Computer-aided diagnosis for distinguishing between triple-negative breast cancer and fibroadenomas based on ultrasound texture features

Authors

  • Moon Woo Kyung,

    1. Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul 110-744, South Korea
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  • Huang Yao-Sian,

    1. Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan, Republic of China
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  • Lo Chung-Ming,

    1. Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan, Republic of China
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  • Huang Chiun-Sheng,

    1. Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 10041, Taiwan, Republic of China and Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan, Republic of China
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  • Bae Min Sun,

    1. Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul 110-744, South Korea
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  • Kim Won Hwa,

    1. Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul 110-744, South Korea
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  • Chen Jeon-Hor,

    1. Center for Functional Onco-Imaging and Department of Radiological Science, University of California, Irvine, California 92868 and Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung 82445, Taiwan, Republic of China
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  • Chang Ruey-Feng

    1. Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan, Republic of China and Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan, Republic of China
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    • a)

      Author to whom correspondence should be addressed. Electronic mail: rfchang@csie.ntu.edu.tw; Telephone: +886-2-33664888∼331; Fax: +886-2-23628167.


Abstract

Purpose:

Triple-negative breast cancer (TNBC), an aggressive subtype, is frequently misclassified as fibroadenoma due to benign morphologic features on breast ultrasound (US). This study aims to develop a computer-aided diagnosis (CAD) system based on texture features for distinguishing between TNBC and benign fibroadenomas in US images.

Methods:

US images of 169 pathology-proven tumors (mean size, 1.65 cm; range, 0.7–3.0 cm) composed of 84 benign fibroadenomas and 85 TNBC tumors are used in this study. After a tumor is segmented out using the level-set method, morphological, conventional texture, and multiresolution gray-scale invariant texture feature sets are computed using a best-fitting ellipse, gray-level co-occurrence matrices, and the ranklet transform, respectively. The linear support vector machine with leave-one-out cross-validation schema is used as a classifier, and the diagnostic performance is assessed with receiver operating characteristic curve analysis.

Results:

The Az values of the morphology, conventional texture, and multiresolution gray-scale invariant texture feature sets are 0.8470 [95% confidence intervals (CIs), 0.7826–0.8973], 0.8542 (95% CI, 0.7911–0.9030), and 0.9695 (95% CI, 0.9376–0.9865), respectively. The Az of the CAD system based on the combined feature sets is 0.9702 (95% CI, 0.9334–0.9882).

Conclusions:

The CAD system based on texture features extracted via the ranklet transform may be useful for improving the ability to discriminate between TNBC and benign fibroadenomas.

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