Moon Woo Kyung, Chen I-Ling, Chang Jung Min, Shin Sung Ui, Lo Chung-Ming, Chang Ruey-Feng
Department of Radiology, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea.
Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
Ultrasonics. 2017 Apr;76:70-77. doi: 10.1016/j.ultras.2016.12.017. Epub 2016 Dec 28.
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.
超声筛查（US）越来越多地被用作乳腺致密女性乳腺钼靶检查的补充手段，仅通过超声检测出的癌症中，超过80%的肿瘤直径为1厘米或更小。提出了一种基于肿瘤大小的自适应计算机辅助诊断（CAD）系统，利用定量形态学和纹理特征对超声筛查图像中检测到的乳腺肿瘤进行分类。在本研究中，一个包含156个肿瘤（78个良性和78个恶性）的数据库被分为两个不同肿瘤大小（<1厘米和⩾1厘米）的子集，以探讨CAD系统性能的提升。经过自适应调整后，整个数据库CAD的准确率、灵敏度、特异性和Az值分别从73.1%（114/156）、73.1%（57/78）、73.1%（57/78）和0.790提高到81.4%（127/156）、83.3%（65/78）、79.5%（62/78）和0.852。在肿瘤大于1厘米的数据子集中，性能分别从66.2%（51/77）、68.3%（28/41）、63.9%（23/36）和0.703提高到81.8%（63/77）、85.4%（35/41）、77.8%（28/36）和0.855。所提出的CAD系统有助于对超声筛查中检测到的乳腺肿瘤进行分类。
