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Development and Validation of an MRI-based Radiomics Nomogram for Assessing Deep Myometrial Invasion in Early Stage Endometrial Adenocarcinoma

  • Author Footnotes
    # Yaoxin Wang and Qiu Bi contributed equally to this work
    Yaoxin Wang
    Footnotes
    # Yaoxin Wang and Qiu Bi contributed equally to this work
    Affiliations
    The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China

    Department of MRI, the First People's Hospital of Yunnan Province, Kunming, Yunnan, China
    Search for articles by this author
  • Author Footnotes
    # Yaoxin Wang and Qiu Bi contributed equally to this work
    Qiu Bi
    Footnotes
    # Yaoxin Wang and Qiu Bi contributed equally to this work
    Affiliations
    The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China

    Department of MRI, the First People's Hospital of Yunnan Province, Kunming, Yunnan, China
    Search for articles by this author
  • Yuchen Deng
    Affiliations
    The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China

    Department of MRI, the First People's Hospital of Yunnan Province, Kunming, Yunnan, China
    Search for articles by this author
  • Zihao Yang
    Affiliations
    The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China

    Department of MRI, the First People's Hospital of Yunnan Province, Kunming, Yunnan, China
    Search for articles by this author
  • Yang Song
    Affiliations
    MR Scientific Marketing, Siemens Healthcare, Shanghai, China
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  • Yunzhu Wu
    Affiliations
    MR Scientific Marketing, Siemens Healthcare, Shanghai, China
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  • Kunhua Wu
    Correspondence
    Address correspondence to: K.W.
    Affiliations
    The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China

    Department of MRI, the First People's Hospital of Yunnan Province, Kunming, Yunnan, China
    Search for articles by this author
  • Author Footnotes
    # Yaoxin Wang and Qiu Bi contributed equally to this work

      Rationale and Objectives

      To establish a radiomics nomogram for detecting deep myometrial invasion (DMI) in early stage endometrioid adenocarcinoma (EAC).

      Materials and Methods

      A total of 266 patients with stage I EAC were divided into training (n = 185) and test groups (n = 81). Logistic regression were used to identify clinical predictors. Radiomics features were extracted and selected from multiparameter MR images. The important clinical factors and radiomics features were integrated into a nomogram. A receiver operating characteristic curve was used to evaluate the nomogram. Two radiologists evaluated MR images with or without the help of the nomogram to detect DMI. The clinical benefit of using the nomogram was evaluated by decision curve analysis (DCA) and by calculating net reclassification index (NRI) and integrated discrimination index (IDI).

      Results

      Age and CA125 were independent clinical predictors. The area under the curves of the clinical parameters, radiomics signature and nomogram in evaluating DMI were 0.744, 0.869 and 0.883, respectively. The accuracies of the two radiologists increased from 79.0% and 80.2% to 90.1% and 92.5% when they used the nomogram. The NRI of the two radiologists were 0.262 and 0.318, and the IDI were 0.322 and 0.405. According to DCA, the nomogram showed a higher net benefit than the radiomics signature or unaided radiologists. Cross-validation showed the outcome of radiomics analysis may not be influenced by changes in field strength.

      Conclusion

      The radiomics nomogram based on radiomics features and clinical factors can help radiologists evaluate DMI and improve their accuracy in predicting DMI in early stage EAC.

      Key Words

      Abbreviations:

      endometrial carcinoma ((EC)), International Federation of Gynecology and Obstetrics ((FIGO)), endometrioid adenocarcinoma ((EAC)), deep myometrial invasion ((DMI)), magnetic resonance imaging ((MRI)), dynamic contrast-enhanced MRI ((DCE-MRI)), T2-weighted image ((T2WI)), diffusion weighted imaging ((DWI)), apparent diffusion coefficient ((ADC)), late contrast-enhanced T1-weighted imaging ((LCE-T1WI)), region of interest ((ROI)), intraclass correlation coefficient ((ICC)), least absolute shrinkage and selection operator ((LASSO)), decision curve analysis ((DCA)), net reclassification index ((NRI)), integrated discrimination index ((IDI)), receiver operating characteristic ((ROC)), areas under the curves ((AUCs)), maximal correlation coefficient ((MCC))
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