Perfusion MRI as the predictive/prognostic and pharmacodynamic biomarkers in recurrent malignant glioma treated with bevacizumab: a systematic review and a time-to-event meta-analysis

The apparent diffusion coefficient can serve as a predictor of survival in patients with gliomas

BACKGROUND AND PURPOSE

Magnetic resonance imaging is indispensable for the preoperative diagnosis of glioma. This study aimed to investigate the role of the apparent diffusion coefficient values as predictors of survival in patients with gliomas.

METHODS AND MATERIALS

A retrospective analysis was conducted on 101 patients with gliomas who underwent surgery between 2015 and 2020. Diffusion-weighted MRI was performed before the surgery. The regions of interest were categorized into parenchymal area, non-enhancing peritumoral area, and necrotic or cystic area. All the patients were divided into three subgroups: the parenchyma group, the non-enhancing peritumoral signal abnormality group, and the necrosis or cyst group. Univariate and multivariate analyses were performed using COX regression.

RESULTS

In the parenchymal group, Ki67, P53, IDH, and the high or low ADC values were identified as independent prognosticators for disease-free survival, while Ki67, IDH, and the high or low ADC values for overall survival. In the non-enhancing peritumoral signal abnormality group, Ki67, P53, IDH, and the ADC parenchymal area/ADC non-enhancing peritumoral area ratio were identified as independent prognostic factors for disease-free survival, while Ki67, IDH, and the ADC parenchymal area/ADC non-enhancing peritumoral area ratio for overall survival. In the necrosis or cyst group, Ki67 was significantly associated with disease-free survival, while Ki67 and the ADC value of the necrotic or cystic area for overall survival.

CONCLUSIONS

The ADC values, including the ADC value in the parenchymal area, the ADC parenchymal area/ADC non-enhancing peritumoral area ratio, and the ADC value in the necrotic or cystic area, can serve as an efficient and potential index for predicting the survival of patients with glioma.

Evaluation of the neoadjuvant chemotherapy response in osteosarcoma using the MRI DWI-based machine learning radiomics nomogram

Objective

To evaluate the value of a nomogram combined MRI Diffusion Weighted Imaging (DWI) and clinical features to predict the treatment response of Neoadjuvant Chemotherapy (NAC) in patients with osteosarcoma.

Methods

A retrospective analysis was conducted on 209 osteosarcoma patients admitted into two bone cancer treatment centers (133 males, 76females; mean age 16.31 ± 11.42 years) from January 2016 to January 2022. Patients were classified as pathological good responders (pGRs) if postoperative histopathological examination revealed ≥90% tumor necrosis, and non-pGRs if <90%. Their clinical features were subjected to univariate and multivariate analysis, and features with statistically significance were utilized to construct a clinical signature using machine learning algorithms. Apparent diffusion coefficient (ADC) values pre-NAC (ADC 0) and post two chemotherapy cycles (ADC 1) were recorded. Regions of interest (ROIs) were delineated from pre-treatment DWI images (b=1000 s/mm²) for radiomic features extraction. Variance thresholding, SelectKBest, and LASSO regression were used to select features with strong relevance, and three machine learning models (Logistic Regression, RandomForest and XGBoost) were used to construct radiomics signatures for predicting treatment response. Finally, the clinical and radiomics signatures were integrated to establish a comprehensive nomogram model. Predictive performance was assessed using ROC curve analysis, with model clinical utility appraised through AUC and decision curve analysis (DCA).

Results

Of the 209patients, 51 (24.4%) were pGRs, while 158 (75.6%) were non-pGRs. No significant ADC1 difference was observed between groups (P>0.05), but pGRs had a higher ADC 0 (P<0.01). ROC analysis indicated an AUC of 0.681 (95% CI: 0.482-0.862) for ADC 0 at the threshold of ≥1.37×10-3 mm²/s, achieving 74.7% sensitivity and 75.7% specificity. The clinical and radiomics models reached AUCs of 0.669 (95% CI: 0.401-0.826) and 0.768 (95% CI: 0.681-0.922) respectively in the test set. The combined nomogram displayed superior discrimination with an AUC of 0.848 (95% CI: 0.668-0.951) and 75.8% accuracy. The DCA suggested the clinical utility of the nomogram.

Conclusion

The nomogram based on combined radiomics and clinical features outperformed standalone clinical or radiomics model, offering enhanced accuracy in evaluating NAC response in osteosarcoma. It held significant promise for clinical applications.

MR Perfusion Imaging for Gliomas

Accurate diagnosis and treatment evaluation of patients with gliomas is imperative to make clinical decisions. Multiparametric MR perfusion imaging reveals physiologic features of gliomas that can help classify them according to their histologic and molecular features as well as distinguish them from other neoplastic and nonneoplastic entities. It is also helpful in distinguishing tumor recurrence or progression from radiation necrosis, pseudoprogression, and pseudoresponse, which is difficult with conventional MR imaging. This review provides an update on MR perfusion imaging for the diagnosis and treatment monitoring of patients with gliomas following standard-of-care chemoradiation therapy and other treatment regimens such as immunotherapy.

Multimodel habitats constructed by perfusion and/or diffusion MRI predict isocitrate dehydrogenase mutation status and prognosis in high-grade gliomas

AIM

To determine whether tumour vascular and cellular heterogeneity of high-grade glioma (HGG) is predictive of isocitrate dehydrogenase (IDH) mutation status and overall survival (OS) by using tumour habitat-based analysis constructed by perfusion and/or diffusion magnetic resonance imaging (MRI).

