The adaptive computer-aided diagnosis system based on tumor sizes for the classification of breast tumors detected at screening ultrasound

Woo Kyung Moon, I. Ling Chen, Jung Min Chang, Sung Ui Shin, Chung Ming Lo, Ruey Feng Chang

Research output: Contribution to journalArticle

15 Citations (Scopus)

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 1 cm 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 (<1 cm and ⩾1 cm) 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 1 cm, 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.

Original languageEnglish
Pages (from-to)70-77
Number of pages8
JournalUltrasonics
Volume76
DOIs
Publication statusPublished - Apr 1 2017

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Keywords

  • Breast cancer
  • Computer-aided diagnosis
  • Screening ultrasound

ASJC Scopus subject areas

  • Medicine(all)
  • Acoustics and Ultrasonics

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