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

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.

Study Overview

• Status: Recruiting
• Conditions: Breast Cancer Diagnosis
• Intervention / Treatment: Diagnostic test: Breast digital tomosynthesis

• Study Type: Observational
• Enrollment (Anticipated): 200

Contacts and Locations

• Study Contact: Name: Valeria Landoni; Phone Number: +39 06 52665602; Email: valeria.landoni@ifo.it

Study Locations

• Italy
  • Napoli, Italy, 80138
    • Recruiting
    • Università Degli Studi Di Napoli Federico Ii
    • Contact: Giovanni Mettivier
  • Rome, Italy, 00144
    • Recruiting
    • "Regina Elena" National Cancer Institute

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Women undergoing mammography

Description

Inclusion Criteria:

• Patients who refer to the Regina Elena for diagnostic mammography tests
• Informed consent

Exclusion Criteria:

• presence of prostheses, artifacts, outcomes of a study in the breast intervention under the study

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Regina Elena Cancer Institute

Study record dates

Study Major Dates

• Study Start (Actual): October 20, 2020
• Primary Completion (Actual): November 25, 2022
• Study Completion (Anticipated): December 31, 2023

Study Registration Dates

• First Submitted: March 28, 2023
• First Submitted That Met QC Criteria: May 12, 2023
• First Posted (Actual): May 15, 2023

Study Record Updates

• Last Update Posted (Actual): May 15, 2023
• Last Update Submitted That Met QC Criteria: May 12, 2023
• Last Verified: March 1, 2023

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Pathologic Processes; Skin Diseases; Neoplasms; Neoplasms by Site; Breast Diseases; Breast Neoplasms; Disease
• Other Study ID Numbers: RS1414/20

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No