School of Electronics and Information, and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China; College of Information Engineering, Shenzhen University, Shenzhen 518060, China; School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China.
School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China.
Comput Methods Programs Biomed. 2018 Mar;156:73-83. doi: 10.1016/j.cmpb.2017.12.028. Epub 2017 Dec 23.
Breast cancer is still considered as the most common form of cancer as well as the leading causes of cancer deaths among women all over the world. We aim to provide a web-based breast ultrasound database for online training inexperienced radiologists and giving computer-assisted diagnostic information for detection and classification of the breast tumor.
We introduce a web database which stores breast ultrasound images from breast cancer patients as well as their diagnostic information. A web-based training system using a feature scoring scheme based on Breast Imaging Reporting and Data System (BI-RADS) US lexicon was designed. A computer-aided diagnosis (CAD) subsystem was developed to assist the radiologists to make scores on the BI-RADS features for an input case. The training system possesses 1669 scored cases, where 412 cases are benign and 1257 cases are malignant. It was tested by 31 users including 12 interns, 11 junior radiologists, and 8 experienced senior radiologists.
This online training system automatically creates case-based exercises to train and guide the newly employed or resident radiologists for the diagnosis of breast cancer using breast ultrasound images based on the BI-RADS. After the trainings, the interns and junior radiologists show significant improvement in the diagnosis of the breast tumor with ultrasound imaging (p-value < .05); meanwhile the senior radiologists show little improvement (p-value > .05).
The online training system can improve the capabilities of early-career radiologists in distinguishing between the benign and malignant lesions and reduce the misdiagnosis of breast cancer in a quick, convenient and effective manner.
乳腺癌仍然被认为是全世界女性最常见的癌症类型，也是癌症死亡的主要原因。我们旨在提供一个基于网络的乳房超声数据库，用于在线培训经验不足的放射科医生，并为乳房肿瘤的检测和分类提供计算机辅助诊断信息。
我们介绍了一个存储乳腺癌患者乳房超声图像及其诊断信息的基于网络的数据库。设计了一个基于乳腺影像报告和数据系统（BI-RADS）超声词汇的基于网络的培训系统，使用基于特征的评分方案。开发了计算机辅助诊断（CAD）子系统，以协助放射科医生对输入病例进行 BI-RADS 特征评分。该培训系统拥有 1669 个评分病例，其中 412 例为良性，1257 例为恶性。该系统由 31 名用户进行了测试，其中包括 12 名实习生、11 名初级放射科医生和 8 名经验丰富的高级放射科医生。
该在线培训系统可根据 BI-RADS 自动创建基于病例的练习，以培训和指导新入职或住院放射科医生使用乳房超声图像诊断乳腺癌。培训后，实习生和初级放射科医生在使用超声成像诊断乳房肿瘤方面的能力有了显著提高（p 值<0.05）；而高级放射科医生的提高则不显著（p 值>0.05）。
在线培训系统可以提高早期职业放射科医生在区分良性和恶性病变方面的能力，并以快速、方便和有效的方式降低乳腺癌的误诊率。
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