Background:Conventional MRI protocols fail to probe marginal tumour infiltration in glioblastoma, hindering surgery and radiotherapy planning. This study aimed to demonstrate development and biological validation of a putative imaging biomarker (IB) for characterising glioblastoma infiltration, following principles outlined in the cancer imaging biomarker roadmap. Methods:This IB is based upon spatial change in apparent diffusion coefficient (ADC) measures across a macroscopic tumour boundary. We systematically assessed whether ADC slope at the tumour margin (marginal diffusion slope-MDS) could (a) describe an underlying infiltrative phenotype based on reported links between ADC and tumour cellularity validated using a preclinical model, and (b) predict clinical outcome in a single-centre exemplar prospective human cohort study. Results:Preclinical results showed a strong, spatially-resolved, negative correlation between marginal ADC and underlying tumour cell density in coregistered MRI-histology datasets from a glioblastoma model. Clinical results (n = 18) showed a positive linear correlation between MDS and clinical outcome, with higher MDS (i.e., steeper marginal ADC slope) associated with longer survival (Pearson's correlation was 0.636, P < .005). Cox proportional hazard analysis yielded a survival model (P = .013) with MDS significantly associated with overall survival (OS) controlling for age (age P = .35, MDS P = .010). The hazard ratio for each MDS standard deviation was 0.47 (range 0.25-0.89), indicating that higher MDS predicts longer survival. Conclusions:In alignment with the Imaging Biomarker Roadmap consensus, we biologically validated MDS as a biomarker of infiltrative phenotype in a preclinical model and demonstrated its predictive value for OS in humans as a prelude to larger clinical validation studies.

Many positron emission tomography (PET) imaging studies in health and disease of the translocator protein 18 kDa (TSPO) using different radioligands have been published, however, only few separately reported left and right regions of interest in the brain. Thus, TSPO binding in healthy brains using [ 11 C]( R )PK11195 datasets of 76 participants from two PET sites was assessed for symmetry. Structural MRI scans were used for brain segmentation and to define six regions of interest (thalamus, putamen, temporal, frontal, parietal, and occipital cortex) with a probabilistic brain atlas. The simplified reference tissue model with bilateral grey matter cerebellar reference tissue input function was used to estimate distribution volume ratios (DVR). On a global level, the right hemisphere had higher DVR than the left side (p < 0.001, Cohen’s d = 1.14 for grey matter and 1.17 for grey matter and white matter regions of interest). There were statistically significantly greater DVRs in all right regions with p < 0.001 except in the occipital cortex (p = 0.012, Cohen’s d = 0.29). This asymmetry was independent of age, sex, and handedness evaluated using a linear mixed-effects model. These results demonstrate that [ 11 C]( R )PK11195 has an asymmetric binding distribution in the human brain, which needs to be accounted for in clinical studies.

Journal of Magnetic Resonance ImagingEarly View Editorial Editorial for "Improving Microstructural Estimation in Time-Dependent Diffusion MRI with a Bayesian Method" Daniel Lewis PhD, Corresponding Author Daniel Lewis PhD [email protected] orcid.org/0000-0001-6831-6990 Geoffrey Jefferson Brain Research Centre, University of Manchester, Manchester, UK Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK Email: [email protected]Search for more papers by this authorXiaoping Zhu PhD, Xiaoping Zhu PhD orcid.org/0000-0002-4351-121X Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UKSearch for more papers by this authorAlan Jackson PhD, Alan Jackson PhD Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UKSearch for more papers by this author Daniel Lewis PhD, Corresponding Author Daniel Lewis PhD [email protected] orcid.org/0000-0001-6831-6990 Geoffrey Jefferson Brain Research Centre, University of Manchester, Manchester, UK Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK Email: [email protected]Search for more papers by this authorXiaoping Zhu PhD, Xiaoping Zhu PhD orcid.org/0000-0002-4351-121X Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UKSearch for more papers by this authorAlan Jackson PhD, Alan Jackson PhD Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UKSearch for more papers by this author First published: 20 May 2024 https://doi.org/10.1002/jmri.29449 Level of Evidence: 5. Technical Efficacy: Stage 1. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. References 1Wang Q-P, Lei D-Q, Yuan Y, Xiong N-X. Accuracy of ADC derived from DWI for differentiating high-grade from low-grade gliomas: Systematic review and meta-analysis. Medicine (Baltimore) 2020; 99:e19254. 10.1097/MD.0000000000019254 PubMedWeb of Science®Google Scholar 2Zhang H, Liu K, Ba R, et al. Histological and molecular classifications of pediatric glioma with time-dependent diffusion MRI-based microstructural mapping. Neuro Oncol 2023; 25: 1146-1156. 10.1093/neuonc/noad003 PubMedWeb of Science®Google Scholar 3Panagiotaki E, Walker-Samuel S, Siow B, et al. Noninvasive quantification of solid tumor microstructure using VERDICT MRI. Cancer Res 2014; 74: 1902-1912. 10.1158/0008-5472.CAN-13-2511 CASPubMedWeb of Science®Google Scholar 4Reynaud O, Winters KV, Hoang DM, Wadghiri YZ, Novikov DS, Kim SG. Pulsed and oscillating gradient MRI for assessment of cell size and extracellular space (POMACE) in mouse gliomas. NMR Biomed 2016; 29: 1350-1363. 10.1002/nbm.3577 PubMedWeb of Science®Google Scholar 5Jiang X, Li H, Xie J, et al. In vivo imaging of cancer cell size and cellularity using temporal diffusion spectroscopy. Magn Reson Med 2017; 78: 156-164. 10.1002/mrm.26356 CASPubMedWeb of Science®Google Scholar 6Wu J, Kang T, Lan X, et al. IMPULSED model based cytological feature estimation with U-net: Application to human brain tumor at 3T. Magn Reson Med 2023; 89: 411-422. 10.1002/mrm.29429 PubMedWeb of Science®Google Scholar 7Iima M, Yamamoto A, Kataoka M, et al. Time-dependent diffusion MRI to distinguish malignant from benign head and neck tumors. J Magn Reson Imaging 2019; 50: 88-95. 10.1002/jmri.26578 PubMedWeb of Science®Google Scholar 8Wu D, Jiang K, Li H, et al. Time-dependent diffusion MRI for quantitative microstructural mapping of prostate cancer. Radiology 2022; 303: 578-587. 10.1148/radiol.211180 PubMedWeb of Science®Google Scholar 9Umezawa E, Ishihara D, Kato R. A Bayesian approach to diffusional kurtosis imaging. Magn Reson Med 2021; 86: 1110-1124. 10.1002/mrm.28741 PubMedWeb of Science®Google Scholar 10Liu K, Lin Z, Zheng T, et al. Improving microstructural estimation in time-dependent diffusion MRI with a Bayesian method. J Magn Reson Imaging 2024. https://doi.org/10.1002/jmri.29434. 10.1002/jmri.29434 Google Scholar Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation

