Artificial Intelligence in Mammography-Based Breast Cancer Screening
NCT ID: NCT04156880
Last Updated: 2024-02-07
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.
WITHDRAWN
OBSERVATIONAL
2020-07-01
2023-12-31
Brief Summary
Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.
As the corner stone of BC screening, mammography is recognized as one of useful imaging modalities to reduce BC mortality, by virtue of early detection of BC. However, mammography interpretation is inherently subjective assessment, and prone to overdiagnosis.
In recent years, artificial intelligence (AI)-Computer Aided Diagnosis (CAD) systems, characterized by embedded deep-learning algorithms, have entered into the field of BC screening as an aid for radiologist, with purpose to optimize conventional CAD system with weakness of hand-crafted features extraction. For now, stand-alone performance of novel AI-CAD tools have demonstrated promising accuracy and efficiency in BC diagnosis, largely attributed to utilization of convolution neural network(CNNs), and some of them have already achieved radiologist-like level. On the other hand, radiologists' performance on BC screening has shown to be enhanced, by leveraging AI-CAD system as decision support tool. As increasing implementation of commercial AI-CAD system, robust evaluation of its usefulness and cost-effectiveness in clinical circumstances should be undertaken in scenarios mimicking real life before broad adoption, like other emerging and promising technologies. This requires to validate AI-CAD systems in BC screening on multiple, diverse and representative datasets and also to estimate the interface between reader and system. This proposed study seeks to investigate the breast cancer diagnostic performance of AI-CAD system used for reading mammograms. In this work, we will employ a commercially available AI-CAD tool based on deep-learning algorithms (IBM Watson Imaging AI Solution) to identify and characterize the suspicious breast lesions on mammograms. The potential cancer lesions can be labeled and their mammographic features and malignancy probability will be automatically reported. After AI post-processing, we shall further carry out statistical analysis to determine the accuracy of AI-CAD system for BC risk prediction.
Related Clinical Trials
Explore similar clinical trials based on study characteristics and research focus.
AI-Based Self-Supervised Learning Model Using Non-Contrast Breast MRI for Early Screening and Clinical Utility Evaluation
NCT07205276
Artificial Intelligence Model-Assisted Accurate Diagnosis of Early-Stage Breast Cancer
NCT07063667
Using Deep Learning Methods to Analyze Automated Breast Ultrasound and Hand-held Ultrasound Images, to Establish a Diagnosis, Therapy Assessment and Prognosis Prediction Model of Breast Cancer.
NCT04270032
Multi-center Study of Deep Learning AI in Breast Mass
NCT05443672
Remote Breast Cancer Screening Study
NCT04527510
Detailed Description
Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.
Conditions
See the medical conditions and disease areas that this research is targeting or investigating.
Study Design
Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.
COHORT
RETROSPECTIVE
Interventions
Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.
mammography
standard mammography including craniocaudal (CC) and mediolateral oblique (MLO) views
Eligibility Criteria
Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.
Inclusion Criteria
* Histopathology-proven diagnosis is available for patients with breast malignancy, including invasive breast cancer, carcinoma in situ, and borderline lesion et al.
* As reference standard of benign nature, results from pathology or clinical long-term follow-up (\>=2 years) examinations are available for cases without breast malignancy.
Exclusion Criteria
* Patients without available pathologic diagnosis or long-term follow-up (\>=2 years) examinations.
* Patients who had undergone breast surgical intervention (e.g. lumpectomy and mammoplasty) prior to first mammography.
* Patients diagnosed with other kinds of malignancy, concurrent with metastasis or infiltration/invasion to breast.
FEMALE
Yes
Sponsors
Meet the organizations funding or collaborating on the study and learn about their roles.
IBM China/Hong Kong Limited
UNKNOWN
Chinese University of Hong Kong
OTHER
Responsible Party
Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.
Professor Winnie W.C. Chu
Professor
Locations
Explore where the study is taking place and check the recruitment status at each participating site.
The Chinese University of Hong Kong, Prince of Wale Hospital
Hong Kong, Shatin, Hong Kong
Countries
Review the countries where the study has at least one active or historical site.
Other Identifiers
Review additional registry numbers or institutional identifiers associated with this trial.
2019.629
Identifier Type: -
Identifier Source: org_study_id
More Related Trials
Additional clinical trials that may be relevant based on similarity analysis.