MATERIALS AND METHODS

Seventy-eight HGG patients that met the 2021 World Health Organization WHO Classification of Tumors of the Central Nervous System, 5th edition (WHO CNS5), were enrolled to predict IDH mutation status, of which 32 grade 4 patients with unmethylated O6-methylguanine-DNA methyltransferase (MGMT) promoter were enrolled for prognostic analysis. The deep-learning-based model nnU-Net and K-means clustering algorithm were applied to construct the Traditional Habitat, Vascular Habitat (VH), Cellular Density Habitat (DH), and their Combined Habitat (CH). Quantitative parameters were extracted and compared between IDH-mutant and IDH-wild-type patients, respectively, and the prediction potential was evaluated by receiver operating characteristic (ROC) curve analysis. OS was analysed using Kaplan-Meier survival analysis and the log-rank test.

RESULTS

Compared with IDH-mutants, median relative cerebral blood volume (rCBVmedian) values in the whole enhancing tumour (WET), VH1, VH3, CH1-4 habitats were significantly increased in IDH-wild-type HGGs (all p<0.05). Additionally, the accuracy of rCBVmedian values in CH1 outperformed other habitats in identifying IDH mutation status (p<0.001) at a cut-off value of 4.83 with AUC of 0.815. Kaplan-Meier survival analysis highlighted significant differences in OS between the populations dichotomised by the median of rCBVmedian in WET, VH1, CH1-3 habitats (all p<0.05).

CONCLUSIONS

The habitat imaging technique may improve the accuracy of predicting IDH mutation status and prognosis, and even provide a new direction for subsequent personalised precision treatment.

Relative cerebral blood volume as response predictor in the treatment of recurrent glioblastoma with anti-angiogenic therapy

BACKGROUND

Glioblastoma is one of the most common brain tumors in adult populations, usually carrying a poor prognosis. While several studies have researched the impact of anti-angiogenic therapies, especially anti-VEFG treatments in glioblastoma, few have attempted to assess its progress using imaging studies.

PURPOSE

We attempted to analyze whether relative cerebral blood volume (rCBV) from dynamic susceptibility-weighted contrast-enhanced MRI (DSC-MRI) could predict response in patients with glioblastoma undergoing Bevacizumab (BVZ) treatment.

METHODS

We performed a retrospective study evaluating patients with recurrent glioblastoma receiving anti-angiogenic therapy with BVZ between 2012 and 2017 in our institution. Patients were scheduled for routine MRIs at baseline and first-month follow-up visits. Studies were processed for DSC-MRI, cT1, and FLAIR images, from which relative cerebral blood volume measurements were obtained. We assessed patient response using the Response Assessment in Neuro-Oncology (RANO) working group criteria and overall survival.

RESULTS

40 patients were included in the study and were classified as Bevacizumab responders and non-responders. The average rCBV before treatment was 4.5 for both groups, and average rCBV was 2.5 for responders and 5.4 for non-responders. ROC curve set a cutoff point of 3.7 for rCBV predictive of response to BVZ. Cox Multivariate analysis only showed rCBV as a predictive factor of OS.

CONCLUSION

A statistically significant difference was found in rCBV between patients who responded and those who did not respond to BVZ treatment. rCBV may be a low-cost and effective marker to assess response to Bevacizumab treatment in GBM.

Perfusion-weighted software written in Python for DSC-MRI analysis

Introduction

Dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion studies in magnetic resonance imaging (MRI) provide valuable data for studying vascular cerebral pathophysiology in different rodent models of brain diseases (stroke, tumor grading, and neurodegenerative models). The extraction of these hemodynamic parameters via DSC-MRI is based on tracer kinetic modeling, which can be solved using deconvolution-based methods, among others. Most of the post-processing software used in preclinical studies is home-built and custom-designed. Its use being, in most cases, limited to the institution responsible for the development. In this study, we designed a tool that performs the hemodynamic quantification process quickly and in a reliable way for research purposes.

Methods

The DSC-MRI quantification tool, developed as a Python project, performs the basic mathematical steps to generate the parametric maps: cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), signal recovery (SR), and percentage signal recovery (PSR). For the validation process, a data set composed of MRI rat brain scans was evaluated: i) healthy animals, ii) temporal blood-brain barrier (BBB) dysfunction, iii) cerebral chronic hypoperfusion (CCH), iv) ischemic stroke, and v) glioblastoma multiforme (GBM) models. The resulting perfusion parameters were then compared with data retrieved from the literature.

Results

A total of 30 animals were evaluated with our DSC-MRI quantification tool. In all the models, the hemodynamic parameters reported from the literature are reproduced and they are in the same range as our results. The Bland-Altman plot used to describe the agreement between our perfusion quantitative analyses and literature data regarding healthy rats, stroke, and GBM models, determined that the agreement for CBV and MTT is higher than for CBF.

Conclusion

An open-source, Python-based DSC post-processing software package that performs key quantitative perfusion parameters has been developed. Regarding the different animal models used, the results obtained are consistent and in good agreement with the physiological patterns and values reported in the literature. Our development has been built in a modular framework to allow code customization or the addition of alternative algorithms not yet implemented.

Skull metastases and osseous venous malformations: The role of diffusion-weighted and dynamic contrast-enhanced MRI

BACKGROUND AND PURPOSE

Skull metastasis (SM) is a common secondary malignancy. We evaluated the diagnostic performance of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (MRI) in differentiating SM from osseous venous malformations and SM of various origins.

METHODS

This study included 31 patients with SM (median age, 64 years; range, 41-87 years; 29 women; 24 and 7 patients with breast and non-small cell lung cancer, respectively) and 16 with osseous venous malformations (median age, 68 years; range, 20-81 years; 10 women) who underwent both DWI and dynamic contrast-enhanced MRI between January 2015 and October 2021. Normalized mean apparent diffusion coefficients (ADCs) and dynamic contrast-enhanced MRI parameters were compared between SM and osseous venous malformations, and between breast cancer and non-small cell lung cancer. Multivariate stepwise logistic regression analyses were performed to identify statistically significant parameters.

