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Celebrating the beginning of international journal collaboration

  • Shinji NaganawaEmail author
  • Yukunori Korogi
Editorial
  • 65 Downloads

Our society, Japan Radiological Society (JRS), is collaborating with many countries’ radiological society. We have leadership meetings at the occasion of large radiological congress such as RSNA (Radiological Society of North America), ECR (European Congress of Radiology), and each radiological societies’ meetings. We are exchanging speakers, young fellows, posters and various information as well as promoting friendship. This year, we begin journal collaboration with Italy, France and Turkey, by exchanging review articles. Of course, even for the invited review articles, we perform the peer-review process. In this issue of the Japanese Journal of Radiology (JJR), the first review article from Italy “Pelvic floor dysfunctions: how to image patients?” by Francesca Iacobellis is published [1]. We would like to celebrate this memorable moment with our readers.

Journal collaboration including the exchanging manuscript is aiming not only to further increase the friendship, but also to increase awareness of each journal. Exchanging the review articles will facilitate the improvements as follows; (1) complementing each other's strengths and weaknesses, (2) by increasing the variety of the nationality of the author, increasing the number of subscribers and subscribed libraries, (3) increasing the awareness of the journal and then increasing the number of citations and (4) by further increase the awareness of the journal, further increasing in the number of submissions of high-quality original articles.

In these years, we have been trying to further improve the quality of JJR (or make the quality of JJR much better) by various efforts. For example, we are gathering the papers with high readers’ interest such as AI (artificial intelligence) [2, 3, 4, 5, 6, 7, 8, 9]. We are intending to provide novel information, the results of high-quality scientific and clinical studies, and up-to-date useful educational information to all readers, ultimately serving to the people’s health care. All these efforts including the journal collaboration with exchanging manuscripts may also increase the impact factor (IF). An increase in IF should be a secondary product of journal improvements and not the ultimate goal. However, if good papers are gathered in the journal due to the increase in IF and our readers can have better services, we think that it may be possible to temporarily set the increase in the impact factor as one of the measures.

Notes

References

  1. 1.
    Iacobellis F. Pelvic floor dysfunctions: how to image patients? Jpn J Radiol. 2019.  https://doi.org/10.1007/s11604-019-00903-6(Epub ahead of print).CrossRefGoogle Scholar
  2. 2.
    Higaki T, Nakamura Y, Tatsugami F, Nakaura T, Awai K. Improvement of image quality at CT and MRI using deep learning. Jpn J Radiol. 2019;37(1):73–80.  https://doi.org/10.1007/s11604-018-0796-2(Epub 2018 Nov 29. Review).CrossRefGoogle Scholar
  3. 3.
    Sakai K, Yamada K. Machine learning studies on major brain diseases: 5-year trends of 2014–2018. Jpn J Radiol. 2019;37(1):34–72.  https://doi.org/10.1007/s11604-018-0794-4(Epub 2018 Nov 29. Review).CrossRefPubMedGoogle Scholar
  4. 4.
    Ueda D, Shimazaki A, Miki Y. Technical and clinical overview of deep learning in radiology. Jpn J Radiol. 2019;37(1):15–33.  https://doi.org/10.1007/s11604-018-0795-3(Epub 2018 Dec 1. Review).CrossRefPubMedGoogle Scholar
  5. 5.
    Alis D, Bagcilar O, Senli YD, Yergin M, Isler C, Kocer N, Islak C, Kizilkilic O. Machine learning-based quantitative texture analysis of conventional MRI combined with ADC maps for assessment of IDH1 mutation in high-grade gliomas. Jpn J Radiol. 2019.  https://doi.org/10.1007/s11604-019-00902-7(Epub ahead of print).CrossRefPubMedGoogle Scholar
  6. 6.
    Yamada K, Mori S. The day when computers read between lines. Jpn J Radiol. 2019;37(5):351–3.  https://doi.org/10.1007/s11604-019-00833-3(Epub 2019 Mar 25. Review).CrossRefPubMedGoogle Scholar
  7. 7.
    Fujioka T, Kubota K, Mori M, Kikuchi Y, Katsuta L, Kasahara M, Oda G, Ishiba T, Nakagawa T, Tateishi U. Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network. Jpn J Radiol. 2019;37(6):466–72.  https://doi.org/10.1007/s11604-019-00831-5(Epub 2019 Mar 19).CrossRefGoogle Scholar
  8. 8.
    Nakata N. Recent technical development of artificial intelligence for diagnostic medical imaging. Jpn J Radiol. 2019;37(2):103–8.  https://doi.org/10.1007/s11604-018-0804-6(Epub 2019 Jan 31. Review).CrossRefGoogle Scholar
  9. 9.
    Kobayashi Y, Ishibashi M, Kobayashi H. How will "democratization of artificial intelligence" change the future of radiologists? Jpn J Radiol. 2019;37(1):9–14.  https://doi.org/10.1007/s11604-018-0793-5(Epub 2018 Dec 21. Review).CrossRefGoogle Scholar

Copyright information

© Japan Radiological Society 2019

Authors and Affiliations

  1. 1.Department of RadiologyNagoya University Graduate School of MedicineNagoyaJapan
  2. 2.Department of RadiologyUniversity of Occupational and Environmental HealthKitakyushuJapan

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