This study aimed to develop and evaluate a new DCE-MRI processing technique that combines LEGATOS, a dual-temporal resolution DCE-MRI technique, with multi-kinetic models. This technique enables high spatial resolution interrogation of flow and permeability effects, which is currently challenging to achieve. Twelve patients with neurofibromatosis type II-related vestibular schwannoma (20 tumours) undergoing bevacizumab therapy were imaged at 1.5 T both before and at 90 days following treatment. Using the new technique, whole-brain, high spatial resolution images of the contrast transfer coefficient (Ktrans), vascular fraction (vp), extravascular extracellular fraction (ve), capillary plasma flow (Fp), and the capillary permeability-surface area product (PS) could be obtained, and their predictive value was examined. Of the five microvascular parameters derived using the new method, baseline PS exhibited the strongest correlation with the baseline tumour volume (p = 0.03). Baseline ve showed the strongest correlation with the change in tumour volume, particularly the percentage tumour volume change at 90 days after treatment (p < 0.001), and PS demonstrated a larger reduction at 90 days after treatment (p = 0.0001) when compared to Ktrans or Fp alone. Both the capillary permeability-surface area product (PS) and the extravascular extracellular fraction (ve) significantly differentiated the ‘responder’ and ‘non-responder’ tumour groups at 90 days (p < 0.05 and p < 0.001, respectively). These results highlight that this novel DCE-MRI analysis approach can be used to evaluate tumour microvascular changes during treatment and the need for future larger clinical studies investigating its role in predicting antiangiogenic therapy response.

To assess the effect of varying iodine flow rate (IFR) and iodine concentration on the quality of virtual unenhanced (VUE) images of the abdomen obtained with dual-energy CT.

There is a clinical need for noninvasive biomarkers of tumor hypoxia for prognostic and predictive studies, radiotherapy planning, and therapy monitoring. Oxygen-enhanced MRI (OE-MRI) is an emerging imaging technique for quantifying the spatial distribution and extent of tumor oxygen delivery in vivo. In OE-MRI, the longitudinal relaxation rate of protons (ΔR1) changes in proportion to the concentration of molecular oxygen dissolved in plasma or interstitial tissue fluid. Therefore, well-oxygenated tissues show positive ΔR1. We hypothesized that the fraction of tumor tissue refractory to oxygen challenge (lack of positive ΔR1, termed "Oxy-R fraction") would be a robust biomarker of hypoxia in models with varying vascular and hypoxic features. Here, we demonstrate that OE-MRI signals are accurate, precise, and sensitive to changes in tumor pO2 in highly vascular 786-0 renal cancer xenografts. Furthermore, we show that Oxy-R fraction can quantify the hypoxic fraction in multiple models with differing hypoxic and vascular phenotypes, when used in combination with measurements of tumor perfusion. Finally, Oxy-R fraction can detect dynamic changes in hypoxia induced by the vasomodulator agent hydralazine. In contrast, more conventional biomarkers of hypoxia (derived from blood oxygenation-level dependent MRI and dynamic contrast-enhanced MRI) did not relate to tumor hypoxia consistently. Our results show that the Oxy-R fraction accurately quantifies tumor hypoxia noninvasively and is immediately translatable to the clinic.

