MR image-based radiomics to differentiate type Ι and type ΙΙ epithelial ovarian cancers



Epithelial ovarian cancers (EOC) can be divided into type I and type II according to etiology and prognosis. Accurate subtype differentiation can substantially impact patient management. In this study, we aimed to construct an MR image–based radiomics model to differentiate between type I and type II EOC.


In this multicenter retrospective study, a total of 294 EOC patients from January 2010 to February 2019 were enrolled. Quantitative MR imaging features were extracted from the following axial sequences: T2WI FS, DWI, ADC, and CE-T1WI. A combined model was constructed based on the combination of these four MR sequences. The diagnostic performance was evaluated by ROC-AUC. In addition, an occlusion test was carried out to identify the most critical region for EOC differentiation.


The combined radiomics model exhibited superior diagnostic capability over all four single-parametric radiomics models, both in internal and external validation cohorts (AUC of 0.806 and 0.847, respectively). The occlusion test revealed that the most critical region for differential diagnosis was the border zone between the solid and cystic components, or the less compact areas of solid component on direct visual inspection.


MR image–based radiomics modeling can differentiate between type I and type II EOC and identify the most critical region for differential diagnosis.

Key Points

• Combined radiomics models exhibited superior diagnostic capability over all four single-parametric radiomics models, both in internal and external validation cohorts (AUC of 0.834 and 0.847, respectively).

• The occlusion test revealed that the most crucial region for differentiating type Ι and type ΙΙ EOC was the border zone between the solid and cystic components, or the less compact areas of solid component on direct visual inspection on T2WI FS.

• The light-combined model (constructed by T2WI FS, DWI, and ADC sequences) can be used for patients who are not suitable for contrast agent use.

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Fig. 1
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Apparent diffusion coefficient


Contrast-enhanced T1-weighted imaging


Diffusion-weighted imaging


Epithelial ovarian cancer




Fat-suppressed T2-weighted imaging


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This study has received funding from the National Natural Science Foundation of China (No. 81871439, No. 81501439, No. 81571772, No. 81471628); Key R&D Program of Jiangsu (No. BE2017671); Shanghai Municipal Commission (No. 20184Y0049); Foundation of Jinshan Hospital, Shanghai Medical College, Fudan University (No. 2018-JSYYQH-03); and Science and Technology Commission Shanghai Municipality (No. 19411972000); and Chinese Academy of Sciences-Iranian Vice Presidency for Science and Technology Silk Road Science Fund (No. GJHZ1857).

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Corresponding authors

Correspondence to Xin Gao or Jinwei Qiang.

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The scientific guarantor of this publication is Jinwei Qiang.

Conflict of interest

Dr. Pickhardt is an advisor to Bracco and a shareholder in SHINE and Elucent. All remaining authors have declared no conflicts of interest.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical Approval

Institutional Review Board approval was obtained.


• retrospective

• diagnostic or prognostic study

• multicenter study

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Jian, J., Li, Y., Pickhardt, P.J. et al. MR image-based radiomics to differentiate type Ι and type ΙΙ epithelial ovarian cancers. Eur Radiol (2020).

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  • Diagnosis
  • Epithelial ovarian cancer
  • Magnetic resonance imaging
  • Machine learning
  • radiomics