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European Radiology
Published in: European Radiology 10/2020

01-10-2020 | Magnetic Resonance Imaging | Urogenital

MRI texture features differentiate clinicopathological characteristics of cervical carcinoma

Authors: Mandi Wang, Jose A. U. Perucho, Ka Yu Tse, Mandy M. Y. Chu, Philip Ip, Elaine Y. P. Lee

Published in: European Radiology | Issue 10/2020

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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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Metadata
Title
MRI texture features differentiate clinicopathological characteristics of cervical carcinoma
Authors
Mandi Wang
Jose A. U. Perucho
Ka Yu Tse
Mandy M. Y. Chu
Philip Ip
Elaine Y. P. Lee
Publication date
01-10-2020
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 10/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-020-06913-7

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