RESULTS

Plasma volume and time-to-maximum enhancement were the most statistically significant parameters for differentiating SM from osseous venous malformations, with an area under the receiver operating characteristic curve of 0.962. The normalized mean ADC and peak enhancement values were the most statistically significant parameters for differentiating breast cancer from non-small cell lung cancer, with an area under the curve of 0.924.

CONCLUSIONS

Our results highlight the efficacious diagnostic performance of DWI and dynamic contrast-enhanced MRI in distinguishing SM from osseous venous malformations and differentiating SM of various origins.

Perfusion and diffusion-weighted imaging parameters: Comparison between pre- and postbiopsy MRI for high-grade glioma

We aimed to evaluate the differences in dynamic susceptibility contrast (DSC)- magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) parameters between the pre- and postbiopsy MRI obtained before treatment in patients with diffuse midline glioma, H3K27-altered. The data of 25 patients with pathologically proven diffuse midline glioma, H3K27-altered, were extracted from our hospital's database between January 2017 and August 2021. Twenty (median age, 13 years; range, 3-52 years; 12 women) and 8 (13.5 years; 5-68 years; 1 woman) patients underwent preoperative DSC-MRI and DWI before and after biopsy, respectively. The normalized corrected relative cerebral blood volume (ncrCBV), normalized relative cerebral blood flow (nrCBF), and normalized maximum, mean, and minimum apparent diffusion coefficient (ADC) were calculated using the volumes-of-interest of the tumor and normal-appearing reference region. The macroscopic postbiopsy changes (i.e., biopsy tract, tissue defect, and hemorrhage) were meticulously excluded from the postbiopsy measurements. The DSC-MRI and DWI parameters of the pre- and postbiopsy groups were compared using the Mann-Whitney U test. The ncrCBV was significantly lower in the postbiopsy group than in the prebiopsy group [prebiopsy group: median 1.293 (range, 0.513 to 2.547) versus postbiopsy group: 0.877 (0.748 to 1.205), P = .016]. No significant difference was observed in the nrCBF and normalized ADC values, although the median nrCBF was lower in the postbiopsy group. The DSC-MRI parameters differed between the pre- and postbiopsy MRI obtained pretreatment, although the macroscopic postbiopsy changes were carefully excluded from the analysis. The results emphasize the potential danger of integrating and analyzing DSC-MRI parameters derived from pre- and postbiopsy MRI.

Efficacy and safety of recombinant human endostatin combined with whole-brain radiation therapy in patients with brain metastases from non-small cell lung cancer

BACKGROUND AND PURPOSE

Brain metastasis (BM) is the leading cause of poor prognosis in non-small cell lung cancer (NSCLC) patients. To date, whole-brain radiation therapy (WBRT) is a standard treatment for patients with multiple BMs, while its effectiveness is currently unsatisfactory. This study aimed to investigate the effects of Rh-endostatin combined with WBRT on NSCLC patients with BMs.

MATERIALS AND METHODS

A total of 43 patients with BM were randomly divided into two groups. The Rh-endostatin combination group (n=19) received Rh-endostatin combined with WBRT, and the radiation group (n=24) received WBRT only. The primary endpoint of the study was progression-free survival (PFS), and the secondary endpoints were intracranial progression free survival (iPFS), overall survival (OS), objective response rate (ORR), and changes in the cerebral blood volume (CBV) and cerebral blood flow (CBF).

RESULTS

Median PFS (mPFS) was 8.1 months in the Rh-endostatin combination group and 4.9 months in the radiation group (95%CI 0.2612-0.9583, p=0·0428). Besides, the median iPFS was 11.6 months in the Rh-endostatin combination group and 4.8 months in the radiation group (95%CI 0.2530-0.9504, p=0·0437). OS was 14.2 months in the Rh-endostatin combination group and 6.4 months in the radiation group (95%CI 0.2508-1.026, p=0·0688). CBV and CBF in the Rh-endostatin combination group were better improved than that in the radiation group, which indicated that Rh-endostatin might improve local blood supply and microcirculation.

CONCLUSION

Rh-endostatin showed better survival and improved cerebral perfusion parameters, which may provide further insights into the application of Rh-endostatin for NSCLC patients with BMs.

Hemodynamic Imaging in Cerebral Diffuse Glioma-Part B: Molecular Correlates, Treatment Effect Monitoring, Prognosis, and Future Directions

Gliomas, and glioblastoma in particular, exhibit an extensive intra- and inter-tumoral molecular heterogeneity which represents complex biological features correlating to the efficacy of treatment response and survival. From a neuroimaging point of view, these specific molecular and histopathological features may be used to yield imaging biomarkers as surrogates for distinct tumor genotypes and phenotypes. The development of comprehensive glioma imaging markers has potential for improved glioma characterization that would assist in the clinical work-up of preoperative treatment planning and treatment effect monitoring. In particular, the differentiation of tumor recurrence or true progression from pseudoprogression, pseudoresponse, and radiation-induced necrosis can still not reliably be made through standard neuroimaging only. Given the abundant vascular and hemodynamic alterations present in diffuse glioma, advanced hemodynamic imaging approaches constitute an attractive area of clinical imaging development. In this context, the inclusion of objective measurable glioma imaging features may have the potential to enhance the individualized care of diffuse glioma patients, better informing of standard-of-care treatment efficacy and of novel therapies, such as the immunotherapies that are currently increasingly investigated. In Part B of this two-review series, we assess the available evidence pertaining to hemodynamic imaging for molecular feature prediction, in particular focusing on isocitrate dehydrogenase (IDH) mutation status, MGMT promoter methylation, 1p19q codeletion, and EGFR alterations. The results for the differentiation of tumor progression/recurrence from treatment effects have also been the focus of active research and are presented together with the prognostic correlations identified by advanced hemodynamic imaging studies. Finally, the state-of-the-art concepts and advancements of hemodynamic imaging modalities are reviewed together with the advantages derived from the implementation of radiomics and machine learning analyses pipelines.

