Diffusion tensor invasive phenotypes can predict progression-free survival in glioblastomas

Diffusion tensor imaging in detecting gliomas sub-regions of infiltration, local and remote recurrences: a systematic review

Given that glioma cells tend to infiltrate and migrate along WM tracts, leading to demyelination and axonal injuries, Diffusion Tensor Imaging (DTI) emerged as a promising tool for identifying major "high-risk areas" of recurrence within the peritumoral brain zone (PBZ) or at a distance throughout the adjacents white matter tracts. Of our systematic review is to answer the following research question: In patients with brain tumor, is DTI able to recognizes within the peri-tumoral brain zone (PBZ) areas more prone to local (near the surgical cavity) or remote recurrence compared to the conventional imaging techniques?. We conducted a comprehensive literature search to identify relevant studies in line with the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) guidelines. 15 papers were deemed compatible with our research question and included. To enhance the paper's readability, we have categorized our findings into two distinct groups: the first delves into the role of DTI in detecting PBZ sub-regions of infiltration and local recurrences (n = 8), while the second group explores the feasibility of DTI in detecting white matter tract infiltration and remote recurrences (n = 7). DTI values and, within a broader framework, radiomics investigations can provide precise, voxel-by-voxel insights into the state of PBZ and recurrences. Better defining the regions at risk for potential recurrence within the PBZ and along WM bundles will allow targeted therapy.

Revealing the biology behind MRI signatures in high grade glioma

Magnetic resonance imaging (MRI) measurements are routinely collected during the treatment of high-grade gliomas (HGGs) to characterize tumor boundaries and guide surgical tumor resection. Using spatially matched MRI and transcriptomics we discovered HGG tumor biology captured by MRI measurements. We strategically overlaid the spatially matched omics characterizations onto a pre-existing transcriptional map of glioblastoma multiforme (GBM) to enhance the robustness of our analyses. We discovered that T1+C measurements, designed to capture vasculature and blood brain barrier (BBB) breakdown and subsequent contrast extravasation, also indirectly reveal immune cell infiltration. The disruption of the vasculature and BBB within the tumor creates a permissive infiltrative environment that enables the transmigration of anti-inflammatory macrophages into tumors. These relationships were validated through histology and enrichment of genes associated with immune cell transmigration and proliferation. Additionally, T2-weighted (T2W) and mean diffusivity (MD) measurements were associated with angiogenesis and validated using histology and enrichment of genes involved in neovascularization. Furthermore, we establish an unbiased approach for identifying additional linkages between MRI measurements and tumor biology in future studies, particularly with the integration of novel MRI techniques. Lastly, we illustrated how noninvasive MRI can be used to map HGG biology spatially across a tumor, and this provides a platform to develop diagnostics, prognostics, or treatment efficacy biomarkers to improve patient outcomes.

Association of MRI Volume Parameters in Predicting Patient Outcome at Time of Initial Diagnosis of Glioblastoma

PURPOSE

Patients with glioblastoma (GBM) may demonstrate varying patterns of infiltration and relapse. Improving the ability to predict these patterns may influence the management strategies at the time of initial diagnosis. This study aims to examine the impact of the ratio (T2/T1) of the non-enhancing volume in T2-weighted images (T2) to the enhancing volume in MRI T1-weighted gadolinium-enhanced images (T1gad) on patient outcome.

METHODS AND MATERIALS

A retrospective audit was performed from established prospective databases in patients managed consecutively with radiation therapy (RT) for GBM between 2016 and 2019. Patient, tumour and treatment-related factors were assessed in relation to outcome. Volumetric data from the initial diagnostic MRI were obtained via the manual segmentation of the T1gd and T2 abnormalities. A T2/T1 ratio was calculated from these volumes. The initial relapse site was assessed on MRI in relation to the site of the original T1gad volume and surgical cavity. The major endpoints were median relapse-free survival (RFS) from the date of diagnosis and site of initial relapse (defined as either local at the initial surgical site or any distance more than 20 mm from initial T1gad abnormality). The analysis was performed for association between known prognostic factors as well as the radiological factors using log-rank tests for subgroup comparisons, with correction for multiple comparisons.

