Reevaluating the imaging definition of tumor progression: perfusion MRI quantifies recurrent glioblastoma tumor fraction, pseudoprogression, and radiation necrosis to predict survival

Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high-grade gliomas

Despite the widespread clinical use of dynamic susceptibility contrast (DSC) MRI, DSC-MRI methodology has not been standardized, hindering its utilization for response assessment in multicenter trials. Recently, the DSC-MRI Standardization Subcommittee of the Jumpstarting Brain Tumor Drug Development Coalition issued an updated consensus DSC-MRI protocol compatible with the standardized brain tumor imaging protocol (BTIP) for high-grade gliomas that is increasingly used in the clinical setting and is the default MRI protocol for the National Clinical Trials Network. After reviewing the basis for controversy over DSC-MRI protocols, this paper provides evidence-based best practices for clinical DSC-MRI as determined by the Committee, including pulse sequence (gradient echo vs spin echo), BTIP-compliant contrast agent dosing (preload and bolus), flip angle (FA), echo time (TE), and post-processing leakage correction. In summary, full-dose preload, full-dose bolus dosing using intermediate (60°) FA and field strength-dependent TE (40-50 ms at 1.5 T, 20-35 ms at 3 T) provides overall best accuracy and precision for cerebral blood volume estimates. When single-dose contrast agent usage is desired, no-preload, full-dose bolus dosing using low FA (30°) and field strength-dependent TE provides excellent performance, with reduced contrast agent usage and elimination of potential systematic errors introduced by variations in preload dose and incubation time.

Validation of combined use of DWI and percentage signal recovery-optimized protocol of DSC-MRI in differentiation of high-grade glioma, metastasis, and lymphoma

PURPOSE

With conventional MRI, it is often difficult to effectively differentiate between contrast-enhancing brain tumors, including primary central nervous system lymphoma (PCNSL), high-grade glioma (HGG), and metastasis. This study aimed to assess the discrimination ability of the parameters obtained from DWI and the percentage signal recovery- (PSR-) optimized protocol of DSC-MRI between these three tumor types at an initial step.

METHODS

DSC-MRI using a PSR-optimized protocol (TR/TE = 1500/30 ms, flip angle = 90°, no preload) and DWI of 99 solitary enhancing tumors (60 HGGs, 24 metastases, 15 PCNSLs) were retrospectively assessed before treatment. rCBV, PSR, ADC in the tumor core and rCBV, and ADC in peritumoral edema were measured. The differences were evaluated using one-way ANOVA, and the diagnostic performance was evaluated using ROC curve analysis.

RESULTS

PSR in the tumor core showed the best discriminating performance in differentiating these three tumor types with AUC values of 0.979 for PCNSL vs. others and 0.947 for HGG vs. metastasis. The ADC was only helpful in the tumor core and distinguishing PCNSLs from others (AUC = 0.897).

CONCLUSION

Different from CBV-optimized protocols (preload, intermediate FA), PSR derived from the PSR-optimized protocol seems to be the most important parameter in the differentiation of HGGs, metastases, and PCNSLs at initial diagnosis. This property makes PSR remarkable and carries the need for comprehensive DSC-MRI protocols, which provides PSR sensitivity and CBV accuracy together, such as the preload use of the PSR-optimized protocol before the CBV-optimized protocol.

Role of Radiological Intervention in Brain Tumor: A Meta-Analysis

Background

This meta-analysis highlights the diagnostic efficacy of computed tomography (CT), computed tomography angiography (CTA), magnetic resonance image (MRI), as well as magnetic resonance spectroscopy (MRS). This paper assesses the detection of the primary outcome comprising choline/creatine ratio, relative cerebral blood volume (rCBV), as well as choline/N-acetyl aspartate. Cochrane, Medline, ScienceDirect, Google Scholar, and EMBASE databases were searched for extracting the relevant studies.

Methods

A sample of 12 studies on radiologic assessment of brain tumors was selected.

Results

The evidence provides that the heterogeneity exists concerning the CBV of 311.623, I2 = 96.12%, with a significance value of P < 0.001. The pooled difference showed rCBV mean (as 2.18, 95% confidence interval = 0.85 to 3.50) substantially enhances lesion.

Conclusion

The study concluded that radiological interventions, particularly the combination of MRS and MRI, help in the brain patient's precise diagnosis and treatment.

Synthetic generation of DSC-MRI-derived relative CBV maps from DCE MRI of brain tumors

PURPOSE

Perfusion MRI with gadolinium-based contrast agents is useful for diagnosis and treatment response evaluation of brain tumors. Dynamic susceptibility contrast (DSC) MRI and dynamic contrast enhanced (DCE) MRI are two gadolinium-based contrast agent perfusion imaging techniques that provide complementary information about the tumor vasculature. However, each requires a separate administration of a gadolinium-based contrast agent. The purpose of this retrospective study was to determine the feasibility of synthesizing relative cerebral blood volume (rCBV) maps, as computed from DSC MRI, from DCE MRI of brain tumors.

METHODS

One hundred nine brain-tumor patients underwent both DCE and DSC MRI. Relative CBV maps were computed from the DSC MRI, and blood plasma volume fraction maps were computed from the DCE MRIs. Conditional generative adversarial networks were developed to synthesize rCBV maps from the DCE MRIs. Tumor-to-white matter ratios were calculated from real rCBV, synthetic rCBV, and plasma volume fraction maps and compared using correlation analysis. Real and synthetic rCBV in white and gray matter regions were also compared.

RESULTS

Pearson correlation analysis showed that both the tumor rCBV and tumor-to-white matter ratios in the synthetic and real rCBV maps were strongly correlated (ρ = 0.87, P < .05 and ρ = 0.86, P < .05, respectively). Tumor plasma volume fraction and real rCBV were not strongly correlated (ρ = 0.47). Bland-Altman analysis showed a mean difference between the synthetic and real rCBV tumor-to-white matter ratios of 0.20 with a 95% confidence interval of ±0.47.

CONCLUSION

Realistic rCBV maps can be synthesized from DCE MRI and contain quantitative information, enabling robust brain-tumor perfusion imaging of DSC and DCE parameters with a single gadolinium-based contrast agent administration.

Differentiation of Treatment-Related Effects from Glioma Recurrence Using Machine Learning Classifiers Based Upon Pre-and Post-Contrast T1WI and T2 FLAIR Subtraction Features: A Two-Center Study

Purpose

We propose three support vector machine (SVM) classifiers, using pre-and post-contrast T2 fluid-attenuated inversion recovery (FLAIR) subtraction and/or pre-and post-contrast T1WI subtraction, to differentiate treatment-related effects (TRE) from glioma recurrence.

Materials and Methods

Fifty-six postoperative high-grade glioma patients with suspicious progression after radiotherapy and chemotherapy from two centers were studied. Pre-and post-contrast T1WI and T2 FLAIR were collected. Each pre-contrast image was voxel-wise subtracted from the co-registered post-contrast image. Dataset was randomly split into training, and testing on a 7:3 ratio, accordingly subjected to a five fold cross validation. Best feature subsets were selected by Pearson correlation coefficient and recursive feature elimination, whereupon a radiomics classifier was built with SVM. The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).

Results

In all, 186 features were extracted on each subtraction map. Top nine T1WI subtraction features, top thirteen T2 FLAIR subtraction features and top thirteen combination features were selected to build optimal SVM classifiers accordingly. The accuracies/AUCs/sensitivity/specificity/PPV/NPV of SVM based on sole T1WI subtraction were 80.00%/80.00% (CI: 0.5370–1.0000)/100%/70.00%/62.50%/100%. Those results of SVM based on sole T2 FLAIR subtraction were 86.67%/84.00% (CI: 0.5962–1.0000)/100%/80%/71.43%/100%. Those results of SVM based on both T1WI subtraction and T2 FLAIR subtraction were 93.33%/94.00% (CI: 0.7778–1.0000)/100%/90%/83.33%/100%, respectively.

Conclusion

Pre- and post-contrast T2 FLAIR subtraction provided added value for diagnosis between recurrence and TRE. SVM based on a combination of T1WI and T2 FLAIR subtraction maps was superior to the sole use of T1WI or T2 FLAIR for differentiating TRE from recurrence. The SVM classifier based on combination of pre-and post-contrast subtraction T2 FLAIR and T1WI imaging allowed for the accurate differential diagnosis of TRE from recurrence, which is of paramount importance for treatment management of postoperative glioma patients after radiation therapy.

