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      A deep look into radiomics

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          Abstract

          Radiomics is a process that allows the extraction and analysis of quantitative data from medical images. It is an evolving field of research with many potential applications in medical imaging. The purpose of this review is to offer a deep look into radiomics, from the basis, deeply discussed from a technical point of view, through the main applications, to the challenges that have to be addressed to translate this process in clinical practice. A detailed description of the main techniques used in the various steps of radiomics workflow, which includes image acquisition, reconstruction, pre-processing, segmentation, features extraction and analysis, is here proposed, as well as an overview of the main promising results achieved in various applications, focusing on the limitations and possible solutions for clinical implementation. Only an in-depth and comprehensive description of current methods and applications can suggest the potential power of radiomics in fostering precision medicine and thus the care of patients, especially in cancer detection, diagnosis, prognosis and treatment evaluation.

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          Radiomics: Images Are More than Pictures, They Are Data

          This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
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            3D Slicer as an image computing platform for the Quantitative Imaging Network.

            Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer. Copyright © 2012 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                camilla.scapicchio@med.unipi.it
                Journal
                Radiol Med
                Radiol Med
                La Radiologia Medica
                Springer Milan (Milan )
                0033-8362
                1826-6983
                2 July 2021
                2 July 2021
                2021
                : 126
                : 10
                : 1296-1311
                Affiliations
                [1 ]GRID grid.5395.a, ISNI 0000 0004 1757 3729, Academic Radiology, Department of Translational Research, , University of Pisa, ; Via Roma 67, 56126 Pisa, Italy
                [2 ]CNR-IFAC Institute of Applied Physics “N. Carrara”, 50019 Sesto Fiorentino, Italy
                [3 ]GRID grid.5395.a, ISNI 0000 0004 1757 3729, Academic Radiology, Department of Surgical, Medical, Molecular Pathology and Emergency Medicine, , University of Pisa, ; Via Roma 67, 56126 Pisa, Italy
                [4 ]GRID grid.416315.4, Department of Radiology, , Azienda Ospedaliero Universitaria (A.O.U.), ; Monserrato (Cagliari),Cagliari, Italy
                [5 ]Italian Society of Medical and Interventional Radiology, SIRM Foundation, Via della Signora 2, 20122 Milano, Italy
                Author information
                http://orcid.org/0000-0001-5984-0408
                http://orcid.org/0000-0002-6668-499X
                http://orcid.org/0000-0002-3759-7512
                http://orcid.org/0000-0002-5120-886X
                http://orcid.org/0000-0003-2870-3771
                http://orcid.org/0000-0001-7950-4559
                Article
                1389
                10.1007/s11547-021-01389-x
                8520512
                34213702
                294369a0-5f3f-44a0-9af7-f36de387b775
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 9 February 2021
                : 15 June 2021
                Funding
                Funded by: H2020-EU-PRIMAGE project
                Award ID: 826494
                Award Recipient :
                Funded by: H2020-EU-CHAIMELEON project
                Award ID: 952172
                Award Recipient :
                Funded by: H2020-EU-EuCanImage project
                Award ID: 952103
                Award Recipient :
                Funded by: H2020-EU-Procancer-I project
                Award ID: 952159
                Award Recipient :
                Funded by: Università di Pisa
                Categories
                Computer Application
                Custom metadata
                © Italian Society of Medical Radiology 2021

                radiomics,medical imaging,features,imaging biomarkers,personalized medicine

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