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Applications of radiomics and machine learning for radiotherapy of malignant brain tumors.
Kocher, M, Ruge, MI, Galldiks, N, Lohmann, P
Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]. 2020;(10):856-867
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Abstract
BACKGROUND Magnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use of radiotherapy in patients with malignant brain tumors. METHODS This study is based on comprehensive literature research on machine learning and radiomics analyses in neuroimaging and their potential application for radiotherapy in patients with malignant glioma or brain metastases. RESULTS Feature-based radiomics and deep learning-based machine learning methods can be used to improve brain tumor diagnostics and automate various steps of radiotherapy planning. In glioma patients, important applications are the determination of WHO grade and molecular markers for integrated diagnosis in patients not eligible for biopsy or resection, automatic image segmentation for target volume planning, prediction of the location of tumor recurrence, and differentiation of pseudoprogression from actual tumor progression. In patients with brain metastases, radiomics is applied for additional detection of smaller brain metastases, accurate segmentation of multiple larger metastases, prediction of local response after radiosurgery, and differentiation of radiation injury from local brain metastasis relapse. Importantly, high diagnostic accuracies of 80-90% can be achieved by most approaches, despite a large variety in terms of applied imaging techniques and computational methods. CONCLUSION Clinical application of automated image analyses based on radiomics and artificial intelligence has a great potential for improving radiotherapy in patients with malignant brain tumors. However, a common problem associated with these techniques is the large variability and the lack of standardization of the methods applied.
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Harnessing the new emerging imaging technologies to uncover the role of Ca2+ signalling in plant nutrient homeostasis.
Vigani, G, Costa, A
Plant, cell & environment. 2019;(10):2885-2901
Abstract
Increasing crop yields by using ecofriendly practices is of high priority to tackle problems regarding food security and malnutrition worldwide. A sustainable crop production requires a limited use of fertilizer and the employment of plant varieties with improved ability to acquire nutrients from soil. To reach these goals, the scientific community aims to understand plant nutrients homeostasis by deciphering the nutrient sensing and signalling mechanisms of plants. Several lines of evidence about the involvement of Ca2+ as the signal of an impaired nutrient availability have been reported. Ca2+ signalling is a tightly regulated process that requires specific protein toolkits to perceive external stimuli and to induce the specific responses in the plant needed to survive. Here, we summarize both older and recent findings concerning the involvement of Ca2+ signalling in the homeostasis of nutrients. In this review, we present new emerging technologies, based on the use of genetically encoded Ca2+ sensors and advanced microscopy, which offer the chance to perform in planta analyses of Ca2+ dynamics at cellular resolution. The harnessing of these technologies with different genetic backgrounds and subjected to different nutritional stresses will provide important insights to the still little-known mechanisms of nutrient sensing in plants.
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Radiomics derived from amino-acid PET and conventional MRI in patients with high-grade gliomas.
Lohmann, P, Kocher, M, Steger, J, Galldiks, N
The quarterly journal of nuclear medicine and molecular imaging : official publication of the Italian Association of Nuclear Medicine (AIMN) [and] the International Association of Radiopharmacology (IAR), [and] Section of the Society of.... 2018;(3):272-280
Abstract
Radiomics is a technique that uses high-throughput computing to extract quantitative features from tomographic medical images such as MRI and PET that usually are beyond visual perception. Importantly, the radiomics approach can be performed using neuroimages that have already been acquired during the routine follow-up of the patients allowing an additional data evaluation at low cost. In Neuro-Oncology, these features can potentially be used for differential diagnosis of newly diagnosed cerebral lesions suggestive for brain tumors or for the prediction of response to a neurooncological treatment option. Furthermore, especially in the light of the recent update of the World Health Organization classification of brain tumors, radiomics also has the potential to non-invasively assess important prognostic and predictive molecular markers such as a mutation in the isocitrate dehydrogenase gene or a 1p/19q codeletion which are not accessible by conventional visual interpretation of MRI or PET findings. This review summarizes the current status of the rapidly evolving field of radiomics with a special focus on patients with high-grade gliomas.
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Clinical quantitative susceptibility mapping (QSM): Biometal imaging and its emerging roles in patient care.
