Computer-aided diagnosis of breast masses using quantified BI-RADS findings
Journal
Computer Methods and Programs in Biomedicine
Journal Volume
111
Journal Issue
1
Pages
84-92
Date Issued
2013
Author(s)
Abstract
The information from radiologists was utilized in the proposed computer-aided diagnosis (CAD) for breast tumor classification. The ultrasound (US) database used in this study contained 166 benign and 78 malignant masses. For each mass, six quantitative feature sets were used to describe the radiologists' grading of six Breast Imaging Reporting and Data System (BI-RADS) categories including shape, orientation, margins, lesion boundary, echo pattern, and posterior acoustic features on breast US. The descriptive abilities were between 76% and 82% and the predicted descriptors were then used for tumor classification. Using receiver operating characteristic curve for evaluation, the area under curve (AUC) of the proposed CAD was slightly better than that of a conventional CAD based on the combination of all quantitative features (0.96 vs. 0.93, p= 0.18). The partial AUC over 90% sensitivity of the proposed CAD was significantly better than that of the conventional CAD (0.90 vs. 0.76, p< 0.05). In conclusion, the computer-aided analysis with qualitative information from radiologists showed a promising result for breast tumor classification. ? 2013 Elsevier Ireland Ltd.
Subjects
Breast cancer; Breast Imaging Reporting and Data System; Computer-assisted diagnosis; Ultrasound
SDGs
Other Subjects
Breast Cancer; Breast imaging reporting and data systems; Breast tumor classifications; Computer assisted diagnosis; Qualitative information; Quantitative features; Receiver operating characteristic curves; Tumor classification; Computer aided analysis; Medical imaging; Tumors; Ultrasonics; Computer aided diagnosis; article; benign tumor; breast tumor; cancer classification; computer aided diagnosis; diagnosis; human; major clinical study; sensitivity analysis; statistical analysis; ultrasound; Adult; Aged; Algorithms; Breast Neoplasms; Diagnosis, Computer-Assisted; Female; Fibroadenoma; Fibrocystic Breast Disease; Humans; Middle Aged; Retrospective Studies; Young Adult
Type
journal article