RESULTS

One hundred and seventy-seven patients with GBM were managed consecutively with RT between 2016 and 2019 and were eligible for the analysis. The median age was 62 years. Seventy-four percent were managed under a 60Gy (Stupp) protocol, whilst 26% were on a 40Gy (Elderly) protocol. Major neuroanatomical subsites were Lateral Temporal (18%), Anterior Temporal (13%) and Medial Frontal (10%). Median volumes on T1gd and T2 were 20 cm3 (q1-3:8-43) and 37 cm3 (q1-3: 17-70), respectively. The median T2/T1 ratio was 2.1. For the whole cohort, the median OS was 16.0 months (95%CI:14.1-18.0). One hundred and forty-eight patients have relapsed with a median RFS of 11.4 months (95%CI:10.4-12.5). A component of distant relapse was evident in 43.9% of relapses, with 23.6% isolated relapse. Better ECOG performance Status (p = 0.007), greater extent of resection (p = 0.020), MGMT methylation (p < 0.001) and RT60Gy Dose (p = 0.050) were associated with improved RFS. Although the continuous variable of initial T1gd volume (p = 0.39) and T2 volume (p = 0.23) were not associated with RFS, the lowest T2/T1 quartile (reflecting a relatively lower T2 volume compared to T1gd volume) was significantly associated with improved RFS (p = 0.016) compared with the highest quartile. The lowest T2/T1 ratio quartile was also associated with a lower risk of distant relapse (p = 0.031).

CONCLUSION

In patients diagnosed with GBM, the volumetric parameters of the diagnostic MRI with a ratio of T2 and T1gad abnormality may assist in the prediction of relapse-free survival and patterns of relapse. A further understanding of these relationships has the potential to impact the design of future radiation therapy target volume delineation protocols.

Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures

Sampling restrictions have hindered the comprehensive study of invasive non-enhancing (NE) high-grade glioma (HGG) cell populations driving tumor progression. Here, we present an integrated multi-omic analysis of spatially matched molecular and multi-parametric magnetic resonance imaging (MRI) profiling across 313 multi-regional tumor biopsies, including 111 from the NE, across 68 HGG patients. Whole exome and RNA sequencing uncover unique genomic alterations to unresectable invasive NE tumor, including subclonal events, which inform genomic models predictive of geographic evolution. Infiltrative NE tumor is alternatively enriched with tumor cells exhibiting neuronal or glycolytic/plurimetabolic cellular states, two principal transcriptomic pathway-based glioma subtypes, which respectively demonstrate abundant private mutations or enrichment in immune cell signatures. These NE phenotypes are non-invasively identified through normalized K2 imaging signatures, which discern cell size heterogeneity on dynamic susceptibility contrast (DSC)-MRI. NE tumor populations predicted to display increased cellular proliferation by mean diffusivity (MD) MRI metrics are uniquely associated with EGFR amplification and CDKN2A homozygous deletion. The biophysical mapping of infiltrative HGG potentially enables the clinical recognition of tumor subpopulations with aggressive molecular signatures driving tumor progression, thereby informing precision medicine targeting.

Preoperative Diagnosis and Molecular Characterization of Gliomas With Liquid Biopsy and Radiogenomics

