|Title:||Computer-aided diagnosis for distinguishing between triple-negative breast cancer and fibroadenomas based on ultrasound texture features||Authors:||Moon, W.K.
|Issue Date:||2015||Journal Volume:||42||Journal Issue:||6||Start page/Pages:||3024-3035||Source:||Medical Physics||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. ? 2015 American Association of Physicists in Medicine.
|DOI:||10.1118/1.4921123||metadata.dc.subject.other:||Diseases; Numerical methods; Statistical methods; Support vector machines; Textures; Tumors; Ultrasonics; Breast Cancer; fibroadenoma; Gray scale; Gray-level co-occurrence matrix; Leave-one-out cross validations; Linear Support Vector Machines; Receiver operating characteristic curve analysis; Triple-negative breast cancers; Computer aided diagnosis; adult; Article; breast fibroadenoma; computer assisted diagnosis; echomammography; female; human; major clinical study; receiver operating characteristic; retrospective study; support vector machine; triple negative breast cancer; tumor volume; aged; computer assisted diagnosis; differential diagnosis; echography; fibroadenoma; image processing; middle aged; procedures; triple negative breast cancer; Adult; Aged; Diagnosis, Computer-Assisted; Diagnosis, Differential; Female; Fibroadenoma; Humans; Image Processing, Computer-Assisted; Middle Aged; Support Vector Machine; Triple Negative Breast Neoplasms
|Appears in Collections:||醫學系|
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