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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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Amide proton transfer imaging of tumors: theory, clinical applications, pitfalls, and future directions.
Kamimura, K, Nakajo, M, Yoneyama, T, Takumi, K, Kumagae, Y, Fukukura, Y, Yoshiura, T
Japanese journal of radiology. 2019;(2):109-116
Abstract
Amide proton transfer (APT) imaging is an emerging molecular magnetic resonance imaging technique based on chemical exchange saturation transfer (CEST). APT imaging has shown promise in oncologic imaging, especially in the imaging of brain tumors. This review article illustrates the theory of CEST/APT imaging and describes the clinical utility, pitfalls, and potential for future development of APT imaging.
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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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Pitfalls in liver MRI: Technical approach to avoiding misdiagnosis and improving image quality.
Yacoub, JH, Elsayes, KM, Fowler, KJ, Hecht, EM, Mitchell, DG, Santillan, C, Szklaruk, J
Journal of magnetic resonance imaging : JMRI. 2019;(1):41-58
Abstract
The following is an illustrative review of common pitfalls in liver MRI that may challenge interpretation. This article reviews common technical and diagnostic challenges encountered when interpreting dynamic multiphasic T1 -weighted imaging, hepatobiliary phase imaging, and diffusion-weighted imaging of the liver. Additionally, each section includes suggestions for avoiding diagnostic and technical errors. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:41-58.