title: The adaptive computer-aided diagnosis system based on tumor sizes for the classification of breast tumors detected at screening ultrasound.
abstract: Screening ultrasound (US) is increasingly used as a supplement to mammography in women with dense breasts, and more than 80% of cancers detected by US alone are 1cm or smaller. An adaptive computer-aided diagnosis (CAD) system based on tumor size was proposed to classify breast tumors detected at screening US images using quantitative morphological and textural features. In the present study, a database containing 156 tumors (78 benign and 78 malignant) was separated into two subsets of different tumor sizes (<1cm and ⩾1cm) to explore the improvement in the performance of the CAD system. After adaptation, the accuracies, sensitivities, specificities and Az values of the CAD for the entire database increased from 73.1% (114/156), 73.1% (57/78), 73.1% (57/78), and 0.790 to 81.4% (127/156), 83.3% (65/78), 79.5% (62/78), and 0.852, respectively. In the data subset of tumors larger than 1cm, the performance improved from 66.2% (51/77), 68.3% (28/41), 63.9% (23/36), and 0.703 to 81.8% (63/77), 85.4% (35/41), 77.8% (28/36), and 0.855, respectively. The proposed CAD system can be helpful to classify breast tumors detected at screening US.
authors.affiliation: Department of Radiology, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea.
authors.forename: Woo Kyung
authors.initials: WK
authors.lastname: Moon
authors.affiliation: Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
authors.forename: I-Ling
authors.initials: IL
authors.lastname: Chen
authors.affiliation: Department of Radiology, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea.
authors.forename: Jung Min
authors.initials: JM
authors.lastname: Chang
authors.affiliation: Department of Radiology, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea.
authors.forename: Sung Ui
authors.initials: SU
authors.lastname: Shin
authors.affiliation: Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan. Electronic address: buddylo@tmu.edu.tw.
authors.forename: Chung-Ming
authors.initials: CM
authors.lastname: Lo
authors.affiliation: Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan. Electronic address: rfchang@csie.ntu.edu.tw.
authors.forename: Ruey-Feng
authors.initials: RF
authors.lastname: Chang
mesh_terms: D000328:Adult; D000368:Aged; D001707:Biopsy, Needle; D000071060:Breast Density; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D008875:Middle Aged; D012680:Sensitivity and Specificity; D016217:Ultrasonography, Mammary
keywords: Breast cancer; Computer-aided diagnosis; Screening ultrasound