Gliomas are a heterogenous group of central nervous system tumors with different outcomes and different therapeutic needs. Glioblastoma, the most common subtype in adults, has a very poor prognosis and disabling consequences. The World Health Organization (WHO) classification specifies that the typing and grading of gliomas should include molecular markers. The molecular characterization of gliomas has implications for prognosis, treatment planning, and prediction of treatment response. At present, gliomas are diagnosed via tumor resection or biopsy, which are always invasive and frequently risky methods. In recent years, however, substantial advances have been made in developing different methods for the molecular characterization of tumors through the analysis of products shed in body fluids. Known as liquid biopsies, these analyses can potentially provide diagnostic and prognostic information, guidance on choice of treatment, and real-time information on tumor status. In addition, magnetic resonance imaging (MRI) is another good source of tumor data; radiomics and radiogenomics can link the imaging phenotypes to gene expression patterns and provide insights to tumor biology and underlying molecular signatures. Machine and deep learning and computational techniques can also use quantitative imaging features to non-invasively detect genetic mutations. The key molecular information obtained with liquid biopsies and radiogenomics can be useful not only in the diagnosis of gliomas but can also help predict response to specific treatments and provide guidelines for personalized medicine. In this article, we review the available data on the molecular characterization of gliomas using the non-invasive methods of liquid biopsy and MRI and suggest that these tools could be used in the future for the preoperative diagnosis of gliomas.

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.

Motor Functional Reorganization Is Triggered by Tumor Infiltration Into the Primary Motor Area and Repeated Surgery

In patients with gliomas, motor deficits are not always observed, even though tumor cells infiltrate into the motor area. Currently, it is recognized that this phenomenon can occur through the neuroplasticity potential. The aim of this study is to investigate the characteristics of motor functional reorganization in gliomas. Out of 100 consecutive patients who underwent awake surgery, 29 patients were assessed as regards their motor function and were retrospectively explored to determine whether positive motor responses were elicited. A total of 73 positive mapping sites from 27 cases were identified, and their spatial anatomical locations and activated region by functional MRI were analyzed. Additionally, the factors promoting neuroplasticity were analyzed through multiple logistic regression analysis. As a result, a total of 60 points (21 cases) were found in place, while 13 points (17.8%) were found to be shifted from anatomical localization. Reorganizations were classified into three categories: Type 1 (move to ipsilateral different gyrus) was detected at nine points (four cases), and they moved into the postcentral gyrus. Type 2 (move within the ipsilateral precentral gyrus) was detected at four points (two cases). Unknown type (two cases) was categorized as those whose motor functional cortex was moved to other regions, although we could not find the compensated motor area. Two factors for the onset of reorganization were identified: tumor cells infiltrate into the primary motor area and repeated surgery (p < 0.0001 and p = 0.0070, respectively). Our study demonstrated that compensation can occur mainly in two ways, and it promoted repeated surgery and infiltration of tumor into the primary motor area.

Multi-scale segmentation in GBM treatment using diffusion tensor imaging

Glioblastoma (GBM) is the commonest primary malignant brain tumor in adults, and despite advances in multi-modality therapy, the outlook for patients has changed little in the last 10 years. Local recurrence is the predominant pattern of treatment failure, hence improved local therapies (surgery and radiotherapy) are needed to improve patient outcomes. Currently segmentation of GBM for surgery or radiotherapy (RT) planning is labor intensive, especially for high-dimensional MR imaging methods that may provide more sensitive indicators of tumor phenotype. Automating processing and segmentation of these images will aid treatment planning. Diffusion tensor magnetic resonance imaging is a recently developed technique (DTI) that is exquisitely sensitive to the ordered diffusion of water in white matter tracts. Our group has shown that decomposition of the tensor information into the isotropic component (p - shown to represent tumor invasion) and the anisotropic component (q - shown to represent the tumor bulk) can provide valuable prognostic information regarding tumor infiltration and patient survival. However, tensor decomposition of DTI data is not commonly used for neurosurgery or radiotherapy treatment planning due to difficulties in segmenting the resultant image maps. For this reason, automated techniques for segmentation of tensor decomposition maps would have significant clinical utility. In this paper, we modified a well-established convolutional neural network architecture (CNN) for medical image segmentation and used it as an automatic multi-sequence GBM segmentation based on both DTI image maps (p and q maps) and conventional MRI sequences (T2-FLAIR and T1 weighted post contrast (T1c)). In this proof-of-concept work, we have used multiple MRI sequences, each with individually defined ground truths for better understanding of the contribution of each image sequence to the segmentation performance. The high accuracy and efficiency of our proposed model demonstrates the potential of utilizing diffusion MR images for target definition in precision radiation treatment planning and surgery in routine clinical practice.

