Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2025-12-24 @ 6:43 PM
Ignite Modification Date:
2025-12-24 @ 6:43 PM
NCT ID:
NCT05968157
Brief Summary:
Accurate risk assessment is essential for the success of population screening programs and early detection efforts in breast cancer. Mirai is a new deep learning model based on full resolution mammograms.
Mirai is a mammography-based deep learning model designed to predict risk at multiple timepoints, leverage potentially missing risk factor information, and produce predictions that are consistent across mammography machines. Mirai was trained on a large dataset from Massachusetts General Hospital (MGH) in the United States and found to be significantly more accurate than the Tyrer-Cuzick model, a current clinical standard.
The primary aim of this study is to prospectively quantify the clinical benefit (i.e. MRI/CEM cancer detection rate) of Mirai-based guidelines and to compare them to the current standard of care.
1. Conduct a prospective study where patients who are identified as high risk by Mirai guidelines are invited to receive supplemental MRI within 12 months.
2. Compare cancer outcomes between patients only identified as high risk by Mirai and patients identified as high risk by existing guidelines The secondary aim is to study the impact of new guidelines by race and ethnicity, to ensure equitable improvements in cancer screening.
Study:
NCT05968157
Study Brief:
Protocol Section:
NCT05968157