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

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

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.

Methods

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.

Results

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.

Conclusions

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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Abbreviations

ADC:

Apparent diffusion coefficient

CE-T1WI:

Contrast-enhanced T1-weighted imaging

DWI:

Diffusion-weighted imaging

EOC:

Epithelial ovarian cancer

MP:

Multi-parameter

T2WI FS:

Fat-suppressed T2-weighted imaging

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Funding

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).

Author information

Author notes

  1. Junming Jian and Yong’ai Li contributed equally to this work.

Authors and Affiliations

  1. University of Science and Technology of China, Hefei, 230026, Anhui, China

    Junming Jian & Xingyu Zhao

  2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No.88 Keling Road, Suzhou New District, Suzhou, 215163, Jiangsu, China

    Junming Jian, Wei Xia, Rui Zhang, Xingyu Zhao, Jiayi Zhang, Xiaodong Wu & Xin Gao

  3. Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Jinshan District, Shanghai, 201508, China

    Yong’ai Li & Jinwei Qiang

  4. Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, USA

    Perry J. Pickhardt

  5. Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China

    Zhang He

  6. Department of Radiology, Xinhua Hospital, Medical College of Shanghai Jiao Tong University, Shanghai, 200092, China

    Shuhui Zhao

  7. Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China

    Songqi Cai

  8. Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, 200082, China

    Guofu Zhang

  9. Department of Radiology, Affiliated Hospital of Nantong University, Nantong, 226001, Jiangsu, China

    Jingxuan Jiang

  10. Department of Radiology, Guangdong Women and Children Hospital, Guangzhou, 511400, Guangdong, China

    Yan Zhang

  11. Department of Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221000, Jiangsu, China

    Keying Wang

  12. Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China

    Guangwu Lin

  13. Department of Radiology, Cancer Hospital, Nantong University, Nantong, 226361, Jiangsu, China

    Feng Feng

Authors
  1. Junming Jian
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  2. Yong’ai Li
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  3. Perry J. Pickhardt
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  4. Wei Xia
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  5. Zhang He
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  6. Rui Zhang
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  19. Jinwei Qiang
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Corresponding authors

Correspondence to Xin Gao or Jinwei Qiang.

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Guarantor

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.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

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• 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 31, 403–410 (2021). https://doi.org/10.1007/s00330-020-07091-2

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  • DOI: https://doi.org/10.1007/s00330-020-07091-2

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