Radiomics in Oncology: A 10-Year Bibliometric Analysis

被引:39
作者
Ding, Haoran
Wu, Chenzhou
Liao, Nailin
Zhan, Qi
Sun, Weize
Huang, Yingzhao
Jiang, Zhou
Li, Yi [1 ]
机构
[1] Sichuan Univ, West China Hosp Stomatol, State Key Lab Oral Dis, Chengdu, Peoples R China
基金
中国国家自然科学基金;
关键词
radiomics; oncology; bibliometric analysis; hotspots; trends; CELL LUNG-CANCER; EMERGING TRENDS; BRAIN-TUMOR; FDG-PET; FEATURES; IMPACT; IMAGES; CLASSIFICATION; RECONSTRUCTION; PREDICTION;
D O I
10.3389/fonc.2021.689802
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Objectives: To date, radiomics has been applied in oncology for over a decade and has shown great progress. We used a bibliometric analysis to analyze the publications of radiomics in oncology to clearly illustrate the current situation and future trends and encourage more researchers to participate in radiomics research in oncology. Methods: Publications for radiomics in oncology were downloaded from the Web of Science Core Collection (WoSCC). WoSCC data were collected, and CiteSpace was used for a bibliometric analysis of countries, institutions, journals, authors, keywords, and references pertaining to this field. The state of research and areas of focus were analyzed through burst detection. Results: A total of 7,199 pieces of literature concerning radiomics in oncology were analyzed on CiteSpace. The number of publications has undergone rapid growth and continues to increase. The USA and Chinese Academy of Sciences are found to be the most prolific country and institution, respectively. In terms of journals and co-cited journals, Scientific Reports is ranked highest with respect to the number of publications, and Radiology is ranked highest among co-cited journals. Moreover, Jie Tian has published the most publications, and Phillipe Lambin is the most cited author. A paper published by Gillies et al. presents the highest citation counts. Artificial intelligence (AI), segmentation methods, and the use of radiomics for classification and diagnosis in oncology are major areas of focus in this field. Test-retest statistics, including reproducibility and statistical methods of radiomics research, the relation between genomics and radiomics, and applications of radiomics to sarcoma and intensity-modulated radiotherapy, are frontier areas of this field. Conclusion: To our knowledge, this is the first study to provide an overview of the literature related to radiomics in oncology and may inspire researchers from multiple disciplines to engage in radiomics-related research.
引用
收藏
页数:12
相关论文
共 66 条
[1]   Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer [J].
Abdollahi, Hamid ;
Mofid, Bahram ;
Shiri, Isaac ;
Razzaghdoust, Abolfazl ;
Saadipoor, Afshin ;
Mahdavi, Arash ;
Galandooz, Hassan Maleki ;
Mahdavi, Seied Rabi .
RADIOLOGIA MEDICA, 2019, 124 (06) :555-567
[2]   Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach [J].
Aerts, Hugo J. W. L. ;
Velazquez, Emmanuel Rios ;
Leijenaar, Ralph T. H. ;
Parmar, Chintan ;
Grossmann, Patrick ;
Cavalho, Sara ;
Bussink, Johan ;
Monshouwer, Rene ;
Haibe-Kains, Benjamin ;
Rietveld, Derek ;
Hoebers, Frank ;
Rietbergen, Michelle M. ;
Leemans, C. Rene ;
Dekker, Andre ;
Quackenbush, John ;
Gillies, Robert J. ;
Lambin, Philippe .
NATURE COMMUNICATIONS, 2014, 5
[3]   Artificial intelligence in cancer imaging: Clinical challenges and applications [J].
Bi, Wenya Linda ;
Hosny, Ahmed ;
Schabath, Matthew B. ;
Giger, Maryellen L. ;
Birkbak, Nicolai J. ;
Mehrtash, Alireza ;
Allison, Tavis ;
Arnaout, Omar ;
Abbosh, Christopher ;
Dunn, Ian F. ;
Mak, Raymond H. ;
Tamimi, Rulla M. ;
Tempany, Clare M. ;
Swanton, Charles ;
Hoffmann, Udo ;
Schwartz, Lawrence H. ;
Gillies, Robert J. ;
Huang, Raymond Y. ;
Aerts, Hugo J. W. L. .
CA-A CANCER JOURNAL FOR CLINICIANS, 2019, 69 (02) :127-157
[4]   Relevance of apparent diffusion coefficient features for a radiomics-based prediction of response to induction chemotherapy in sinonasal cancer [J].
Bologna, Marco ;
Calareso, Giuseppina ;
Resteghini, Carlo ;
Sdao, Silvana ;
Montin, Eros ;
Corino, Valentina ;
Mainardi, Luca ;
Licitra, Lisa ;
Bossi, Paolo .
NMR IN BIOMEDICINE, 2022, 35 (04)
[5]   A Collaborative Enterprise for Multi-Stakeholder Participation in the Advancement of Quantitative Imaging [J].
Buckler, Andrew J. ;
Bresolin, Linda ;
Dunnick, N. Reed ;
Sullivan, Daniel C. .
RADIOLOGY, 2011, 258 (03) :906-914
[6]   Emerging trends and new developments in regenerative medicine: a scientometric update (2000 - 2014) [J].
Chen, Chaomei ;
Dubin, Rachael ;
Kim, Meen Chul .
EXPERT OPINION ON BIOLOGICAL THERAPY, 2014, 14 (09) :1295-1317
[7]   CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature [J].
Chen, CM .
JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, 2006, 57 (03) :359-377
[8]   Publication trends and hot spots in postoperative cognitive dysfunction research: A 20-year bibliometric analysis [J].
Chen, Sifan ;
Zhang, Yizhe ;
Dai, Wanbing ;
Qi, Siyi ;
Tian, Weitian ;
Gu, Xiyao ;
Chen, Xuemei ;
Yu, Weifeng ;
Tian, Jie ;
Su, Diansan .
JOURNAL OF CLINICAL ANESTHESIA, 2020, 67
[9]   Cancer Statistics in China, 2015 [J].
Chen, Wanqing ;
Zheng, Rongshou ;
Baade, Peter D. ;
Zhang, Siwei ;
Zeng, Hongmei ;
Bray, Freddie ;
Jemal, Ahmedin ;
Yu, Xue Qin ;
He, Jie .
CA-A CANCER JOURNAL FOR CLINICIANS, 2016, 66 (02) :115-132
[10]   The Quantitative Imaging Network: NCI's Historical Perspective and Planned Goals [J].
Clarke, Laurence P. ;
Nordstrom, Robert J. ;
Zhang, Huiming ;
Tandon, Pushpa ;
Zhang, Yantian ;
Redmond, George ;
Farahani, Keyvan ;
Kelloff, Gary ;
Henderson, Lori ;
Shankar, Lalitha ;
Deye, James ;
Capala, Jacek ;
Jacobs, Paula .
TRANSLATIONAL ONCOLOGY, 2014, 7 (01) :1-4