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MR imaging of epithelial ovarian cancer: a combined model to predict histologic subtypes

  • Magnetic Resonance
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

To compare the performance of clinical features, conventional MR image features, ADC value, T2WI, DWI, DCE-MRI radiomics, and a combined multiple features model in predicting the type of epithelial ovarian cancer (EOC).

Methods

In this retrospective analysis, 61 EOC patients were confirmed by histology. Significant features (p < 0.05) by multivariate logistic regression were retained to establish a clinical model, conventional MRI morphological model, ADC model, and traditional model. The radiomics model included FS-T2WI, DWI, and DCE-MRI, and also, a multisequence model was established. A total of 1070 radiomics features of each sequence were extracted; then, univariate analysis and LASSO were used to select important features. Traditional models were combined with a combined radiomics model to establish a mixed model. The predictive performance was validated by receiver operating characteristic curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). A stratified analysis was conducted to compare the differences between the combined radiomics model and the traditional model in identifying early- and late-stage EOC.

Results

Traditional models showed the highest performance (AUC = 0.96). The performance of the mixed model (AUC = 0.97) was not significantly different from that of the traditional model. The calibration curve showed that the traditional model had the highest reliability. Stratified analysis showed the potential of the combined radiomics model in the early distinction of the two tumor types.

Conclusion

The traditional model is an effective tool to distinguish EOC type I/II. Combined radiomics models have the potential to better distinguish EOC types in early FIGO stage disease.

Key Points

• The combined radiomics model resulted in a better predictive model than that from a single sequence model.

• The traditional model showed higher classification accuracy than the combined radiomics model.

• Combined radiomics models have the potential to better distinguish EOC types in early FIGO stage disease.

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Abbreviations

ADC:

Apparent diffusion coefficient

DCA:

Decision curve analysis

DWI:

Diffusion-weighted imaging

EOC:

Epithelial ovarian cancer

GLCM:

Gray-level cooccurrence matrix

GLDM:

Gray-level dependence matrix

GLRLM:

Gray-level run-length matrix

GLSZM:

Gray-level size zone matrix

ICC:

Intraclass correlation coefficients

LASSO:

Least absolute shrinkage selection operator

MAPK:

Mitogen-activated protein kinase

MRI:

Magnetic resonance imaging

ROC:

Receiver operating characteristic

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Funding

This study has received funding by Inner Mongolia Natural Science Foundation: Based on IVIM-DWI Prostate Cancer Imaging Biomarkers and Molecular Pathology Basic Research, No.: 2017MS(LH)0837.

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Author notes

  1. LuoDan Qian and JiaLiang Ren contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China

    Qian LuoDan, Liu AiShi, Gao Yang, Hao FenE, Zhao Lei, Wu Hui & Niu GuangMing

  2. GE Healthcare (Shanghai) Co., Ltd., Shanghai, 210000, China

    Ren JiaLiang

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  1. Qian LuoDan
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  6. Zhao Lei
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Corresponding authors

Correspondence to Wu Hui or Niu GuangMing.

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Guarantor

The scientific guarantor of this publication is GuangMing Niu.

Conflict of interest

One of the authors of this manuscript (JiaLiang Ren) is an employee GE Healthcare. The remaining authors declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Statistician JiaLiang Ren kindly provided all statistical work for this study.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Qian, L., Ren, J., Liu, A. et al. MR imaging of epithelial ovarian cancer: a combined model to predict histologic subtypes. Eur Radiol 30, 5815–5825 (2020). https://doi.org/10.1007/s00330-020-06993-5

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  • DOI: https://doi.org/10.1007/s00330-020-06993-5

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