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Study on the prognosis predictive model of COVID-19 patients based on CT radiomics.

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  • معلومة اضافية
    • المصدر:
      Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
    • بيانات النشر:
      Original Publication: London : Nature Publishing Group, copyright 2011-
    • الموضوع:
      Tomography, X-Ray Computed*; COVID-19/*diagnostic imaging; Aged ; COVID-19/etiology ; Diagnosis, Computer-Assisted/methods ; Female ; Humans ; Male ; Middle Aged ; Models, Theoretical ; Prognosis ; ROC Curve
    • نبذة مختصرة :
      Making timely assessments of disease progression in patients with COVID-19 could help offer the best personalized treatment. The purpose of this study was to explore an effective model to predict the outcome of patients with COVID-19. We retrospectively included 188 patients (124 in the training set and 64 in the test set) diagnosed with COVID-19. Patients were divided into aggravation and improvement groups according to the disease progression. Three kinds of models were established, including the radiomics, clinical, and combined model. Receiver operating characteristic curves, decision curves, and Delong's test were used to evaluate and compare the models. Our analysis showed that all the established prediction models had good predictive performance in predicting the progress and outcome of COVID-19.
    • References:
      J Clin Med. 2020 Dec 24;10(1):. (PMID: 33374452)
      J Med Virol. 2020 Sep;92(9):1449-1459. (PMID: 32242947)
      IEEE J Biomed Health Inform. 2020 Dec;24(12):3585-3594. (PMID: 33166256)
      N Engl J Med. 2020 Apr 30;382(18):1708-1720. (PMID: 32109013)
      Med Image Anal. 2021 Jan;67:101844. (PMID: 33091743)
      Med Decis Making. 2006 Nov-Dec;26(6):565-74. (PMID: 17099194)
      N Engl J Med. 2013 Jun 13;368(24):2277-85. (PMID: 23697469)
      Radiology. 2017 Mar;282(3):665-675. (PMID: 27700229)
      Invest New Drugs. 2018 Aug;36(4):601-607. (PMID: 29075985)
      Radiology. 2014 Feb;270(2):320-5. (PMID: 24471381)
      BMJ. 2020 Mar 26;368:m1091. (PMID: 32217556)
      Eur J Cancer. 2012 Mar;48(4):441-6. (PMID: 22257792)
      Radiographics. 2017 Sep-Oct;37(5):1483-1503. (PMID: 28898189)
      J Comput Assist Tomogr. 2020 Sep/Oct;44(5):640-646. (PMID: 32842058)
      Lancet. 2020 Feb 15;395(10223):497-506. (PMID: 31986264)
      Radiology. 2020 Oct;297(1):E216-E222. (PMID: 32324101)
      Radiology. 2020 Aug;296(2):E32-E40. (PMID: 32101510)
      Cell. 2018 Feb 22;172(5):1122-1131.e9. (PMID: 29474911)
      Lancet Respir Med. 2020 May;8(5):475-481. (PMID: 32105632)
      BMC Med. 2020 Feb 28;18(1):57. (PMID: 32106852)
      Lancet. 2020 Mar 28;395(10229):1054-1062. (PMID: 32171076)
      BMJ Open. 2020 Feb 25;10(2):e033898. (PMID: 32102816)
      Lancet Infect Dis. 2020 Apr;20(4):425-434. (PMID: 32105637)
      J Clin Med. 2020 Jan 17;9(1):. (PMID: 31963480)
      Theranostics. 2020 Mar 15;10(10):4606-4613. (PMID: 32292517)
      Eur Radiol. 2021 Jun;31(6):4367-4376. (PMID: 33274405)
      Aging (Albany NY). 2020 Jul 20;12(14):13860-13868. (PMID: 32688346)
      Br J Radiol. 2021 Jan 1;94(1117):20200634. (PMID: 33296222)
    • Grant Information:
      81873910 National Natural Science Foundation of China; 2019YFC0118100 National Key Research and Development Program of China
    • الموضوع:
      Date Created: 20210603 Date Completed: 20210616 Latest Revision: 20210616
    • الموضوع:
      20240829
    • الرقم المعرف:
      PMC8172890
    • الرقم المعرف:
      10.1038/s41598-021-90991-0
    • الرقم المعرف:
      34078950