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MRI texture features differentiate clinicopathological characteristics of cervical carcinoma

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Abstract

Objectives

To evaluate MRI texture analysis in differentiating clinicopathological characteristics of cervical carcinoma (CC).

Methods

Patients with newly diagnosed CC who underwent pre-treatment MRI were retrospectively reviewed. Texture analysis was performed using commercial software (TexRAD). Largest single-slice ROIs were manually drawn around the tumour on T2-weighted (T2W) images, apparent diffusion coefficient (ADC) maps and contrast-enhanced T1-weighted (T1c) images. First-order texture features were calculated and compared among histological subtypes, tumour grades, FIGO stages and nodal status using the Mann-Whitney U test. Feature selection was achieved by elastic net. Selected features from different sequences were used to build the multivariable support vector machine (SVM) models and the performances were assessed by ROC curves and AUC.

Results

Ninety-five patients with FIGO stage IB~IVB were evaluated. A number of texture features from multiple sequences were significantly different among all the clinicopathological subgroups (p < 0.05). Texture features from different sequences were selected to build the SVM models. The AUCs of SVM models for discriminating histological subtypes, tumour grades, FIGO stages and nodal status were 0.841, 0.850, 0.898 and 0.879, respectively.

Conclusions

Texture features derived from multiple sequences were helpful in differentiating the clinicopathological signatures of CC. The SVM models with selected features from different sequences offered excellent diagnostic discrimination of the tumour characteristics in CC.

Key Points

• First-order texture features are able to differentiate clinicopathological signatures of cervical carcinoma.

• Combined texture features from different sequences can offer excellent diagnostic discrimination of the tumour characteristics in cervical carcinoma.

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Abbreviations

ACA:

Adenocarcinoma

ADC:

Apparent diffusion coefficient

AUC:

Area under the curve

CC:

Cervical carcinoma

DWI:

Diffusion-weighted imaging

MPP:

Mean of positive pixels

MRI:

Magnetic resonance imaging

ROI:

Region of interest

SCC:

Squamous cell carcinoma

SD:

Standard deviation

SSF:

Spatial scale filter

SVM:

Support vector machine

T1c:

Contrast-enhanced T1-weighted

T2W:

T2-weighted

VOI:

Volume of interest

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Funding

This study has received funding from the General Research Fund (GRF, No. 17119916) of the Research Grants Council (RGC), Hong Kong.

Author information

Author notes

  1. Elaine Y. P. Lee

    Present address: Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, Queen Mary Hospital, The University of Hong Kong, Hong Kong, Hong Kong

  2. Mandi Wang and Jose A. U. Perucho contributed equally to this work.

Authors and Affiliations

  1. Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, Queen Mary Hospital, The University of Hong Kong, Hong Kong, Hong Kong

    Mandi Wang & Jose A. U. Perucho

  2. Department of Obstetrics & Gynaecology, Li Ka Shing Faculty of Medicine, Queen Mary Hospital, The University of Hong Kong, Hong Kong, Hong Kong

    Ka Yu Tse

  3. Department of Obstetrics & Gynaecology, Queen Mary Hospital, Hong Kong, Hong Kong

    Mandy M. Y. Chu

  4. Department of Pathology, Li Ka Shing Faculty of Medicine, Queen Mary Hospital, The University of Hong Kong, Hong Kong, Hong Kong

    Philip Ip

Authors
  1. Mandi Wang
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Corresponding author

Correspondence to Elaine Y. P. Lee.

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Guarantor

The scientific guarantor of this publication is Elaine Y.P. Lee.

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

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board (Reference No. UW 17-389) approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in Academic Radiology that evaluated the clinical value of diffusion kurtosis imaging and not assessed texture analysis.

Methodology

• Retrospective

• Observational

• Performed at one institution

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Wang, M., Perucho, J.A.U., Tse, K.Y. et al. MRI texture features differentiate clinicopathological characteristics of cervical carcinoma. Eur Radiol 30, 5384–5391 (2020). https://doi.org/10.1007/s00330-020-06913-7

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  • DOI: https://doi.org/10.1007/s00330-020-06913-7

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