Lentiviral Vector Induced Modeling of High-Grade Spinal Cord Glioma in Minipigs

BACKGROUND

Prior studies have applied driver mutations targeting the RTK/RAS/PI3K and p53 pathways to induce the formation of high-grade gliomas in rodent models. In the present study, we report the production of a high-grade spinal cord glioma model in pigs using lentiviral gene transfer.

METHODS

Six Gottingen Minipigs received thoracolumbar (T14-L1) lateral white matter injections of a combination of lentiviral vectors, expressing platelet-derived growth factor beta (PDGF-B), constitutive HRAS, and shRNA-p53 respectively. All animals received injection of control vectors into the contralateral cord. Animals underwent baseline and endpoint magnetic resonance imaging (MRI) and were evaluated daily for clinical deficits. Hematoxylin and eosin (H&E) and immunohistochemical analysis was conducted. Data are presented using descriptive statistics including relative frequencies, mean, standard deviation, and range.

RESULTS

100% of animals (n = 6/6) developed clinical motor deficits ipsilateral to the oncogenic lentiviral injections by a three-week endpoint. MRI scans at endpoint demonstrated contrast enhancing mass lesions at the site of oncogenic lentiviral injection and not at the site of control injections. Immunohistochemistry demonstrated positive staining for GFAP, Olig2, and a high Ki-67 proliferative index. Histopathologic features demonstrate consistent and reproducible growth of a high-grade glioma in all animals.

CONCLUSIONS

Lentiviral gene transfer represents a feasible pathway to glioma modeling in higher order species. The present model is the first lentiviral vector induced pig model of high-grade spinal cord glioma and may potentially be used in preclinical therapeutic development programs.

Performance of Standardized Relative CBV for Quantifying Regional Histologic Tumor Burden in Recurrent High-Grade Glioma: Comparison against Normalized Relative CBV Using Image-Localized Stereotactic Biopsies

BACKGROUND AND PURPOSE

Perfusion MR imaging measures of relative CBV can distinguish recurrent tumor from posttreatment radiation effects in high-grade gliomas. Currently, relative CBV measurement requires normalization based on user-defined reference tissues. A recently proposed method of relative CBV standardization eliminates the need for user input. This study compares the predictive performance of relative CBV standardization against relative CBV normalization for quantifying recurrent tumor burden in high-grade gliomas relative to posttreatment radiation effects.

MATERIALS AND METHODS

We recruited 38 previously treated patients with high-grade gliomas (World Health Organization grades III or IV) undergoing surgical re-resection for new contrast-enhancing lesions concerning for recurrent tumor versus posttreatment radiation effects. We recovered 112 image-localized biopsies and quantified the percentage of histologic tumor content versus posttreatment radiation effects for each sample. We measured spatially matched normalized and standardized relative CBV metrics (mean, median) and fractional tumor burden for each biopsy. We compared relative CBV performance to predict tumor content, including the Pearson correlation (r), against histologic tumor content (0%-100%) and the receiver operating characteristic area under the curve for predicting high-versus-low tumor content using binary histologic cutoffs (≥50%; ≥80% tumor).

RESULTS

Across relative CBV metrics, fractional tumor burden showed the highest correlations with tumor content (0%-100%) for normalized (r = 0.63, P < .001) and standardized (r = 0.66, P < .001) values. With binary cutoffs (ie, ≥50%; ≥80% tumor), predictive accuracies were similar for both standardized and normalized metrics and across relative CBV metrics. Median relative CBV achieved the highest area under the curve (normalized = 0.87, standardized = 0.86) for predicting ≥50% tumor, while fractional tumor burden achieved the highest area under the curve (normalized = 0.77, standardized = 0.80) for predicting ≥80% tumor.

CONCLUSIONS

Standardization of relative CBV achieves similar performance compared with normalized relative CBV and offers an important step toward workflow optimization and consensus methodology.

Imaging of intratumoral heterogeneity in high-grade glioma

High-grade glioma (HGG), and particularly Glioblastoma (GBM), can exhibit pronounced intratumoral heterogeneity that confounds clinical diagnosis and management. While conventional contrast-enhanced MRI lacks the capability to resolve this heterogeneity, advanced MRI techniques and PET imaging offer a spectrum of physiologic and biophysical image features to improve the specificity of imaging diagnoses. Published studies have shown how integrating these advanced techniques can help better define histologically distinct targets for surgical and radiation treatment planning, and help evaluate the regional heterogeneity of tumor recurrence and response assessment following standard adjuvant therapy. Application of texture analysis and machine learning (ML) algorithms has also enabled the emerging field of radiogenomics, which can spatially resolve the regional and genetically distinct subpopulations that coexist within a single GBM tumor. This review focuses on the latest advances in neuro-oncologic imaging and their clinical applications for the assessment of intratumoral heterogeneity.

Clinical Imaging for Diagnostic Challenges in the Management of Gliomas: A Review

Neuroimaging plays a critical role in the management of patients with gliomas. While conventional magnetic resonance imaging (MRI) remains the standard imaging modality, it is frequently insufficient to inform clinical decision-making. There is a need for noninvasive strategies for reliably distinguishing low-grade from high-grade gliomas, identifying important molecular features of glioma, choosing an appropriate target for biopsy, delineating target area for surgery or radiosurgery, and distinguishing tumor progression (TP) from pseudoprogression (PsP). One recent advance is the identification of the T2/fluid-attenuated inversion recovery mismatch sign on standard MRI to identify isocitrate dehydrogenase mutant astrocytomas. However, to meet other challenges, neuro-oncologists are increasingly turning to advanced imaging modalities. Diffusion-weighted imaging modalities including diffusion tensor imaging and diffusion kurtosis imaging can be helpful in delineating tumor margins and better visualization of tissue architecture. Perfusion imaging including dynamic contrast-enhanced MRI using gadolinium or ferumoxytol contrast agents can be helpful for grading as well as distinguishing TP from PsP. Positron emission tomography is useful for measuring tumor metabolism, which correlates with grade and can distinguish TP/PsP in the right setting. Magnetic resonance spectroscopy can identify tissue by its chemical composition, can distinguish TP/PsP, and can identify molecular features like 2-hydroxyglutarate. Finally, amide proton transfer imaging measures intracellular protein content, which can be used to identify tumor grade/progression and distinguish TP/PsP.

In Vivo Measurement of Brain Tissue Response After Irradiation: Comparison of T2 Relaxation, Apparent Diffusion Coefficient, and Electrical Conductivity

Radiation therapy (RT) has been widely used as a powerful treatment tool to address cancerous tissues because of its ability to control cell growth. Its ionizing radiation damages the DNA of cancerous tissues, leading to cell death. Medical imaging, however, still has limitations regarding the reliability of its assessment of tissue response and in predicting the treatment effect because of its inability to provide contrast information on the gradual, minute tissue changes after RT. A recently developed magnetic resonance (MR)-based conductivity imaging method may provide direct, highly sensitive information on this tissue response because its contrast mechanism is based on the concentration and mobility of ions in intracellular and extracellular spaces. In this feasibility study, we applied T2-weighted, diffusion-weighted, and electrical conductivity imaging to mouse brain, thus, using the MR imaging to map the tissue response after radiation exposure. To evaluate the degree of response, we measured the T2 relaxation, apparent diffusion coefficient (ADC), and electrical conductivity of brain tissues before and after irradiation. The conductivity images, which showed significantly higher sensitivity than other MR imaging methods, indicated that the contrast is distinguishable in different ways at different areas of the brain. Future studies will focus on verifying these results and the long-term evaluation of conductivity changes using various irradiation methods for clinical applications.

Perfusion MRI-Based Fractional Tumor Burden Differentiates between Tumor and Treatment Effect in Recurrent Glioblastomas and Informs Clinical Decision-Making

BACKGROUND AND PURPOSE

Fractional tumor burden better correlates with histologic tumor volume fraction in treated glioblastoma than other perfusion metrics such as relative CBV. We defined fractional tumor burden classes with low and high blood volume to distinguish tumor from treatment effect and to determine whether fractional tumor burden can inform treatment-related decision-making.

MATERIALS AND METHODS

Forty-seven patients with high-grade gliomas (primarily glioblastoma) with recurrent contrast-enhancing lesions on DSC-MR imaging were retrospectively evaluated after surgical sampling. Histopathologic examination defined treatment effect versus tumor. Normalized relative CBV thresholds of 1.0 and 1.75 were used to define low, intermediate, and high fractional tumor burden classes in each histopathologically defined group. Performance was assessed with an area under the receiver operating characteristic curve. Consensus agreement among physician raters reporting hypothetic changes in treatment-related decisions based on fractional tumor burden was compared with actual real-time treatment decisions.

