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      Promoting the application of pediatric radiomics via an integrated medical engineering approach

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          Abstract

          Radiomics is widely used in adult tumors but has been rarely applied to the field of pediatrics. Promoting the application of radiomics in pediatric diseases, especially in the early diagnosis and stratified treatment of tumors, is of great value to the realization of the WHO 2030 “Global Initiative for Childhood Cancer.” This paper discusses the general characteristics of radiomics, the particularity of its application to pediatric diseases, and the current status and prospects of pediatric radiomics. Radiomics is a data‐driven science, and the combination of medicine and engineering plays a decisive role in improving data quality, data diversity, and sample size. Compared with adult radiomics, pediatric radiomics is significantly different in data type, disease spectrum, disease staging, and progression. Some progress has been made in the identification, classification, stratification, survival prediction, and prognosis of tumor diseases. In the future, big data applications from multiple centers and cross‐talent training should be strengthened to improve the benefits for clinical workers and children.

          Abstract

          Radiomics is widely used in adult tumors but has been rarely applied to the field of pediatrics. Promoting the application of radiomics in pediatric diseases, especially in the early diagnosis and stratified treatment of tumors, is of great value to the realization of the WHO 2030 “Global Initiative for Childhood Cancer.” This paper discusses the general characteristics of radiomics, the particularity of its application to pediatric diseases, and the current status and prospects of pediatric radiomics.

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          Most cited references30

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          The FAIR Guiding Principles for scientific data management and stewardship

          There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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            Radiomics: the facts and the challenges of image analysis

            Radiomics is an emerging translational field of research aiming to extract mineable high-dimensional data from clinical images. The radiomic process can be divided into distinct steps with definable inputs and outputs, such as image acquisition and reconstruction, image segmentation, features extraction and qualification, analysis, and model building. Each step needs careful evaluation for the construction of robust and reliable models to be transferred into clinical practice for the purposes of prognosis, non-invasive disease tracking, and evaluation of disease response to treatment. After the definition of texture parameters (shape features; first-, second-, and higher-order features), we briefly discuss the origin of the term radiomics and the methods for selecting the parameters useful for a radiomic approach, including cluster analysis, principal component analysis, random forest, neural network, linear/logistic regression, and other. Reproducibility and clinical value of parameters should be firstly tested with internal cross-validation and then validated on independent external cohorts. This article summarises the major issues regarding this multi-step process, focussing in particular on challenges of the extraction of radiomic features from data sets provided by computed tomography, positron emission tomography, and magnetic resonance imaging.
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              The future of digital health with federated learning

              Data-driven machine learning (ML) has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how federated learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.
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                Author and article information

                Contributors
                xuntaoyin@gmail.com
                Journal
                Cancer Innov
                Cancer Innov
                10.1002/(ISSN)2770-9183
                CAI2
                Cancer Innovation
                John Wiley and Sons Inc. (Hoboken )
                2770-9191
                2770-9183
                19 February 2023
                August 2023
                : 2
                : 4 ( doiID: 10.1002/cai2.v2.4 )
                : 302-311
                Affiliations
                [ 1 ] Department of Radiology, Guangzhou Women and Children's Medical Center Guangdong Provincial Clinical Research Center for Child Health Guangzhou China
                [ 2 ] Lianying Intelligent Medical Technology (Chengdu) Co., Ltd. Chengdu China
                [ 3 ] Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China
                Author notes
                [*] [* ] Correspondence Xuntao Yin, Department of Radiology, Guangzhou Women and Children's Medical Center, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou 510623, China.

                Email: xuntaoyin@ 123456gmail.com

                Author information
                http://orcid.org/0000-0002-9667-4848
                http://orcid.org/0000-0002-8228-6908
                Article
                CAI244
                10.1002/cai2.44
                10686116
                38089752
                8715a1d2-33f7-4b89-a63e-e1c5845ea808
                © 2022 The Authors. Cancer Innovation published by John Wiley & Sons Ltd. on behalf of Tsinghua University Press.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 19 November 2022
                : 10 October 2022
                : 27 November 2022
                Page count
                Figures: 4, Tables: 2, Pages: 10, Words: 5251
                Funding
                Funded by: Science and Technology Innovation 2030‐Major project of “Brain Science and Brain‐Inspired Research”
                Award ID: 2021ZD0200522
                Categories
                Review
                Reviews
                Custom metadata
                2.0
                August 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.5 mode:remove_FC converted:29.11.2023

                radiomics,pediatrics,oncology,survival prediction
                radiomics, pediatrics, oncology, survival prediction

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