scholarly journals NIMG-16. EXPLORATORY EVALUATION OF Q-SPACE TRAJECTORY IMAGING PARAMETERS AS NOVEL IMAGING BIOMARKERS FOR GLIOMAS

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii150-ii150
Author(s):  
Parikshit Juvekar ◽  
Filip Szczepankiewicz ◽  
Thomas Noh ◽  
Carl-Fredrik Westin ◽  
Alexandra Golby
Keyword(s):  
Diffusion Mri ◽  
Tumor Tissue ◽  
De Novo ◽  
Tumor Grade ◽  
Imaging Biomarkers ◽  
Tumor Treatment ◽  
Tissue Microstructure ◽  
Non Invasive ◽  
Recurrent Tumors ◽  
Diffusion Parameters

Abstract Neuroimaging offers a non-invasive means to probe tumor tissue in order to inform decision making at all phases of brain tumor treatment. Diffusion MRI is particularly sensitive to tumor tissue microstructure, with greater heterogeneity being reflected as a larger diffusional kurtosis. Q-Space Trajectory Imaging (QTI) uses tensor-valued diffusion encoding (encoding along multiple directions per shot) to disentangle Isotropic Mean Kurtosis (MKi) from Anisotropic Mean Kurtosis (MKa), which are otherwise conflated in the Total Mean Kurtosis (MKt). To test whether disentangling MKi and MKa facilitates a more specific probe of tumor tissue heterogeneity and malignancy, we investigated if QTI parameters could distinguish low- from high-grade gliomas and enhancing from non-enhancing regions using pre-operative QTI imaging of 13 W.H.O. grade I-II and 18 grade III-IV glioma patients. We also analyzed these features separately for de novo and recurrent tumors. Regions of Interest (ROIs) were drawn on QTI maps, with support from T1 and T2-weighted images, for enhancing region, non-enhancing region, necrotic cavity, cyst, edema and resection cavity. ROC was used to gauge QTI parameter performance in classifying tumor characteristics. MKi was found to be the strongest predictor of tumor grade (AUC = 0.74, p = 0.019). MKi and MKt separated de novo from recurrent tumors (AUC = 0.80 and 0.76, p < 0.05). MKt separated enhancing regions from non-enhancing regions with an AUC of 0.88 in all tumors and 0.97 in de novo tumors. Our preliminary results highlight that tensor-valued diffusion MRI and QTI analysis have the potential to non-invasively characterize tumor grade. Further, MKt accurately differentiated enhancing from non-enhancing tumors and could potentially substitute for gadolinium injection in some situations thereby decreasing risk, time, and cost. Our ongoing studies in larger groups aim to further correlate molecular tumor markers (eg. IDH1 status) with these diffusion parameters.

scholarly journals Overall Survival Prediction in Renal Cell Carcinoma Patients Using Computed Tomography Radiomic and Clinical Information

2021 ◽  
Author(s):  
Zahra Khodabakhshi ◽  
Mehdi Amini ◽  
Shayan Mostafaei ◽  
Atlas Haddadi Avval ◽  
Mostafa Nazari ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Overall Survival ◽  
Cell Carcinoma ◽  
Clinical Features ◽  
Renal Cell ◽  
Radical Nephrectomy ◽  
Clinical Information ◽  
Tumor Grade ◽  
Imaging Biomarkers ◽  
Area Density

AbstractThe aim of this work is to investigate the applicability of radiomic features alone and in combination with clinical information for the prediction of renal cell carcinoma (RCC) patients’ overall survival after partial or radical nephrectomy. Clinical studies of 210 RCC patients from The Cancer Imaging Archive (TCIA) who underwent either partial or radical nephrectomy were included in this study. Regions of interest (ROIs) were manually defined on CT images. A total of 225 radiomic features were extracted and analyzed along with the 59 clinical features. An elastic net penalized Cox regression was used for feature selection. Accelerated failure time (AFT) with the shared frailty model was used to determine the effects of the selected features on the overall survival time. Eleven radiomic and twelve clinical features were selected based on their non-zero coefficients. Tumor grade, tumor malignancy, and pathology t-stage were the most significant predictors of overall survival (OS) among the clinical features (p < 0.002, < 0.02, and < 0.018, respectively). The most significant predictors of OS among the selected radiomic features were flatness, area density, and median (p < 0.02, < 0.02, and < 0.05, respectively). Along with important clinical features, such as tumor heterogeneity and tumor grade, imaging biomarkers such as tumor flatness, area density, and median are significantly correlated with OS of RCC patients.

scholarly journals Cardiac Computed Tomography Radiomics for the Non-Invasive Assessment of Coronary Inflammation

Cells ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 879
Author(s):  
Kevin Cheng ◽  
Andrew Lin ◽  
Jeremy Yuvaraj ◽  
Stephen J. Nicholls ◽  
Dennis T.L. Wong
Keyword(s):  
Cardiac Computed Tomography ◽  
Quantitative Information ◽  
Imaging Biomarkers ◽  
Great Promise ◽  
Non Invasive ◽  
Quantitative Image ◽  
Artery Disease ◽  
Therapeutic Decision Making ◽  
Underlying Pathophysiology