Pretreatment ADC Histogram Analysis as a Prognostic Imaging Biomarker for Patients with Recurrent Glioblastoma Treated with Bevacizumab: A Systematic Review and Meta-analysis

BACKGROUND

The mean ADC value of the lower Gaussian curve (ADC_L) derived from the bi-Gaussian curve-fitting histogram analysis has been reported as a predictive/prognostic imaging biomarker in patients with recurrent glioblastoma treated with bevacizumab; however, its systematic summary has been lacking.

PURPOSE

We applied a systematic review and meta-analysis to investigate the predictive/prognostic performance of ADC_L in patients with recurrent glioblastoma treated with bevacizumab.

DATA SOURCES

We performed a literature search using PubMed, Scopus, and EMBASE.

STUDY SELECTION

A total of 1344 abstracts were screened, of which 83 articles were considered potentially relevant. Data were finally extracted from 6 studies including 578 patients.

DATA ANALYSIS

Forest plots were generated to illustrate the hazard ratios of overall survival and progression-free survival. The heterogeneity across the studies was assessed using the Cochrane Q test and I² values.

DATA SYNTHESIS

The pooled hazard ratios for overall survival and progression-free survival in patients with an ADC_L lower than the cutoff values were 1.89 (95% CI, 1.53-2.31) and 1.98 (95% CI, 1.54-2.55) with low heterogeneity among the studies. Subgroup analysis of the bevacizumab-free cohort showed a pooled hazard ratio for overall survival of 1.20 (95% CI, 1.08-1.34) with low heterogeneity.

LIMITATIONS

The conclusions are limited by the difference in the definition of recurrence among the included studies.

CONCLUSIONS

This systematic review with meta-analysis supports the prognostic value of ADC_L in patients with recurrent glioblastoma treated with bevacizumab, with a low ADC_L demonstrating decreased overall survival and progression-free survival. On the other hand, the predictive role of ADC_L for bevacizumab treatment was not confirmed.

Dynamic susceptibility contrast and diffusion-weighted MRI in posterior fossa pilocytic astrocytoma and medulloblastoma

BACKGROUND AND PURPOSE

The utility of perfusion MRI in distinguishing between pilocytic astrocytoma (PA) and medulloblastoma (MB) is unclear. This study aimed to evaluate the diagnostic and prognostic performance of dynamic susceptibility contrast (DSC)-MRI parameters and apparent diffusion coefficient (ADC) values between PA and MB.

METHODS

Between January 2012 and August 2021, 49 (median, 7 years [range, 1-28 years]; 28 females) and 35 (median, 8 years [1-24 years]; 12 females) patients with pathologically confirmed PA and MB, respectively, were included. The normalized relative cerebral blood volume and flow (nrCBV and nrCBF) and mean and minimal normalized ADC (nADCmean and nADCmin) values were calculated using volume-of-interest analyses. Diagnostic performance and Pearson's correlation with progression-free survival were also evaluated.

RESULTS

The MB group showed a significantly higher nrCBV and nrCBF (nrCBV: 1.69 [0.93-4.23] vs. 0.95 [range, 0.37-2.28], p = .0032; nrCBF: 1.62 [0.93-3.16] vs. 1.07 [0.46-2.26], p = .0084) and significantly lower nADCmean and nADCmin (nADCmean: 0.97 [0.70-1.68] vs. 2.21 [1.44-2.80], p < .001; nADCmin: 0.50 [0.19-0.89] vs. 1.42 [0.89-2.20], p < .001) than the PA group. All parameters exhibited good diagnostic ability (accuracy >0.80) with nADCmin achieving the highest score (accuracy = 1). A moderate correlation was found between nADCmean and progression-free survival for MB (r = 0.44, p = .0084).

CONCLUSIONS

DSC-MRI parameters and ADC values were useful for distinguishing between PA and MB. A lower ADC indicated an unfavorable MB prognosis, but the DSC-MRI parameters did not correlate with progression-free survival in either group.

Assessing the reproducibility of high temporal and spatial resolution dynamic contrast-enhanced magnetic resonance imaging in patients with gliomas

Temporal and spatial resolution of dynamic contrast-enhanced MR imaging (DCE-MRI) is critical to reproducibility, and the reproducibility of high-resolution (HR) DCE-MRI was evaluated. Thirty consecutive patients suspected to have brain tumors were prospectively enrolled with written informed consent. All patients underwent both HR-DCE (voxel size, 1.1 × 1.1 × 1.1 mm³; scan interval, 1.6 s) and conventional DCE (C-DCE; voxel size, 1.25 × 1.25 × 3.0 mm³; scan interval, 4.0 s) MRI. Regions of interests (ROIs) for enhancing lesions were segmented twice in each patient with glioblastoma (n = 7) to calculate DCE parameters (K^trans, V_p, and V_e). Intraclass correlation coefficients (ICCs) of DCE parameters were obtained. In patients with gliomas (n = 25), arterial input functions (AIFs) and DCE parameters derived from T2 hyperintense lesions were obtained, and DCE parameters were compared according to WHO grades. ICCs of HR-DCE parameters were good to excellent (0.84–0.95), and ICCs of C-DCE parameters were moderate to excellent (0.66–0.96). Maximal signal intensity and wash-in slope of AIFs from HR-DCE MRI were significantly greater than those from C-DCE MRI (31.85 vs. 7.09 and 2.14 vs. 0.63; p < 0.001). Both 95^th percentile K^trans and V_e from HR-DCE and C-DCE MRI could differentiate grade 4 from grade 2 and 3 gliomas (p < 0.05). In conclusion, HR-DCE parameters generally showed better reproducibility than C-DCE parameters, and HR-DCE MRI provided better quality of AIFs.

