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Open Access Highly Accessed Research article

Diagnostic assessment by dynamic contrast-enhanced and diffusion-weighted magnetic resonance in differentiation of breast lesions under different imaging protocols

Hongmin Cai1, Lizhi Liu2, Yanxia Peng3, Yaopan Wu2* and Li Li2*

Author Affiliations

1 School of Computer Science & Engineering, South China University of Technology, Guangzhou 510006, People’s Republic of China

2 Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Imaging Diagnosis and Interventional Center, Guangzhou 510060, People’s Republic of China

3 Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, People’s Republic of China

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BMC Cancer 2014, 14:366  doi:10.1186/1471-2407-14-366

Published: 24 May 2014

Abstract

Background

The apparent diffusion coefficient (ADC) is a highly diagnostic factor in discriminating malignant and benign breast masses in diffusion-weighted magnetic resonance imaging (DW-MRI). The combination of ADC and other pictorial characteristics has improved lesion type identification accuracy. The objective of this study was to reassess the findings on an independent patient group by changing the magnetic field from 1.5-Tesla to 3.0-Tesla.

Methods

This retrospective study consisted of a training group of 234 female patients, including 85 benign and 149 malignant lesions, imaged using 1.5-Tesla MRI, and a test group of 95 female patients, including 19 benign and 85 malignant lesions, imaged using 3.0-Tesla MRI. The lesion of interest was segmented from the raw image and four sets of measurements describing the morphology, kinetics, DW-MRI, and texture of the pictorial properties of each lesion were obtained. Each lesion was characterized by 28 features in total. Three classical machine-learning algorithms were used to build prediction models on the training group, which evaluated the prognostic performance of the multi-sided features in three scenarios. To reduce information redundancy, five highly diagnostic factors were selected to obtain a compact yet informative characterization of the lesion status.

Results

Three classification models were built on the training of 1.5-Tesla patients and were tested on the independent 3.0-Tesla test group. The following results were found. i) Characterization of breast masses in a multi-sided way dramatically increased prediction performance. The usage of all features gave a higher performance in both sensitivity and specificity than any individual feature groups or their combinations. ii) ADC was a highly effective factor in improving the sensitivity in discriminating malignant from benign masses. iii) Five features, namely ADC, Sum Average, Entropy, Elongation, and Sum Variance, were selected to achieve the highest performance in diagnosis of the 3.0-Tesla patient group.

Conclusions

The combination of ADC and other multi-sided characteristics can increase the capability of discriminating malignant and benign breast lesions, even under different imaging protocols. The selected compact feature subsets achieved a high diagnostic performance and thus are promising in clinical applications for discriminating lesion type and for personalized treatment planning.

Keywords:
Diffusion-weighted imaging; Breast mass; Quantitative morphology and texture features; Computer-aided diagnosis; Classifier; Feature subset selection