Discrimination of malignant and benign breast masses using automatic segmentation and features extracted from dynamic contrast‑enhanced and diffusion‑weighted MRI

Jiang,Xinhua; Xie,Fei; Liu,Lizhi; Peng,Yanxia; Cai,Hongmin; Li,Li

doi:10.3892/ol.2018.8805

Discrimination of malignant and benign breast masses using automatic segmentation and features extracted from dynamic contrast‑enhanced and diffusion‑weighted MRI

Authors:
- Xinhua Jiang
- Fei Xie
- Lizhi Liu
- Yanxia Peng
- Hongmin Cai
- Li Li
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Affiliations: Department of Medical Imaging, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat‑sen University Cancer Center, Guangzhou, Guangdong 510060, P.R. China, Department of Radiology, The Third Affiliated Hospital of Sun Yat‑sen University, Guangzhou, Guangdong 510630, P.R. China, Department of Computer Science, School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong 510006, P.R. China
Published online on: May 24, 2018 https://doi.org/10.3892/ol.2018.8805
Pages: 1521-1528
Copyright: © Jiang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Magnetic resonance imaging exhibits high sensitivity but low specificity for breast cancer. The present study aimed to investigate whether combining morphology, texture features and kinetic features with diffusion‑weighted imaging using quantitative analysis improves the accuracy of discriminating malignant from benign breast masses. In total, 104 and 171 malignant lesions in 205 women were included. Additionally, 13 texture and 11 morphology features were computed from each lesion using a semi‑automated segmentation method. To increase prediction accuracy, a newly designed classification model, difference‑weighted local hyperplane, was used for statistical analysis of the combined effects of the features for predicting lesion type. The mean apparent diffusion coefficient (ADC) value for each lesion was calculated. Diagnostic performances of morphology and texture features, kinetic features and ADC alone and the combination of them were evaluated using receiver operating characteristics analysis. Malignant lesions had lower mean ADCs than benign lesions. By using 10‑fold cross validation scheme, combined morphological and kinetic features achieved a diagnostic average accuracy of 0.87. Adding an ADC threshold of 1.37x10‑3 mm2/sec increased the overall averaged accuracy to 0.90. A multivariate model combining ADC values with 6 morphological and kinetic parameters best discriminated malignant from benign lesions. Incorporating morphology and texture features, kinetic features and ADC into a multivariable diagnostic model improves the discriminatory power of breast lesions.

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