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Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution.

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  • Additional Information
    • Source:
      Publisher: Springer Nature Country of Publication: England NLM ID: 101172931 Publication Model: Electronic Cited Medium: Internet ISSN: 1470-7330 (Electronic) Linking ISSN: 14707330 NLM ISO Abbreviation: Cancer Imaging Subsets: MEDLINE
    • Publication Information:
      Publication: <2014- > : London : Springer Nature
      Original Publication: London : e-med, c2000]-
    • Subject Terms:
    • Abstract:
      Background: As artificial intelligence methods for the diagnosis of disease advance, we aimed to evaluate machine learning in the predictive task of distinguishing between malignant and benign breast lesions on an independent clinical magnetic resonance imaging (MRI) dataset within a single institution for subsequent use as a computer aid for radiologists.
      Methods: Computer analysis was conducted on consecutive dynamic contrast-enhanced MRI (DCE-MRI) studies from 1483 breast cancer and 496 benign patients who underwent MRI examinations between February 2015 and October 2017; with the age ranges of the cancer and benign patients being 19 to 77 and 16 to 76 years old, respectively. Cases were separated into a training dataset (years 2015 & 2016; 1444 cases) and an independent testing dataset (year 2017; 535 cases) based solely on MRI examination date. After radiologist indication of the lesion, the computer automatically segmented and extracted radiomic features, which were subsequently merged with a support-vector machine (SVM) to yield a lesion signature. Area under the receiving operating characteristic (ROC) curve (AUC) with 95% confidence intervals (CI) served as the primary figure of merit in the statistical evaluation for this clinical classification task.
      Results: In the task of distinguishing malignant and benign breast lesions DCE-MRI, the trained predictive model yielded an AUC value of 0.89 (95% CI: 0.858, 0.922) on the independent image set. AUC values of 0.88 (95% CI: 0.845, 0.926) and 0.90 (95% CI: 0.837, 0.940) were obtained for mass lesions only and non-mass lesions only, respectively. Compared with actual clinical management decisions, the predictive model achieved 99.5% sensitivity with 9.6% fewer recommended biopsies.
      Conclusion: On an independent, consecutive clinical dataset within a single institution, a trained machine learning system yielded promising performance in distinguishing between malignant and benign breast lesions.
    • Grant Information:
      U01 CA195564 United States CA NCI NIH HHS; CA195564 National Institutes of Health (US); 81801781 National Natural Science Foundation of China
    • Contributed Indexing:
      Keywords: Artificial intelligence (AI); Breast cancer; Computer-aided diagnosis; Independent statistical testing; Machine learning; Quantitative MRI; Radiomics
    • Accession Number:
      0 (Contrast Media)
    • Publication Date:
      Date Created: 20190920 Date Completed: 20200106 Latest Revision: 20200106
    • Publication Date:
      20200827
    • Accession Number:
      PMC6751793
    • Accession Number:
      10.1186/s40644-019-0252-2
    • Accession Number:
      31533838
  • Citations
    • ABNT:
      JI, Y. et al. Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution. Cancer imaging : the official publication of the International Cancer Imaging Society, [s. l.], v. 19, n. 1, p. 64, 2019. DOI 10.1186/s40644-019-0252-2. Disponível em: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdc&AN=31533838. Acesso em: 26 set. 2020.
    • AMA:
      Ji Y, Li H, Edwards AV, et al. Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution. Cancer imaging : the official publication of the International Cancer Imaging Society. 2019;19(1):64. doi:10.1186/s40644-019-0252-2
    • APA:
      Ji, Y., Li, H., Edwards, A. V., Papaioannou, J., Ma, W., Liu, P., & Giger, M. L. (2019). Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution. Cancer Imaging : The Official Publication of the International Cancer Imaging Society, 19(1), 64. https://doi.org/10.1186/s40644-019-0252-2
    • Chicago/Turabian: Author-Date:
      Ji, Yu, Hui Li, Alexandra V Edwards, John Papaioannou, Wenjuan Ma, Peifang Liu, and Maryellen L Giger. 2019. “Independent Validation of Machine Learning in Diagnosing Breast Cancer on Magnetic Resonance Imaging within a Single Institution.” Cancer Imaging : The Official Publication of the International Cancer Imaging Society 19 (1): 64. doi:10.1186/s40644-019-0252-2.
    • Harvard:
      Ji, Y. et al. (2019) ‘Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution’, Cancer imaging : the official publication of the International Cancer Imaging Society, 19(1), p. 64. doi: 10.1186/s40644-019-0252-2.
    • Harvard: Australian:
      Ji, Y, Li, H, Edwards, AV, Papaioannou, J, Ma, W, Liu, P & Giger, ML 2019, ‘Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution’, Cancer imaging : the official publication of the International Cancer Imaging Society, vol. 19, no. 1, p. 64, viewed 26 September 2020, .
    • MLA:
      Ji, Yu, et al. “Independent Validation of Machine Learning in Diagnosing Breast Cancer on Magnetic Resonance Imaging within a Single Institution.” Cancer Imaging : The Official Publication of the International Cancer Imaging Society, vol. 19, no. 1, Sept. 2019, p. 64. EBSCOhost, doi:10.1186/s40644-019-0252-2.
    • Chicago/Turabian: Humanities:
      Ji, Yu, Hui Li, Alexandra V Edwards, John Papaioannou, Wenjuan Ma, Peifang Liu, and Maryellen L Giger. “Independent Validation of Machine Learning in Diagnosing Breast Cancer on Magnetic Resonance Imaging within a Single Institution.” Cancer Imaging : The Official Publication of the International Cancer Imaging Society 19, no. 1 (September 18, 2019): 64. doi:10.1186/s40644-019-0252-2.
    • Vancouver/ICMJE:
      Ji Y, Li H, Edwards AV, Papaioannou J, Ma W, Liu P, et al. Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution. Cancer imaging : the official publication of the International Cancer Imaging Society [Internet]. 2019 Sep 18 [cited 2020 Sep 26];19(1):64. Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdc&AN=31533838