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The Additional Value Evaluation of Tri-parametric MRI in Identifying Muscle-invasive Status in Bladder Cancer

  • Author Footnotes
    # Yan Liu and Xiaopan Xu contributed to this work equally.
    Yan Liu
    Footnotes
    # Yan Liu and Xiaopan Xu contributed to this work equally.
    Affiliations
    School of Biomedical Engineering, Air Force Medical University, No. 169 Changle West Road, Xi'an, SN 710032, China

    Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, SN 710032, China
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  • Author Footnotes
    # Yan Liu and Xiaopan Xu contributed to this work equally.
    Xiaopan Xu
    Footnotes
    # Yan Liu and Xiaopan Xu contributed to this work equally.
    Affiliations
    School of Biomedical Engineering, Air Force Medical University, No. 169 Changle West Road, Xi'an, SN 710032, China

    Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, SN 710032, China
    Search for articles by this author
  • Huanjun Wang
    Affiliations
    Department of Radiology, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, GD 510080, China
    Search for articles by this author
  • Yang Liu
    Affiliations
    School of Biomedical Engineering, Air Force Medical University, No. 169 Changle West Road, Xi'an, SN 710032, China

    Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, SN 710032, China
    Search for articles by this author
  • Yang Wang
    Affiliations
    Department of Radiology, the First Affiliated Hospital, Air Force Medical University, Xi'an, SN 710032, China
    Search for articles by this author
  • Qi Dong
    Affiliations
    School of Biomedical Engineering, Air Force Medical University, No. 169 Changle West Road, Xi'an, SN 710032, China

    Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, SN 710032, China
    Search for articles by this author
  • Ziqi Li
    Affiliations
    School of Biomedical Engineering, Air Force Medical University, No. 169 Changle West Road, Xi'an, SN 710032, China

    Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, SN 710032, China
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  • Yan Guo
    Affiliations
    Department of Radiology, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, GD 510080, China
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  • Hongbing Lu
    Correspondence
    Address correspondence to: H. L.
    Affiliations
    School of Biomedical Engineering, Air Force Medical University, No. 169 Changle West Road, Xi'an, SN 710032, China

    Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, SN 710032, China
    Search for articles by this author
  • Author Footnotes
    # Yan Liu and Xiaopan Xu contributed to this work equally.

      Rationale and Objectives

      Identification of muscle-invasive status (MIS) of bladder cancer (BCa) is critical for treatment decisions. The Vesical Imaging-Reporting and Data System (VI-RADS) has been widely used in preoperatively predicting MIS using tri-parametric MR imaging including T2-weighted (T2W), diffusion-weighted (DW), and dynamic contrast-enhanced (DCE) sequences. While the diagnostic values of radiomics features from bi-parametric MRI such as T2W + DW to identification of MIS have been reported, whether the tri-parametric MRI could provide additional diagnostic value to the radiomics prediction task, and how to integrate DCE features into the radiomics model, which is the objectives of this study, remain unknown.

      Materials and Methods

      Patients with postoperatively confirmed BCa lesions (150 in non-muscle-invasive BCa and 56 in muscle-invasive BCa groups) were retrospectively included. Their T2W, DW with apparent diffusion coefficient (ADC) maps, and DCE sequences were acquired using a 3.0T MR system. Regions of interest were manually depicted and VI-RADS scores were assessed by three radiologists. Three predictive models were developed by the radiomics features extracted from sequence combinations of T2W + DW (Model one), T2W + DCE (Model two), and T2W + DW + DCE (Model three), respectively, using the least absolute shrinkage and selection operator. The performance of each model was quantitatively assessed on both the training (n = 165) and testing (n = 41) cohorts. Then a 10 times five-fold cross validation was conducted to assess the overall performance.

      Results

      Three models achieved area under the curve of 0.888, 0.869, and 0.901 in the cross validation, respectively. The tri-parametric model performed significantly superior than the two bi-parametric models and VI-RADS. The analysis of feature coefficients derived from least absolute shrinkage and selection operator algorithm showed features from the tri-parametric MRI are effective in MIS discrimination.

      Conclusion

      The tri-parametric MRI provides additional value to the radiomics-based identification of MIS.

      Key Words

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