Applications of radiomics and machine learning for radiotherapy of malignant brain tumors

Background

Magnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use of radiotherapy in patients with malignant brain tumors.

Methods

This study is based on comprehensive literature research on machine learning and radiomics analyses in neuroimaging and their potential application for radiotherapy in patients with malignant glioma or brain metastases.

Results

Feature-based radiomics and deep learning-based machine learning methods can be used to improve brain tumor diagnostics and automate various steps of radiotherapy planning. In glioma patients, important applications are the determination of WHO grade and molecular markers for integrated diagnosis in patients not eligible for biopsy or resection, automatic image segmentation for target volume planning, prediction of the location of tumor recurrence, and differentiation of pseudoprogression from actual tumor progression. In patients with brain metastases, radiomics is applied for additional detection of smaller brain metastases, accurate segmentation of multiple larger metastases, prediction of local response after radiosurgery, and differentiation of radiation injury from local brain metastasis relapse. Importantly, high diagnostic accuracies of 80–90% can be achieved by most approaches, despite a large variety in terms of applied imaging techniques and computational methods.

Conclusion

Clinical application of automated image analyses based on radiomics and artificial intelligence has a great potential for improving radiotherapy in patients with malignant brain tumors. However, a common problem associated with these techniques is the large variability and the lack of standardization of the methods applied.

Characterizing tumor invasiveness of glioblastoma using multiparametric magnetic resonance imaging

OBJECTIVE

The objective of this study was to characterize the abnormalities revealed by diffusion tensor imaging (DTI) using MR spectroscopy (MRS) and perfusion imaging, and to evaluate the prognostic value of a proposed quantitative measure of tumor invasiveness by combining contrast-enhancing (CE) and DTI abnormalities in patients with glioblastoma.

METHODS

Eighty-four patients with glioblastoma were recruited preoperatively. DTI was decomposed into isotropic (p) and anisotropic (q) components. The relative cerebral blood volume (rCBV) was calculated from the dynamic susceptibility contrast imaging. Values of N-acetylaspartate, myoinositol, choline (Cho), lactate (Lac), and glutamate + glutamine (Glx) were measured from multivoxel MRS and normalized as ratios to creatine (Cr). Tumor regions of interest (ROIs) were manually segmented from the CE T1-weighted (CE-ROI) and DTI-q (q-ROI) maps. Perfusion and metabolic characteristics of these ROIs were measured and compared. The relative invasiveness coefficient (RIC) was calculated as a ratio of the characteristic radii of CE-ROI and q-ROI. The prognostic significance of RIC was tested using Kaplan-Meier and multivariate Cox regression analyses.

RESULTS

The Cho/Cr, Lac/Cr, and Glx/Cr in q-ROI were significantly higher than CE-ROI (p = 0.004, p = 0.005, and p = 0.007, respectively). CE-ROI had significantly higher rCBV values than q-ROI (p < 0.001). A higher RIC was associated with worse survival in a multivariate overall survival (OS) model (hazard ratio [HR] 1.40, 95% confidence interval [CI] 1.06-1.85, p = 0.016) and progression-free survival (PFS) model (HR 1.55, 95% CI 1.16-2.07, p = 0.003). An RIC cutoff value of 0.89 significantly predicted shorter OS (median 384 vs 605 days, p = 0.002) and PFS (median 244 vs 406 days, p = 0.001).

CONCLUSIONS

DTI-q abnormalities displayed higher tumor load and hypoxic signatures compared with CE abnormalities, whereas CE regions potentially represented the tumor proliferation edge. Integrating the extents of invasion visualized by DTI-q and CE images into clinical practice may lead to improved treatment efficacy.

