Artificial Intelligence in Mammography-Based Breast Cancer Screening
Study Purpose
Breast cancer (BC) is the most common cancer among women in worldwide and the second leading cause of cancer-related death. 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.
Recruitment Criteria
Accepts Healthy Volunteers
Healthy volunteers are participants who do not have a disease or condition, or related conditions or symptoms |
Yes |
Study Type
An interventional clinical study is where participants are assigned to receive one or more interventions (or no intervention) so that researchers can evaluate the effects of the interventions on biomedical or health-related outcomes. An observational clinical study is where participants identified as belonging to study groups are assessed for biomedical or health outcomes. Searching Both is inclusive of interventional and observational studies. |
Observational |
Eligible Ages | N/A and Over |
Gender | Female |
Trial Details
Trial ID:
This trial id was obtained from ClinicalTrials.gov, a service of the U.S. National Institutes of Health, providing information on publicly and privately supported clinical studies of human participants with locations in all 50 States and in 196 countries. |
NCT04156880 |
Phase
Phase 1: Studies that emphasize safety and how the drug is metabolized and excreted in humans. Phase 2: Studies that gather preliminary data on effectiveness (whether the drug works in people who have a certain disease or condition) and additional safety data. Phase 3: Studies that gather more information about safety and effectiveness by studying different populations and different dosages and by using the drug in combination with other drugs. Phase 4: Studies occurring after FDA has approved a drug for marketing, efficacy, or optimal use. |
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Lead Sponsor
The sponsor is the organization or person who oversees the clinical study and is responsible for analyzing the study data. |
Chinese University of Hong Kong |
Principal Investigator
The person who is responsible for the scientific and technical direction of the entire clinical study. |
N/A |
Principal Investigator Affiliation | N/A |
Agency Class
Category of organization(s) involved as sponsor (and collaborator) supporting the trial. |
Other |
Overall Status | Not yet recruiting |
Countries | |
Conditions
The disease, disorder, syndrome, illness, or injury that is being studied. |
Breast Cancer |
Contact Information
This trial has no sites locations listed at this time. If you are interested in learning more, you can contact the trial's primary contact:
Chiu Wing CHU
For additional contact information, you can also visit the trial on clinicaltrials.gov.