Development of Artificial Intelligence System for Detection and Diagnosis of Breast Lesion Using Mammography
24 de julio de 2021 actualizado por: Sun Ying-Shi, Peking University Cancer Hospital & Institute
This project aims to establish a comprehensive artificial intelligence system for detecting and qualitative diagnosing breast lesions.
Mammary images will be used to construct a diagnosis method based on deep learning.
The system is proposed to automatically analyze the type of mammary glands, automatically identify and mark all breast lesions on the mammography images, provide the malignancy probability judgment of the lesions, the BI-RADS classification and the clinical suggestion, and also automatically generate the structured diagnosis report.
Descripción general del estudio
Estado
Terminado
Condiciones
Intervención / Tratamiento
Descripción detallada
This is a multi-center study.The project contains a retrospective part(3000 samples anticipated) and a prospective part(7000 samples anticipated).
In the retrospective part, investigators collected subjects with mammary images to design the deep learning method and construct a detective and diagnostic model for breast lesions.
In the prospective part, investigators validate the accuracy of the constructed deep learning method, and established artificial intelligence system focusing on mammary diagnosis.
Investigators will also explore the application pattern of the artificial intelligence system in clinical practice.
Tipo de estudio
De observación
Inscripción (Actual)
5809
Contactos y Ubicaciones
Esta sección proporciona los datos de contacto de quienes realizan el estudio e información sobre dónde se lleva a cabo este estudio.
Ubicaciones de estudio
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Beijing
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Beijing, Beijing, Porcelana, 100142
- Beijing Cancer Hospital
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Beijing, Beijing, Porcelana
- Beijing Chao Yang Women and Children's Health Hospital
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Beijing, Beijing, Porcelana
- Beijing Da Xing People's Hospital
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Beijing, Beijing, Porcelana
- Beijing Hang Tian Centre Hospital
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Beijing, Beijing, Porcelana
- Beijing Nan Jiao Cancer Hospital
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Beijing, Beijing, Porcelana
- Beijing Shi Jing Shan Hospital
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Beijing, Beijing, Porcelana
- Beijing Shun Yi Qu Hospital
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Beijing, Beijing, Porcelana
- Beijing Shun Yi Woman and Children Health Hospital
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Criterios de participación
Los investigadores buscan personas que se ajusten a una determinada descripción, denominada criterio de elegibilidad. Algunos ejemplos de estos criterios son el estado de salud general de una persona o tratamientos previos.
Criterio de elegibilidad
Edades elegibles para estudiar
18 años y mayores (Adulto, Adulto Mayor)
Acepta Voluntarios Saludables
No
Géneros elegibles para el estudio
Femenino
Método de muestreo
Muestra de probabilidad
Población de estudio
Women with suspected Breast Lesion
Descripción
Inclusion Criteria:
- the X-ray images of the breast were complete
- the results of pathological diagnosis or more than 2 years of mammography follow-up were available
- subject signs informed consent(this item was only for prospective study cases)
Exclusion Criteria:
- there exists pathological diagnosis of breast lesions when receiving mammography
- there lacks pathological diagnosis or 2 years of mammography follow-up
- subject withdraws(this item was only for prospective study cases)
Plan de estudios
Esta sección proporciona detalles del plan de estudio, incluido cómo está diseñado el estudio y qué mide el estudio.
¿Cómo está diseñado el estudio?
Detalles de diseño
Cohortes e Intervenciones
Grupo / Cohorte |
Intervención / Tratamiento |
|---|---|
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mammography group
women who receives mammography because of suspected breast lesion(s)
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When a woman comes to the clinic to receive mammography.
Then a radiologist will give a BI-RADS classification after reviewing the images.
If a BI-RADS 4/5 is obtained, the woman will receive pathological biopsy to ensure there is a benign or malignant lesion.
If a BI-RADS 3 is obtained, the woman will be followed up by a half-year interval until two year after the first mammography.