A Review of Radiomics and Deep Predictive Modeling in Glioma Characterization

Recent developments in glioma categorization based on biological genotypes and application of computational machine learning or deep learning based predictive models using multi-modal MRI biomarkers to assess these genotypes provides potential assurance for optimal and personalized treatment plans and efficacy. Artificial intelligence based quantified assessment of glioma using MRI derived hand-crafted or auto-extracted features have become crucial as genomic alterations can be associated with MRI based phenotypes. This survey integrates all the recent work carried out in state-of-the-art radiomics, and Artificial Intelligence based learning solutions related to molecular diagnosis, prognosis, and treatment monitoring with the aim to create a structured resource on radiogenomic analysis of glioma. Challenges such as inter-scanner variability, requirement of benchmark datasets, prospective validations for clinical applicability are discussed with further scope for designing optimal solutions for glioma stratification with immediate recommendations for further diagnostic decisions and personalized treatment plans for glioma patients.

MRI biomarkers in neuro-oncology

The central role of MRI in neuro-oncology is undisputed. The technique is used, both in clinical practice and in clinical trials, to diagnose and monitor disease activity, support treatment decision-making, guide the use of focused treatments and determine response to treatment. Despite recent substantial advances in imaging technology and image analysis techniques, clinical MRI is still primarily used for the qualitative subjective interpretation of macrostructural features, as opposed to quantitative analyses that take into consideration multiple pathophysiological features. However, the field of quantitative imaging and imaging biomarker development is maturing. The European Imaging Biomarkers Alliance (EIBALL) and Quantitative Imaging Biomarkers Alliance (QIBA) are setting standards for biomarker development, validation and implementation, as well as promoting the use of quantitative imaging and imaging biomarkers by demonstrating their clinical value. In parallel, advanced imaging techniques are reaching the clinical arena, providing quantitative, commonly physiological imaging parameters that are driving the discovery, validation and implementation of quantitative imaging and imaging biomarkers in the clinical routine. Additionally, computational analysis techniques are increasingly being used in the research setting to convert medical images into objective high-dimensional data and define radiomic signatures of disease states. Here, I review the definition and current state of MRI biomarkers in neuro-oncology, and discuss the clinical potential of quantitative image analysis techniques.

Cerebral microcirculation in glioblastoma: A major determinant of diagnosis, resection, and drug delivery

Glioblastoma (GBM) is the most common primary brain tumor with a dismal prognosis. Current standard of treatment is safe maximal tumor resection followed by chemotherapy and radiation. Altered cerebral microcirculation and elevated blood-tumor barrier (BTB) permeability in tumor periphery due to glioma-induced vascular dysregulation allow T1 contrast-enhanced visualization of resectable tumor boundaries. Newer tracers that label the tumor and its vasculature are being increasingly used for intraoperative delineation of glioma boundaries for even more precise resection. Fluorescent 5-aminolevulinic acid (5-ALA) and indocyanine green (ICG) are examples of such intraoperative tracers. Recently, magnetic resonance imaging (MRI)-based MR thermometry is being employed for laser interstitial thermal therapy (LITT) for glioma debulking. However, aggressive, fatal recurrence always occurs. Postsurgical chemotherapy is hampered by the inability of most drugs to cross the blood-brain barrier (BBB). Understanding postsurgical changes in brain microcirculation and permeability is crucial to improve chemotherapy delivery. It is important to understand whether any microcirculatory indices can differentiate between true recurrence and radiation necrosis. LITT leads to peri-ablation BBB opening that persists for several weeks. Whether it can be a conduit for chemotherapy delivery is yet to be explored. This review will address the role of cerebral microcirculation in such emerging ideas in GBM diagnosis and therapy.

Tumor segmentation analysis at different post-contrast time points: A possible source of variability of quantitative DCE-MRI parameters in locally advanced breast cancer

PURPOSE

to assess if tumor segmentation analysis performed at different post-contrast time points (TPs) on dynamic images could influence the extraction of dynamic contrast enhanced (DCE)-MRI parameters in locally advanced breast cancer (LABC), and potentially represent a source of variability.

METHOD

forty patients with forty-two LABC lesions were prospectively enrolled and underwent breast DCE-MRI examination at 3 T. On post-processed dynamic images, enhancing tumor lesions were manually segmented at four different TPs: at the first post-contrast dynamic image in which the lesion was appreciable (TP 1) and at 1, 5 and 10 min after contrast-agent administration (TPs 2, 3 and 4, respectively) and corresponding DCE-MRI parameters were extracted. Friedman's test followed by Bonferroni-adjusted Wilcoxon signed rank test for post-hoc analysis was used to compare DCE-MRI parameters. Intra- and inter-observer reliability of DCE-MRI parameters measurements was assessed using the Intraclass Correlation Coefficient (ICC) analysis.

RESULTS

Ktrans, Kep and iAUC were significantly higher when extracted from ROIs placed at TP1 and progressively decreased from TP 2-4. The intra-observer reliability ranged from good to excellent (ICC's: 0.894 to 0.990). The inter-observer reliability varied from moderate to excellent (0.770 to 0.942). The inter-observer reliability was significantly higher for Ktrans and Kep extracted at TPs1 and 2 as compared to TPs 3 and 4.

CONCLUSIONS

A significant variability of DCE-MRI quantitative parameters occurs when tumor segmentation is performed at different TPs. We suggest to performing tumor delineation at an established TP, preferably the earliest, in order to extract reliable and comparable DCE-MRI data.