RESULTS

Mean lower fractional tumor burden, high fractional tumor burden, and relative CBV of the contrast-enhancing volume were significantly different between treatment effect and tumor (P = .002, P < .001, and P < .001), with tumor having significantly higher fractional tumor burden and relative CBV and lower fractional tumor burden. No significance was found with intermediate fractional tumor burden. Performance of the area under the receiver operating characteristic curve was the following: high fractional tumor burden, 0.85; low fractional tumor burden, 0.7; and relative CBV, 0.81. In comparing treatment decisions, there were disagreements in 7% of tumor and 44% of treatment effect cases; in the latter, all disagreements were in cases with scattered atypical cells.

CONCLUSIONS

High fractional tumor burden and low fractional tumor burden define fractions of the contrast-enhancing lesion volume with high and low blood volume, respectively, and can differentiate treatment effect from tumor in recurrent glioblastomas. Fractional tumor burden maps can also help to inform clinical decision-making.

Imaging-guided precision medicine in glioblastoma patients treated with immune checkpoint modulators: research trend and future directions in the field of imaging biomarkers and artificial intelligence

Immunotherapies that employ immune checkpoint modulators (ICMs) have emerged as an effective treatment for a variety of solid cancers, as well as a paradigm shift in the treatment of cancers. Despite this breakthrough, the median survival time of glioblastoma patients has remained at about 2 years. Therefore, the safety and anti-cancer efficacy of combination therapies that include ICMs are being actively investigated. Because of the distinct mechanisms of ICMs, which restore the immune system’s anti-tumor capacity, unconventional immune-related phenomena are increasingly being reported in terms of tumor response and progression, as well as adverse events. Indeed, immunotherapy response assessments for neuro-oncology (iRANO) play a central role in guiding cancer patient management and define a “wait and see strategy” for patients treated with ICMs in monotherapy with progressive disease on MRI. This article deciphers emerging research trends to ameliorate four challenges unaddressed by the iRANO criteria: (1) patient selection, (2) identification of immune-related phenomena other than pseudoprogression (i.e., hyperprogression, the abscopal effect, immune-related adverse events), (3) response assessment in combination therapies including ICM, and (4) alternatives to MRI. To this end, our article provides a structured approach for standardized selection and reporting of imaging modalities to enable the use of precision medicine by deciphering the characteristics of the tumor and its immune environment. Emerging preclinical or clinical innovations are also discussed as future directions such as immune-specific targeting and implementation of artificial intelligence algorithms.

Imaging Glioblastoma Posttreatment: Progression, Pseudoprogression, Pseudoresponse, Radiation Necrosis

Radiographic monitoring of posttreatment glioblastoma is important for clinical trials and determining next steps in management. Evaluation for tumor progression is confounded by the presence of treatment-related radiographic changes, making a definitive determination less straight-forward. The purpose of this article was to describe imaging tools available for assessing treatment response in glioblastoma, as well as to highlight the definitions, pathophysiology, and imaging features typical of true progression, pseudoprogression, pseudoresponse, and radiation necrosis.

Systematic assessment of multi-echo dynamic susceptibility contrast MRI using a digital reference object

PURPOSE

Brain tumor dynamic susceptibility contrast (DSC) MRI is adversely impacted by T₁ and T 2 ∗ contrast agent leakage effects that result in inaccurate hemodynamic metrics. While multi-echo acquisitions remove T₁ leakage effects, there is no consensus on the optimal set of acquisition parameters. Using a computational approach, we systematically evaluated a wide range of acquisition strategies to determine the optimal multi-echo DSC-MRI perfusion protocol.

METHODS

Using a population-based DSC-MRI digital reference object (DRO), we assessed the influence of preload dosing (no preload and full dose preload), field strength (1.5 and 3T), pulse sequence parameters (echo time, repetition time, and flip angle), and leakage correction on relative cerebral blood volume (rCBV) and flow (rCBF) accuracy. We also compared multi-echo DSC-MRI protocols with standard single-echo protocols.

RESULTS

Multi-echo DSC-MRI is highly consistent across all protocols, and multi-echo rCBV (with or without use of a preload dose) had higher accuracy than single-echo rCBV. Regression analysis showed that choice of repetition time and flip angle had minimal impact on multi-echo rCBV and rCBV, indicating the potential for significant flexibility in acquisition parameters. The echo time combination had minimal impact on rCBV, though longer echo times should be avoided, particularly at higher field strengths. Leakage correction improved rCBV accuracy in all cases. Multi-echo rCBF was less biased than single-echo rCBF, although rCBF accuracy was reduced overall relative to rCBV.

CONCLUSIONS

Multi-echo acquisitions were more robust than single-echo, essentially decoupling both repetition time and flip angle from rCBV accuracy. Multi-echo acquisitions obviate the need for preload dosing, although leakage correction to remove residual T 2 ∗ leakage effects remains compulsory for high rCBV accuracy.

Moving Toward a Consensus DSC-MRI Protocol: Validation of a Low-Flip Angle Single-Dose Option as a Reference Standard for Brain Tumors

BACKGROUND AND PURPOSE

DSC-MR imaging using preload, intermediate (60°) flip angle and postprocessing leakage correction has gained traction as a standard methodology. Simulations suggest that DSC-MR imaging with flip angle = 30° and no preload yields relative CBV practically equivalent to the reference standard. This study tested this hypothesis in vivo.

MATERIALS AND METHODS

Eighty-four patients with brain lesions were enrolled in this 3-institution study. Forty-three patients satisfied the inclusion criteria. DSC-MR imaging (3T, single-dose gadobutrol, gradient recalled-echo-EPI, TE = 20-35 ms, TR = 1.2-1.63 seconds) was performed twice for each patient, with flip angle = 30°-35° and no preload (P-), which provided preload (P+) for the subsequent intermediate flip angle = 60°. Normalized relative CBV and standardized relative CBV maps were generated, including postprocessing with contrast agent leakage correction (C+) and without (C-) contrast agent leakage correction. Contrast-enhancing lesion volume, mean relative CBV, and contrast-to-noise ratio obtained with 30°/P-/C-, 30°/P-/C+, and 60°/P+/C- were compared with 60°/P+/C+ using the Lin concordance correlation coefficient and Bland-Altman analysis. Equivalence between the 30°/P-/C+ and 60°/P+/C+ protocols and the temporal SNR for the 30°/P- and 60°/P+ DSC-MR imaging data was also determined.

RESULTS

Compared with 60°/P+/C+, 30°/P-/C+ had closest mean standardized relative CBV (P = .61), highest Lin concordance correlation coefficient (0.96), and lowest Bland-Altman bias (μ = 1.89), compared with 30°/P-/C- (P = .02, Lin concordance correlation coefficient = 0.59, μ = 14.6) and 60°/P+/C- (P = .03, Lin concordance correlation coefficient = 0.88, μ = -10.1) with no statistical difference in contrast-to-noise ratios across protocols. The normalized relative CBV and standardized relative CBV were statistically equivalent at the 10% level using either the 30°/P-/C+ or 60°/P+/C+ protocols. Temporal SNR was not significantly different for 30°/P- and 60°/P+ (P = .06).

CONCLUSIONS

Tumor relative CBV derived from low-flip angle, no-preload DSC-MR imaging with leakage correction is an attractive single-dose alternative to the higher dose reference standard.

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.

Residual Tumor Volume, Cell Volume Fraction, and Tumor Cell Kill During Fractionated Chemoradiation Therapy of Human Glioblastoma using Quantitative Sodium MR Imaging

PURPOSE

Spatial and temporal patterns of response of human glioblastoma to fractionated chemoradiation are described by changes in the bioscales of residual tumor volume (RTV), tumor cell volume fraction (CVF), and tumor cell kill (TCK), as derived from tissue sodium concentration (TSC) measured by quantitative sodium MRI at 3 Tesla. These near real-time patterns during treatment are compared with overall survival.

EXPERIMENTAL DESIGN

Bioscales were mapped during fractionated chemoradiation therapy in patients with glioblastomas (n = 20) using TSC obtained from serial quantitative sodium MRI at 3 Tesla and a two-compartment model of tissue sodium distribution. The responses of these parameters in newly diagnosed human glioblastomas undergoing treatment were compared with time-to-disease progression and survival.

