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

24 juli 2021 uppdaterad av: 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.

Studieöversikt

Status

Avslutad

Betingelser

Intervention / Behandling

Detaljerad beskrivning

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.

Studietyp

Observationell

Inskrivning (Faktisk)

5809

Kontakter och platser

Det här avsnittet innehåller kontaktuppgifter för dem som genomför studien och information om var denna studie genomförs.

Studieorter

  • 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

Deltagandekriterier

Forskare letar efter personer som passar en viss beskrivning, så kallade behörighetskriterier. Några exempel på dessa kriterier är en persons allmänna hälsotillstånd eller tidigare behandlingar.

Urvalskriterier

Åldrar som är berättigade till studier

18 år och äldre (Vuxen, Äldre vuxen)

Tar emot friska volontärer

Nej

Kön som är behöriga för studier

Kvinna

Testmetod

Sannolikhetsprov

Studera befolkning

Women with suspected Breast Lesion

Beskrivning

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

Det här avsnittet ger detaljer om studieplanen, inklusive hur studien är utformad och vad studien mäter.

Hur är studien utformad?

Designdetaljer

Kohorter och interventioner

Grupp / Kohort
Intervention / Behandling
mammography group
women who receives mammography because of suspected breast lesion(s)
Diagnostiskt 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.

Vad mäter studien?

Primära resultatmått

Resultatmått
Åtgärdsbeskrivning
Tidsram
benign-malignant diagnosis accuracy
Tidsram: 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
Tidsram: 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

Sekundära resultatmått

Resultatmått
Åtgärdsbeskrivning
Tidsram
lesion detection accuracy
Tidsram: 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)

Samarbetspartners och utredare

Det är här du hittar personer och organisationer som är involverade i denna studie.

Sponsor

Peking University Cancer Hospital & Institute

Samarbetspartners

Peking University

Utredare

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

Studieavstämningsdatum

Dessa datum spårar framstegen för inlämningar av studieposter och sammanfattande resultat till ClinicalTrials.gov. Studieposter och rapporterade resultat granskas av National Library of Medicine (NLM) för att säkerställa att de uppfyller specifika kvalitetskontrollstandarder innan de publiceras på den offentliga webbplatsen.

Studera stora datum

Studiestart (Faktisk)

5 april 2018

Primärt slutförande (Faktisk)

4 maj 2020

Avslutad studie (Faktisk)

4 maj 2020

Studieregistreringsdatum

Först inskickad

17 april 2018

Först inskickad som uppfyllde QC-kriterierna

12 oktober 2018

Första postat (Faktisk)

17 oktober 2018

Uppdateringar av studier

Senaste uppdatering publicerad (Faktisk)

27 juli 2021

Senaste inskickade uppdateringen som uppfyllde QC-kriterierna

24 juli 2021

Senast verifierad

1 juli 2021

Mer information

Termer relaterade till denna studie

Andra studie-ID-nummer

  • BCA-AI

Plan för individuella deltagardata (IPD)

Planerar du att dela individuella deltagardata (IPD)?

NEJ

Läkemedels- och apparatinformation, studiedokument

Studerar en amerikansk FDA-reglerad läkemedelsprodukt

Nej

Studerar en amerikansk FDA-reglerad produktprodukt

Nej

Denna information hämtades direkt från webbplatsen clinicaltrials.gov utan några ändringar. Om du har några önskemål om att ändra, ta bort eller uppdatera dina studieuppgifter, vänligen kontakta register@clinicaltrials.gov. Så snart en ändring har implementerats på clinicaltrials.gov, kommer denna att uppdateras automatiskt även på vår webbplats .

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