional imaging.

Fractional Anisotropy Correlates with Overall Survival in Glioblastoma

OBJECTIVE

Glioblastoma (GB) is an infiltrative disease that results in microstructural damage on a cellular level. Fractional anisotropy (FA) is an important estimate of diffusion tensor imaging (DTI) that can be used to assess microstructural integrity. The aim of this study was to examine the correlation between FA values and overall survival (OS) in patients with GB.

METHODS

This retrospective single-center study included 122 consecutive patients with GB (50 women; median age, 63 years) with preoperative MRI including fluid attenuated inversion recovery (FLAIR), contrast-enhanced T1-weighted sequences, and DTI. FA and apparent diffusion coefficient (ADC) values in contrast-enhancing lesions (FA-CEL, FA-ADC), nonenhancing lesions, and central tumor regions were correlated to histopathologic and clinical parameters. Univariate and multivariate survival analyses were performed.

RESULTS

Patients with low FA-CEL (median <0.31) showed significantly improved OS in univariate analysis (P = 0.028). FA-CEL also showed a positive correlation with Ki-67 proliferation index (P = 0.003). However, in a multivariate survival model, FA values could not be identified as independent prognostic parameters beside established factors such as age and Karnofsky performance scale score. FA values in nonenhancing lesions and central tumor regions and mean ADC values had no distinct influence on OS.

CONCLUSIONS

FA values can provide prognostic information regarding OS in patients with GB. There is a correlation between FA-CEL values and Ki-67 proliferation index, a marker for malignancy. Noninvasive identification of more aggressive GB growth patterns might be beneficial for preoperative risk evaluation and estimation of prognosis.

Automatic Analysis of Cellularity in Glioblastoma and Correlation with ADC Using Trajectory Analysis and Automatic Nuclei Counting

Objective

Several studies have analyzed a correlation between the apparent diffusion coefficient (ADC) derived from diffusion-weighted MRI and the tumor cellularity of corresponding histopathological specimens in brain tumors with inconclusive findings. Here, we compared a large dataset of ADC and cellularity values of stereotactic biopsies of glioblastoma patients using a new postprocessing approach including trajectory analysis and automatic nuclei counting.

Materials and Methods

Thirty-seven patients with newly diagnosed glioblastomas were enrolled in this study. ADC maps were acquired preoperatively at 3T and coregistered to the intraoperative MRI that contained the coordinates of the biopsy trajectory. 561 biopsy specimens were obtained; corresponding cellularity was calculated by semi-automatic nuclei counting and correlated to the respective preoperative ADC values along the stereotactic biopsy trajectory which included areas of T1-contrast-enhancement and necrosis.

Results

There was a weak to moderate inverse correlation between ADC and cellularity in glioblastomas that varied depending on the approach towards statistical analysis: for mean values per patient, Spearman’s ρ = -0.48 (p = 0.002), for all trajectory values in one joint analysis Spearman’s ρ = -0.32 (p < 0.001). The inverse correlation was additionally verified by a linear mixed model.

Conclusions

Our data confirms a previously reported inverse correlation between ADC and tumor cellularity. However, the correlation in the current article is weaker than the pooled correlation of comparable previous studies. Hence, besides cell density, other factors, such as necrosis and edema might influence ADC values in glioblastomas.

Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models

Purpose To evaluate whether radiomic feature-based magnetic resonance (MR) imaging signatures allow prediction of survival and stratification of patients with newly diagnosed glioblastoma with improved accuracy compared with that of established clinical and radiologic risk models. Materials and Methods Retrospective evaluation of data was approved by the local ethics committee and informed consent was waived. A total of 119 patients (allocated in a 2:1 ratio to a discovery [n = 79] or validation [n = 40] set) with newly diagnosed glioblastoma were subjected to radiomic feature extraction (12 190 features extracted, including first-order, volume, shape, and texture features) from the multiparametric (contrast material-enhanced T1-weighted and fluid-attenuated inversion-recovery imaging sequences) and multiregional (contrast-enhanced and unenhanced) tumor volumes. Radiomic features of patients in the discovery set were subjected to a supervised principal component (SPC) analysis to predict progression-free survival (PFS) and overall survival (OS) and were validated in the validation set. The performance of a Cox proportional hazards model with the SPC analysis predictor was assessed with C index and integrated Brier scores (IBS, lower scores indicating higher accuracy) and compared with Cox models based on clinical (age and Karnofsky performance score) and radiologic (Gaussian normalized relative cerebral blood volume and apparent diffusion coefficient) parameters. Results SPC analysis allowed stratification based on 11 features of patients in the discovery set into a low- or high-risk group for PFS (hazard ratio [HR], 2.43; P = .002) and OS (HR, 4.33; P < .001), and the results were validated successfully in the validation set for PFS (HR, 2.28; P = .032) and OS (HR, 3.45; P = .004). The performance of the SPC analysis (OS: IBS, 0.149; C index, 0.654; PFS: IBS, 0.138; C index, 0.611) was higher compared with that of the radiologic (OS: IBS, 0.175; C index, 0.603; PFS: IBS, 0.149; C index, 0.554) and clinical risk models (OS: IBS, 0.161, C index, 0.640; PFS: IBS, 0.139; C index, 0.599). The performance of the SPC analysis model was further improved when combined with clinical data (OS: IBS, 0.142; C index, 0.696; PFS: IBS, 0.132; C index, 0.637). Conclusion An 11-feature radiomic signature that allows prediction of survival and stratification of patients with newly diagnosed glioblastoma was identified, and improved performance compared with that of established clinical and radiologic risk models was demonstrated. (©) RSNA, 2016 Online supplemental material is available for this article.