RESULTS

RTV following tumor resection showed decreased CVF due to disruption of normal cell packing by edema and infiltrating tumor cells. CVF showed either increases back toward normal as infiltrating tumor cells were killed, or decreases as cancer cells continued to infiltrate and extend tumor margins. These highly variable tumor responses showed no correlation with time-to-progression or overall survival.

CONCLUSIONS

These bioscales indicate that fractionated chemoradiotherapy of glioblastomas produces variable responses with low cell killing efficiency. These parameters are sensitive to real-time changes within the treatment volume while remaining stable elsewhere, highlighting the potential to individualize therapy earlier in management, should alternative strategies be available.

Long-term outcome data from 121 patients treated with Gamma Knife stereotactic radiosurgery as salvage therapy for focally recurrent high-grade gliomas

INTRODUCTION

We examined patient outcomes after Gamma Knife stereotactic radiosurgery (GKSRS) salvage therapy for recurrent high-grade gliomas (HGGs) to determine whether tumor grade or lesion size affected overall survival (OS) and progression-free survival (PFS).

METHODS

This single-center retrospective study assessed radiographic response and clinical outcomes following GKSRS salvage treatment of recurrent malignant gliomas (January 2005-March 2014).

RESULTS

A total of 121 patients (67 female) with 132 tumors were treated. Median (range) PFS was 4.7 (3.9-5.4) months for the cohort, 6.8 (4.6-8.9) months for initial grade 2 tumors, 4.2 (1.9-6.5) months for initial grade 3 tumors, and 4.3 (3.7-4.9) months for initial grade 4 tumors. Patients with small lesions (≤6.7 cm³; n = 53) had significantly longer median (range) PFS (6.8 [4.8-8.8], P=0.02).

CONCLUSIONS

GKSRS offers meaningful salvage therapy with minimal morbidity in appropriately selected patients with focally recurrent HGGs.

[C-methionine PET in assessment of brain lesions in patients with glial tumors after combined treatment]

Positron emission tomography (PET) with amino acid-based radiopharmaceuticals is considered as an effective method to diagnose continued growth of cerebral gliomas, but the variability of ¹¹C-methionine uptake by brain lesions of different genesis after combined treatment still remains poorly understood. The aim of this study was to explore the information value of ¹¹C-methionine PET in delimitating progression of cerebral gliomas and stable disease and to assess the risk of tumor recurrence at different values of the ¹¹C-methionine uptake index. MATERIAL AND METHODS: We performed a retrospective analysis of the results of ¹¹C-methionine PET or PET/CT in 324 patients suspected for continued growth of cerebral tumor based on magnetic resonance imaging (MRI) findings. A quantitative analysis of the results included calculation of the ¹¹C-methionine uptake index (UI). RESULTS: A ROC analysis revealed that the specificity of PET in the diagnosis of continued tumor growth (CTG) was 98%, and the sensitivity was 71% for a UI of more than 1.9. We found that 98% of lesions with a negative level of RP uptake were related to radiation brain lesions (RBLs) or residual tumors combined with radiation pathomorphims. The UI in a range of 1.2-1.6 in 75% of lesions characterized a stable disease, but 25.5% of the lesions represented continued glioma growth. The proportion of recurrences increased to 40% in a UI range of 1.6-1.9, and 95.5% of brain lesions with a UI of more than 1.9 were tumor recurrences. Therefore, high ¹¹C-methionine uptake with the UI above 1.9 in brain lesions characterized by radiological signs of disease progression is a highly specific indicator of CTG; however, the UI may significantly vary during tumor growth, and a substantial fraction of recurrent gliomas may have lower radiopharmaceutical uptake. In the case of borderline UI values, early dynamic control or complementary additional MRI or CT techniques should be used.

Optimization of Acquisition and Analysis Methods for Clinical Dynamic Susceptibility Contrast MRI Using a Population-Based Digital Reference Object

BACKGROUND AND PURPOSE

The accuracy of DSC-MR imaging CBV maps in glioblastoma depends on acquisition and analysis protocols. Multisite protocol heterogeneity has challenged standardization initiatives due to the difficulties of in vivo validation. This study sought to compare the accuracy of routinely used protocols using a digital reference object.

MATERIALS AND METHODS

The digital reference object consisted of approximately 10,000 simulated voxels recapitulating typical signal heterogeneity encountered in vivo. The influence of acquisition and postprocessing methods on CBV reliability was evaluated across 6912 parameter combinations, including contrast agent dosing schemes, pulse sequence parameters, field strengths, and postprocessing methods. Accuracy and precision were assessed using the concordance correlation coefficient and coefficient of variation.

RESULTS

Across all parameter space, the optimal protocol included full-dose contrast agent preload and bolus, intermediate (60°) flip angle, 30-ms TE, and postprocessing with a leakage-correction algorithm (concordance correlation coefficient = 0.97, coefficient of variation = 6.6%). Protocols with no preload or fractional dose preload and bolus using these acquisition parameters were generally less robust. However, a protocol with no preload, full-dose bolus, and low (30°) flip angle performed very well (concordance correlation coefficient = 0.93, coefficient of variation = 8.7% at 1.5T and concordance correlation coefficient = 0.92, coefficient of variation = 8.2% at 3T).

CONCLUSIONS

Schemes with full-dose preload and bolus maximize CBV accuracy and reduce variability, which could enable smaller sample sizes and more reliable detection of CBV changes in clinical trials. When a lower total contrast agent dose is desired, use of a low flip angle, no preload, and full-dose bolus protocol may provide an attractive alternative.

Utility of Magnetic Resonance Perfusion Imaging in Quantifying Active Tumor Fraction and Radiation Necrosis in Recurrent Intracranial Tumors

BACKGROUND

Ancillary criteria to identify tumor recurrence such as the McDonald criteria or Response Assessment in Neuro-Oncology criteria can provide false diagnoses. Magnetic resonance perfusion (MRP) imaging has been proposed to differentiate post-treatment changes from recurrence. We investigated the utility of MRP to quantify the histological fraction of active tumor (AT), treatment-related changes, and radiation necrosis in recurrent post-treatment intracranial tumors.

METHODS

We conducted an exploratory single-blind study of patients with intracranial glioblastoma or metastases with previous radiation therapy and MRP before surgery. Biopsy specimens (n = 19) were analyzed for the percentage of AT, radiation necrosis, and treatment effect. Nonparametric Spearman's rho analysis and multivariable analysis of covariance were performed to assess the correlation between quantitative MRP and AT histological fraction.

RESULTS

The mean patient age was 58 ± 11.5 years. The mean relative cerebral blood volume (rCBV) and relative cerebral blood flow (rCBF) were 1.33 ± 0.71 and 1.34 ± 0.73, respectively. On analysis of covariance, significant associations were identified between increased rCBF (P = 0.0004) and increased rCBV (P = 0.007) and percentage of AT. A significant interaction was identified between rCBF and rCBV and tumor histological features (glioblastoma vs. metastases; P = 0.003 and P = 0.03, respectively). An rCBF >1 predicted a mean AT fraction of ≥53% for all intracranial tumors and 74% for glioblastoma.

CONCLUSION

MRP can help quantitatively predict tumor recurrence and/or progression for glioblastomas. The AT histological fraction correlated with quantitative radiologic measurements, including rCBV and rCBF. For metastases, MRP might not be as useful in predicting the AT fraction. Clinicians must be judicious with their use of MRP in predicting tumor recurrence and radiation necrosis.

Multisite Concordance of DSC-MRI Analysis for Brain Tumors: Results of a National Cancer Institute Quantitative Imaging Network Collaborative Project

BACKGROUND AND PURPOSE

Standard assessment criteria for brain tumors that only include anatomic imaging continue to be insufficient. While numerous studies have demonstrated the value of DSC-MR imaging perfusion metrics for this purpose, they have not been incorporated due to a lack of confidence in the consistency of DSC-MR imaging metrics across sites and platforms. This study addresses this limitation with a comparison of multisite/multiplatform analyses of shared DSC-MR imaging datasets of patients with brain tumors.

MATERIALS AND METHODS

DSC-MR imaging data were collected after a preload and during a bolus injection of gadolinium contrast agent using a gradient recalled-echo-EPI sequence (TE/TR = 30/1200 ms; flip angle = 72°). Forty-nine low-grade (n = 13) and high-grade (n = 36) glioma datasets were uploaded to The Cancer Imaging Archive. Datasets included a predetermined arterial input function, enhancing tumor ROIs, and ROIs necessary to create normalized relative CBV and CBF maps. Seven sites computed 20 different perfusion metrics. Pair-wise agreement among sites was assessed with the Lin concordance correlation coefficient. Distinction of low- from high-grade tumors was evaluated with the Wilcoxon rank sum test followed by receiver operating characteristic analysis to identify the optimal thresholds based on sensitivity and specificity.