3D-ASL perfusion correlates with VEGF expression and overall survival in glioma patients: Comparison of quantitative perfusion and pathology on accurate spatial location-matched basis

BACKGROUND

There is a need for an imaging-based tool for measuring vascular endothelial growth factor (VEGF) expression and overall survival (OS) in patients with glioma.

PURPOSE

To assess the correlation between cerebral blood flow (CBF), measured by 3D pseudo-continuous arterial spin-labeling (3D-ASL), and VEGF expression in gliomas on the basis of coregistered localized biopsy, and investigate whether CBF correlated with survival month (SM) in glioma patients.

STUDY TYPE

Prospective cohort.

SUBJECTS

Thirty-seven patients with gliomas from whom 63 biopsy specimens were obtained.

SEQUENCE

3D-ASL acquired with a 3.0T MR unit.

ASSESSMENT

Biopsy specimens were grouped as high-grade (HGG) or low-grade glioma (LGG). CBF measurements were spatially matched with VEGF expression by coregistered localized biopsies, and the CBF value was correlated with quantitative VEGF expression for each specimen. Patients' survival information was derived and connected with CBF.

STATISTICAL TESTS

Patients' OS was analyzed by Kaplan-Meier and Cox-regression methods. VEGF expression and CBF were compared in both LGG and HGG. The Spearman rank correlation was calculated for CBF and VEGF expression, SM. Significance level, P < 0.05.

RESULTS

CBF-derived 3D-ASL positively correlated significantly with VEGF expression in both LGG (31 specimens) and HGG (32 specimens), r = 0.604 (P < 0.001) and r = 0.665 (P < 0.001), respectively. LGG and HGG together gave a correlation coefficient r = 0.728 (P < 0.001). Median survival for LGG and HGG patients was 34.19 and 17.17 months, respectively (P = 0.037); CBF value negatively correlated significantly with SM with r = -0.714 (P < 0.001) regardless of glioma grade. CBF was an independent risk factor for OS with HR = 1.027 (P = 0.044), 1.028 (P = 0.010) for univariate/multivariate regression analysis.

DATA CONCLUSION

CBF determined by 3D-ASL correlates with VEGF expression in glioma and is an independent risk factor for OS in these patients.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:209-220.

Functional Parameters Derived from Magnetic Resonance Imaging Reflect Vascular Morphology in Preclinical Tumors and in Human Liver Metastases

Purpose: Tumor vessels influence the growth and response of tumors to therapy. Imaging vascular changes in vivo using dynamic contrast-enhanced MRI (DCE-MRI) has shown potential to guide clinical decision making for treatment. However, quantitative MR imaging biomarkers of vascular function have not been widely adopted, partly because their relationship to structural changes in vessels remains unclear. We aimed to elucidate the relationships between vessel function and morphology in vivo Experimental Design: Untreated preclinical tumors with different levels of vascularization were imaged sequentially using DCE-MRI and CT. Relationships between functional parameters from MR (iAUC, K trans, and BATfrac) and structural parameters from CT (vessel volume, radius, and tortuosity) were assessed using linear models. Tumors treated with anti-VEGFR2 antibody were then imaged to determine whether antiangiogenic therapy altered these relationships. Finally, functional-structural relationships were measured in 10 patients with liver metastases from colorectal cancer.Results: Functional parameters iAUC and K trans primarily reflected vessel volume in untreated preclinical tumors. The relationships varied spatially and with tumor vascularity, and were altered by antiangiogenic treatment. In human liver metastases, all three structural parameters were linearly correlated with iAUC and K trans For iAUC, structural parameters also modified each other's effect.Conclusions: Our findings suggest that MR imaging biomarkers of vascular function are linked to structural changes in tumor vessels and that antiangiogenic therapy can affect this link. Our work also demonstrates the feasibility of three-dimensional functional-structural validation of MR biomarkers in vivo to improve their biological interpretation and clinical utility. Clin Cancer Res; 24(19); 4694-704. ©2018 AACR.

Perfusion Magnetic Resonance Imaging Changes in Normal Appearing Brain Tissue after Radiotherapy in Glioblastoma Patients may Confound Longitudinal Evaluation of Treatment Response

Background

The aim of this study was assess acute and early delayed radiation-induced changes in normal-appearing brain tissue perfusion as measured with perfusion magnetic resonance imaging (MRI) and the dependence of these changes on the fractionated radiotherapy (FRT) dose level.

Patients and methods

Seventeen patients with glioma WHO grade III-IV treated with FRT were included in this prospective study, seven were excluded because of inconsistent FRT protocol or missing examinations. Dynamic susceptibility contrast MRI and contrast-enhanced 3D-T1-weighted (3D-T1w) images were acquired prior to and in average (standard deviation): 3.1 (3.3), 34.4 (9.5) and 103.3 (12.9) days after FRT. Pre-FRT 3D-T1w images were segmented into white- and grey matter. Cerebral blood volume (CBV) and cerebral blood flow (CBF) maps were calculated and co-registered patient-wise to pre-FRT 3D-T1w images. Seven radiation dose regions were created for each tissue type: 0-5 Gy, 5-10 Gy, 10-20 Gy, 20-30 Gy, 30-40 Gy, 40-50 Gy and 50-60 Gy. Mean CBV and CBF were calculated in each dose region and normalised (nCBV and nCBF) to the mean CBV and CBF in 0-5 Gy white- and grey matter reference regions, respectively.

Results

Regional and global nCBV and nCBF in white- and grey matter decreased after FRT, followed by a tendency to recover. The response of nCBV and nCBF was dose-dependent in white matter but not in grey matter.

