Evaluation of Microbubbles as Contrast Agents for Ultrasonography and Magnetic Resonance Imaging

Background: Microbubbles (MBs) can serve as an ultrasound contrast agent, and has the potential for magnetic resonance imaging (MRI). Due to the relatively low effect of MBs on MRI, it is necessary to develop new MBs that are more suitable for MRI. In this study, we evaluate the properties of SonoVueH and custom-made Fe 3O 4-nanoparticle-embedded microbubbles (Fe3O4-MBs) in terms of contrast agents for ultrsonography (US) and MRI. Methodology/Principal Findings: A total of 20 HepG2 subcutaneous-tumor-bearing nude mice were randomly assigned to 2 groups (i.e., n = 10 mice each group), one for US test and the other for MRI test. Within each group, two tests were performed for each mouse. The contrast agent for the first test is SonoVueH, and the second is Fe 3O 4-MBs. US was performed using a Technos MPX US system (Esaote, Italy) with a contrast-tuned imaging (CnTI TM) mode. MRI was performed using a 7.0T Micro-MRI (PharmaScan, Bruker Biospin GmbH, Germany) with an EPI-T2 * sequence. The data of signal-to-noise ratio (SNR) from the region-of-interest of each US and MR image was calculated by ImageJ (National Institute of Health, USA). In group 1, enhancement of SonoVueH was significantly higher than Fe 3O 4-MBs on US (P,0.001). In group 2, negative enhancement of Fe3O4-MBs was significantly higher than SonoVueH on MRI (P,0.001). The time to peak showed no significant differences between US and MRI, both of which used the same MBs (P.0.05). The SNR analysis of the enhancement process reveals a strong negative correlation in both cases (i.e., SonoVueH r=20.733, Fe 3O 4-MBs r = 20.903

Sensitivity of MRI Tumor Biomarkers to VEGFR Inhibitor Therapy in an Orthotopic Mouse Glioma Model

MRI biomarkers of tumor edema, vascular permeability, blood volume, and average vessel caliber are increasingly being employed to assess the efficacy of tumor therapies. However, the dependence of these biomarkers on a number of physiological factors can compromise their sensitivity and complicate the assessment of therapeutic efficacy. Here we examine the response of these MRI tumor biomarkers to cediranib, a potent vascular endothelial growth factor receptor (VEGFR) inhibitor, in an orthotopic mouse glioma model. A significant increase in the tumor volume and relative vessel caliber index (rVCI) and a slight decrease in the water apparent diffusion coefficient (ADC) were observed for both control and cediranib treated animals. This contrasts with a clinical study that observed a significant decrease in tumor rVCI, ADC and volume with cediranib therapy. While the lack of a difference between control and cediranib treated animals in these biomarker responses might suggest that cediranib has no therapeutic benefit, cediranib treated mice had a significantly increased survival. The increased survival benefit of cediranib treated animals is consistent with the significant decrease observed for cediranib treated animals in the relative cerebral blood volume (rCBV), relative microvascular blood volume (rMBV), transverse relaxation time (T2), blood vessel permeability (Ktrans), and extravascular-extracellular space (νe). The differential response of pre-clinical and clinical tumors to cediranib therapy, along with the lack of a positive response for some biomarkers, indicates the importance of evaluating the whole spectrum of different tumor biomarkers to properly assess the therapeutic response and identify and interpret the therapy-induced changes in the tumor physiology

Reliability of dynamic contrast-enhanced magnetic resonance imaging data in primary brain tumours: a comparison of Tofts and shutter speed models

Purpose To investigate the robustness of pharmacokinetic modelling of DCE-MRI brain tumour data and to ascertain reliable perfusion parameters through a model selection process and a stability test. Methods DCE-MRI data of 14 patients with primary brain tumours were analysed using the Tofts model (TM), the extended Tofts model (ETM), the shutter speed model (SSM) and the extended shutter speed model (ESSM). A no-effect model (NEM) was implemented to assess overfitting of data by the other models. For each lesion, the Akaike Information Criteria (AIC) was used to build a 3D model selection map. The variability of each pharmacokinetic parameter extracted from this map was assessed with a noise propagation procedure, resulting in voxel-wise distributions of the coefficient of variation (CV). Results The model selection map over all patients showed NEM had the best fit in 35.5% of voxels, followed by ETM (32%), TM (28.2%), SSM (4.3%) and ESSM (<0.1%). In analysing the reliability of Ktrans, when considering regions with a CV<20%, ≈25% of voxels were found to be stable across all patients. The remaining 75% of voxels were considered unreliable. Conclusions The majority of studies quantifying DCE-MRI data in brain tumours only consider a single model and whole-tumour statistics for the output parameters. Appropriate model selection, considering tissue biology and its effects on blood brain barrier permeability and exchange conditions, together with an analysis on the reliability and stability of the calculated parameters, is critical in processing robust brain tumour DCE-MRI data

RECIST revised: implications for the radiologist. A review article on the modified RECIST guideline

The purpose of this review article is to familiarize radiologists with the recently revised Response Evaluation Criteria in Solid Tumours (RECIST), used in many anticancer drug trials to assess response and progression rate. The most important modifications are: a reduction in the maximum number of target lesions from ten to five, with a maximum of two per organ, with a longest diameter of at least 10 mm; in lymph nodes (LNs) the short axis rather than the long axis should be measured, with normal LN measuring <10 mm, non-target LN ≥10 mm but <15 mm and target LN ≥15 mm; osteolytic lesions with a soft tissue component and cystic tumours may serve as target lesions; an additional requirement for progressive disease (PD) of target lesions is not only a ≥20% increase in the sum of the longest diameter (SLD) from the nadir but also a ≥5 mm absolute increase in the SLD (the other response categories of target lesion are unchanged); PD of non-target lesions can only be applied if the increase in non-target lesions is representative of change in overall tumour burden; detailed imaging guidelines. Alternative response criteria in patients with hepatocellular carcinoma and gastrointestinal stromal tumours are discussed

The 2019 Mathematical Oncology Roadmap.

Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research. Mathematical Oncology-defined here simply as the use of mathematics in cancer research-complements and overlaps with a number of other fields that rely on mathematics as a core methodology. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier; make predictions of response to therapy; design adaptive, patient-specific treatment plans to overcome therapy resistance; and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between Two Beasts: mathematics and cancer.NIH (R01CA16437, R01NS060752, U54CA210180, U54CA143970, U54193489, U01CA220378)James S. McDonnell FoundationBen & Catherine Ivy Foundatio

Imaging biomarker roadmap for cancer studies.

Imaging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing 'translational gaps' through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored 'roadmap'. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use.Development of this roadmap received support from Cancer Research UK and the Engineering and Physical Sciences Research Council (grant references A/15267, A/16463, A/16464, A/16465, A/16466 and A/18097), the EORTC Cancer Research Fund, and the Innovative Medicines Initiative Joint Undertaking (grant agreement number 115151), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and European Federation of Pharmaceutical Industries and Associations (EFPIA) companies' in kind contribution