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Preoperative prediction of parametrial invasion in early-stage cervical cancer with MRI-based radiomics nomogram

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

Abstract

Purpose

To develop and identify a MRI-based radiomics nomogram for the preoperative prediction of parametrial invasion (PMI) in patients with early-stage cervical cancer (ECC).

Materials and methods

All 137 patients with ECC (FIGO stages IB–IIA) underwent T2WI and DWI scans before radical hysterectomy surgery. The radiomics signatures were calculated with the radiomics features which were extracted from T2WI and DWI and selected by the least absolute shrinkage and selection operation regression. The support vector machine (SVM) models were built using radiomics signatures derived from T2WI and joint T2WI and DWI respectively to evaluate the performance of radiomics signatures for distinguishing patients with PMI. A radiomics nomogram was drawn based on the radiomics signatures with a better performance, patient’s age, and pathological grade; its discrimination and calibration performances were estimated.

Results

For T2WI and joint T2WI and DWI, the radiomics signatures yielded an AUC of 0.797 (95% CI, 0.682–0.911) vs 0.946 (95% CI, 0.899–0.994), and 0.780 (95% CI, 0.641–0.920) vs 0.921 (95% CI, 0.832–1) respectively in the primary and validation cohorts. The radiomics nomogram, integrating the radiomics signatures from joint T2WI and DWI, patient’s age, and pathological grade, showed excellent discrimination, with C-index values of 0.969 (95% CI, 0.933–1) and 0.941 (95% CI, 0.868–1) in the primary and validation cohorts, respectively. The calibration curve showed a good agreement.

Conclusions

The radiomics nomogram performed well for the preoperative prediction of PMI in patients with ECC and may be used as a supplementary tool to provide individualized treatment plans for patients with ECC.

Key Points

• No previously reported study that has utilized radiomics nomogram to preoperatively predict PMI for patients with ECC.

• Radiomics model involves radiomics features extracted from joint T2WI and DWI which characterize the heterogeneity between tumors in patients with ECC.

• Radiomics nomogram can assist clinicians with individualized treatment decision-making for patients with ECC.

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Abbreviations

CI:

Confidence interval

ECC:

Early-stage cervical cancer

FIGO:

International Federation of Gynecology and Obstetrics

H–L:

Hosmer-Lemeshow test

LASSO:

Least absolute shrinkage and selection operator

PMI:

Parametrial invasion

ROC:

Receiver operating curve

SCC:

Ag squamous cell carcinoma antigen level

SVM:

Support vector machine

T2WI:

T2-weighted imaging

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Funding

This study has received funding by the National Key Research and Development Program of China (Grant No. 2017YFA0205202) and partially funded by the National Natural Science Foundation of China (Grant No. 61672422).

Author information

Author notes

  1. Tao Wang and Tingting Gao contributed equally to this work.

Authors and Affiliations

  1. Department of Medical Imaging, First Affiliated Hospital of Xi’an Jiaotong University, No.277, West Yanta Road, Xi’an, 710061, Shaanxi, People’s Republic of China

    Tao Wang & Ming Zhang

  2. Department of Radiology, Shaanxi Provincial People’s Hospital, Xi’an, 710068, Shaanxi, People’s Republic of China

    Tao Wang

  3. School of Life Science and Technology, Xidian University, Xi’an, 710071, Shaanxi, People’s Republic of China

    Tingting Gao, Yubo Wang & Liyu Huang

  4. Center Laboratory, Shaanxi Provincial People’s Hospital, Xi’an, 710068, Shaanxi, People’s Republic of China

    Hua Guo

  5. Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA

    Xiaobo Zhou

  6. Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, 100080, People’s Republic of China

    Jie Tian

Authors
  1. Tao Wang
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  2. Tingting Gao
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  3. Hua Guo
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  7. Liyu Huang
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Corresponding authors

Correspondence to Liyu Huang or Ming Zhang.

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Guarantor

The scientific guarantor of this publication is Liyu Huang.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors (Tingting Gao) has significant statistical expertise.

Informed consent

This is a retrospective study for images’ analyses.

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

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Wang, T., Gao, T., Guo, H. et al. Preoperative prediction of parametrial invasion in early-stage cervical cancer with MRI-based radiomics nomogram. Eur Radiol 30, 3585–3593 (2020). https://doi.org/10.1007/s00330-019-06655-1

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  • DOI: https://doi.org/10.1007/s00330-019-06655-1

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