What does literature teach about digital pathology? A bibliometric study in Web of Science

  1. Jesús López-Belmonte 1
  2. Adrián Segura-Robles 1
  3. William C. Cho 1
  4. María-Elena Parra-González 1
  5. Antonio J. Moreno-Guerrero 1
  1. 1 Universidad de Granada
    info

    Universidad de Granada

    Granada, España

    ROR https://ror.org/04njjy449

Revista:
IJERI: International journal of Educational Research and Innovation

ISSN: 2386-4303

Año de publicación: 2021

Número: 16

Páginas: 106-121

Tipo: Artículo

DOI: 10.46661/IJERI.4918 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: IJERI: International journal of Educational Research and Innovation

Resumen

La patología digital (DIPA) se ha convertido en una disciplina efectiva que genera un entorno gráfico para diagnosticar e interpretar la información patológica de las personas. Al analizar la literatura existente sobre DIPA, se produjo una brecha de conocimiento al no informar un estudio que ha analizado bibliométricamente las publicaciones sobre el tema. El objetivo de este estudio es analizar la producción científica y el rendimiento alcanzado por el término patología digital en la base de datos de Web of Science (WoS). Para ello, se ha llevado a cabo una metodología basada en la bibliometría, complementada con la técnica de mapeo científico para buscar, registrar, analizar y predecir la literatura científica sobre el estado de la cuestión. Hemos trabajado con una unidad de análisis de 1222 documentos reportados desde la base de datos de WoS. Los resultados muestran que no hay un tema de investigación en el campo de estudio de DIPA que se destaque del resto. Se puede observar una brecha conceptual en el desarrollo temático, dado que no hay un tema que se repita en todos los periodos, donde las conexiones son más temáticas que conceptuales. Hay documentos clave para diferentes temas. Los temas principales han sido muy diferentes a lo largo de los años como la telepatología y la inteligencia artificial.