Semi-automated construction of patient individualised clinical target volumes for radiotherapy treatment of glioblastoma utilising diffusion tensor decomposition maps

OBJECTIVES

Glioblastoma multiforme (GBM) is a highly infiltrative primary brain tumour with an aggressive clinical course. Diffusion tensor imaging (DT-MRI or DTI) is a recently developed technique capable of visualising subclinical tumour spread into adjacent brain tissue. Tensor decomposition through p and q maps can be used for planning of treatment. Our objective was to develop a tool to automate the segmentation of DTI decomposed p and q maps in GBM patients in order to inform construction of radiotherapy target volumes.

METHODS

Chan-Vese level set model is applied to segment the p map using the q map as its initial starting point. The reason of choosing this model is because of the robustness of this model on either conventional MRI or only DTI. The method was applied on a data set consisting of 50 patients having their gross tumour volume delineated on their q map and Chan-Vese level set model uses these superimposed masks to incorporate the infiltrative edges.

RESULTS

The expansion of tumour boundary from q map to p map is clearly visible in all cases and the Dice coefficient (DC) showed a mean similarity of 74% across all 50 patients between the manually segmented ground truth p map and the level set automatic segmentation.

CONCLUSION

Automated segmentation of the tumour infiltration boundary using DTI and tensor decomposition is possible using Chan-Vese level set methods to expand q map to p map. We have provided initial validation of this technique against manual contours performed by experienced clinicians.

ADVANCES IN KNOWLEDGE

This novel automated technique to generate p maps has the potential to individualise radiation treatment volumes and act as a decision support tool for the treating oncologist.

Clinical Significance of Fractional Anisotropy Measured in Peritumoral Edema as a Biomarker of Overall Survival in Glioblastoma: Evidence Using Correspondence Analysis

Introduction

Fractional anisotropy (FA), a diffusion tensor image (DTI) derived biomarker is related to invasion, infiltration, and extension of glioblastoma (GB). We aimed to evaluate FA values and their association with intervals of overall survival (OS).

Materials and Methods

Retrospective study conducted in 36 patients with GB included 23 (63.9%) males, 46 ± 14 y; and 13 (36.1%) females, 53 ± 13; followed up for 36 months. We measured FA at edema, enhancing rim, and necrosis. We created two categorical variables using levels of FA and intervals of OS to evaluate their relationships. Kaplan-Meier method and correspondence analysis evaluated the association between OS (grouped in 7 six-month intervals) and FA measurements.

Results

Median FA values were higher in healthy brain regions (0.351), followed by peritumoral edema (0.190), enhancing ring (0.116), and necrosis (0.071). Pair-wise comparisons among tumor regions showed a significant difference, P < 0.001. The median OS for all patients was 19.3 months; variations in the OS curves among subgroups was significant χ² (3) = 8.48, P = 0.037. Correspondence analysis showed a significant association between FA values in the edema region and the survival intervals χ² (18) = 30.996, P = 0.029.

Conclusions

Alternative multivariate assessment using correspondence analysis might supplement the traditional survival analysis in patients with GB. A close follow-up of the variability of FA in the peritumoral edema region is predictive of the OS within specific six-month interval subgroup. Further studies should focus on predictive models combining surgical and DTI biomarkers.

Intratumoral Heterogeneity of Glioblastoma Infiltration Revealed by Joint Histogram Analysis of Diffusion Tensor Imaging

BACKGROUND

Glioblastoma is a heterogeneous disease characterized by its infiltrative growth, rendering complete resection impossible. Diffusion tensor imaging (DTI) shows potential in detecting tumor infiltration by reflecting microstructure disruption.

OBJECTIVE

To explore the heterogeneity of glioblastoma infiltration using joint histogram analysis of DTI, to investigate the incremental prognostic value of infiltrative patterns over clinical factors, and to identify specific subregions for targeted therapy.

METHODS

A total of 115 primary glioblastoma patients were prospectively recruited for surgery and preoperative magnetic resonance imaging. The joint histograms of decomposed anisotropic and isotropic components of DTI were constructed in both contrast-enhancing and nonenhancing tumor regions. Patient survival was analyzed with joint histogram features and relevant clinical factors. The incremental prognostic values of histogram features were assessed using receiver operating characteristic curve analysis. The correlation between the proportion of diffusion patterns and tumor progression rate was tested using Pearson correlation.

