참고문헌
- Choe YH. Announcement of new editor-in-chief for the Korean Journal of Radiology. Korean J Radiol 2020;21:1283
- Coronavirus disease 2019 (COVID-19). Jamanetwork.com Web site. https://jamanetwork.com/journals/jama/pages/coronavirusalert#clinical-information. Accessed December 5, 2020
- Special focus: COVID-19. Pubs.rsna.org Web site. https://pubs.rsna.org/2019-ncov_articles. Accessed December 5, 2020
- Jung HK, Kim JY, Lee MS, Lee JY, Park JS, Hyun M, et al. Characteristics of COVID-19 patients who progress to pneumonia on follow-up chest radiograph: 236 patients from a single isolated cohort in Daegu, South Korea. Korean J Radiol 2020;21:1265-1272 https://doi.org/10.3348/kjr.2020.0564
- Zheng Y, Xiao A, Yu X, Zhao Y, Lu Y, Li X, et al. Development and validation of a prognostic nomogram based on clinical and CT features for adverse outcome prediction in patients with COVID-19. Korean J Radiol 2020;21:1007-1017 https://doi.org/10.3348/kjr.2020.0485
- Hwang EJ, Kim H, Yoon SH, Goo JM, Park CM. Implementation of a deep learning-based computer-aided detection system for the interpretation of chest radiographs in patients suspected for COVID-19. Korean J Radiol 2020;21:1150-1160 https://doi.org/10.3348/kjr.2020.0536
- Park B, Park J, Lim JK, Shin KM, Lee J, Seo H, et al. Prognostic implication of volumetric quantitative CT analysis in patients with COVID-19: a multicenter study in Daegu, Korea. Korean J Radiol 2020;21:1256-1264 https://doi.org/10.3348/kjr.2020.0567
- Sun D, Li X, Guo D, Wu L, Chen T, Fang Z, et al. CT quantitative analysis and its relationship with clinical features for assessing the severity of patients with COVID-19. Korean J Radiol 2020;21:859-868 https://doi.org/10.3348/kjr.2020.0293
- Yin X, Min X, Nan Y, Feng Z, Li B, Cai W, et al. Assessment of the severity of coronavirus disease: quantitative computed tomography parameters versus semiquantitative visual score. Korean J Radiol 2020;21:998-1006 https://doi.org/10.3348/kjr.2020.0423
- Lan L, Xu D, Xia C, Wang S, Yu M, Xu H. Early CT findings of coronavirus disease 2019 (COVID-19) in asymptomatic children: a single-center experience. Korean J Radiol 2020;21:919-924 https://doi.org/10.3348/kjr.2020.0231
- Liu Z, Jin C, Wu CC, Liang T, Zhao H, Wang Y, et al. Association between initial chest CT or clinical features and clinical course in patients with coronavirus disease 2019 pneumonia. Korean J Radiol 2020;21:736-745 https://doi.org/10.3348/kjr.2020.0171
- Yu M, Liu Y, Xu D, Zhang R, Lan L, Xu H. Prediction of the development of pulmonary fibrosis using serial thin-section CT and clinical features in patients discharged after treatment for COVID-19 pneumonia. Korean J Radiol 2020;21:746-755 https://doi.org/10.3348/kjr.2020.0215
- Yoon SH, Lee KH, Kim JY, Lee YK, Ko H, Kim KH, et al. Chest radiographic and CT findings of the 2019 novel coronavirus disease (COVID-19): analysis of nine patients treated in Korea. Korean J Radiol 2020;21:494-500 https://doi.org/10.3348/kjr.2020.0132
- Won SY, Park YW, Park M, Ahn SS, Kim J, Lee SK. Quality reporting of radiomics analysis in mild cognitive impairment and Alzheimer's disease: a roadmap for moving forward. Korean J Radiol 2020;21:1345-1354 https://doi.org/10.3348/kjr.2020.0715
- 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 https://doi.org/10.1148/radiol.2019192515
- England JR, Cheng PM. Artificial intelligence for medical image analysis: a guide for authors and reviewers. AJR Am J Roentgenol 2019;212:513-519 https://doi.org/10.2214/AJR.18.20490
- Mongan J, Moy L, Kahn Jr CE. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiology: Artificial Intelligence 2020;2:e200029
- Sounderajah V, Ashrafian H, Aggarwal R, De Fauw J, Denniston AK, Greaves F, et al. Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: The STARD-AI Steering Group. Nat Med 2020;26:807-808 https://doi.org/10.1038/s41591-020-0941-1
- Collins GS, Moons KGM. Reporting of artificial intelligence prediction models. Lancet 2019;393:1577-1579 https://doi.org/10.1016/S0140-6736(19)30037-6
- Park JE, Park SY, Kim HJ, Kim HS. Reproducibility and generalizability in radiomics modeling: possible strategies in radiologic and statistical perspectives. Korean J Radiol 2019;20:1124-1137 https://doi.org/10.3348/kjr.2018.0070
- Moy L, Bluemke D; Radiology Editorial Board. The radiology scientific expert panel. Radiology 2020;296:E105