Conclusions

Our data suggest that radiation-induced perfusion changes occur in normal-appearing brain tissue after FRT. This can cause an overestimation of relative tumour perfusion using dynamic susceptibility contrast MRI, and can thus confound tumour treatment evaluation.

Pharmacodynamic and Pharmacokinetic Markers For Anti-angiogenic Cancer Therapy: Implications for Dosing and Selection of Patients

Angiogenesis is integral to tumour growth and invasion, and is a key target for cancer therapeutics. However, for many of the licensed indications, only a modest clinical benefit has been observed for both monoclonal antibody and small-molecule tyrosine kinase inhibitor anti-angiogenic therapy. Pre-clinical and clinical studies have attempted to evaluate circulating, imaging, genomic, pharmacokinetic, and pharmacodynamic markers that may aid both the selection of patients for treatment and define dosing. Correct dosing is likely to be critical in the context of vascular normalization to allow better delivery of concomitant anti-cancer therapy and novel imaging techniques hold much promise in the early evaluation of pharmacodynamic response to improve efficacy.

A predictive value of von Willebrand factor for early response to Bevacizumab therapy in recurrent glioma

Bevacizumab (BV), a neutralizing monoclonal antibody against the vascular endothelial growth factor ligand, is recognized as a potent anti-angiogenic agent with antitumor activity. The aim of this single-center, retrospective, longitudinal study was to investigate the possible predictive value of baseline demographic, clinical and laboratory parameters for early 3-month response to BV therapy in patients with recurrent glioma. Forty-nine patients with recurrent glioma received BV at 10 mg/kg intravenously every 3 weeks alone or in association with chemotherapy were included in this study. Blood samples were collected from all patients before the first (baseline), the second and the third administration of BV. After 3 months of BV therapy, patients with partial response were defined as responders whereas patients with stable or progressive disease were defined as non-responders. The median overall follow-up was 8 months (range 1-73), the median overall survival (OS) was 8 months (95% CI 6-10) and the median progression free survival (PFS) was 4 months (95% CI 3-5). Thirty-five % of patients were responders and showed significantly lower von Willebrand factor (VWF) levels than non-responders at all sample times (p < .02 for all). Also, on multivariate analysis the baseline VWF value was the only predictor for an early response to BV therapy. Furthermore, D-dimer and prothrombin fragment 1+2 were predictive factors for OS while Karnofsky performance status resulted predictive for PFS. VWF antigen value is a possible predictive biomarker for an early 3-month response to BV therapy in recurrent glioma.

Diffusion Weighted/Tensor Imaging, Functional MRI and Perfusion Weighted Imaging in Glioblastoma-Foundations and Future

In this article, we review the basics of diffusion tensor imaging and functional MRI, their current utility in preoperative neurosurgical mapping, and their limitations. We also discuss potential future applications, including implementation of resting state functional MRI. We then discuss perfusion and diffusion-weighted imaging and their application in advanced neuro-oncologic practice. We explain how these modalities can be helpful in guiding surgical biopsies and differentiating recurrent tumor from treatment related changes.

Relative cerebral blood volume is a potential biomarker in late delayed radiation-induced brain injury

PURPOSE

To assess whether relative cerebral blood volume (rCBV) can provide information to reliably evaluate the stages of late delayed radiation-induced brain injury.

MATERIALS AND METHODS

Forty patients diagnosed with late delayed radiation-induced brain injury were enrolled. The patients were examined using a 1.5T magnetic resonance imaging (MRI) system equipped with an 8-channel head coil. An echo planar imaging (EPI) sequence was used in perfusion-weighted imaging (PWI). The location of 1H-MR spectroscopy scanning was acquired by a point-resolved spectroscopy sequence. Lesions of the temporal lobe were divided into one of two groups according to rCBV value: rCBV<1 (low rCBV [group 1; n = 45]); and rCBV>1 (elevated rCBV [group 2; n = 14]). PWI and MRS parameters, as well as morphological lesion types, in these two groups were compared. Morphological severity was assessed independently and agreed on by two imaging specialists (J.L. and H.X.S., with 16 and 24 years' experience, respectively). If necessary, a third imaging professor (Z.M.H.) with 30 years' experience resolved disagreement(s). Standards for evaluating morphological lesion types were based on previously published criteria. After testing the skewness of data, the Mann-Whitney U-test or Student's t-test was used, as appropriate.

RESULTS

rCBV, relative cerebral blood flow (rCBF), and relative mean transit time (rMTT) in group 2 (n = 14) were significantly higher than in group 1 (n = 45) (rCBV: 1.21 ± 0.38 vs. 0.72 ± 0.32, respectively; P < 0.001; rCBF: 1.13 ± 0.02 vs. 0.74 ± 0.04, respectively; P < 0.001; rMTT: 1.10 ± 0.26 vs. 0.96 ± 0.20, P < 0.001). The levels of choline-containing compounds (CHO) / creatine (Cr) and CHO/N-acetylaspartate (NAA) in group 1 were significantly greater than in group 2 (CHO/Cr: 1.89 ± 1.83 vs. 1.22 ± 1.31, respectively; P = 0.016; CHO/NAA: 1.85 ± 3.50 vs. 1.17 ± 0.75, respectively; P = 0.022). More severe morphological lesions were present in lesions with low rCBV compared with elevated rCBV (overall severity: 7.00 ± 4.25 vs. 5.00 ± 5.13, respectively; P = 0.029).

CONCLUSION

Elevated rCBV accompanied by a more conservative metabolic pattern and milder lesion(s) may represent a less advanced stage in the development of late delayed radiation-induced brain injury.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1112-1118.

