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Artificial Intelligence in Mammography-Based Breast Cancer Screening

NCT04156880 · Status: WITHDRAWN · Type: OBSERVATIONAL

Last updated 2024-02-07

No results posted yet for this study

Summary

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.

Conditions

Interventions

OTHER

mammography

standard mammography including craniocaudal (CC) and mediolateral oblique (MLO) views

Sponsors & Collaborators

  • IBM China/Hong Kong Limited

    collaborator UNKNOWN

  • Chinese University of Hong Kong

    lead OTHER

Eligibility

Sex
FEMALE
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2020-07-01
Primary Completion
2023-12-31
Completion
2023-12-31

Countries

  • Hong Kong

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04156880 on ClinicalTrials.gov