Ultrasonic automatic classification algorithm in the diagnosis of breast masses
10.3760/cma.j.cn131148-20220415-00265
- VernacularTitle:应用超声自动分类算法辅助乳腺肿块诊断的研究
- Author:
Ying WANG
1
;
Ruolan ZHAO
;
Xiaofang WU
;
Jiawei TIAN
Author Information
1. 河北医科大学第二医院腺体外科，石家庄 050000
- Keywords:
Ultrasonography;
Computer-aided diagnosis;
Breast mass;
Classification
- From:
Chinese Journal of Ultrasonography
2022;31(12):1065-1070
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the clinical effectiveness of an automatic computer-aided diagnosis(CAD) method in benign and malignant breast masses discrimination.Methods:The ultrasound images of 539 patients from the Second Hospital of Harbin Medical University and the Second Hospital of Hebei Medical University between 2012 to 2019 were analyzed retrospectively. According to the fifth Breast Imaging Reporting and Data System (BI-RADS), four breast radiologists first sent the case into a BI-RADS category with the original ultrasound image. Then with the CAD result, radiologists gave a category again. Pathology results and clinical data were not available to the radiologists during the diagnosis process. The histological and follow-up results were used as the gold standard. The accuracy of CAD automatic classification, radiologists′ diagnosis before and after CAD application were compared using the ROC curves. The accuracy, sensitivity and specificity of the diagnosis were also calculated.Results:The classification algorithm has a good performance in benign and malignant breast masses discrimination.When the cutoff value was 0.495, the accuracy, sensitivity and specificity were 0.878, 0.868 and 0.886 respectively. When the cutoff value was 0.203, the sensitivity and specificity was 0.981 and 0.337 respectively. With the CAD method, the radiologists improved their diagnostic performance. The total area under the ROC curve for the four radiologists increased from 0.775 to 0.871( P<0.001). The total sensitivity increased from 0.786 to 0.842, and the specificity increased from 0.681 to 0.813. Conclusions:The automatic classification algorithm in this study provides quantitative reference for doctors′ diagnosis. It has the potential to improve junior radiologists′ diagnostic performance in differentiating benign and malignant breast masses.
