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

다국어 초록 (Multilingual Abstract)

<P><B>Abstract</B></P> <P>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 <I>Az</I> 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.</P> <P><B>Highlights</B></P> <P> <UL> <LI> An adaptive filtering is introduced into a computer-aided diagnosis (CAD) system to highlight the characteristic of breast tumors detected in screening ultrasound (US). </LI> <LI> The adaptive filtering enhances the CAD system to emphasize the meaningfulness of tumor size, allows a new regularization technique to be embedded, and increasing the classification accuracy. </LI> <LI> For the classification between malignant and benign tumors with two kinds of tumor size ( < 1cm and ⩾ 1cm), especially in the tumors larger or equal to 1cm, the proposed CAD was more robust than conventional CAD. </LI> <LI> The CAD system using various quantitative US features would provide a promising diagnostic suggestion for classifying the breast tumors detected at screening US images. </LI> </UL> </P>