RESULTS

For normalized relative CBV and normalized CBF, 93% and 94% of entries showed good or excellent cross-site agreement (0.8 ≤ Lin concordance correlation coefficient ≤ 1.0). All metrics could distinguish low- from high-grade tumors. Optimum thresholds were determined for pooled data (normalized relative CBV = 1.4, sensitivity/specificity = 90%:77%; normalized CBF = 1.58, sensitivity/specificity = 86%:77%).

CONCLUSIONS

By means of DSC-MR imaging data obtained after a preload of contrast agent, substantial consistency resulted across sites for brain tumor perfusion metrics with a common threshold discoverable for distinguishing low- from high-grade tumors.

Magnetic resonance perfusion image features uncover an angiogenic subgroup of glioblastoma patients with poor survival and better response to antiangiogenic treatment

Background

In previous clinical trials, antiangiogenic therapies such as bevacizumab did not show efficacy in patients with newly diagnosed glioblastoma (GBM). This may be a result of the heterogeneity of GBM, which has a variety of imaging-based phenotypes and gene expression patterns. In this study, we sought to identify a phenotypic subtype of GBM patients who have distinct tumor-image features and molecular activities and who may benefit from antiangiogenic therapies.

Methods

Quantitative image features characterizing subregions of tumors and the whole tumor were extracted from preoperative and pretherapy perfusion magnetic resonance (MR) images of 117 GBM patients in 2 independent cohorts. Unsupervised consensus clustering was performed to identify robust clusters of GBM in each cohort. Cox survival and gene set enrichment analyses were conducted to characterize the clinical significance and molecular pathway activities of the clusters. The differential treatment efficacy of antiangiogenic therapy between the clusters was evaluated.

Results

A subgroup of patients with elevated perfusion features was identified and was significantly associated with poor patient survival after accounting for other clinical covariates (P values <.01; hazard ratios > 3) consistently found in both cohorts. Angiogenesis and hypoxia pathways were enriched in this subgroup of patients, suggesting the potential efficacy of antiangiogenic therapy. Patients of the angiogenic subgroups pooled from both cohorts, who had chemotherapy information available, had significantly longer survival when treated with antiangiogenic therapy (log-rank P=.022).

Conclusions

Our findings suggest that an angiogenic subtype of GBM patients may benefit from antiangiogenic therapy with improved overall survival.

Pseudoprogression of brain tumors

This review describes the definition, incidence, clinical implications, and magnetic resonance imaging (MRI) findings of pseudoprogression of brain tumors, in particular, but not limited to, high-grade glioma. Pseudoprogression is an important clinical problem after brain tumor treatment, interfering not only with day-to-day patient care but also the execution and interpretation of clinical trials. Radiologically, pseudoprogression is defined as a new or enlarging area(s) of contrast agent enhancement, in the absence of true tumor growth, which subsides or stabilizes without a change in therapy. The clinical definitions of pseudoprogression have been quite variable, which may explain some of the differences in reported incidences, which range from 9-30%. Conventional structural MRI is insufficient for distinguishing pseudoprogression from true progressive disease, and advanced imaging is needed to obtain higher levels of diagnostic certainty. Perfusion MRI is the most widely used imaging technique to diagnose pseudoprogression and has high reported diagnostic accuracy. Diagnostic performance of MR spectroscopy (MRS) appears to be somewhat higher, but MRS is less suitable for the routine and universal application in brain tumor follow-up. The combination of MRS and diffusion-weighted imaging and/or perfusion MRI seems to be particularly powerful, with diagnostic accuracy reaching up to or even greater than 90%. While diagnostic performance can be high with appropriate implementation and interpretation, even a combination of techniques, however, does not provide 100% accuracy. It should also be noted that most studies to date are small, heterogeneous, and retrospective in nature. Future improvements in diagnostic accuracy can be expected with harmonization of acquisition and postprocessing, quantitative MRI and computer-aided diagnostic technology, and meticulous evaluation with clinical and pathological data.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

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.

Contrast-Enhanced Perfusion MR Imaging to Differentiate Between Recurrent/Residual Brain Neoplasms and Radiation Necrosis

Purpose:

To determine the value of dynamic susceptibility contrast enhanced (DSC) MRI (magnetic resonance imaging) perfusion in the characterization of newly developed/enlarging lesions within irradiated regions after treatment of brain tumors.

Methods:

This prospective cross-sectional study covered 23 patients, 12 females and 11 males. All cases initially presented with histologically proven malignant brain tumors and underwent surgical intervention followed by radiotherapy (+/- chemotherapy). On follow up imaging, they presented with newly developed/progressively enhancing mass lesions at the sites of the primary tumors. All patients then underwent conventional MRI, DSC MRI perfusion and MR spectroscopy.

Results:

In our study, we found DSC MR perfusion to be a useful non-invasive method for differentiating recurrent brain tumors from radiation necrosis. This approach allows hemodynamic measurements to be obtained within the brain as the relative cerebral blood volume (rCBV) to complement the anatomic information obtained with conventional contrast enhanced MR imaging. The sensitivity and specificity of DSC MR perfusion for differentiation were found to be 77.8% and 80.0%, respectively.

Conclusion:

DSC MR perfusion is a promising technique in differentiating recurrent brain tumors from radiation necrosis as it has acceptable spatial resolution and can be routinely performed in the same settings after conventional MRI.

Biophysical Modeling of In Vivo Glioma Response After Whole-Brain Radiation Therapy in a Murine Model of Brain Cancer

PURPOSE

To develop and investigate a set of biophysical models based on a mechanically coupled reaction-diffusion model of the spatiotemporal evolution of tumor growth after radiation therapy.

METHODS AND MATERIALS

Post-radiation therapy response is modeled using a cell death model (Md), a reduced proliferation rate model (Mp), and cell death and reduced proliferation model (Mdp). To evaluate each model, rats (n = 12) with C6 gliomas were imaged with diffusion-weighted magnetic resonance imaging (MRI) and contrast-enhanced MRI at 7 time points over 2 weeks. Rats received either 20 or 40 Gy between the third and fourth imaging time point. Diffusion-weighted MRI was used to estimate tumor cell number within enhancing regions in contrast-enhanced MRI data. Each model was fit to the spatiotemporal evolution of tumor cell number from time point 1 to time point 5 to estimate model parameters. The estimated model parameters were then used to predict tumor growth at the final 2 imaging time points. The model prediction was evaluated by calculating the error in tumor volume estimates, average surface distance, and voxel-based cell number.

RESULTS

For both the rats treated with either 20 or 40 Gy, significantly lower error in tumor volume, average surface distance, and voxel-based cell number was observed for the Mdp and Mp models compared with the Md model. The Mdp model fit, however, had significantly lower sum squared error compared with the Mp and Md models.

CONCLUSIONS

The results of this study indicate that for both doses, the Mp and Mdp models result in accurate predictions of tumor growth, whereas the Md model poorly describes response to radiation therapy.

Noninvasive Glioblastoma Testing: Multimodal Approach to Monitoring and Predicting Treatment Response

Glioblastoma is the most aggressive adult primary brain tumor which is incurable despite intensive multimodal treatment. Inter- and intratumoral heterogeneity poses one of the biggest barriers in the diagnosis and treatment of glioblastoma, causing differences in treatment response and outcome. Noninvasive prognostic and predictive tests are highly needed to complement the current armamentarium. Noninvasive testing of glioblastoma uses multiple techniques that can capture the heterogeneity of glioblastoma. This set of diagnostic approaches comprises advanced MRI techniques, nuclear imaging, liquid biopsy, and new integrated approaches including radiogenomics and radiomics. New treatment options such as agents targeted at driver oncogenes and immunotherapy are currently being developed, but benefit for glioblastoma patients still has to be demonstrated. Understanding and unraveling tumor heterogeneity and microenvironment can help to create a treatment regime that is patient-tailored to these specific tumor characteristics. Improved noninvasive tests are crucial to this success. This review discusses multiple diagnostic approaches and their effect on predicting and monitoring treatment response in glioblastoma.

