Korean Journal of Radiology

  • 제24권1호
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  • Pages.15-18
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  • 2023
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  • 1229-6929(pISSN)
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  • 2005-8330(eISSN)
대한영상의학회 (The Korean Society of Radiology)
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Looking Back at 2022 and ahead to 2023 for the Korean Journal of Radiology

  • Seong Ho Park (Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center)
  • 투고 : 2022.12.03
  • 심사 : 2022.12.03
  • 발행 : 2023.01.01
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참고문헌

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  6. Quaderi N. Journal citation reports 2022: COVID-19 research continues to drive increased citation impact. clarivate. com Web site. https://clarivate.com/blog/journal-citationreports-2022-covid-19-research-continues-to-drive-increasedcitation-impact/. Accessed December 2, 2022
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  16. Park SH. Guides for the successful conduct and reporting of systematic review and meta-analysis of diagnostic test accuracy studies. Korean J Radiol 2022;23:295-297
  17. Park SH, Han K. How to clearly and accurately report odds ratio and hazard ratio in diagnostic research studies? Korean J Radiol 2022;23:777-784
  18. Park SH, Han K, Park SY. Mistakes to avoid for accurate and transparent reporting of survival analysis in imaging research. Korean J Radiol 2021;22:1587-1593
  19. Atzen SL, Bluemke DA. How to write the perfect abstract for Radiology. Radiology 2022;305:498-501
  20. Atzen SL, Bluemke DA. Top 10 tips for writing your scientific Paper: the Radiology scientific style guide. Radiology 2022;304:1-2
  21. Bluemke DA, Moy L, Bredella MA, Ertl-Wagner BB, Fowler KJ, Goh VJ, et al. Assessing radiology research on artificial intelligence: a brief guide for authors, reviewers, and readers-from the radiology editorial board. Radiology 2020;294:487-489
  22. Chang JM, Ha SM. Regional lymphadenopathy following COVID-19 vaccination in patients with or suspicious of breast cancer: a quick summary of current key facts and recommendations. Korean J Radiol 2022;23:691-695
  23. Futterer JJ, Nagarajah J. Research highlight: 68Ga-PSMA-11 PET imaging for pelvic nodal metastasis in prostate cancer. Korean J Radiol 2022;23:293-294
  24. Goo JM, Jung KW, Kim HY, Kim Y. Potential overdiagnosis with CT lung cancer screening in Taiwanese female: status in South Korea. Korean J Radiol 2022;23:571-573
  25. Han K, Jung I. Restricted mean survival time for survival analysis: a quick guide for clinical researchers. Korean J Radiol 2022;23:495-499
  26. Kim HC. Role of Yttrium-90 radioembolization for colorectal hepatic metastasis. Korean J Radiol 2022;23:156-158
  27. Kwapisz L, Bruining DH, Fletcher JG. Using MR enterography and CT enterography for routine Crohn's surveillance: how we do it now, and how we hope to do it in the future. Korean J Radiol 2022;23:1-5
  28. Lee MW, Rhim H. Research highlight: how to use technical and oncologic outcomes of image-guided tumor ablation according to guidelines by Society of Interventional Oncology and DATECAN? Korean J Radiol 2022;23:385-388
  29. Lee S, Shin HJ, Kim S, Kim EK. Successful implementation of an artificial intelligence-based computer-aided detection system for chest radiography in daily clinical practice. Korean J Radiol 2022;23:847-852
  30. Lee SH, Moon WK. Glandular tissue component on breast ultrasound in dense breasts: a new imaging biomarker for breast cancer risk. Korean J Radiol 2022;23:574-580
  31. Park JE, Vollmuth P, Kim N, Kim HS. Research highlight: use of generative images created with artificial intelligence for brain tumor imaging. Korean J Radiol 2022;23:500-504
  32. Park SH, Choi JI, Fournier L, Vasey B. Randomized clinical trials of artificial intelligence in medicine: why, when, and how? Korean J Radiol 2022;23:1119-1125
  33. Youk JH, Kim EK. Research highlight: artificial intelligence for ruling out negative examinations in screening breast MRI. Korean J Radiol 2022;23:153-155
  34. Bluemke DA. The new radiology. Radiology 2019;290:275-276
  35. Gormly KL. High-resolution T2-weighted MRI to evaluate rectal cancer: why variations matter. Korean J Radiol 2021;22:1475-1480
  36. Heo S, Lee SS, Kim SY, Lim YS, Park HJ, Yoon JS, et al. Prediction of decompensation and death in advanced chronic liver disease using deep learning analysis of gadoxetic acid-enhanced MRI. Korean J Radiol 2022;23:1269-1280
  37. Kim DW, Ahn H, Kim KW, Lee SS, Kim HJ, Ko Y, et al. Prognostic value of sarcopenia and myosteatosis in patients with resectable pancreatic ductal adenocarcinoma. Korean J Radiol 2022;23:1055-1066
  38. Kim YS, Jang MJ, Lee SH, Kim SY, Ha SM, Kwon BR, et al. Use of artificial intelligence for reducing unnecessary recalls at screening mammography: a simulation study. Korean J Radiol 2022;23:1241-1250
  39. Lee JH, Kim KH, Lee EH, Ahn JS, Ryu JK, Park YM, et al. Improving the performance of radiologists using artificial intelligence-based detection support software for mammography: a multi-reader study. Korean J Radiol 2022;23:505-516
  40. Park HJ, Yoon JS, Lee SS, Suk HI, Park B, Sung YS, et al. Deep learning-based assessment of functional liver capacity using gadoxetic acid-enhanced hepatobiliary phase MRI. Korean J Radiol 2022;23:720-731
  41. Yoo H, Kim EY, Kim H, Choi YR, Kim MY, Hwang SH, et al. Artificial intelligence-based identification of normal chest radiographs: a simulation study in a multicenter health screening cohort. Korean J Radiol 2022;23:1009-1018
  42. Park SH, Han K, Jang HY, Park JE, Lee JG, Kim DW, et al. Methods for clinical evaluation of artificial intelligence algorithms for medical diagnosis. Radiology 2022 Nov 8. [Epub]. https://doi.org/10.1148/radiol.220182
  43. Mongan J, Moy L, Kahn CE Jr. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell 2020;2:e200029
  44. Liu X, Rivera SC, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension. BMJ 2020;370:m3164
  45. Rivera SC, Liu X, Chan AW, Denniston AK, Calvert MJ; SPIRITAI and CONSORT-AI Working Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension. BMJ 2020;370:m3210
  46. Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS, Denaxas S, et al. Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. BMJ 2022;377:e070904
  47. Collins GS, Dhiman P, Andaur Navarro CL, Ma J, Hooft L, Reitsma JB, et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open 2021;11:e048008
  48. Sounderajah V, Ashrafian H, Golub RM, Shetty S, De Fauw J, Hooft L, et al. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol. BMJ Open 2021;11:e047709