Artificial Intelligence for Automated Diagnosis of Breast Cancer
AICAMAMMELLA
Study of an Artificial Intelligence Algorithm for the Classification of Digital Tomosynthesis Breast Images for Automated Breast Cancer Diagnosis
1 other identifier
observational
200
1 country
2
Brief Summary
Mammography is a two-dimensional imaging technique which involves the tissues overlapping under the projective image; dense glandular tissue above or below the lesion can reduce the visibility of the lesion. The trouble could be the interpretation of the image obtained which may lead to the inability to visualize a fist stage cancer and the probability that to a healthy person will be diagnosed a pathology that is not present (false positive). The introduction of an almost three-dimensional technique imaging called breast digital tomosynthesis (DBT) can overcome most limitations. In the last 5 years image analysis methods based on Artificial Intelligence (, AI) have also been massively introduced in breast cancer detection. The study is a prospective observational study based on Artificial intelligence whose the mail goal is to develop a method to identify a lesion.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Oct 2020
Typical duration for all trials
2 active sites
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
October 20, 2020
CompletedPrimary Completion
Last participant's last visit for primary outcome
November 25, 2022
CompletedFirst Submitted
Initial submission to the registry
March 28, 2023
CompletedFirst Posted
Study publicly available on registry
May 15, 2023
CompletedStudy Completion
Last participant's last visit for all outcomes
December 31, 2023
CompletedMay 15, 2023
March 1, 2023
2.1 years
March 28, 2023
May 12, 2023
Conditions
Outcome Measures
Primary Outcomes (1)
Artificial Intelligence system to detect a lesion
Lesion detction is based on breast density, case type, BIRADS assessment categories, mammographic appearance, size and pathological profile of malignant lesions
12 months
Interventions
Introduction of an almost three-dimensional imaging technique called breast digital tomosynthesis
Eligibility Criteria
Women undergoing mammography
You may qualify if:
- Patients who refer to the Regina Elena for diagnostic mammography tests
- Informed consent
You may not qualify if:
- presence of prostheses, artifacts, outcomes of a study in the breast intervention under the study
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (2)
Università degli Studi di Napoli Federico II
Napoli, 80138, Italy
"Regina Elena" National Cancer Institute
Rome, 00144, Italy
MeSH Terms
Conditions
Interventions
Condition Hierarchy (Ancestors)
Intervention Hierarchy (Ancestors)
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- OTHER
- Time Perspective
- PROSPECTIVE
- Target Duration
- 24 Months
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
March 28, 2023
First Posted
May 15, 2023
Study Start
October 20, 2020
Primary Completion
November 25, 2022
Study Completion
December 31, 2023
Last Updated
May 15, 2023
Record last verified: 2023-03