An Update on the Approach to the Imaging of Brain Tumors

PURPOSE OF REVIEW

Neuroimaging plays a critical role in diagnosis of brain tumors and in assessment of response to therapy. However, challenges remain, including accurately and reproducibly assessing response to therapy, defining endpoints for neuro-oncology trials, providing prognostic information, and differentiating progressive disease from post-therapeutic changes particularly in the setting of antiangiogenic and other novel therapies.

RECENT FINDINGS

Recent advances in the imaging of brain tumors include application of advanced MRI imaging techniques to assess tumor response to therapy and analysis of imaging features correlating to molecular markers, grade, and prognosis. This review aims to summarize recent advances in imaging as applied to current diagnostic and therapeutic neuro-oncologic challenges.

Reirradiation with concurrent bevacizumab for recurrent high-grade gliomas in adult patients

PURPOSE

To analyse feasibility, prognostic factors and patterns of recurrence after concurrent reirradiation and bevacizumab for recurrent high-grade gliomas.

PATIENTS AND METHODS

Between 2009 and 2015, 35 patients (median 57-year-old; 21 men, 14 women) with WHO grade III (n=11) or grade IV (n=24) gliomas were included in this retrospective and consecutive single-centre study. All patients received bevacizumab (median number of treatments: 12) concomitant with reirradiation (median dose: 45Gy, median number of fractions: 18) for recurrence with median 22 months (range: 5.6-123.7 months) from first irradiation (median dose: 60Gy).

RESULTS

The median follow-up was 9.2 months from reirradiation. The median overall survival from reirradiation was 10.5 months (95% confidence interval [95% CI]: 4.9-16.1) and the progression-free survival from reirradiation was 6.7 months (95% CI: 2.9-10.5). The median overall survival from initial diagnosis was 44.6 months (95% CI: 32-57.1). No grade 3 toxicity or above was reported. Prognostic factors significantly correlated with better overall survival in univariate analysis were: age at least 55 (P=0.024), initial surgery (P=0.003), and 2Gy equivalent dose (EQD2) at least 50Gy at reirradiation (P=0.046). Twenty-two patients bevacizumab-naïve at time of reirradiation had a significantly increased overall survival from reirradiation compared to patients treated with reirradiation after bevacizumab failure (17.7 vs. 5.4 months, P<0.001) as well as overall survival from initial diagnosis (58.9 vs. 33.5 months, P=0.006). This outcome was similar in patients with initial glioblastomas (P=0.018) or anaplastic gliomas (P=0.021). There was no correlation between overall survival and gross tumour volume or planning target volume, frontal localization, or number of salvage therapies before reirradiation (P>0.05).

CONCLUSIONS

Concomitant reirradiation with bevacizumab in high-grade recurrent gliomas shows encouraging results in terms of survival and toxicities. Our data suggest that reirradiation should be favoured at initiation of bevacizumab, with EQD2 at least 50Gy.

Long-Term Follow-up of 25 Cases of Biopsy-Proven Radiation Necrosis or Post-Radiation Treatment Effect Treated With Magnetic Resonance-Guided Laser Interstitial Thermal Therapy

BACKGROUND

Magnetic resonance-guided laser-induced thermal therapy (MRgLITT) is a minimally invasive surgical treatment for progressive neoplasms and post-radiation treatment effect (PRTE).

OBJECTIVE

To evaluate the radiographic response and efficacy of MRgLITT for biopsy-confirmed PRTE and the quality-of-life outcomes of patients following MRgLITT.

METHODS

We conducted a single-center retrospective study of radiographic responses and clinical outcomes of 25 patients with previously treated primary or secondary brain neoplasms (World Health Organization grades 4 [n = 8], 3 [n = 5], 2 [n = 5]) and metastatic brain tumors (n = 7). MRgLITT was applied directly following stereotactic needle biopsy confirming PRTE without any evidence of tumor presence.

RESULTS

Mean overall survival times (months) for grades 4 and 3 and for metastatic brain tumors were 39.2 (standard error [SE], 7.6; 95% confidence interval [CI], 24.3-54.1), 29.1 (SE, 7.7; 95% CI, 14.0-44.2), and 55.9 (SE, 10.0; 95% CI, 36.3-75.4), respectively. Mean progression-free survival times after MRgLITT were 9.1 (SE, 3.6; 95% CI, 2.1-16.1), 8.5 (SE, 2.4; 95% CI, 3.9-13.2), and 11.4 (SE, 3.9; 95% CI, 3.8-19.0), respectively. Mean survival times after MRgLITT were 13.1 (SE, 2.3; 95% CI, 8.5-17.6), 12.2 (SE, 4.0; 95% CI, 4.4-20.0), and 19.2 (SE, 5.3; 95% CI, 8.9-29.6), respectively. The SF-36 indicated significant overall effects on mental health (P = .029) and vitality (P = .005).

CONCLUSION

MRgLITT may be a viable option for patients with symptomatic advancing PRTE and is less invasive than open craniotomy. Although our results suggest a positive effect for MRgLITT on PRTE, prospective randomized trials with larger numbers of patients are needed to validate the study results.

ABBREVIATIONS

cRBV, relative cerebral blood volumeHIF1a, hypoxia-inducible factor 1aIMRT, intensity-modulated radiation therapyKPS, Karnofsky Performance StatusLITT, laser-induced thermal therapyMBT, metastatic brain tumorMRgLITT, magnetic resonance-guided laser-induced thermal therapyPRTE, post-radiation treatment effectSRS, stereotactic radiosurgeryVEGF, vascular endothelial growth factorWBXRT, whole brain radiation therapy.

MRI assessment of treatment delivery for interstitial photodynamic therapy of high-grade glioma in a preclinical model

BACKGROUND

High-grade gliomas are primary brain tumors that have shown increasing incidence and unfavorable outcomes. Local control is crucial to the management of this pathology. Photodynamic therapy (PDT), based on the light-induced activation of a photosensitizer (PS), achieves local treatment by inducing selective lesions in tumor tissue.

OBJECTIVES

Previous studies have reported the outcomes of PDT for glioblastoma via immunohistological data. Our study aimed to evaluate MRI findings, including diffusion, and perfusion sequences, compared with immunohistological data from the same population to address the efficiency of light fractionation.

MATERIALS AND METHODS

Twenty-six "nude" rats grafted with human U87 cells into the right putamen underwent PDT. After PS precursor (5-ALA) intake, an optical fiber was introduced into the tumor. The rats were randomized into the following groups: those without illumination and those that received two or five fractions of light. Treatment effects were assessed with early high-field MRI to measure the volume of necrosis and edema using diffusion and perfusion sequences; the MRI results were compared with immunohistology results, including necrosis and apoptosis markers.

RESULTS

Elevated diffusion values were observed on MRI in the centers of the tumors of the treated animals, especially in the 5-fraction group (P < 0.01). Perfusion was decreased around the treatment site, especially in the 5-fraction group (P = 0.024). The MRI findings were consistent with previously published histological data. The median volume of necrosis was significantly different between the sham group and treated groups, 0 mm³ versus 2.67 mm³ , P < 0.001. The same trend was previously observed in histology data when grading the absence or presence of necrosis and when the presence of necrosis was significantly more predominant for the treated group than for the untreated group (P < 001). Additionally, cell death represented by apoptosis marker data (TUNEL method) was significantly higher in the 5-fraction group than in the 2-fraction group (P = 0.01).

CONCLUSION

Diffusion and perfusion MRI revealed histological lesions. Interstitial PDT (iPDT) induced specific lesions in the tumor tissue, which were observed with MRI and confirmed by histopathological analysis. Thus, MRI may provide a non-invasive and reliable tool to assess treatment outcomes after PDT. Lasers Surg. Med. 50:460-468, 2018. © 2017 Wiley Periodicals, Inc.

