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.
| Original language | English |
|---|---|
| Pages (from-to) | 3024-3035 |
| Number of pages | 12 |
| Journal | Medical Physics |
| Volume | 42 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - Jun 1 2015 |
| Externally published | Yes |
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Keywords
- breast cancer
- fibroadenoma
- gray-scale invariant features
- ranklet transform
- triple-negative breast cancer
ASJC Scopus subject areas
- Biophysics
- Radiology Nuclear Medicine and imaging
Cite this
Computer-aided diagnosis for distinguishing between triple-negative breast cancer and fibroadenomas based on ultrasound texture features. / Moon, Woo Kyung; Huang, Yao Sian; Lo, Chung Ming; Huang, Chiun Sheng; Bae, Min Sun; Kim, Won Hwa; Chen, Jeon Hor; Chang, Ruey Feng.
In: Medical Physics, Vol. 42, No. 6, 01.06.2015, p. 3024-3035.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Computer-aided diagnosis for distinguishing between triple-negative breast cancer and fibroadenomas based on ultrasound texture features
AU - Moon, Woo Kyung
AU - Huang, Yao Sian
AU - Lo, Chung Ming
AU - Huang, Chiun Sheng
AU - Bae, Min Sun
AU - Kim, Won Hwa
AU - Chen, Jeon Hor
AU - Chang, Ruey Feng
PY - 2015/6/1
Y1 - 2015/6/1
N2 - 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.
AB - 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.
KW - breast cancer
KW - fibroadenoma
KW - gray-scale invariant features
KW - ranklet transform
KW - triple-negative breast cancer
UR - http://www.scopus.com/inward/record.url?scp=84931266806&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84931266806&partnerID=8YFLogxK
U2 - 10.1118/1.4921123
DO - 10.1118/1.4921123
M3 - Article
C2 - 26127055
AN - SCOPUS:84931266806
VL - 42
SP - 3024
EP - 3035
JO - Medical Physics
JF - Medical Physics
SN - 0094-2405
IS - 6
ER -