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Differentiating atypical meningioma from anaplastic meningioma using diffusion weighted imaging

  • Tao Han
    Affiliations
    Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China

    Second Clinical School, Lanzhou University, Lanzhou 730000, China

    Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China

    Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
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  • Jing Zhang
    Affiliations
    Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China

    Second Clinical School, Lanzhou University, Lanzhou 730000, China

    Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China

    Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
    Search for articles by this author
  • Xianwang Liu
    Affiliations
    Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China

    Second Clinical School, Lanzhou University, Lanzhou 730000, China

    Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China

    Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
    Search for articles by this author
  • Bin Zhang
    Affiliations
    Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China

    Second Clinical School, Lanzhou University, Lanzhou 730000, China

    Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China

    Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
    Search for articles by this author
  • Liangna Deng
    Affiliations
    Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China

    Second Clinical School, Lanzhou University, Lanzhou 730000, China

    Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China

    Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
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  • Xiaoqiang Lin
    Affiliations
    Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China

    Second Clinical School, Lanzhou University, Lanzhou 730000, China

    Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China

    Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
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  • Mengyuan Jing
    Affiliations
    Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China

    Second Clinical School, Lanzhou University, Lanzhou 730000, China

    Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China

    Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
    Search for articles by this author
  • Junlin Zhou
    Correspondence
    Corresponding author at: Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China.
    Affiliations
    Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China

    Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China

    Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
    Search for articles by this author
Published:December 12, 2021DOI:https://doi.org/10.1016/j.clinimag.2021.12.004

      Abstract

      Purpose

      To explore the value of MRI conventional features and apparent diffusion coefficient (ADC) on the differential diagnosis of atypical meningioma (AtM) and anaplastic meningioma (AnM).

      Materials and methods

      This retrospective study analyzed the preoperative clinical data, MRI conventional features, and DWI data of 55 AtM and 25 AnM confirmed by pathology in our hospital. The clinical features, MRI conventional features, ADCmean, ADCmin, and relative ADC (rADC) values were compared between the two tumors by Chi-square test or an independent sample t-test. Receiver operating characteristic curve (ROC) and binary logistic regression analysis were used to evaluate the diagnostic efficacy of each parameter to differentiate between these tumors.

      Results

      The MRI conventional features had a certain ability to distinguish AnM and AtM, with an area under the curve value (AUC) of 0.824 (95% CI, 0.723–0.900). The ADCmean, ADCmin, and rADC values were significantly higher in AtM compared to AnM (all P < 0.05). ADCmean had the best identification effect with an AUC of 0.867 (95% CI, 0.772–0.933) among them, at an cut-off of 0.817 × 10−3 mm2/s, the sensitivity and specificity of distinguishing AtM from AnM were 78.18% and 88.00%, respectively. A combination of ADCmean and MRI conventional features showed the optimum discrimination ability for the two tumors, the AUC, sensitivity, specificity, and accuracy were 0.918 (95% CI, 0.835–0.967), 80.00%, 94.55%, and 90.00%, respectively.

      Conclusion

      MRI conventional features combined with ADCmean, as a non-invasive method, has potential clinical value in the preoperative diagnosis of AtM and AnM.

      Keywords

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