Most tumours, even those of the same histological type and grade, demonstrate considerable biological heterogeneity. Variations in genomic subtype, growth factor expression and local microenvironmental factors can result in regional variations within individual tumours. For example, localised variations in tumour cell proliferation, cell death, metabolic activity and vascular structure will be accompanied by variations in oxygenation status, pH and drug delivery that may directly affect therapeutic response. Documenting and quantifying regional heterogeneity within the tumour requires histological or imaging techniques. There is increasing evidence that quantitative imaging biomarkers can be used in vivo to provide important, reproducible and repeatable estimates of tumoural heterogeneity. In this article we review the imaging methods available to provide appropriate biomarkers of tumour structure and function. We also discuss the significant technical issues involved in the quantitative estimation of heterogeneity and the range of descriptive metrics that can be derived. Finally, we have reviewed the existing clinical evidence that heterogeneity metrics provide additional useful information in drug discovery and development and in clinical practice.

A new dual temporal resolution‐based, high spatial resolution, pharmacokinetic parametric mapping method is described ‐ improved coverage and spatial resolution using dual injection dynamic contrast‐enhanced (ICE‐DICE) MRI. In a dual‐bolus dynamic contrast‐enhanced‐MRI acquisition protocol, a high temporal resolution prebolus is followed by a high spatial resolution main bolus to allow high spatial resolution parametric mapping for cerebral tumors. The measured plasma concentration curves from the dual‐bolus data were used to reconstruct a high temporal resolution arterial input function. The new method reduces errors resulting from uncertainty in the temporal alignment of the arterial input function, tissue response function, and sampling grid. The technique provides high spatial resolution 3D pharmacokinetic maps (voxel size 1.0 × 1.0 × 2.0 mm 3 ) with whole brain coverage and greater parameter accuracy than that was possible with the conventional single temporal resolution methods. High spatial resolution imaging of brain lesions is highly desirable for small lesions and to support investigation of heterogeneity within pathological tissue and peripheral invasion at the interface between diseased and normal brain. The new method has the potential to be used to improve dynamic contrast‐enhanced‐MRI techniques in general. Magn Reson Med, 2012. © 2011 Wiley Periodicals, Inc.

Assessment of perfusion and capillary permeability is important in both malignant and nonmalignant lung disease. Kinetic modeling of T(1)-weighted dynamic contrast-enhanced MRI (DCE-MRI) data may provide such an assessment. This study establishes the feasibility and interrelationship of kinetic modeling approaches designed to estimate microvascular properties in malignant and nonmalignant tissues of the lung. DCE-MRI data were acquired using a low molecular weight contrast agent with 4-sec temporal resolution in lung cancer patients. A model-free parameterization and three kinetic models of increasing complexity, each related to the classical Kety model, were applied. Comparison of an extended Kety model and the adiabatic approximation to the tissue homogeneity (AATH) model using Akaike's Information Criterion suggested that in most cases the best description of the lung tumor data is obtained using the AATH model. In the normal lung parenchyma the temporal resolution was insufficient to separate effects of flow and contrast agent leakage and in this case the extended Kety model yielded the best fit to the data.

PURPOSE:To characterize human gliomas using T1-weighted dynamic contrast-enhanced MRI (DCE-MRI), and directly compare three pharmacokinetic analysis techniques: a conventional established technique and two novel techniques that aim to reduce erroneous overestimation of the volume transfer constant between plasma and the extravascular extracellular space (EES) (Ktrans) in areas of high blood volume. MATERIALS AND METHODS:Eighteen patients with high-grade gliomas underwent DCE-MRI. Three kinetic models were applied to estimate Ktrans and fractional blood plasma volume (vp). We applied the Tofts and Kermode (TK) model without arterial input function (AIF) estimation, the TK model modified to include vp and AIF estimation (mTK), and a "first pass" variant of the TK model (FP). RESULTS:KTK values were considerably higher than KmTK and KFP values (P <0.001). KmTK and KFP were more comparable and closely correlated (rho=0.744), with KmTK generally higher than KFP (P <0.001). Estimates of vp(mTK) and vp(FP) also showed a significant difference (P <0.001); however, these values were very closely correlated (rho=0.901). KTK parameter maps showed "pseudopermeability" effects displaying numerous vessels. These were not visualized on KmTK and KFP maps but appeared on the corresponding vp maps, indicating a failure of the TK model in commonly occurring vascular regions. CONCLUSION:Both of the methods that incorporate a measured AIF and an estimate of vp provide similar pathophysiological information and avoid erroneous overestimation of Ktrans in areas of significant vessel density, and thus allow a more accurate estimation of endothelial permeability.