Radiomics, via the extraction of quantitative information from conventional radiologic images, can identify imperceptible imaging biomarkers that can advance the characterization of coronary plaques and the surrounding adipose tissue. Such an approach can unravel the underlying pathophysiology of atherosclerosis which has the potential to aid diagnostic, prognostic and, therapeutic decision making. Several studies have demonstrated that radiomic analysis can characterize coronary atherosclerotic plaques with a level of accuracy comparable, if not superior, to current conventional qualitative and quantitative image analysis. While there are many milestones still to be reached before radiomics can be integrated into current clinical practice, such techniques hold great promise for improving the imaging phenotyping of coronary artery disease.

scholarly journals Positron Emission Tomography in Animal Models of Alzheimer’s Disease Amyloidosis

2021 ◽  
Author(s):  
Ruiqing Ni
Keyword(s):  
Positron Emission Tomography ◽  
Animal Models ◽  
Amyloid Beta ◽  
Emission Tomography ◽  
Imaging Biomarkers ◽  
Preclinical Research ◽  
Non Invasive ◽  
Positron Emission ◽  
Cerebral Amyloid ◽  
On Chip

Animal models of Alzheimer&rsquo;s disease amyloidosis that recapitulate cerebral amyloid-beta pathology have been widely used in preclinical research, and have greatly enabled the mechanistic understanding of Alzheimer&rsquo;s disease and the development of therapeutics. Comprehensive deep phenotyping of the pathophysiological and biochemical features in these animal models are essential. Recent advances in positron emission tomography have allowed the non-invasive visualization of the alterations in the brain of animal models as well as in patients with Alzheimer&rsquo;s disease, These tools have facilitated our understanding of disease mechanisms, and provided longitudinal monitoring of treatment effect in animal models of Alzheimer&rsquo;s disease amyloidosis. In this review, we focus on recent positron emission tomography studies of cerebral amyloid-beta accumulation, hypoglucose metabolism, synaptic and neurotransmitter receptor deficits (cholinergic and glutamatergic system), blood-brain barrier impairment and neuroinflammation (microgliosis and astrocytosis) in animal models of Alzheimer&rsquo;s disease amyloidosis. We further propose the emerging targets and tracers for reflecting the pathophysiological changes, and discuss outstanding challenges in disease animal models and future outlook in on-chip characterization of imaging biomarkers towards clinical translation.

Optical-Thermal Response and Dynamic Thermal Damage of Bio-Tissue During Laser Thermotherapy

2003 ◽  
Author(s):  
G. M. Zhu ◽  
W. Liu ◽  
T. F. Zeng ◽  
K. Yang
Keyword(s):  
Thermal Injury ◽  
Thermal Damage ◽  
Tumor Tissue ◽  
Local Heating ◽  
Damage Model ◽  
Thermal Response ◽  
Tumor Treatment ◽  
Thermal Coagulation ◽  
Laser Head ◽  
Temperature Damage

Laser thermotherapy is a technique used for tumor treatment. It generates a local heating, causes thermal coagulation of living tissue and eliminates the tumor. Precise heating of tumor tissue with healthy minimum thermal injury to adjacent tissue is essential to thermotherapy. Understanding of heat transfer and optical-thermal interaction is important for control of temperature and design of thermotherapy. This study applies the Arrhenius damage model to describe the heat-induced change of optical properties. It calculates the distribution temperature, damage and optical-thermal response of bio-tissue during the laser treatment, and shows how these factors affect the effectiveness of laser thermotherapy. Similar research has been performed by Kim and coworkers [1996], Iizuka and coworkers [2000], and Whelan and coworkers [2000]. This study relaxes some conditions in previous investigations. It reveals the importance and the effect of size of the laser head.

Optimal Diffusion Encoding Strategies for Fiber Mapping in Diffusion MRI

2009 ◽  
pp. 90-107 ◽  
Author(s):  
Dimitrios C. Karampinos ◽  
Robert Dawe ◽  
Konstantinos Arfanakis ◽  
John G. Georgiadis
Keyword(s):  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Cerebral White Matter ◽  
Tissue Microstructure ◽  
High Order Schemes ◽  
Selection Strategies ◽  
Diffusion Characteristics ◽  
Encoding Strategies ◽  
White Matter Connectivity ◽  
Selection Of

Diffusion Magnetic Resonance Imaging (diffusion MRI) can provide important information about tissue microstructure by probing the diffusion of water molecules in a biological tissue. Although originally proposed for the characterization of cerebral white matter connectivity and pathologies, its implementation has extended to many other areas of the human body. In a parallel development, a number of diffusion models have been proposed in order to extract the underlying tissue microstructural properties from the diffusion MRI signal. The present study reviews the basic considerations that have to be taken into account in the selection of the diffusion encoding parameters in diffusion MRI acquisition. Both diffusion tensor imaging (DTI) and high-order schemes are reviewed. The selection of these parameters relies strongly on requirements of the adopted diffusion model and the diffusion characteristics of the tissue under study. The authors review several successful parameter selection strategies for the imaging of the human brain, and conclude with the basics of parameter optimization on promising applications of the technique on other tissues, such as the spinal cord, the myocardium, and the skeletal muscles.

