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A case-oriented web-based training system for breast cancer diagnosis.
Huang, Qinghua; Huang, Xianhai; Liu, Longzhong; Lin, Yidi; Long, Xingzhang; Li, Xuelong.
Afiliación
  • Huang Q; 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 Engineerin
  • Huang X; School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China.
  • Liu L; Department of Ultrasound, The Cancer Center of Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong, China. Electronic address: liulzh@sysucc.org.cn.
  • Lin Y; Department of Ultrasound, The Cancer Center of Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong, China.
  • Long X; Department of Ultrasound, The Cancer Center of Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong, China.
  • Li X; Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, Shaanxi, China.
Comput Methods Programs Biomed ; 156: 73-83, 2018 Mar.
Article en En | MEDLINE | ID: mdl-29428078
ABSTRACT
BACKGROUND AND

OBJECTIVE:

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.

METHODS:

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.

RESULTS:

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).

CONCLUSIONS:

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.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Radiología / Neoplasias de la Mama / Ultrasonografía Mamaria Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies Límite: Female / Humans Idioma: En Revista: Comput Methods Programs Biomed Asunto de la revista: INFORMATICA MEDICA Año: 2018 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Radiología / Neoplasias de la Mama / Ultrasonografía Mamaria Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies Límite: Female / Humans Idioma: En Revista: Comput Methods Programs Biomed Asunto de la revista: INFORMATICA MEDICA Año: 2018 Tipo del documento: Article