Tracking knowledge evolution, hotspots and future directions of emerging technologies in cancers research: a bibliometrics review

Due to various environmental pollution issues, cancers have become the “first killer” of human beings in the 21st century and their control has become a global strategy of human health. The increasing development of emerging information technologies has provided opportunities for prevention, early detection, diagnosis, intervention, prognosis, nursing, and rehabilitation of cancers. In recent years, the literature associated with emerging technologies in cancer has grown rapidly, but few studies have used bibliometrics and a visualization approach to conduct deep mining and reveal a panorama of this field. To explore the dynamic knowledge evolution of emerging information technologies in cancer literature, we comprehensively analyzed the development status and research hotspots in this field from bibliometrics perspective. We collected 7,136 articles (2000-2017) from the Web of Science database and visually displayed the dynamic knowledge evolution process via the analysis on time-sequence changes, spatial distribution, knowledge base, and hotspots. Much institutional cooperation occurs in this field, and research groups are relatively concentrated. BMC Bioinformatics, PLOS One, Journal of Urology, Scientific Reports, and Bioinformatics are the top five journals in this field. Research hotspots are mainly concentrated in two dimensions: the disease dimension (e.g., cancer, breast cancer, and prostate cancer), and the technical dimension (e.g., robotics, machine learning, data mining, and etc.). The emerging technologies in cancer research is fast ascending and promising. This study also provides researchers with panoramic knowledge of this field, as well as research hotspots and future directions.

[1]  Matthew E Falagas,et al.  Comparison of PubMed, Scopus, Web of Science, and Google Scholar: strengths and weaknesses , 2007, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.

[2]  José M Merigó,et al.  Influential journals in health research: a bibliometric study , 2016, Globalization and Health.

[3]  Isabel Gómez,et al.  Analysis of the structure of international scientific cooperation networks through bibliometric indicators , 1999, Scientometrics.

[4]  J. Ferlay,et al.  Global Cancer Statistics, 2002 , 2005, CA: a cancer journal for clinicians.

[5]  Ning Wang,et al.  Trends of triple negative breast cancer research (2007–2015) , 2016, Medicine.

[6]  Shang Gao,et al.  Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends , 2016, BMC Research Notes.

[7]  Blaise Cronin,et al.  Bibliometrics and beyond: some thoughts on web-based citation analysis , 2001, J. Inf. Sci..

[8]  Sharon Swee-Lin Tan,et al.  Electronic Health Records: How Can IS Researchers Contribute to Transforming Healthcare? , 2016, MIS Q..

[9]  Carmen C. Y. Poon,et al.  Unobtrusive Sensing and Wearable Devices for Health Informatics , 2014, IEEE Transactions on Biomedical Engineering.

[10]  Paul G Nagy,et al.  Cloud computing in medical imaging. , 2013, Medical physics.

[11]  N Yue,et al.  SU-F-J-207: Non-Small Cell Lung Cancer Patient Survival Prediction with Quantitative Tumor Textures Analysis in Baseline CT. , 2016, Medical physics.

[12]  Chaomei Chen,et al.  CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature , 2006, J. Assoc. Inf. Sci. Technol..

[13]  C. Lynch.  Big data: How do your data grow? , 2008, Nature.

[14]  R. Sarin,et al.  Cancer research in India: national priorities, global results. , 2014, The Lancet. Oncology.

[15]  R. Chang,et al.  Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images , 2001, Breast Cancer Research and Treatment.

[16]  Min Song,et al.  Trajectory analysis of drug-research trends in pancreatic cancer on PubMed and ClinicalTrials.gov , 2016, J. Informetrics.

[17]  Yue Wang,et al.  Evolutionary features of academic articles co-keyword network and keywords co-occurrence network: Based on two-mode affiliation network , 2016 .

[18]  E. Yan,et al.  Identifying Liver Cancer and Its Relations with Diseases, Drugs, and Genes: A Literature-Based Approach , 2016, PloS one.

[19]  Derek R. Smith.  Citation Analysis and Impact Factor Trends of 5 Core Journals in Occupational Medicine, 1985-2006 , 2008, Archives of environmental & occupational health.

[20]  Ramakanth Kavuluru,et al.  Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations , 2018, J. Biomed. Informatics.

