Quo vadis Radiomics? Bibliometric analysis of 10-year Radiomics journey.
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| Title: | Quo vadis Radiomics? Bibliometric analysis of 10-year Radiomics journey. |
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| Authors: | Volpe, Stefania1,2 (AUTHOR), Mastroleo, Federico1,3 (AUTHOR) federico.mastroleo@ieo.it, Krengli, Marco3,4 (AUTHOR), Jereczek-Fossa, Barbara Alicja1,2 (AUTHOR) |
| Source: | European Radiology. Oct2023, Vol. 33 Issue 10, p6736-6745. 10p. 1 Color Photograph, 2 Diagrams, 2 Charts, 3 Graphs. |
| Subjects: | Bibliometrics, Radiomics, Thematic maps, Image analysis, Deep learning |
| Geographic Terms: | China |
| Abstract: | Objectives: Radiomics is the high-throughput extraction of mineable and—possibly—reproducible quantitative imaging features from medical imaging. The aim of this work is to perform an unbiased bibliometric analysis on Radiomics 10 years after the first work became available, to highlight its status, pitfalls, and growing interest. Methods: Scopus database was used to investigate all the available English manuscripts about Radiomics. R Bibliometrix package was used for data analysis: a cumulative analysis of document categories, authors affiliations, country scientific collaborations, institution collaboration networks, keyword analysis, comprehensive of co-occurrence network, thematic map analysis, and 2021 sub-analysis of trend topics was performed. Results: A total of 5623 articles and 16,833 authors from 908 different sources have been identified. The first available document was published in March 2012, while the most recent included was released on the 31st of December 2021. China and USA were the most productive countries. Co-occurrence network analysis identified five words clusters based on top 50 authors' keywords: Radiomics, computed tomography, radiogenomics, deep learning, tomography. Trend topics analysis for 2021 showed an increased interest in artificial intelligence (n = 286), nomogram (n = 166), hepatocellular carcinoma (n = 125), COVID-19 (n = 63), and X-ray computed (n = 60). Conclusions: Our work demonstrates the importance of bibliometrics in aggregating information that otherwise would not be available in a granular analysis, detecting unknown patterns in Radiomics publications, while highlighting potential developments to ensure knowledge dissemination in the field and its future real-life applications in the clinical practice. Clinical relevance statement: This work aims to shed light on the state of the art in radiomics, which offers numerous tangible and intangible benefits, and to encourage its integration in the contemporary clinical practice for more precise imaging analysis. Key Points: • ML-based bibliometric analysis is fundamental to detect unknown pattern of data in Radiomics publications. • A raising interest in the field, the most relevant collaborations, keywords co-occurrence network, and trending topics have been investigated. • Some pitfalls still exist, including the scarce standardization and the relative lack of homogeneity across studies. [ABSTRACT FROM AUTHOR] |
| Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 172041152 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Quo vadis Radiomics? Bibliometric analysis of 10-year Radiomics journey. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Volpe%2C+Stefania%22">Volpe, Stefania</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mastroleo%2C+Federico%22">Mastroleo, Federico</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> federico.mastroleo@ieo.it</i><br /><searchLink fieldCode="AR" term="%22Krengli%2C+Marco%22">Krengli, Marco</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jereczek-Fossa%2C+Barbara+Alicja%22">Jereczek-Fossa, Barbara Alicja</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22European+Radiology%22">European Radiology</searchLink>. Oct2023, Vol. 33 Issue 10, p6736-6745. 10p. 1 Color Photograph, 2 Diagrams, 2 Charts, 3 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Bibliometrics%22">Bibliometrics</searchLink><br /><searchLink fieldCode="DE" term="%22Radiomics%22">Radiomics</searchLink><br /><searchLink fieldCode="DE" term="%22Thematic+maps%22">Thematic maps</searchLink><br /><searchLink fieldCode="DE" term="%22Image+analysis%22">Image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Objectives: Radiomics is the high-throughput extraction of mineable and—possibly—reproducible quantitative imaging features from medical imaging. The aim of this work is to perform an unbiased bibliometric analysis on Radiomics 10 years after the first work became available, to highlight its status, pitfalls, and growing interest. Methods: Scopus database was used to investigate all the available English manuscripts about Radiomics. R Bibliometrix package was used for data analysis: a cumulative analysis of document categories, authors affiliations, country scientific collaborations, institution collaboration networks, keyword analysis, comprehensive of co-occurrence network, thematic map analysis, and 2021 sub-analysis of trend topics was performed. Results: A total of 5623 articles and 16,833 authors from 908 different sources have been identified. The first available document was published in March 2012, while the most recent included was released on the 31st of December 2021. China and USA were the most productive countries. Co-occurrence network analysis identified five words clusters based on top 50 authors' keywords: Radiomics, computed tomography, radiogenomics, deep learning, tomography. Trend topics analysis for 2021 showed an increased interest in artificial intelligence (n = 286), nomogram (n = 166), hepatocellular carcinoma (n = 125), COVID-19 (n = 63), and X-ray computed (n = 60). Conclusions: Our work demonstrates the importance of bibliometrics in aggregating information that otherwise would not be available in a granular analysis, detecting unknown patterns in Radiomics publications, while highlighting potential developments to ensure knowledge dissemination in the field and its future real-life applications in the clinical practice. Clinical relevance statement: This work aims to shed light on the state of the art in radiomics, which offers numerous tangible and intangible benefits, and to encourage its integration in the contemporary clinical practice for more precise imaging analysis. Key Points: • ML-based bibliometric analysis is fundamental to detect unknown pattern of data in Radiomics publications. • A raising interest in the field, the most relevant collaborations, keywords co-occurrence network, and trending topics have been investigated. • Some pitfalls still exist, including the scarce standardization and the relative lack of homogeneity across studies. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00330-023-09645-6 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 6736 Subjects: – SubjectFull: Bibliometrics Type: general – SubjectFull: Radiomics Type: general – SubjectFull: Thematic maps Type: general – SubjectFull: Image analysis Type: general – SubjectFull: Deep learning Type: general – SubjectFull: China Type: general Titles: – TitleFull: Quo vadis Radiomics? Bibliometric analysis of 10-year Radiomics journey. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Volpe, Stefania – PersonEntity: Name: NameFull: Mastroleo, Federico – PersonEntity: Name: NameFull: Krengli, Marco – PersonEntity: Name: NameFull: Jereczek-Fossa, Barbara Alicja IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 09387994 Numbering: – Type: volume Value: 33 – Type: issue Value: 10 Titles: – TitleFull: European Radiology Type: main |
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