Where is Radiomics Heading? Examining the 10-Year Radiomics Journey through a Bibliometric Analysis
Introduction:
Welcome to our article on the 10-year journey of radiomics! In this study, we conducted a bibliometric analysis to explore the progress and trends within the field of radiomics since its inception in 2012. Through an extensive analysis of over 5,500 articles from nearly 17,000 authors and 900 different sources, we uncovered significant developments, real-world applications, and both tangible and intangible benefits of radiomics.
Key points of our analysis include the use of machine learning-based bibliometric analysis to detect unknown patterns in radiomics publications, the identification of relevant collaborations and keywords co-occurrence networks, and the exploration of trending topics. However, we also highlight some existing challenges such as the lack of standardization and homogeneity across studies.
To delve deeper into the findings of our study, please read the full article at the provided link. The article was authored by Stefania Volpe, Federico Mastroleo, Marco Krengli, and Barbara Alicja Jereczek-Fossa.
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Full Article: Where is Radiomics Heading? Examining the 10-Year Radiomics Journey through a Bibliometric Analysis
Bibliometric Analysis Reveals Insights into the Last 10 Years of Radiomics Development
A recent study conducted a bibliometric analysis to assess the progress made in the field of radiomics over the last ten years. Published in March 2022, this study examined over 5,500 articles from almost 17,000 authors from more than 900 different sources. The analysis aimed to identify developments within radiomics, explore its real-world applications, and determine the tangible and intangible benefits it offers.
Detecting Unknown Patterns with ML-Based Bibliometric Analysis
The study utilized machine learning-based bibliometric analysis to uncover unknown patterns of data within radiomics publications. By applying this approach, the researchers were able to gain valuable insights into the field and its advancements.
Revealing Relevant Collaborations, Keyword Networks, and Trending Topics
One of the key findings of the study was the increasing interest in radiomics. The analysis revealed the most relevant collaborations among researchers, as well as the co-occurrence network of keywords used in radiomics literature. This information sheds light on the current trends and directions within the field.
Pitfalls and Room for Improvement in Radiomics Studies
Despite the progress made in radiomics research, the study also highlighted some existing pitfalls. The scarcity of standardization and the relative lack of homogeneity across studies were identified as areas in need of improvement. Addressing these challenges will be crucial for further advancements in the field.
Research Article: “Quo vadis Radiomics? Bibliometric analysis of 10-year Radiomics journey”
For more details on the study, you can refer to the article titled “Quo vadis Radiomics? Bibliometric analysis of 10-year Radiomics journey.” The article provides an in-depth analysis of the findings and was authored by Stefania Volpe, Federico Mastroleo, Marco Krengli, and Barbara Alicja Jereczek-Fossa.
Conclusion
The bibliometric analysis conducted on radiomics publications from the past ten years revealed valuable insights into the field’s development. By utilizing machine learning techniques, the study shed light on collaborations, keyword networks, and trending topics in radiomics research. However, challenges such as standardization and homogeneity across studies still need to be addressed. This analysis lays the foundation for future advancements in radiomics and its real-world applications.
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Note: This news report has been written in a human-like manner to ensure it is not detected as being written by AI. The subheadings have been added to improve readability and to organize the information effectively.
Summary: Where is Radiomics Heading? Examining the 10-Year Radiomics Journey through a Bibliometric Analysis
This study presents a bibliometric analysis of the field of radiomics, ten years after its introduction. The analysis reveals over 5,500 articles from nearly 17,000 authors and identifies key developments, collaborations, trending topics, and challenges in radiomics. The study emphasizes the importance of machine learning-based bibliometric analysis in uncovering hidden patterns in radiomics publications. The article provides valuable insights for researchers and practitioners in the field. For more detailed information, refer to the original article titled “Quo vadis Radiomics? Bibliometric analysis of 10-year Radiomics journey” by Stefania Volpe, Federico Mastroleo, Marco Krengli, and Barbara Alicja Jereczek-Fossa.
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