Clinical Validation of an Artificial Intelligence Algorithm to Help Interpret Mammograms

NCT05640011 | Completed

• 1 other identifier: 7964
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

Brief Summary

This aims to clinically validate, on a large population, a tumor detection aid software which has already been trained on a representative French population (from several hospital centers and liberals from several departments in the west and east of France). This population consists of 1000 patients who have been treated for breast cancer (histologically proven by breast biopsy) and whose investigators have mammograms performed at the time of diagnosis. The control population consists of the unaffected breast of each patient (with the exception of the rare cases of bilateral cancers). This innovative software has the main feature of recognizing healthy breast tissue, allowing the radiologist to focus on breast tissue at risk, improving the management of medical time and the management of "difficult" files.

Conditions

Breast Cancer

Keywords

Breast Cancer; Mammography; Artificial intelligence; Tumor detection on mammograms

Outcome Measures

Primary Outcomes (1)

• Evaluate the diagnostic performance (sensitivity, specificity) for operator tumor detection assisted by mammographic diagnostic aid software, and compare it to the diagnostic performance of an unassisted operator.

  This assessment is based on contouring the tumor area on mammograms.

  Files analysed retrospectively from January 01, 2010 to January 01, 2020 will be examined

Eligibility Criteria

• Age: 40 Years - 75 Years
• Sex: female
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adult woman (40 to 75 years old) treated at the University Hospitals of Strasbourg between 2010 and 2020 for breast cancer

You may qualify if:

• Adult woman (40 to 75 years old)
• Treatment at the University Hospitals of Strasbourg between 2010 and 2020 for breast cancer
• including mammography and histological evidence available
• Patient who has already given her consent for the reuse of her anonymous data for research purposes

You may not qualify if:

• Woman who expressed her opposition to participating in the study

Sponsors & Collaborators

• University Hospital, Strasbourg, France: lead

Study Sites (1)

Service de Radiologie 1 - CHU de Strasbourg - France

Strasbourg, 67091, France

Location

MeSH Terms

Conditions

Breast Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 25, 2022
• First Posted: December 7, 2022
• Study Start: April 20, 2021
• Primary Completion: March 20, 2023
• Study Completion: May 20, 2023
• Last Updated: November 8, 2023

Record last verified: 2022-11

Locations

FR (1)