Referencias bibliográficas

  • Baidoshvili, A., Stathonikos, N., Freling, G., Bart, J., Hart, N., Van der Laak…, Van Diest (2018). Validation of a whole-slide image-based teleconsultation network. Histopathology, 73(5), 777–783. http://doi.org/10.1111/his.13673
  • Begley, C.G., & Ellis, L.M. (2012). Raise standards for preclinical cancer research. Nature, 483, 531–533.
  • Benke, K., & Benke, G. (2018). Artificial Intelligence and Big Data in Public Health. International Journal of Environmental Research and Public Health, 15(12), 1-9. http://doi.org/10.3390/ijerph15122796
  • Bizzego, A., Bussola, N., Chierici, M., Maggio, V., Francescatto, M., Cima, L… Furlanello, C. (2019). Evaluating reproducibility of AI algorithms in digital pathology with DAPPER. PLoS computational biology, 15(3), 1-24. https://doi.org/10.1371/journal.pcbi.1006269
  • Corredor, G., Whitney, J., Arias, V., Madabhushi, A., & Romero, E. (2017). Training a cell-level classifier for detecting basal-cell carcinoma by combining human visual attention maps with low-level handcrafted features. Journal of Medical Imaging, 4(2), 1-15. https://doi.org/10.1117/1.JMI.4.2.021105
  • Fauzi, M.F.A., Chen, W., Knight, D., Hampel, H., Frankel, W.L., & Gurcan, M.N. (2019). Tumor Budding Detection System in Whole Slide Pathology Images. Journal of Medical System, 44(38), 1-10.
  • Holliday, D.L., & Speirs, V. (2011). Choosing the right cell line for breast cancer research. Breast Cancer Research, 13(4), 1-7. https://doi.org/10.1186/bcr2889
  • Hsieh, W.-H., Chiu, W.-T., Lee, Y.-S., & Ho, Y.-S. (2004). Bibliometric analysis of Patent Ductus Arteriosus treatments. Scientometrics, 60, 105–215.
  • Klimov, S., Miligy, I.M., Gertych, A., Jiang, Y., Toss, M.S., Rida, P… Aneja, R. (2019) A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk. Breast Cancer Research, 21(1), 1-19. https://doi.org/10.1186/s13058-019-1165-5
  • López-Belmonte, J., Segura-Robles, A., Moreno-Guerrero, A.J., & Parra-González, M.E. (2020). Machine Learning and Big Data in the Impact Literature. A Bibliometric Review with Scientific Mapping in Web of Science. Symmetry, 12(2), 1-15. https://doi.org/10.3390/sym12040495
  • López-Belmonte, J., Moreno-Guerrero, A.J., López-Núñez, J.A., & Pozo-Sánchez, S. (2019). Analysis of the Productive, Structural, and Dynamic Development of Augmented Reality in Higher Education Research on the Web of Science. Applied Science, 9(24), 1-21. https://doi.org/10.3390/app9245306
  • Lugli, A., Kirsch, R., Ajioka, Y., Bosman, F., Cathomas, G., Dawson, H… Quirke, P. (2017). Recommendations for reporting tumor budding in colorectal cancer based on the International Tumor Budding Consensus Conference (ITBCC) 2016. Modern Pathology, 30(9), 1299–1311. https://doi.org/10.1038/modpathol.2017.46
  • Majo, J., Klinkhammer, B.M., Boor, P., & Tiniakos, D. (2019). Pathology and natural history of organ fibrosis. Current Opinion in Pharmacology, 49, 82-89. https://doi.org/10.1016/j.coph.2019.09.009
  • Moreno-Guerrero, A.J., Gómez-García, G., López-Belmonte, J., & Rodríguez-Jiménez, C. (2020). Internet Addiction in the Web of Science Database: A Review of the Literature with Scientific Mapping. International Journal of Environmental Research and Public Health, 17(8), 1-17. https://doi.org/10.3390/ijerph17082753
  • Mukundan, R. (2019). Analysis of Image Feature Characteristics for Automated Scoring of HER2 in Histology Slides. Journal of Imaging, 5(3), 1-12. https://doi.org/10.3390/jimaging5030035
  • Naylor, P., Laé, M., Reyal, F., & Walter, T. (2018). Segmentation of nuclei in histopathology images by deep regression of the distance map. IEEE transactions on medical imaging, 38(2), 448-459. https://doi.org/10.1109/TMI.2018.2865709
  • Norazman, S.H.B., Nakamura, T., Kimura, F., & Yamaguchi, M. (2019). Analysis of quantitative phase obtained by digital holography on H&E-stained pathological samples. Artificial Life and Robotics, 24(1), 38-43. https://doi.org/10.1007/s10015-018-0468-4
  • Patra, S.K., Bhattacharya, P., & Verma, N. (2006). Bibliometric Study of Literature on Bibliometrics. DESIDOC Journal of Library & Information Technology, 26(1), 27–32. https://doi.org/10.14429/djlit.26.1.3672
  • Rodríguez-García, A.M., López-Belmonte, J., Agreda, M., & Moreno-Guerrero, A.J. (2019) Productive, Structural and Dynamic Study of the Concept of Sustainability in the Educational Field. Sustainability, 11(20), 1-12. https://doi.org/10.3390/su11205613
  • Saxena, P., & Goyal, A. (2019). Study of Computerized Segmentation & Classification Techniques: An Application to Histopathological Imagery. Journal of Computing and Informatics, 43(4), 561-572. https://doi.org/10.31449/inf.v43i4.2142
  • Tavolara, T.E., Khan, M.K., Arole, V., Chen, W., Frankel, W., & Gurcan, M.N. (2019) A modular cGAN classification framework: Application to colorectal tumor detection. Scientific Reports, 9, 1-8.
  • Williams, B.J., Bottoms, D., Clark, D., & Treanor, D. (2019). Future-proofing pathology part 2: building a business case for digital pathology. Journal of clinical pathology, 72(3), 198-205. https://doi.org/10.1136/jclinpath-2017-204926
  • Zaidi, M., Fu, F., Cojocari, D., McKee, T.D., & Wouter, B.G. (2019). Quantitative Visualization of Hypoxia and Proliferation Gradients Within Histological Tissue Sections. Frontiers in Bioengineering and Biotechnology, 7, 1-9. https://doi.org/10.3389/fbioe.2019.00397
  • Zwanenburg, A., Vallières, M., Abdalah, M.A., Aerts, H.J.W.L., Andrearczyk, V., Ashrafinia, S…, Löck, S. (2020) The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology, 295(2), 1-16. https://doi.org/10.1148/radiol.2020191145