[21]  F. D. de Gruijl,et al.  Predictions of skin cancer incidence in the Netherlands up to 2015 , 2005, The British journal of dermatology.

[22]  Grant Lewison,et al.  Radiation Therapy Research: A Global Analysis 2001-2015. , 2018, International journal of radiation oncology, biology, physics.

[23]  S. Mane,et al.  Predicting neuroendocrine tumor (carcinoid) neoplasia using gene expression profiling and supervised machine learning , 2009, Cancer.

[24]  N. Restifo.  A "big data" view of the tumor "immunome". , 2013, Immunity.

[25]  W. Klein,et al.  Bibliometrics , 2005, Social work in health care.

[26]  Dickson K. W. Chiu,et al.  Efficient and robust large medical image retrieval in mobile cloud computing environment , 2014, Inf. Sci..

[27]  Nilanjan Sarkar,et al.  Robot assisted real-time tumor manipulation for breast biopsy , 2008, 2008 IEEE International Conference on Robotics and Automation.

[28]  Tianyong Hao,et al.  A bibliometric analysis of natural language processing in medical research , 2018, BMC Medical Informatics and Decision Making.

[29]  S. Morrison,et al.  Prospective identification of tumorigenic breast cancer cells , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[30]  Christian Etmann,et al.  Deep learning for tumor classification in imaging mass spectrometry , 2017, Bioinform..

[31]  Derek R. Smith.  Citation Analysis and Impact Factor Trends of 5 Core Journals in Occupational Medicine, 1975–1984 , 2010, Archives of environmental & occupational health.

[32]  H. Zuckerman.  Patterns of Name Ordering Among Authors of Scientific Papers: A Study of Social Symbolism and Its Ambiguity , 1968, American Journal of Sociology.

[33]  Min Song,et al.  Analyzing the field of bioinformatics with the multi-faceted topic modeling technique , 2017, BMC Bioinformatics.

[34]  Richard J. Epstein,et al.  Computational prediction of multidisciplinary team decision-making for adjuvant breast cancer drug therapies: a machine learning approach , 2016, BMC Cancer.

[35]  E Hirt,et al.  Wearable devices for telemedicine applications , 2005, Journal of telemedicine and telecare.

[36]  Lida Xu,et al.  The internet of things: a survey , 2014, Information Systems Frontiers.

[37]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[38]  Changyong Liang,et al.  A case-based reasoning system based on weighted heterogeneous value distance metric for breast cancer diagnosis , 2017, Artif. Intell. Medicine.

[39]  Roland Fischer,et al.  Publication trends of shared decision making in 15 high impact medical journals: a full-text review with bibliometric analysis , 2014, BMC Medical Informatics and Decision Making.

[40]  L. Sobin,et al.  Classification of isolated tumor cells , 2003, Cancer.

[41]  Changyong Liang,et al.  Visualizing the knowledge structure and evolution of big data research in healthcare informatics , 2017, Int. J. Medical Informatics.

[42]  J. Chiang,et al.  A computational text analysis and visualization study , 2017 .

[43]  Michael M. Hopkins,et al.  Strategic intelligence on emerging technologies: Scientometric overlay mapping , 2013, J. Assoc. Inf. Sci. Technol..

[44]  Guo Zhang,et al.  Productivity and influence in bioinformatics: A bibliometric analysis using PubMed central , 2014, J. Assoc. Inf. Sci. Technol..

[45]  Jeffrey W. Pollard,et al.  Macrophage Diversity Enhances Tumor Progression and Metastasis , 2010, Cell.

[46]  Wolfgang Glänzel,et al.  A new methodological approach to bibliographic coupling and its application to the national, regional and institutional level , 2005, Scientometrics.

[47]  G. Hussey,et al.  A bibliometric analysis of cancer research in South Africa: study protocol , 2015, BMJ Open.

[48]  T. Masui,et al.  Union for International Cancer Control International Session: Healthcare Economics: The significance of the UN summit non‐communicable diseases political declaration in Asia , 2013, Cancer science.

[49]  A. Jemal,et al.  Global cancer statistics , 2011, CA: a cancer journal for clinicians.

[50]  Randy H. Katz,et al.  Above the Clouds: A Berkeley View of Cloud Computing , 2009 .