Bevacizumab for malignant gliomas: current indications, mechanisms of action and resistance, and markers of response

Vascular endothelial growth factor (VEGF) is an attractive target of antiangiogenic therapy in glioblastomas. Bevacizumab (Bev), a humanized anti-VEGF antibody, is associated with the improvement of progression-free survival and performance status in patients with glioblastoma. However, randomized trials uniformly suggest that these favorable clinical effects of Bev do not translate into an overall survival benefit. The mechanisms of action of Bev appear to include the inhibition of tumor angiogenesis, as well as indirect effects such as the depletion of niches for glioma stem cells and stimulation of antitumor immunity. Although several molecules/pathways have been reported to mediate adaptation and resistance to Bev, including the activation of alternative pro-angiogenic pathways, the resistance mechanisms have not been fully elucidated; for example, the mechanism that reinduces tumor hypoxia remains unclarified. The identification of imaging characteristics or biomarkers predicting the response to Bev, as well as the better understanding of the mechanisms of action and resistance, is crucial to improve the overall clinical outcome and optimize individual therapy. In this article, the authors review the results of important clinical trials/studies, the current understanding of the mechanisms of action and resistance, and the knowledge of imaging characteristics and biomarkers predicting the response to Bev.

Posttreatment DSC-MRI is Predictive of Early Treatment Failure in Children with Supratentorial High-Grade Glioma Treated with Erlotinib

BACKGROUND AND PURPOSE

The role of perfusion imaging in the management of pediatric high grade glioma is unclear. We evaluated the ability of dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) to determine grade, evaluate post-treatment response and predict treatment failure.

MATERIAL AND METHODS

In this study 22 patients with high-grade glioma underwent biopsy and were treated with concurrent and sequential radiotherapy and erlotinib as part of a phase I/II clinical trial (NCT00124657). Preradiotherapy, immediate postradiotherapy, 6‑month and treatment failure DSC MR images were reviewed, registered, and processed for the ratio of cerebral blood flow (CBF) and cerebral blood volume (CBV). Processed, derived perfusion, and T1-weighted images (T1WI), T2WI, and fluid attenuation inversion recovery (FLAIR) MRI sequences were used for segmentation and extraction of tumor perfusion parameters at all time points. Patient, tumor, treatment, and outcome data were summarized and related to perfusion data.

RESULTS

Regional CBF in tumors increased from diagnosis to postradiotherapy, while they decreased to levels below those at diagnosis from postradiotherapy to 6‑month follow-up. At 6 months, the median regional CBF was higher in tumors that progressed (median 1.16) than in those that did not (median, 0.95; P < 0.05). Patients with regional CBF ratios above 1.4 at diagnosis had shorter survival times than did those with regional CBF ratios below 1.4 (P = 0.77). Tumors with a regional CBV above 1.15 at the postradiotherapy (1-3 months) follow-up scan were associated with an earlier time to death than that of tumors with a regional CBV below 1.15 (P < 0.05).

CONCLUSION

Posttreatment perfusion characteristics are prognostic and may help predict survival. Overall, perfusion MRI is useful for managing pediatric high-grade glioma and should be incorporated into future clinical trials.

Advanced MRI Techniques in the Monitoring of Treatment of Gliomas

OPINION STATEMENT

With advances in treatments and survival of patients with glioblastoma (GBM), it has become apparent that conventional imaging sequences have significant limitations both in terms of assessing response to treatment and monitoring disease progression. Both 'pseudoprogression' after chemoradiation for newly diagnosed GBM and 'pseudoresponse' after anti-angiogenesis treatment for relapsed GBM are well-recognised radiological entities. This in turn has led to revision of response criteria away from the standard MacDonald criteria, which depend on the two-dimensional measurement of contrast-enhancing tumour, and which have been the primary measure of radiological response for over three decades. A working party of experts published RANO (Response Assessment in Neuro-oncology Working Group) criteria in 2010 which take into account signal change on T2/FLAIR sequences as well as the contrast-enhancing component of the tumour. These have recently been modified for immune therapies, which are associated with specific issues related to the timing of radiological response. There has been increasing interest in quantification and validation of physiological and metabolic parameters in GBM over the last 10 years utilising the wide range of advanced imaging techniques available on standard MRI platforms. Previously, MRI would provide structural information only on the anatomical location of the tumour and the presence or absence of a disrupted blood-brain barrier. Advanced MRI sequences include proton magnetic resonance spectroscopy (MRS), vascular imaging (perfusion/permeability) and diffusion imaging (diffusion weighted imaging/diffusion tensor imaging) and are now routinely available. They provide biologically relevant functional, haemodynamic, cellular, metabolic and cytoarchitectural information and are being evaluated in clinical trials to determine whether they offer superior biomarkers of early treatment response than conventional imaging, when correlated with hard survival endpoints. Multiparametric imaging, incorporating different combinations of these modalities, improves accuracy over single imaging modalities but has not been widely adopted due to the amount of post-processing analysis required, lack of clinical trial data, lack of radiology training and wide variations in threshold values. New techniques including diffusion kurtosis and radiomics will offer a higher level of quantification but will require validation in clinical trial settings. Given all these considerations, it is clear that there is an urgent need to incorporate advanced techniques into clinical trial design to avoid the problems of under or over assessment of treatment response.

Brain Vascular Imaging Techniques

Recent major improvements in a number of imaging techniques now allow for the study of the brain in ways that could not be considered previously. Researchers today have well-developed tools to specifically examine the dynamic nature of the blood vessels in the brain during development and adulthood; as well as to observe the vascular responses in disease situations in vivo. This review offers a concise summary and brief historical reference of different imaging techniques and how these tools can be applied to study the brain vasculature and the blood-brain barrier integrity in both healthy and disease states. Moreover, it offers an overview on available transgenic animal models to study vascular biology and a description of useful online brain atlases.