Clinical parameters outweigh diffusion- and perfusion-derived MRI parameters in predicting survival in newly diagnosed glioblastoma

BACKGROUND

The purpose of this study was to determine the relevance of clinical data, apparent diffusion coefficient (ADC), and relative cerebral blood volume (rCBV) from dynamic susceptibility contrast (DSC) perfusion and the volume transfer constant (k^trans) from dynamic contrast-enhanced (DCE) perfusion for predicting overall survival (OS) and progression-free survival (PFS) in newly diagnosed treatment-naïve glioblastoma patients.

METHODS

Preoperative MR scans including standardized contrast-enhanced T1 (cT1), T2 - fluid-attenuated inversion recovery (FLAIR), ADC, DSC, and DCE of 125 patients with subsequent histopathologically confirmed glioblastoma were performed on a 3 Tesla MRI scanner. ADC, DSC, and DCE parameters were analyzed in semiautomatically segmented tumor volumes on contrast-enhanced (CE) cT1 and hyperintense signal changes on T2 FLAIR (ED). Univariate and multivariable Cox regression analyses including age, sex, extent of resection (EOR), and KPS were performed to assess the influence of each parameter on OS and PFS.

RESULTS

Univariate Cox regression analysis demonstrated a significant association of age, KPS, and EOR with PFS and age, KPS, EOR, lower ADC, and higher rCBV with OS. Multivariable analysis showed independent significance of male sex, KPS, EOR, and increased rCBV_CE for PFS, and age, sex, KPS, and EOR for OS.

CONCLUSIONS

MRI parameters help to predict OS in a univariate Cox regression analysis, and increased rCBV_CE is associated with shorter PFS in the multivariable model. In summary, however, our findings suggest that the relevance of MRI parameters is outperformed by clinical parameters in a multivariable analysis, which limits their prognostic value for survival prediction at the time of initial diagnosis.

Grading diffuse gliomas without intense contrast enhancement by amide proton transfer MR imaging: comparisons with diffusion- and perfusion-weighted imaging

OBJECTIVES

To investigate whether amide proton transfer (APT) MR imaging can differentiate high-grade gliomas (HGGs) from low-grade gliomas (LGGs) among gliomas without intense contrast enhancement (CE).

METHODS

This retrospective study evaluated 34 patients (22 males, 12 females; age 36.0 ± 11.3 years) including 20 with LGGs and 14 with HGGs, all scanned on a 3T MR scanner. Only tumours without intense CE were included. Two neuroradiologists independently performed histogram analyses to measure the 90th-percentile (APT₉₀) and mean (APT_mean) of the tumours' APT signals. The apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) were also measured. The parameters were compared between the groups with Student's t-test. Diagnostic performance was evaluated with receiver operating characteristic (ROC) analysis.

RESULTS

The APT₉₀ (2.80 ± 0.59 % in LGGs, 3.72 ± 0.89 in HGGs, P = 0.001) and APT_mean (1.87 ± 0.49 % in LGGs, 2.70 ± 0.58 in HGGs, P = 0.0001) were significantly larger in the HGGs compared to the LGGs. The ADC and rCBV values were not significantly different between the groups. Both the APT₉₀ and APT_mean showed medium diagnostic performance in this discrimination.

CONCLUSIONS

APT imaging is useful in discriminating HGGs from LGGs among diffuse gliomas without intense CE.