At each follow up, she will receive mammography.
If a BI-RADS 4/5 is obtained at follow up, she will receive pathological biopsy; if a BI-RADS 1/2/3 is obtained at follow up, she will be followed up by a half-year interval until two year.
If a BI-RADS 1/2 is obtained at the first mammography, the woman will receive a second mammography after two year.
During the study period, breast examination and results will be recorded for every subject.
Radiologists will give the diagnosis with and without AI support.
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¿Qué mide el estudio?
Medidas de resultado primarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
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benign-malignant diagnosis accuracy
Periodo de tiempo: from the first mammography to pathological result obtained(an average of 3 weeks if mammography BI-RADS 4 or 5 obtained)
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the accuracy of the AI model, radiogist with AI support, radiologist alone for binary diagnosis of a benign or malignant breast lesion according to pathology.
If either one mammography of BI-RADS 4/5 in the first examination or during the two year' follow up examination is obtained,a pathological examination is performed, the lesion is judged benign or malignant according to pathological results.
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from the first mammography to pathological result obtained(an average of 3 weeks if mammography BI-RADS 4 or 5 obtained)
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benign-malignant diagnosis accuracy
Periodo de tiempo: from the first mammography to 2-year-after mammography
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the accuracy of the AI model, radiogist with AI support, radiologist alone for binary diagnosis of a benign or malignant breast lesion according to follow up.
If a 2-year mammography of BI-RADS 1/2/3 is obtained, the lesion is considered benign.
If either one mammography of BI-RADS 4/5 during the two year is obtained,a pathological examination is performed to ensure the benign or malignant lesion
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from the first mammography to 2-year-after mammography
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Medidas de resultado secundarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
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lesion detection accuracy
Periodo de tiempo: from the first mammography to radiologist diagnosis (within 3 days after the mammography taken)
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the detection rate of the constructed deep learning method for detecting benign or malignant breast lesion according to radiologist's subjective diagnosis or follow up as reference.
If a radiologist suggests existence of a lesion at the first mammography or at each follow-up mammography during the 2-year period, it is considered that a lesion exists
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from the first mammography to radiologist diagnosis (within 3 days after the mammography taken)
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Colaboradores e Investigadores
Aquí es donde encontrará personas y organizaciones involucradas en este estudio.
Patrocinador
Colaboradores
Investigadores
- Silla de estudio: Ying-Shi Sun, Professor, Peking University Cancer Hospital & Institute
Fechas de registro del estudio
Estas fechas rastrean el progreso del registro del estudio y los envíos de resultados resumidos a ClinicalTrials.gov. Los registros del estudio y los resultados informados son revisados por la Biblioteca Nacional de Medicina (NLM) para asegurarse de que cumplan con los estándares de control de calidad específicos antes de publicarlos en el sitio web público.
Fechas importantes del estudio
Inicio del estudio (Actual)
5 de abril de 2018
Finalización primaria (Actual)
4 de mayo de 2020
Finalización del estudio (Actual)
4 de mayo de 2020
Fechas de registro del estudio
Enviado por primera vez
17 de abril de 2018
Primero enviado que cumplió con los criterios de control de calidad
12 de octubre de 2018
Publicado por primera vez (Actual)
17 de octubre de 2018
Actualizaciones de registros de estudio
Última actualización publicada (Actual)
27 de julio de 2021
Última actualización enviada que cumplió con los criterios de control de calidad
24 de julio de 2021
Última verificación
1 de julio de 2021
Más información
Términos relacionados con este estudio
Otros números de identificación del estudio
- BCA-AI
Plan de datos de participantes individuales (IPD)
¿Planea compartir datos de participantes individuales (IPD)?
NO
Información sobre medicamentos y dispositivos, documentos del estudio
Estudia un producto farmacéutico regulado por la FDA de EE. UU.
No
Estudia un producto de dispositivo regulado por la FDA de EE. UU.
No
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