Advanced MR Techniques for Preoperative Glioma Characterization: Part 2.

Preoperative clinical MRI protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this second part, we review magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), susceptibility-weighted imaging (SWI), MRI-PET, MR elastography (MRE), and MR-based radiomics applications. The first part of this review addresses dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) MRI, arterial spin labeling (ASL), diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting (MRF). EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Advanced MR Techniques for Preoperative Glioma Characterization: Part 1.

Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast-enhanced MRI, arterial spin labeling, diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility-weighted imaging, MRI-PET, MR elastography, and MR-based radiomics applications. Evidence Level: 3 Technical Efficacy: Stage 2.

Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness.

BACKGROUND: Diffusion-weighted imaging (DWI) is commonly used to detect prostate cancer, and a major clinical challenge is differentiating aggressive from indolent disease. PURPOSE: To compare 14 site-specific parametric fitting implementations applied to the same dataset of whole-mount pathologically validated DWI to test the hypothesis that cancer differentiation varies with different fitting algorithms. STUDY TYPE: Prospective. POPULATION: Thirty-three patients prospectively imaged prior to prostatectomy. FIELD STRENGTH/SEQUENCE: 3 T, field-of-view optimized and constrained undistorted single-shot DWI sequence. ASSESSMENT: Datasets, including a noise-free digital reference object (DRO), were distributed to the 14 teams, where locally implemented DWI parameter maps were calculated, including mono-exponential apparent diffusion coefficient (MEADC), kurtosis (K), diffusion kurtosis (DK), bi-exponential diffusion (BID), pseudo-diffusion (BID*), and perfusion fraction (F). The resulting parametric maps were centrally analyzed, where differentiation of benign from cancerous tissue was compared between DWI parameters and the fitting algorithms with a receiver operating characteristic area under the curve (ROC AUC). STATISTICAL TEST: Levene's test, P < 0.05 corrected for multiple comparisons was considered statistically significant. RESULTS: The DRO results indicated minimal discordance between sites. Comparison across sites indicated that K, DK, and MEADC had significantly higher prostate cancer detection capability (AUC range = 0.72-0.76, 0.76-0.81, and 0.76-0.80 respectively) as compared to bi-exponential parameters (BID, BID*, F) which had lower AUC and greater between site variation (AUC range = 0.53-0.80, 0.51-0.81, and 0.52-0.80 respectively). Post-processing parameters also affected the resulting AUC, moving from, for example, 0.75 to 0.87 for MEADC varying cluster size. DATA CONCLUSION: We found that conventional diffusion models had consistent performance at differentiating prostate cancer from benign tissue. Our results also indicated that post-processing decisions on DWI data can affect sensitivity and specificity when applied to radiological-pathological studies in prostate cancer. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 3.

Magnolia extract is effective for the chemoprevention of oral cancer through its ability to inhibit mitochondrial respiration at complex I.

BACKGROUND: Magnolia extract (ME) is known to inhibit cancer growth and metastasis in several cell types in vitro and in animal models. However, there is no detailed study on the preventive efficacy of ME for oral cancer, and the key components in ME and their exact mechanisms of action are not clear. The overall goal of this study is to characterize ME preclinically as a potent oral cancer chemopreventive agent and to determine the key components and their molecular mechanism(s) that underlie its chemopreventive efficacy. METHODS: The antitumor efficacy of ME in oral cancer was investigated in a 4-nitroquinoline-1-oxide (4NQO)-induced mouse model and in two oral cancer orthotopic models. The effects of ME on mitochondrial electron transport chain activity and ROS production in mouse oral tumors was also investigated. RESULTS: ME did not cause detectable side effects indicating that it is a promising and safe chemopreventive agent for oral cancer. Three major key active compounds in ME (honokiol, magnolol and 4-O-methylhonokiol) contribute to its chemopreventive effects. ME inhibits mitochondrial respiration at complex I of the electron transport chain, oxidizes peroxiredoxins, activates AMPK, and inhibits STAT3 phosphorylation, resulting in inhibition of the growth and proliferation of oral cancer cells. CONCLUSION: Our data using highly relevant preclinical oral cancer models, which share histopathological features seen in human oral carcinogenesis, suggest a novel signaling and regulatory role for mitochondria-generated superoxide and hydrogen peroxide in suppressing oral cancer cell proliferation, progression, and metastasis. Video abstract.

Human Fetal Tissue from Elective Abortions in Research and Medicine: Science, Ethics, and the Law.

Since the U.S. Supreme Court issued its landmark decision in 1973 to legalize abortion, over 60 million preborn have been killed by elective abortion. While alive in the womb, these preborn are abandoned and not protected under current law. But once aborted, their body parts are a highly esteemed and prized commodity amongst certain members of the scientific community. Moral discourse is disregarded for the sake of science. The public have been lulled and lured into believing that this practice must continue in order to understand and develop cures for some of the most debilitating diseases of our day. But they are mistaken. This practice is not necessary, especially in light of numerous noncontroversial alternatives. Here, we expose and consider the false and misleading claims regarding human fetal tissue (HFT) in research from scientific, legal, and ethical points of view. We endeavor deeply to understand the depth of the injustice in this practice and what forces promote and maintain it; and by revealing and understanding these forces, we set forth how these inhumane practices can be ended. An accurate portrayal of the history of HFT use in research is provided, along with a close examination of the current state of this practice under existing laws. The serious societal implications are also discussed, which will worsen beyond comprehension if these practices are allowed to continue. The timeliness of this information cannot be overstated, and a thorough understanding is paramount for anyone who desires to know the facts about HFT in research and medicine and its detrimental impact for humanity.

