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Development of Artificial Intelligence System for Detection and Diagnosis of Breast Lesion Using Mammography

24. juli 2021 opdateret af: 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.

Studieoversigt

Status

Afsluttet

Betingelser

Intervention / Behandling

Detaljeret beskrivelse

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.

Undersøgelsestype

Observationel

Tilmelding (Faktiske)

5809

Kontakter og lokationer

Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.

Studiesteder

  • Kina
    • Beijing
      • Beijing, Beijing, Kina, 100142
        • Beijing Cancer Hospital
      • Beijing, Beijing, Kina
        • Beijing Chao Yang Women and Children's Health Hospital
      • Beijing, Beijing, Kina
        • Beijing Da Xing People's Hospital
      • Beijing, Beijing, Kina
        • Beijing Hang Tian Centre Hospital
      • Beijing, Beijing, Kina
        • Beijing Nan Jiao Cancer Hospital
      • Beijing, Beijing, Kina
        • Beijing Shi Jing Shan Hospital
      • Beijing, Beijing, Kina
        • Beijing Shun Yi Qu Hospital
      • Beijing, Beijing, Kina
        • Beijing Shun Yi Woman and Children Health Hospital

Deltagelseskriterier

Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.

Berettigelseskriterier

Aldre berettiget til at studere

18 år og ældre (Voksen, Ældre voksen)

Tager imod sunde frivillige

Ingen

Køn, der er berettiget til at studere

Kvinde

Prøveudtagningsmetode

Sandsynlighedsprøve

Studiebefolkning

Women with suspected Breast Lesion

Beskrivelse

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)

Studieplan

Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

Kohorter og interventioner

Gruppe / kohorte
Intervention / Behandling
mammography group
women who receives mammography because of suspected breast lesion(s)
Diagnostisk test: mammography
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.

Hvad måler undersøgelsen?

Primære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
benign-malignant diagnosis accuracy
Tidsramme: from the first mammography to pathological result obtained(an average of 3 weeks if mammography BI-RADS 4 or 5 obtained)
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.
from the first mammography to pathological result obtained(an average of 3 weeks if mammography BI-RADS 4 or 5 obtained)
benign-malignant diagnosis accuracy
Tidsramme: from the first mammography to 2-year-after mammography
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
from the first mammography to 2-year-after mammography

Sekundære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
lesion detection accuracy
Tidsramme: from the first mammography to radiologist diagnosis (within 3 days after the mammography taken)
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
from the first mammography to radiologist diagnosis (within 3 days after the mammography taken)

Samarbejdspartnere og efterforskere

Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.

Sponsor

Peking University Cancer Hospital & Institute

Samarbejdspartnere

Peking University

Efterforskere

  • Studiestol: Ying-Shi Sun, Professor, Peking University Cancer Hospital & Institute

Datoer for undersøgelser

Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.

Studer store datoer

Studiestart (Faktiske)

5. april 2018

Primær færdiggørelse (Faktiske)

4. maj 2020

Studieafslutning (Faktiske)

4. maj 2020

Datoer for studieregistrering

Først indsendt

17. april 2018

Først indsendt, der opfyldte QC-kriterier

12. oktober 2018

Først opslået (Faktiske)

17. oktober 2018

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

27. juli 2021

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

24. juli 2021

Sidst verificeret

1. juli 2021

Mere information

Begreber relateret til denne undersøgelse

Andre undersøgelses-id-numre

  • BCA-AI

Plan for individuelle deltagerdata (IPD)

Planlægger du at dele individuelle deltagerdata (IPD)?

INGEN

Lægemiddel- og udstyrsoplysninger, undersøgelsesdokumenter

Studerer et amerikansk FDA-reguleret lægemiddelprodukt

Ingen

Studerer et amerikansk FDA-reguleret enhedsprodukt

Ingen

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Kliniske forsøg med mammography

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Sponsorer og samarbejdspartnere

Medicinske tilstande

Narkotikainterventioner

CROs by country

CROs in Gabon

Betingelser

Sjældne sygdomme

Narkotikainterventioner

Kosttilskud

Sponsor / samarbejdspartnere

Placeringer

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