RESULTS

We found that joint histogram features were associated with patient survival and improved survival model performance. Specifically, the proportion of nonenhancing tumor subregion with decreased isotropic diffusion and increased anisotropic diffusion was correlated with tumor progression rate (P = .010, r = 0.35), affected progression-free survival (hazard ratio = 1.08, P < .001), and overall survival (hazard ratio = 1.36, P < .001) in multivariate models.

CONCLUSION

Joint histogram features of DTI showed incremental prognostic values over clinical factors for glioblastoma patients. The nonenhancing tumor subregion with decreased isotropic diffusion and increased anisotropic diffusion may indicate a more infiltrative habitat and potential treatment target.

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.

Radiomics in peritumoral non-enhancing regions: fractional anisotropy and cerebral blood volume improve prediction of local progression and overall survival in patients with glioblastoma

PURPOSE

The peritumoral non-enhancing region (NER) is frequently not removed during the surgical resection of glioblastoma, with most recurrences occurring within the original treatment field. This study determined whether radiomics analysis of the NER can predict local recurrence and overall survival in patients with glioblastoma.

METHODS

Preoperative magnetic resonance imaging (MRI) scans from 83 consecutive patients with glioblastoma were retrospectively reviewed and grouped into training (n = 59) and test sets (n = 24). A total of 6472 radiomic features were extracted from contrast-enhanced T1-weighted and fluid-attenuated inversion recovery images and from fractional anisotropy (FA) and normalized cerebral blood volume (CBV) maps. A diagnostic model to predict 6-month progression was tested using the area under the receiver operating characteristics curve (AUC) and compared with the single parameters of FA and CBV. A survival model was tested using Harrell's C-index and compared with clinical models that included age, sex, Karnofsky performance score, and extent of surgical resection.

RESULTS

Four FA features and six CBV features were selected for the diagnostic model; no features were extracted from conventional MRI. Combined FA and CBV radiomics showed better predictive value for local progression (AUC, 0.79; 95% CI, 0.67-0.90) than single imaging radiomics (AUC, 0.70-0.76) or single imaging parameters (AUC, 0.51-0.54). The combined model (C-index, 0.87) improved prognostication when added to clinical models (C-index, 0.72).

CONCLUSION

Radiomics features using FA and CBV in the NER have the potential to improve prediction of local progression and overall survival in patients with glioblastoma.

Multimodal MRI characteristics of the glioblastoma infiltration beyond contrast enhancement

Our inability to identify the invasive margin of glioblastomas hampers attempts to achieve local control. Diffusion tensor imaging (DTI) has been implemented clinically to delineate the margin of the tumor infiltration, its derived anisotropic (q) values can extend beyond the contrast-enhanced area and correlates closely with the tumor. However, its correlation with tumor infiltration shown on multivoxel proton magnetic resonance spectroscopy¹ (MRS) and perfusion magnetic resonance imaging (MRI) should be investigated. In this study, we aimed to show tissue characteristics of the q-defined peritumoral invasion on MRS and perfusion MRI. Patients with a primary glioblastoma were included (n = 51). Four regions of interest were analyzed; the contrast-enhanced lesion, peritumoral abnormal q region, peritumoral normal q region, and contralateral normal-appearing white matter. MRS, including choline (Cho)/creatinine (Cr), Cho/N-acetyl-aspartate (NAA) and NAA/Cr ratios, and the relative cerebral blood volume (rCBV) were analyzed. Our results showed an increase in the Cho/NAA (p = 0.0346) and Cho/Cr (p = 0.0219) ratios in the peritumoral abnormal q region, suggestive of tumor invasion. The rCBV was marginally elevated (p = 0.0798). Furthermore, the size of the abnormal q regions was correlated with survival; patients with larger abnormal q regions showed better progression-free survival (median 287 versus 53 days, p = 0.001) and overall survival (median 464 versus 274 days, p = 0.006) than those with smaller peritumoral abnormal q regions of interest. These results support how the DTI q abnormal area identifies tumor activity beyond the contrast-enhanced area, especially correlating with MRS.

Accurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer Learning

BACKGROUND AND PURPOSE

MR imaging-based modeling of tumor cell density can substantially improve targeted treatment of glioblastoma. Unfortunately, interpatient variability limits the predictive ability of many modeling approaches. We present a transfer learning method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient's own histologic data.

MATERIALS AND METHODS

We recruited patients with primary glioblastoma undergoing image-guided biopsies and preoperative imaging, including contrast-enhanced MR imaging, dynamic susceptibility contrast MR imaging, and diffusion tensor imaging. We calculated relative cerebral blood volume from DSC-MR imaging and mean diffusivity and fractional anisotropy from DTI. Following image coregistration, we assessed tumor cell density for each biopsy and identified corresponding localized MR imaging measurements. We then explored a range of univariate and multivariate predictive models of tumor cell density based on MR imaging measurements in a generalized one-model-fits-all approach. We then implemented both univariate and multivariate individualized transfer learning predictive models, which harness the available population-level data but allow individual variability in their predictions. Finally, we compared Pearson correlation coefficients and mean absolute error between the individualized transfer learning and generalized one-model-fits-all models.

RESULTS

Tumor cell density significantly correlated with relative CBV (r = 0.33, P < .001), and T1-weighted postcontrast (r = 0.36, P < .001) on univariate analysis after correcting for multiple comparisons. With single-variable modeling (using relative CBV), transfer learning increased predictive performance (r = 0.53, mean absolute error = 15.19%) compared with one-model-fits-all (r = 0.27, mean absolute error = 17.79%). With multivariate modeling, transfer learning further improved performance (r = 0.88, mean absolute error = 5.66%) compared with one-model-fits-all (r = 0.39, mean absolute error = 16.55%).

CONCLUSIONS

Transfer learning significantly improves predictive modeling performance for quantifying tumor cell density in glioblastoma.

Detection of occult neoplastic infiltration in the corpus callosum and prediction of overall survival in patients with glioblastoma using diffusion tensor imaging

OBJECTIVE

Corpus callosum (CC) involvement is a poor prognostic factor in patients with glioblastoma (GBM). The purpose of this study was to determine whether diffusion tensor imaging (DTI) can quantify occult tumor infiltration in the CC and predict the overall survival in GBM patients.

METHODS

Forty-eight patients with pathologically proven GBM and 17 normal subjects were included in this retrospective study. Patients were divided into four groups based on CC invasion and overall survival: long survivors without CC invasion; short survivors without CC invasion; long survivors with CC invasion; short survivors with CC invasion. All patients underwent DTI at 3T MRI scanner. Fractional anisotropy (FA) and mean diffusivity (MD) values were measured from genu, mid-body, and splenium of the CC. The mean values of these parameters were compared between different groups and Kaplan Meier curves were used for prediction of overall survival.

RESULTS

Patients with short survival and CC invasion had the lowest FA values (0.64 ± 0.05) from the CC compared with other groups (p < 0.05). Receiver operator characteristic curve (ROC) analysis indicated that a FA cutoff value of 0.70 was the best predictor for overall survival with an area under the curve (AUC) of 0.77, sensitivity 1, specificity 0.59. Kaplan-Meier survival curves demonstrated that the mean survival time was significantly longer for patients with high FA (>0.70) compared with those with low FA (<0.70) (p < 0.001).

CONCLUSIONS

FA values from the CC can quantify occult tumor infiltration and serve as a sensitive prognostic marker for prediction of overall survival in GBM patients.

Conventional and advanced magnetic resonance imaging in patients with high-grade glioma

Magnetic resonance imaging is integral to the care of patients with high-grade gliomas. Anatomic detail can be acquired with conventional structural imaging, but newer approaches also add capabilities to interrogate image-derived physiologic and molecular characteristics of central nervous system neoplasms. These advanced imaging techniques are increasingly employed to generate biomarkers that better reflect tumor burden and therapy response. The following is an overview of current strategies based on advanced magnetic resonance imaging that are used in the assessment of high-grade glioma patients with an emphasis on how novel imaging biomarkers can potentially advance patient care.