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.

The effect of low b-values on the intravoxel incoherent motion derived pseudodiffusion parameter in liver.

PURPOSE: To examine the effect of low b-values (0 < b < 50 s/mm(2)) on the calculation of the intravoxel incoherent motion (IVIM) derived pseudodiffusion parameter in the normal liver. METHODS: Simulations were performed to examine the effects of adding low b-values on the pseudodiffusion parameter. Low b-values were cumulatively added to the distribution and the IVIM signal was generated with varying pseudodiffusion values. The signal was fit with the IVIM model after the addition of Gaussian noise, and the simulated values were compared with the true values. In addition, the livers of eight control subjects were imaged using respiratory-triggered DWI. Pseudodiffusion was calculated with and without low b-values and compared. RESULTS: Pseudodiffusion tended to be underestimated when low b-values were not included in the b-value distribution as predicted by simulations and confirmed with in vivo imaging. The number of outlier values was also reduced as more low b-values were added. CONCLUSION: In conclusion, this study showed pseudodiffusion in the liver tended to be underestimated when too few low b-values (0 < b < 50 s/mm(2)) were included in the distribution. Therefore, it is recommended to include at least two low b-values when performing liver IVIM studies.

Location of brain tumor intersecting white matter tracts predicts patient prognosis.

Brain tumor cells invade adjacent normal brain along white matter (WM) bundles of axons. We therefore hypothesized that the location of tumor intersecting WM tracts would be associated with differing survival. This study introduces a method, voxel-wise survival analysis (VSA), to determine the relationship between the location of brain tumor intersecting WM tracts and patient prognosis. 113 primary glioblastoma (GBM) patients were retrospectively analyzed for this study. Patient specific tumor location, defined by contrast-enhancement, was combined with diffusion tensor imaging derived tractography to determine the location of axons intersecting tumor enhancement (AXITEs). VSA was then used to determine the relationship between the AXITE location and patient survival. Tumors intersecting the right anterior thalamic radiation (ATR), right inferior fronto-occipital fasciculus (IFOF), right and left cortico-spinal tract (CST), and corpus callosum (CC) were associated with decreased overall survival. Tumors intersecting the CST, body of the CC, right ATR, posterior IFOF, and inferior longitudinal fasciculus are associated with decreased progression-free survival (PFS), while tumors intersecting the right genu of the CC and anterior IFOF are associated with increased PFS. Patients with tumors intersecting the ATR, IFOF, CST, or CC had significantly improved survival prognosis if they were additionally treated with bevacizumab. This study demonstrates the usefulness of VSA by locating AXITEs associated with poor prognosis in GBM patients. This information should be included in patient-physician conversations, therapeutic strategy, and clinical trial design.

Comprehensive characterization of glioblastoma tumor tissues for biomarker identification using mass spectrometry-based label-free quantitative proteomics.

Cancer is a complex disease; glioblastoma (GBM) is no exception. Short survival, poor prognosis, and very limited treatment options make it imperative to unravel the disease pathophysiology. The critically important identification of proteins that mediate various cellular events during disease is made possible with advancements in mass spectrometry (MS)-based proteomics. The objective of our study is to identify and characterize proteins that are differentially expressed in GBM to better understand their interactions and functions that lead to the disease condition. Further identification of upstream regulators will provide new potential therapeutic targets. We analyzed GBM tumors by SDS-PAGE fractionation with internal DNA markers followed by liquid chromatography-tandem mass spectrometry (MS). Brain tissue specimens obtained for clinical purposes during epilepsy surgeries were used as controls, and the quantification of MS data was performed by label-free spectral counting. The differentially expressed proteins were further characterized by Ingenuity Pathway Analysis (IPA) to identify protein interactions, functions, and upstream regulators. Our study identified several important proteins that are involved in GBM progression. The IPA revealed glioma activation with z score 2.236 during unbiased core analysis. Upstream regulators STAT3 and SP1 were activated and CTNNα was inhibited. We verified overexpression of several proteins by immunoblot to complement the MS data. This work represents an important step towards the identification of GBM biomarkers, which could open avenues to identify therapeutic targets for better treatment of GBM patients. The workflow developed represents a powerful and efficient method to identify biomarkers in GBM.

Effect of contrast leakage on the detection of abnormal brain tumor vasculature in high-grade glioma.

Abnormal brain tumor vasculature has recently been highlighted by a dynamic susceptibility contrast (DSC) MRI processing technique. The technique uses independent component analysis (ICA) to separate arterial and venous perfusion. The overlap of the two, i.e. arterio-venous overlap or AVOL, preferentially occurs in brain tumors and predicts response to anti-angiogenic therapy. The effects of contrast agent leakage on the AVOL biomarker have yet to be established. DSC was acquired during two separate contrast boluses in ten patients undergoing clinical imaging for brain tumor diagnosis. Three components were modeled with ICA, which included the arterial and venous components. The percentage of each component as well as a third component were determined within contrast enhancing tumor and compared. AVOL within enhancing tumor was also compared between doses. The percentage of enhancing tumor classified as not arterial or venous and instead into a third component with contrast agent leakage apparent in the time-series was significantly greater for the first contrast dose compared to the second. The amount of AVOL detected within enhancing tumor was also significantly greater with the second dose compared to the first. Contrast leakage results in large signal variance classified as a separate component by the ICA algorithm. The use of a second dose mitigates the effect and allows measurement of AVOL within enhancement.