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      Multiparametric Deep Learning and Radiomics for Tumor Grading and Treatment Response Assessment of Brain Cancer: Preliminary Results

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          Abstract

          Radiomics is an exciting new area of texture research for extracting quantitative and morphological characteristics of pathological tissue. However, to date, only single images have been used for texture analysis. We have extended radiomic texture methods to use multiparametric (mp) data to get more complete information from all the images. These mpRadiomic methods could potentially provide a platform for stratification of tumor grade as well as assessment of treatment response in brain tumors. In brain, multiparametric MRI (mpMRI) are based on contrast enhanced T1-weighted imaging (T1WI), T2WI, Fluid Attenuated Inversion Recovery (FLAIR), Diffusion Weighted Imaging (DWI) and Perfusion Weighted Imaging (PWI). Therefore, we applied our multiparametric radiomic framework (mpRadiomic) on 24 patients with brain tumors (8 grade II and 16 grade IV). The mpRadiomic framework classified grade IV tumors from grade II tumors with a sensitivity and specificity of 93% and 100%, respectively, with an AUC of 0.95. For treatment response, the mpRadiomic framework classified pseudo-progression from true-progression with an AUC of 0.93. In conclusion, the mpRadiomic analysis was able to effectively capture the multiparametric brain MRI texture and could be used as potential biomarkers for distinguishing grade IV from grade II tumors as well as determining true-progression from pseudo-progression.

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          Most cited references19

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          Advances in the molecular genetics of gliomas — implications for classification and therapy

          In 2016, a revised WHO classification of glioma was published, in which molecular data and traditional histological information are incorporated into integrated diagnoses. Herein, the authors highlight the developments in our understanding of the molecular genetics of gliomas that underlie this classification, and review the current landscape of molecular biomarkers used in the classification of disease subtypes. In addition, they discuss how these advances can promote the development of novel pathogenesis-based therapeutic approaches, paving the way to precision medicine.
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            Radiomics: a new application from established techniques.

            The increasing use of biomarkers in cancer have led to the concept of personalized medicine for patients. Personalized medicine provides better diagnosis and treatment options available to clinicians. Radiological imaging techniques provide an opportunity to deliver unique data on different types of tissue. However, obtaining useful information from all radiological data is challenging in the era of "big data". Recent advances in computational power and the use of genomics have generated a new area of research termed Radiomics. Radiomics is defined as the high throughput extraction of quantitative imaging features or texture (radiomics) from imaging to decode tissue pathology and creating a high dimensional data set for feature extraction. Radiomic features provide information about the gray-scale patterns, inter-pixel relationships. In addition, shape and spectral properties can be extracted within the same regions of interest on radiological images. Moreover, these features can be further used to develop computational models using advanced machine learning algorithms that may serve as a tool for personalized diagnosis and treatment guidance.
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              Deep learning and radiomics in precision medicine

              Introduction: The radiological reading room is undergoing a paradigm shift to a symbiosis of computer science and radiology using artificial intelligence integrated with machine and deep learning with radiomics to better define tissue characteristics. The goal is to use integrated deep learning and radiomics with radiological parameters to produce a personalized diagnosis for a patient. Areas covered: This review provides an overview of historical and current deep learning and radiomics methods in the context of precision medicine in radiology. A literature search for ‘Deep Learning’, ‘Radiomics’, ‘Machine learning’, ‘Artificial Intelligence’, ‘Convolutional Neural Network’, ‘Generative Adversarial Network’, ‘Autoencoders’, Deep Belief Networks”, Reinforcement Learning”, and ‘Multiparametric MRI’ was performed in PubMed, ArXiv, Scopus, CVPR, SPIE, IEEE Xplore, and NIPS to identify articles of interest. Expert opinion: In conclusion, both deep learning and radiomics are two rapidly advancing technologies that will unite in the future to produce a single unified framework for clinical decision support with a potential to completely revolutionize the field of precision medicine.
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                Author and article information

                Journal
                10 June 2019
                Article
                1906.04049
                4f08406d-fffb-4ab5-a631-ebbbee9a9a7f

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                94A17, 68T10
                6 pages, 4 figure, 2 tables, radiomics, brain
                eess.IV cs.LG physics.med-ph q-bio.QM

                Quantitative & Systems biology,Medical physics,Artificial intelligence,Electrical engineering

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