scholarly journals A time-dependent diffusion MRI signature of axon caliber variations and beading

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Hong-Hsi Lee ◽  
Antonios Papaioannou ◽  
Sung-Lyoung Kim ◽  
Dmitry S. Novikov ◽  
Els Fieremans
Keyword(s):  
Time Dependence ◽  
Diffusion Mri ◽  
Cellular Level ◽  
3 Dimensional ◽  
Non Invasive ◽  
Brain White Matter ◽  
Cell Dimensions ◽  
Micrometer Scale ◽  
Exciting Possibility ◽  
The Brain

AbstractMRI provides a unique non-invasive window into the brain, yet is limited to millimeter resolution, orders of magnitude coarser than cell dimensions. Here, we show that diffusion MRI is sensitive to the micrometer-scale variations in axon caliber or pathological beading, by identifying a signature power-law diffusion time-dependence of the along-fiber diffusion coefficient. We observe this signature in human brain white matter and identify its origins by Monte Carlo simulations in realistic substrates from 3-dimensional electron microscopy of mouse corpus callosum. Simulations reveal that the time-dependence originates from axon caliber variation, rather than from mitochondria or axonal undulations. We report a decreased amplitude of time-dependence in multiple sclerosis lesions, illustrating the potential sensitivity of our method to axonal beading in a plethora of neurodegenerative disorders. This specificity to microstructure offers an exciting possibility of bridging across scales to image cellular-level pathology with a clinically feasible MRI technique.

Regeneration of testis tissue after ectopic implantation of porcine testis cell aggregates in mice: improved consistency of outcomes and in situ monitoring

2020 ◽  
Vol 32 (6) ◽  
pp. 594 ◽  
Author(s):  
Awang Hazmi Awang-Junaidi ◽  
Jaswant Singh ◽  
Ali Honaramooz
Keyword(s):  
De Novo ◽  
Ultrasound Biomicroscopy ◽  
In Situ Monitoring ◽  
Cell Aggregates ◽  
Tissue Development ◽  
Immunodeficient Mice ◽  
Non Invasive ◽  
Old Donor ◽  
Testis Tissue

Ectopic implantation of donor testis cell aggregates in recipient mice results in de novo formation or regeneration of testis tissue and, as such, provides a unique invivo model for the study of testis development. However, currently the results are inconsistent and the efficiency of the model remains low. This study was designed to: (1) examine several factors that can potentially improve the consistency and efficiency of this model and (2) explore the use of ultrasound biomicroscopy (UBM) for the non-invasive invivo evaluation of implants. Testis cell aggregates, containing ~40% gonocytes, from 1-week-old donor piglets were implanted under the back skin of immunodeficient mice through skin incisions using gel matrices or through subcutaneous injection without using gel matrices. The addition of gel matrices led to inconsistent tissue development; gelatin had the greatest development, followed by collagen, whereas agarose resulted in poor development. The results also depended on the implanted cell numbers since implants with 100×106 cells were larger than those with 50×106 cells. The injection approach for cell implantation was less invasive and resulted in more consistent and efficient testis tissue development. UBM provided promising results as a means of non-invasive monitoring of implants.

scholarly journals The Translational Status of Cancer Liquid Biopsies

2019 ◽  
Author(s):  
Sinisa Bratulic ◽  
Francesco Gatto ◽  
Jens Nielsen
Keyword(s):  
Treatment Outcomes ◽  
Liquid Biopsy ◽  
Tumor Tissue ◽  
Body Fluids ◽  
Precision Oncology ◽  
Liquid Biopsies ◽  
Non Invasive ◽  
Biomarker Research ◽  
Successes And Challenges

Abstract Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. This can be achieved by leveraging omics information for accurate molecular characterization of tumors. Tumor tissue biopsies are currently the main source of information for molecular profiling. However, biopsies are invasive and limited in resolving spatiotemporal heterogeneity in tumor tissues. Alternative non-invasive liquid biopsies can exploit patient’s body fluids to access multiple layers of tumor-specific biological information (genomes, epigenomes, transcriptomes, proteomes, metabolomes, circulating tumor cells, and exosomes). Analysis and integration of these large and diverse datasets using statistical and machine learning approaches can yield important insights into tumor biology and lead to discovery of new diagnostic, predictive, and prognostic biomarkers. Translation of these new diagnostic tools into standard clinical practice could transform oncology, as demonstrated by a number of liquid biopsy assays already entering clinical use. In this review, we highlight successes and challenges facing the rapidly evolving field of cancer biomarker research. Lay Summary Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. The discovery of biomarkers for precision oncology has been accelerated by high-throughput experimental and computational methods, which can inform fine-grained characterization of tumors for clinical decision-making. Moreover, advances in the liquid biopsy field allow non-invasive sampling of patient’s body fluids with the aim of analyzing circulating biomarkers, obviating the need for invasive tumor tissue biopsies. In this review, we highlight successes and challenges facing the rapidly evolving field of liquid biopsy cancer biomarker research.

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