Wang, Y, Spincemaille, P, Liu, Z, Dimov, A, Deh, K, Li, J, Zhang, Y, Yao, Y, Gillen, KM, Wilman, AH, et al
Journal of magnetic resonance imaging : JMRI. 2017;(4):951-971
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Abstract
UNLABELLED Quantitative susceptibility mapping (QSM) has enabled magnetic resonance imaging (MRI) of tissue magnetic susceptibility to advance from simple qualitative detection of hypointense blooming artifacts to precise quantitative measurement of spatial biodistributions. QSM technology may be regarded to be sufficiently developed and validated to warrant wide dissemination for clinical applications of imaging isotropic susceptibility, which is dominated by metals in tissue, including iron and calcium. These biometals are highly regulated as vital participants in normal cellular biochemistry, and their dysregulations are manifested in a variety of pathologic processes. Therefore, QSM can be used to assess important tissue functions and disease. To facilitate QSM clinical translation, this review aims to organize pertinent information for implementing a robust automated QSM technique in routine MRI practice and to summarize available knowledge on diseases for which QSM can be used to improve patient care. In brief, QSM can be generated with postprocessing whenever gradient echo MRI is performed. QSM can be useful for diseases that involve neurodegeneration, inflammation, hemorrhage, abnormal oxygen consumption, substantial alterations in highly paramagnetic cellular iron, bone mineralization, or pathologic calcification; and for all disorders in which MRI diagnosis or surveillance requires contrast agent injection. Clinicians may consider integrating QSM into their routine imaging practices by including gradient echo sequences in all relevant MRI protocols. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2017;46:951-971.
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Phenomic Approaches and Tools for Phytopathologists.
Simko, I, Jimenez-Berni, JA, Sirault, XR
Phytopathology. 2017;(1):6-17
Abstract
Plant phenomics approaches aim to measure traits such as growth, performance, and composition of plants using a suite of noninvasive technologies. The goal is to link phenotypic traits to the genetic information for particular genotypes, thus creating the bridge between the phenome and genome. Application of sensing technologies for detecting specific phenotypic reactions occurring during plant-pathogen interaction offers new opportunities for elucidating the physiological mechanisms that link pathogen infection and disease symptoms in the host, and also provides a faster approach in the selection of genetic material that is resistant to specific pathogens or strains. Appropriate phenomics methods and tools may also allow presymptomatic detection of disease-related changes in plants or to identify changes that are not visually apparent. This review focuses on the use of sensor-based phenomics tools in plant pathology such as those related to digital imaging, chlorophyll fluorescence imaging, spectral imaging, and thermal imaging. A brief introduction is provided for less used approaches like magnetic resonance, soft x-ray imaging, ultrasound, and detection of volatile compounds. We hope that this concise review will stimulate further development and use of tools for automatic, nondestructive, and high-throughput phenotyping of plant-pathogen interaction.
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Survey on computer aided decision support for diagnosis of celiac disease.
Hegenbart, S, Uhl, A, Vécsei, A
Computers in biology and medicine. 2015;:348-58
Abstract
Celiac disease (CD) is a complex autoimmune disorder in genetically predisposed individuals of all age groups triggered by the ingestion of food containing gluten. A reliable diagnosis is of high interest in view of embarking on a strict gluten-free diet, which is the CD treatment modality of first choice. The gold standard for diagnosis of CD is currently based on a histological confirmation of serology, using biopsies performed during upper endoscopy. Computer aided decision support is an emerging option in medicine and endoscopy in particular. Such systems could potentially save costs and manpower while simultaneously increasing the safety of the procedure. Research focused on computer-assisted systems in the context of automated diagnosis of CD has started in 2008. Since then, over 40 publications on the topic have appeared. In this context, data from classical flexible endoscopy as well as wireless capsule endoscopy (WCE) and confocal laser endomicrosopy (CLE) has been used. In this survey paper, we try to give a comprehensive overview of the research focused on computer-assisted diagnosis of CD.
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Visualizing non-Gaussian diffusion: clinical application of q-space imaging and diffusional kurtosis imaging of the brain and spine.
Hori, M, Fukunaga, I, Masutani, Y, Taoka, T, Kamagata, K, Suzuki, Y, Aoki, S
Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine. 2012;(4):221-33
Abstract
Recently, non-Gaussian diffusion-weighted imaging (DWI) techniques, including q-space imaging (QSI) and diffusional kurtosis imaging (DKI), have emerged as advanced methods to evaluate tissue microstructure in vivo using water diffusion. QSI and DKI have shown promising results in clinical applications, such as in the evaluation of brain tumors (e.g., grading gliomas), degenerative diseases (e.g., specific diagnosis of Parkinson disease), demyelinating diseases (e.g., assessment of normal-appearing tissue of multiple sclerosis), and cerebrovascular diseases (e.g., assessment of the microstructural environment of fresh infarctions). Representative metrics in clinical use are the full width at half maximum, also known as the mean displacement of the probability density function curve, which is derived from QSI, and diffusional kurtosis, which is derived from DKI. These new metrics may provide information on tissue structure in addition to that provided by conventional Gaussian DWI investigations that use the apparent diffusion coefficient and fractional anisotropy, recognized indices for evaluating disease and normal development in the brain and spine. In some clinical situations, sensitivity for detecting pathological conditions is higher using QSI and DKI than conventional DWI and diffusion tensor imaging (DTI) because DWI and DTI calculations are based on the assumption that water molecules follow a Gaussian distribution, whereas hindrance of the distribution of water molecules by complex and restricted structures in actual neural tissues produces distributions that are far from Gaussian. We review the technical aspects and clinical applications of QSI and DKI, focusing on clinical use and in vivo studies and highlighting differences from conventional diffusional metrics.
