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Reproducibility of Breast Density and Background Parenchymal Enhancement Grading on Contrast-Enhanced Mammography With AI Support: A Retrospective Single-Centre Study
Sponsor: Graziella di Grezia
Summary
Retrospective single-centre observational study in women who underwent contrast-enhanced mammography (CEM). It assesses the reproducibility of breast density (BD) and background parenchymal enhancement (BPE) grading by five radiologists with and without AI support, develops linear regression and deep neural network (DNN) models to predict BD and BPE, tests BD prediction on an external public dataset, and explores a multi-output DNN as a proof of concept for proxy estimates of bone mineral density and systolic blood pressure. This is a secondary analysis of the cohort also described in NCT06838117 and NCT06838130.
Official title: Beyond Cancer Detection: An AI Framework for Multidimensional Risk Profiling on Contrast-Enhanced Mammography Acronym: AI-CEM-RISK
Key Details
Gender
FEMALE
Age Range
28 Years - 80 Years
Study Type
OBSERVATIONAL
Enrollment
213
Start Date
2022-01-01
Completion Date
2023-12-31
Last Updated
2026-11-03
Healthy Volunteers
No
Interventions
Contrast Enhanced Mammography
Diagnostic Test: contrast-enhanced mammography (Senographe Pristina, GE Healthcare), performed as routine care. Other: AI-assisted grading of breast density and BPE (linear regression and deep neural network).
Locations (1)
Perrino Hospital
Brindisi, Italy