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.

Less Invasive Phenotype Found in Isocitrate Dehydrogenase-mutated Glioblastomas than in Isocitrate Dehydrogenase Wild-Type Glioblastomas: A Diffusion-Tensor Imaging Study

Purpose To explore the diffusion-tensor (DT) imaging-defined invasive phenotypes of both isocitrate dehydrogenase (IDH-1)-mutated and IDH-1 wild-type glioblastomas. Materials and Methods Seventy patients with glioblastoma were prospectively recruited and imaged preoperatively. All patients provided signed consent, and the local research ethics committee approved the study. Patients underwent surgical resection, and tumor samples underwent immunohistochemistry for IDH-1 R132H mutations. DT imaging data were coregistered to the anatomic magnetic resonance study and reconstructed to provide the anisotropic and isotropic components of the DT. The invasive phenotype was determined by using previously published criteria and correlated with IDH-1 mutation status by using the Freeman-Halton extension of the Fisher exact probability test. Results Nine patients had an IDH-1 mutation and 61 had IDH-1 wild type. All of the patients with IDH-1 mutation had a minimally invasive DT imaging phenotype. Among the IDH-1 wild-type tumors, 42 of 61 (69%) were diffusively invasive glioblastomas, 14 of 61 (23%) were locally invasive, and five of 61 (8%) were minimally invasive (P < .001). Conclusion IDH-mutated glioblastomas have a less invasive phenotype compared with IDH wild type. This finding may have implications for individualizing the extent of surgical resection and radiation therapy volumes.

Multimodal MRI can identify perfusion and metabolic changes in the invasive margin of glioblastomas

PURPOSE

To use perfusion and magnetic resonance (MR) spectroscopy to compare the diffusion tensor imaging (DTI)-defined invasive and noninvasive regions. Invasion of normal brain is a cardinal feature of glioblastomas (GBM) and a major cause of treatment failure. DTI can identify invasive regions.

MATERIALS AND METHODS

In all, 50 GBM patients were imaged preoperatively at 3T with anatomic sequences, DTI, dynamic susceptibility perfusion MR (DSCI), and multivoxel spectroscopy. The DTI and DSCI data were coregistered to the spectroscopy data and regions of interest (ROIs) were made in the invasive (determined by DTI), noninvasive regions, and normal brain. Values of relative cerebral blood volume (rCBV), N-acetyl aspartate (NAA), myoinositol (mI), total choline (Cho), and glutamate + glutamine (Glx) normalized to creatine (Cr) and Cho/NAA were measured at each ROI.

RESULTS

Invasive regions showed significant increases in rCBV, suggesting angiogenesis (invasive rCBV 1.64 [95% confidence interval, CI: 1.5-1.76] vs. noninvasive 1.14 [1.09-1.18]; P < 0.001), Cho/Cr (invasive 0.42 [0.38-0.46] vs. noninvasive 0.35 [0.31-0.38]; P = 0.02) and Cho/NAA (invasive 0.54 [0.41-0.68] vs. noninvasive 0.37 [0.29-0.45]; P = < 0.03), suggesting proliferation, and Glx/Cr (invasive 1.54 [1.27-1.82] vs. noninvasive 1.3 [1.13-1.47]; P = 0.028), suggesting glutamate release; and a significantly reduced NAA/Cr (invasive 0.95 [0.85-1.05] vs. noninvasive 1.19 [1.06-1.31]; P = 0.008). The mI/Cr was not different between the three ROIs (invasive 1.2 [0.99-1.41] vs. noninvasive 1.3 [1.14-1.46]; P = 0.68). In the noninvasive regions, the values were not different from normal brain.

CONCLUSION

Combining DTI to identify the invasive region with perfusion and spectroscopy, we can identify changes in invasive regions not seen in noninvasive regions.