Spatial discrimination of glioblastoma and treatment effect with histologically-validated perfusion and diffusion magnetic resonance imaging metrics

The goal of this study is to spatially discriminate tumor from treatment effect (TE), within the contrast-enhancing lesion, for brain tumor patients at all stages of treatment. To this end, the diagnostic accuracy of MRI-derived diffusion and perfusion parameters to distinguish pure TE from pure glioblastoma (GBM) was determined utilizing spatially-correlated biopsy samples. From July 2010 through June 2015, brain tumor patients who underwent pre-operative DWI and DSC-MRI and stereotactic image-guided biopsy were considered for inclusion in this IRB-approved study. MRI-derived parameter maps included apparent diffusion coefficient (ADC), normalized cerebral blood flow (nCBF), normalized and standardized relative cerebral blood volume (nRCBV, sRCBV), peak signal-height (PSR) and percent signal-recovery (PSR). These were co-registered to the Stealth MRI and median values extracted from the spatially-matched biopsy regions. A ROC analysis accounting for multiple subject samples was performed, and the optimal threshold for distinguishing TE from GBM determined for each parameter. Histopathologic diagnosis of pure TE (n = 10) or pure GBM (n = 34) was confirmed in tissue samples from 15 consecutive subjects with analyzable data. Perfusion thresholds of sRCBV (3575; SN/SP% = 79.4/90.0), nRCBV (1.13; SN/SP% = 82.1/90.0), and nCBF (1.05; SN/SP% = 79.4/80.0) distinguished TE from GBM (P < 0.05), whereas ADC, PSR, and PH could not (P > 0.05). The thresholds for CBF and CBV can be applied to lesions with any admixture of tumor or treatment effect, enabling the identification of true tumor burden within enhancing lesions. This approach overcomes current limitations of averaging values from both tumor and TE for quantitative assessments.

Response Assessment in Neuro-Oncology criteria, contrast enhancement and perfusion MRI for assessing progression in glioblastoma

PURPOSE

The purpose of the study was to evaluate Response Assessment in Neuro-Oncology (RANO) criteria in glioblastoma multiforme (GBM), with respect to the Macdonald criteria and changes in contrast-enhancement (CE) volume. Related variations in relative cerebral blood volume (rCBV) were investigated.

METHODS

Forty-three patients diagnosed between 2006 and 2010 were included. All underwent surgical resection, followed by temozolomide-based chemoradiation. MR images were retrospectively reviewed. Times to progression (TTPs) according to RANO criteria, Macdonald criteria and increased CE volume (CE-3D) were compared, and the percentage change in the 75th percentile of rCBV (rCBV75) was evaluated.

RESULTS

After a median follow-up of 22.7 months, a total of 39 patients had progressed according to RANO criteria, 32 according to CE-3D, and 42 according to Macdonald. Median TTPs were 6.4, 9.3, and 6.6 months, respectively. Overall agreement was 79.07% between RANO and CE-3D and 93.02% between RANO and Macdonald. The mean percentage change in rCBV75 at RANO progression onset was over 73% in 87.5% of patients.

CONCLUSIONS

In conclusion, our findings suggest that CE-3D criterion is not yet suitable to assess progression in routine clinical practice. Indeed, the accurate threshold is still not well defined. To date, in our opinion, early detection of disease progression by RANO combined with advanced MRI imaging techniques like MRI perfusion and diffusion remains the best way to assess disease progression. Further investigations that would examine the impact of treatment modifications after progression determined by different criteria on overall survival would be of great value.

Longitudinal DSC-MRI for Distinguishing Tumor Recurrence From Pseudoprogression in Patients With a High-grade Glioma

OBJECTIVE

For patients with high-grade glioma on clinical trials it is important to accurately assess time of disease progression. However, differentiation between pseudoprogression (PsP) and progressive disease (PD) is unreliable with standard magnetic resonance imaging (MRI) techniques. Dynamic susceptibility contrast perfusion MRI (DSC-MRI) can measure relative cerebral blood volume (rCBV) and may help distinguish PsP from PD.

METHODS

A subset of patients with high-grade glioma on a phase II clinical trial with temozolomide, paclitaxel poliglumex, and concurrent radiation were assessed. Nine patients (3 grade III, 6 grade IV), with a total of 19 enhancing lesions demonstrating progressive enhancement (≥25% increase from nadir) on postchemoradiation conventional contrast-enhanced MRI, had serial DSC-MRI. Mean leakage-corrected rCBV within enhancing lesions was computed for all postchemoradiation time points.

RESULTS

Of the 19 progressively enhancing lesions, 10 were classified as PsP and 9 as PD by biopsy/surgery or serial enhancement patterns during interval follow-up MRI. Mean rCBV at initial progressive enhancement did not differ significantly between PsP and PD (2.35 vs. 2.17; P=0.67). However, change in rCBV at first subsequent follow-up (-0.84 vs. 0.84; P=0.001) and the overall linear trend in rCBV after initial progressive enhancement (negative vs. positive slope; P=0.04) differed significantly between PsP and PD.

CONCLUSIONS

Longitudinal trends in rCBV may be more useful than absolute rCBV in distinguishing PsP from PD in chemoradiation-treated high-grade gliomas with DSC-MRI. Further studies of DSC-MRI in high-grade glioma as a potential technique for distinguishing PsP from PD are indicated.

MR-guided radiation therapy: transformative technology and its role in the central nervous system

This review article describes advancement of magnetic resonance imaging technologies in radiation therapy planning, guidance, and adaptation of brain tumors. The potential for MR-guided radiation therapy to improve outcomes and the challenges in its adoption are discussed.

Improving the utility of 1H-MRS for the differentiation of glioma recurrence from radiation necrosis

Proton magnetic resonance spectroscopy (¹H-MRS) has shown promise in distinguishing recurrent high-grade glioma from posttreatment radiation effect (PTRE). The purpose of this study was to establish objective ¹H-MRS criteria based on metabolite peak height ratios to distinguish recurrent tumor (RT) from PTRE. A retrospective analysis of magnetic resonance imaging and ¹H-MRS data was performed. Spectral metabolites analyzed included N-acetylaspartate, choline (Cho), creatine (Cr), lactate (Lac), and lipids (Lip). Quantitative ¹H-MRS criteria to differentiate RT from PTRE were identified using 81 biopsy-matched spectral voxels. A receiver operating characteristic curve analysis was conducted for all metabolite ratio combinations with the pathology diagnosis as the classification variable. Forward discriminant analysis was used to identify ratio variables that maximized the correct classification of RT versus PTRE. Our results were applied to 205 records without biopsy-matched voxels to examine the percent agreement between our criteria and the radiologic diagnoses. Five ratios achieved an acceptable balance [area under the curve (AUC) ≥ 0.700] between sensitivity and specificity for distinguishing RT from PTRE, and each ratio defined a criterion for diagnosing RT. The ratios are as follows: Cho/Cr > 1.54 (sensitivity 66%, specificity 79%), Cr/Cho ≤ 0.63 (sensitivity 65%, specificity 79%), Lac/Cho ≤ 2.67 (sensitivity 85%, specificity 58%), Lac/Lip ≤ 1.64 (sensitivity 54%, specificity 95%), and Lip/Lac > 0.58 (sensitivity 56%, specificity 95%). Application of our ratio criteria in prospective studies may offer an alternative to biopsy or visual spectral pattern recognition to distinguish RT from PTRE in patients with gliomas.

Analysis of heterogeneity in T2-weighted MR images can differentiate pseudoprogression from progression in glioblastoma

PURPOSE

To develop an image analysis technique that distinguishes pseudoprogression from true progression by analyzing tumour heterogeneity in T2-weighted images using topological descriptors of image heterogeneity called Minkowski functionals (MFs).

METHODS

Using a retrospective patient cohort (n = 50), and blinded to treatment response outcome, unsupervised feature estimation was performed to investigate MFs for the presence of outliers, potential confounders, and sensitivity to treatment response. The progression and pseudoprogression groups were then unblinded and supervised feature selection was performed using MFs, size and signal intensity features. A support vector machine model was obtained and evaluated using a prospective test cohort.

RESULTS

The model gave a classification accuracy, using a combination of MFs and size features, of more than 85% in both retrospective and prospective datasets. A different feature selection method (Random Forest) and classifier (Lasso) gave the same results. Although not apparent to the reporting radiologist, the T2-weighted hyperintensity phenotype of those patients with progression was heterogeneous, large and frond-like when compared to those with pseudoprogression.

CONCLUSION

Analysis of heterogeneity, in T2-weighted MR images, which are acquired routinely in the clinic, has the potential to detect an earlier treatment response allowing an early change in treatment strategy. Prospective validation of this technique in larger datasets is required.

Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization

Background

Segmentation of gliomas in multi-parametric (MP-)MR images is challenging due to their heterogeneous nature in terms of size, appearance and location. Manual tumor segmentation is a time-consuming task and clinical practice would benefit from (semi-) automated segmentation of the different tumor compartments.