KEY POINTS

• Amide proton transfer (APT) imaging helps in grading non-enhancing gliomas • High-grade gliomas showed higher APT signal than low-grade gliomas • APT imaging showed better diagnostic performance than diffusion- and perfusion-weighted imaging.

Improved Brain Tumor Classification by Sodium MR Imaging: Prediction of IDH Mutation Status and Tumor Progression

BACKGROUND AND PURPOSE

MR imaging in neuro-oncology is challenging due to inherent ambiguities in proton signal behavior. Sodium-MR imaging may substantially contribute to the characterization of tumors because it reflects the functional status of the sodium-potassium pump and sodium channels.

MATERIALS AND METHODS

Sodium-MR imaging data of patients with treatment-naïve glioma WHO grades I-IV (n = 34; mean age, 51.29 ± 17.77 years) were acquired by using a 7T MR system. For acquisition of sodium-MR images, we applied density-adapted 3D radial projection reconstruction pulse sequences. Proton-MR imaging data were acquired by using a 3T whole-body system.

RESULTS

We demonstrated that the initial sodium signal of a treatment-naïve brain tumor is a significant predictor of isocitrate dehydrogenase (IDH) mutation status (P < .001). Moreover, independent of this correlation, the Cox proportional hazards model confirmed the sodium signal of treatment-naïve brain tumors as a predictor of progression (P = .003). Compared with the molecular signature of IDH mutation status, information criteria of model comparison revealed that the sodium signal is even superior to IDH in progression prediction. In addition, sodium-MR imaging provides a new approach to noninvasive tumor classification. The sodium signal of contrast-enhancing tumor portions facilitates differentiation among most glioma types (P < .001).

CONCLUSIONS

The information of sodium-MR imaging may help to classify neoplasias at an early stage, to reduce invasive tissue characterization such as stereotactic biopsy specimens, and overall to promote improved and individualized patient management in neuro-oncology by novel imaging signatures of brain tumors.

Differentiation of high-grade and low-grade diffuse gliomas by intravoxel incoherent motion MR imaging

BACKGROUND

Our aim was to assess the diagnostic performance of intravoxel incoherent motion (IVIM) MR imaging for differentiating high-grade gliomas (HGGs) from low-grade gliomas (LGGs).

METHODS

Forty-five patients with diffuse glioma (age 50.9 ± 20.4 y; 26 males, 19 females) were assessed with IVIM imaging using 13 b-values (0-1000 s/mm(2)) at 3T. The perfusion fraction (f), true diffusion coefficient (D), and pseudo-diffusion coefficient (D*) were calculated by fitting the bi-exponential model. The apparent diffusion coefficient (ADC) was obtained with 2 b-values (0 and 1000 s/mm(2)). Relative cerebral blood volume was measured by the dynamic susceptibility contrast method. Two observers independently measured D, ADC, D*, and f, and these measurements were compared between the LGG group (n = 16) and the HGG group (n = 29).

RESULTS

Both D (1.26 ± 0.37 mm(2)/s in LGG, 0.94 ± 0.19 mm(2)/s in HGG; P < .001) and ADC (1.28 ± 0.35 mm(2)/s in LGG, 1.03 ± 0.19 mm(2)/s in HGG; P < .01) were lower in the HGG group. D was lower than ADC in the LGG (P < .05) and HGG groups (P < .0001). D* was not different between the groups. The f-values were significantly larger in HGG (17.5 ± 6.3%) than in LGG (5.8 ± 3.8%; P < .0001) and correlated with relative cerebral blood volume (r = 0.85; P < .0001). Receiver operating characteristic analyses showed areas under curve of 0.95 with f, 0.78 with D, 0.73 with ADC, and 0.60 with D*.

CONCLUSION

IVIM imaging is useful in differentiating HGGs from LGGs.

Modern brain tumor imaging

The imaging and clinical management of patients with brain tumor continue to evolve over time and now heavily rely on physiologic imaging in addition to high-resolution structural imaging. Imaging remains a powerful noninvasive tool to positively impact the management of patients with brain tumor. This article provides an overview of the current state-of-the art clinical brain tumor imaging. In this review, we discuss general magnetic resonance (MR) imaging methods and their application to the diagnosis of, treatment planning and navigation, and disease monitoring in patients with brain tumor. We review the strengths, limitations, and pitfalls of structural imaging, diffusion-weighted imaging techniques, MR spectroscopy, perfusion imaging, positron emission tomography/MR, and functional imaging. Overall this review provides a basis for understudying the role of modern imaging in the care of brain tumor patients.