Radiomics is the high-throughput extraction of large amounts of image features from radiographic images, as previously defined in 2012 by Lambin and colleagues in the first publication in the field (1). In the July 2017 issue of Journal (2), Verma and colleagues highlighted the rise of radiomics, which may provide new insights into precision medicine in the next decades. Although the general process of radiomics has been well described (3), a major pitfall should be addressed before these methods can be used to personalize treatments: the lack of standardized procedures for radiomics features extraction (2,3). Lambin et al. recently proposed the radiomics quality score (RQS), assessing the quality of radiomics studies (4), to better address this issue of standardization between institutions (2), but the actual extraction methodologies currently available are clearly inconsistent (5). Scientometric analysis of publications can provide an objective overview of knowledge base and network features in this field of research that may be informative for researchers (6). Therefore, using the SCOPUS database, we performed a scientometric analysis of research on radiomics. On December 7, 2017, we searched in article titles, abstracts, and key words for all documents containing the terms “radiomic” or “radiomics.” A total of 391 research items were retrieved from the SCOPUS database, and errata were excluded. The first articles on radiomics were published by Lambin and colleagues in 2012. Since then, the dramatic increase in the number of published research items (Figure 1A) highlights the urgent need to better standardize radiomics methods. The identification of the scientific networks contributing to the research in the field could be used for that purpose. Therefore, the bibliometric data set exported from SCOPUS was cleaned using OpenRefine software (www.openrefine.org) to identify contributing countries for each published item. Interestingly, although the first article on radiomics was published only five years ago, many countries (n = 35) have already contributed to research in the field, and 95 (24%) published items involved international collaborations. Figure 1. View largeDownload slide International scientific collaborations in radiomics research. A) Number of published items and international collaborations over past years since the first publication on radiomics in 2012. B-C) International scientific network worldwide (B) and in Europe (C). The international scientific network was built using Table2Net and then exported to the Gephi software where the “Map of countries” and “geolayout” were used to visualize the network. The network includes nodes (countries) as well as edges (connection of two countries in a collaborative work). Node size is defined by degree, corresponding to the quantity of direct neighbors of a country in the network. Colour figure online: Node color (from light orange to dark purple) is defined by betweenness centrality, corresponding to the measure of how often a country lies on the shortest path between other countries in the network. Blue and red colored lines connect two countries which collaborated once and more than twice respectively. Figure 1. View largeDownload slide International scientific collaborations in radiomics research. A) Number of published items and international collaborations over past years since the first publication on radiomics in 2012. B-C) International scientific network worldwide (B) and in Europe (C). The international scientific network was built using Table2Net and then exported to the Gephi software where the “Map of countries” and “geolayout” were used to visualize the network. The network includes nodes (countries) as well as edges (connection of two countries in a collaborative work). Node size is defined by degree, corresponding to the quantity of direct neighbors of a country in the network. Colour figure online: Node color (from light orange to dark purple) is defined by betweenness centrality, corresponding to the measure of how often a country lies on the shortest path between other countries in the network. Blue and red colored lines connect two countries which collaborated once and more than twice respectively. We then built and analyzed the scientific worldwide networks including all countries contributing to research in this field. To this end, the cleaned bibliometric data set extracted from the SCOPUS database was imported to Table2Net (http://tools.medialab.sciences-po.fr/table2net/), and the network was visualized using Gephi software (Figure 1, B and C). The scientific network worldwide shows the already close-knit network of relations between the United States and other countries worldwide (Figure 1B). The United States, Germany, and the Netherlands are the leading collaborating countries in terms of betweenness centrality, a measure of how often a country lies on the shortest path between nodes in the network. However, we observed slightly fewer connections between European countries (Figure 1C). This should be improved to enhance the quality of studies performed in Europe. The development of international scientific networks, including groups in North and South America, Europe, and Asia, is critical to promote radiomic-driven precision medicine. Following the success of The Cancer Genome Atlas and the International Cancer Genomics Consortium (7), a large effort based on this international scientific network needs to be engaged worldwide. Notes Affiliations of authors: INSERM 1052, CNRS 5286, Centre de Recherche en Cancérologie de Lyon, Centre Léon Bérard, Univ Lyon, Université Claude Bernard Lyon 1, Lyon, France (JPF); Department of Radiation Oncology, Paris Descartes University, Hôpital Européen Georges Pompidou, Paris, France (JPF, CD, PG, JEB); Centre de Recherche des Cordeliers, Team 22: Information Sciences for Personalized Medicine, INSERM, UMRS1138, Paris, France (JEB). The authors have no conflicts of interest to disclose. References 1 Lambin P, Rios-Velazquez E, Leijenaar Ret al. , Radiomics: Extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012; 48( 4): 441– 446. Google Scholar CrossRef Search ADS PubMed 2 Verma V, Simone CB2nd, Krishnan Set al. , The rise of radiomics and implications for oncologic management. J Natl Cancer Inst. 2017; 109( 7):djx055. 3 Limkin EJ, Sun R, Dercle Let al. , Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol. 2017; 28( 6): 1191– 1206. Google Scholar CrossRef Search ADS PubMed 4 Lambin P, Leijenaar RTH, Deist TMet al. , Radiomics: The bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017; 14( 12): 749– 762. Google Scholar CrossRef Search ADS PubMed 5 Gan J, Wang J, Zhong Het al. , MO-DE-207B-09: A consistent test for radiomics softwares. Med Phys. 2016; 43( 6 part 30): 3706– 3706. Google Scholar CrossRef Search ADS 6 Vezyridis P, Timmons S. Evolution of primary care databases in UK: A scientometric analysis of research output. BMJ Open. 2016; 6( 10): e012785. Google Scholar CrossRef Search ADS PubMed 7 International Cancer Genome Consortium, Hudson TJ, Anderson Wet al. , International network of cancer genome projects. Nature. 2010; 464( 7291): 993– 998. Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please email: email@example.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)
JNCI: Journal of the National Cancer Institute – Oxford University Press
Published: Mar 12, 2018
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