Methods

We present a semi-automated framework for brain tumor segmentation based on non-negative matrix factorization (NMF) that does not require prior training of the method. L1-regularization is incorporated into the NMF objective function to promote spatial consistency and sparseness of the tissue abundance maps. The pathological sources are initialized through user-defined voxel selection. Knowledge about the spatial location of the selected voxels is combined with tissue adjacency constraints in a post-processing step to enhance segmentation quality. The method is applied to an MP-MRI dataset of 21 high-grade glioma patients, including conventional, perfusion-weighted and diffusion-weighted MRI. To assess the effect of using MP-MRI data and the L1-regularization term, analyses are also run using only conventional MRI and without L1-regularization. Robustness against user input variability is verified by considering the statistical distribution of the segmentation results when repeatedly analyzing each patient’s dataset with a different set of random seeding points.

Results

Using L1-regularized semi-automated NMF segmentation, mean Dice-scores of 65%, 74 and 80% are found for active tumor, the tumor core and the whole tumor region. Mean Hausdorff distances of 6.1 mm, 7.4 mm and 8.2 mm are found for active tumor, the tumor core and the whole tumor region. Lower Dice-scores and higher Hausdorff distances are found without L1-regularization and when only considering conventional MRI data.

Conclusions

Based on the mean Dice-scores and Hausdorff distances, segmentation results are competitive with state-of-the-art in literature. Robust results were found for most patients, although careful voxel selection is mandatory to avoid sub-optimal segmentation.

Dynamic susceptibility contrast and dynamic contrast-enhanced MRI characteristics to distinguish microcystic meningiomas from traditional Grade I meningiomas and high-grade gliomas

OBJECTIVE

Microcystic meningioma (MM) is a meningioma variant with a multicystic appearance that may mimic intrinsic primary brain tumors and other nonmeningiomatous tumor types. Dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) MRI techniques provide imaging parameters that can differentiate these tumors according to hemodynamic and permeability characteristics with the potential to aid in preoperative identification of tumor type.

METHODS

The medical data of 18 patients with a histopathological diagnosis of MM were identified through a retrospective review of procedures performed between 2008 and 2012; DSC imaging data were available for 12 patients and DCE imaging data for 6. A subcohort of 12 patients with Grade I meningiomas (i.e., of meningoepithelial subtype) and 54 patients with Grade IV primary gliomas (i.e., astrocytomas) was also included, and all preoperative imaging sequences were analyzed. Clinical variables including patient sex, age, and surgical blood loss were also included in the analysis. Images were acquired at both 1.5 and 3.0 T. The DSC images were acquired at a temporal resolution of either 1500 msec (3.0 T) or 2000 msec (1.5 T). In all cases, parameters including normalized cerebral blood volume (CBV) and transfer coefficient (kTrans) were calculated with region-of-interest analysis of enhancing tumor volume. The normalized CBV and kTrans data from the patient groups were analyzed with 1-way ANOVA, and post hoc statistical comparisons among groups were conducted with the Bonferroni adjustment.

RESULTS

Preoperative DSC imaging indicated mean (± SD) normalized CBVs of 5.7 ± 2.2 ml for WHO Grade I meningiomas of the meningoepithelial subtype (n = 12), 4.8 ± 1.8 ml for Grade IV astrocytomas (n = 54), and 12.3 ± 3.8 ml for Grade I meningiomas of the MM subtype (n = 12). The normalized CBV measured within the enhancing portion of the tumor was significantly higher in the MM subtype than in typical meningiomas and Grade IV astrocytomas (p < 0.001 for both). Preoperative DCE imaging indicated mean kTrans values of 0.49 ± 0.20 min−1 in Grade I meningiomas of the meningoepithelial subtype (n = 12), 0.27 ± 0.12 min−1 for Grade IV astrocytomas (n = 54), and 1.35 ± 0.74 min−1 for Grade I meningiomas of the MM subtype (n = 6). The kTrans was significantly higher in the MM variants than in the corresponding nonmicrocystic Grade 1 meningiomas and Grade IV astrocytomas (p < 0.001 for both). Intraoperative blood loss tended to increase with increased normalized CBV (R = 0.45, p = 0.085).

CONCLUSIONS

An enhancing cystic lesion with a normalized CBV greater than 10.3 ml or a kTrans greater than 0.88 min−1 should prompt radiologists and surgeons to consider the diagnosis of MM rather than traditional Grade I meningioma or high-grade glioma in planning surgical care. Higher normalized CBVs tend to be associated with increased intraoperative blood loss.

Classifying Glioblastoma Multiforme Follow-Up Progressive vs. Responsive Forms Using Multi-Parametric MRI Features

Purpose: The purpose of this paper is discriminating between tumor progression and response to treatment based on follow-up multi-parametric magnetic resonance imaging (MRI) data retrieved from glioblastoma multiforme (GBM) patients.

Materials and Methods: Multi-parametric MRI data consisting of conventional MRI (cMRI) and advanced MRI [i.e., perfusion weighted MRI (PWI) and diffusion kurtosis MRI (DKI)] were acquired from 29 GBM patients treated with adjuvant therapy after surgery. We propose an automatic pipeline for processing advanced MRI data and extracting intensity-based histogram features and 3-D texture features using manually and semi-manually delineated regions of interest (ROIs). Classifiers are trained using a leave-one-patient-out cross validation scheme on complete MRI data. Balanced accuracy rate (BAR)–values are computed and compared between different ROIs, MR modalities, and classifiers, using non-parametric multiple comparison tests.

Results: Maximum BAR–values using manual delineations are 0.956, 0.85, 0.879, and 0.932, for cMRI, PWI, DKI, and all three MRI modalities combined, respectively. Maximum BAR–values using semi-manual delineations are 0.932, 0.894, 0.885, and 0.947, for cMRI, PWI, DKI, and all three MR modalities combined, respectively. After statistical testing using Kruskal-Wallis and post-hoc Dunn-Šidák analysis we conclude that training a RUSBoost classifier on features extracted using semi-manual delineations on cMRI or on all MRI modalities combined performs best.

Conclusions: We present two main conclusions: (1) using T1 post-contrast (T1pc) features extracted from manual total delineations, AdaBoost achieves the highest BAR–value, 0.956; (2) using T1pc-average, T1pc-90th percentile, and Cerebral Blood Volume (CBV) 90th percentile extracted from semi-manually delineated contrast enhancing ROIs, SVM-rbf, and RUSBoost achieve BAR–values of 0.947 and 0.932, respectively. Our findings show that AdaBoost, SVM-rbf, and RUSBoost trained on T1pc and CBV features can differentiate progressive from responsive GBM patients with very high accuracy.

IDH mutation and MGMT promoter methylation are associated with the pseudoprogression and improved prognosis of glioblastoma multiforme patients who have undergone concurrent and adjuvant temozolomide-based chemoradiotherapy

PURPOSE

This study aimed to investigate the potential association between IDH mutation and O⁶-methyl-guanine methyl transferase (MGMT) gene promoter methylation and pseudoprogression disease (psPD) in glioblastoma multiforme (GBM) patients after concurrent temozolomide (TMZ)-based chemoradiotherapy.

METHODS

A total of 157 GBM patients who received concurrent TMZ-based chemoradiotherapy were included in this retrospective study. The association between psPD and a number of demographic and genetic factors, including IDH mutation and MGMT promoter methylation, were analyzed based on logistic regression, Cox regression, and multivariate analysis.

RESULTS

Of the 157 GBM patients, 145 (92.36%) patients, including 38 patients with psPD, 38 patients with early progression (ePD), and 69 patients with non-progression (non-PD), were followed up for six to 56 months. We identified a higher rate of MGMT promoter methylation and IDH1 mutation in psPD patients compared with ePD patients (P=0.002). In addition, MGMT promoter methylation and IDH1 mutation predicted a high probability of psPD development in GBM patients (P=0.001 and P<0.001, respectively). MGMT promoter methylation, IDH1 mutation, Karnofsky performance score (KPS) ≥70, and psPD were associated with a significantly longer overall survival of GBM patients (P=0.001, 0.001, 0.002, and P<0.001, respectively). Both of MGMT promoter methylation and IDH mutation had a cumulative effect on the OS of GBM patients. GBM patients with psPD (39.2±2.1months, P<0.001) had a longer median survival (MS) than GBM patients with ePD (11.9±1.1months) or with non-PD (24.4±2.4months).

CONCLUSION

MGMT promoter methylation and IDH1 mutation were associated with PsPD and predicted a longer median survival in GBM patients after TMZ-based chemoradiotherapy. Genetic analyses of the MGMT promoter and IDH1 may allow us to effectively treat GBM patients.