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The evidence for automated grading in diabetic retinopathy screening.
Fleming, AD, Philip, S, Goatman, KA, Prescott, GJ, Sharp, PF, Olson, JA
Current diabetes reviews. 2011;(4):246-52
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Abstract
Systematic screening for diabetic retinopathy using retinal photography has been shown to reduce the incidence of blindness among people with diabetes. The implementation of diabetic retinopathy screening programmes faces several challenges. Consequently, methods for improving the efficiency of screening are being sought, one of which is the automation of image grading involving detection of images with either disease or of inadequate quality using computer software. This review aims to bring together the available evidence that is suitable for making a judgement about whether automated grading systems could be used effectively in diabetic retinopathy screening. To do this, it focuses on studies made by the few centres who have presented results tests of automated grading software on large sets of patients or screening episodes. It also considers economic model analyses and papers describing the effectiveness of manual grading in order that the effect of replacing stages of manual grading by automated grading can be judged. In conclusion, the review shows that there is sufficient evidence to suggest that automated grading, operating as a disease / no disease grader, is safe and could reduce the workload of manual grading in diabetic retinopathy screening.
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Mapping brain anatomical connectivity using white matter tractography.
Lazar, M
NMR in biomedicine. 2010;(7):821-35
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Abstract
Integration of the neural processes in the human brain is realized through interconnections that exist between different neural centers. These interconnections take place through white matter pathways. White matter tractography is currently the only available technique for the reconstruction of the anatomical connectivity in the human brain noninvasively and in vivo. The trajectory and terminations of white matter pathways are estimated from local orientations of nerve bundles. These orientations are obtained using measurements of water diffusion in the brain. In this article, the techniques for estimating fiber directions from diffusion measurements in the human brain are reviewed. Methods of white matter tractography are described, together with the current limitations of the technique, including sensitivity to image noise and partial voluming. The applications of white matter tractography to the topographical characterization of the white matter connections and the segmentation of specific white matter pathways, and corresponding functional units of gray matter, are discussed. In this context, the potential impact of white matter tractography in mapping the functional systems and subsystems in the human brain, and their interrelations, is described. Finally, the applications of white matter tractography to the study of brain disorders, including fiber tract localization in brains affected by tumors and the identification of impaired connectivity routes in neurologic and neuropsychiatric diseases, are discussed.
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Proton MR spectroscopy of the brain at 3 T: an update.
Di Costanzo, A, Trojsi, F, Tosetti, M, Schirmer, T, Lechner, SM, Popolizio, T, Scarabino, T
European radiology. 2007;(7):1651-62
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Abstract
Proton magnetic resonance spectroscopy ((1)H-MRS) provides specific metabolic information not otherwise observable by any other imaging method. (1)H-MRS of the brain at 3 T is a new tool in the modern neuroradiological armamentarium whose main advantages, with respect to the well-established and technologically advanced 1.5-T (1)H-MRS, include a higher signal-to-noise ratio, with a consequent increase in spatial and temporal resolutions, and better spectral resolution. These advantages allow the acquisition of higher quality and more easily quantifiable spectra in smaller voxels and/or in shorter times, and increase the sensitivity in metabolite detection. However, these advantages may be hampered by intrinsic field-dependent technical issues, such as decreased T(2) signal, chemical shift dispersion errors, J-modulation anomalies, increased magnetic susceptibility, eddy current artifacts, challenges in designing and obtaining appropriate radiofrequency coils, magnetic field instability and safety hazards. All these limitations have been tackled by manufacturers and researchers and have received one or more solutions. Furthermore, advanced (1)H-MRS techniques, such as specific spectral editing, fast (1)H-MRS imaging and diffusion tensor (1)H-MRS imaging, have been successfully implemented at 3 T. However, easier and more robust implementations of these techniques are still needed before they can become more widely used and undertake most of the clinical and research (1)H-MRS applications.