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Digital Mammography: Computer-Aided Breast Cancer Diagnosis

This study is ongoing, but not recruiting participants.
Information provided by (Responsible Party):
Heang-Ping Chan Ph.D, University of Michigan Identifier:
First received: August 7, 2008
Last updated: July 25, 2017
Last verified: July 2017
The purpose of this study is to develop computer programs to assist radiologists in finding breast cancer on mammograms and to compare the computer's accuracy of detecting cancers on direct digital and film mammograms.

Condition Intervention
Tumors, Breast Procedure: digital mammography

Study Type: Interventional
Study Design: Intervention Model: Single Group Assignment
Masking: None (Open Label)
Primary Purpose: Diagnostic
Official Title: Digital Mammography: Computer-Aided Breast Cancer Diagnosis

Resource links provided by NLM:

Further study details as provided by Heang-Ping Chan Ph.D, University of Michigan:

Primary Outcome Measures:
  • Using computer aided programs to assist in detection and characterization of breast lesions in digital mammography. [ Time Frame: Research scan will be completed at the time of scheduled clinical visit. ]

Estimated Enrollment: 500
Study Start Date: June 2000
Estimated Study Completion Date: March 2018
Estimated Primary Completion Date: August 2017 (Final data collection date for primary outcome measure)
Arms Assigned Interventions
Experimental: digital mammogram

Digital mammography is a non-invasive imaging technique to obtain an x-ray image of the breast.

Two-view digital mammogram of the breast with a lesion that has been recommended for biopsy during the subject's regular clinical care. The digital mammogram is then analyzed by a computer program.

Procedure: digital mammography
Using non-invasive digital mammography with computer aided programs to screen, detect and characterize breast lesions/cancer.

Detailed Description:
To develop a computer-aided diagnosis (CAD) system for full field digital mammography (FFDM) using advanced computer vision techniques and to evaluate the effects of CAD on interpretation of digital mammograms (DMs). This system will assist radiologists with the four most important areas in mammographic interpretation: (1) detection of masses, (2) classification of masses, (3) detection of microcalcifications, (4) classification of microcalcifications. The proposed approach is distinctly different from previous approaches in that image information from two-view and bilateral mammograms will be fused with that from the single-view mammogram to improve lesion detection and characterization.

Ages Eligible for Study:   18 Years to 80 Years   (Adult, Senior)
Sexes Eligible for Study:   Female
Accepts Healthy Volunteers:   No

Inclusion Criteria:

  • Females who have been scheduled for mammographic exams.
  • Females who have been recommended for work-up or biopsy due to a suspicious finding on their mammogram.
  • Females who can give informed consent.

Exclusion Criteria:

  • No subject under 18 years of age
  Contacts and Locations
Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the Contacts provided below. For general information, see Learn About Clinical Studies.

Please refer to this study by its identifier: NCT00732433

United States, Michigan
University of Michigan Health System
Ann Arbor, Michigan, United States, 48109
Sponsors and Collaborators
University of Michigan
Principal Investigator: Heang-Ping Chan, Ph.D. University of Michigan
  More Information

Responsible Party: Heang-Ping Chan Ph.D, Principal Investigator, University of Michigan Identifier: NCT00732433     History of Changes
Other Study ID Numbers: 2000-0227
Study First Received: August 7, 2008
Last Updated: July 25, 2017

Additional relevant MeSH terms:
Breast Neoplasms
Neoplasms by Site
Breast Diseases
Skin Diseases processed this record on August 22, 2017