Development of a low-dose mammography technique to calculate women’s breast density to aid breast cancer risk assessment

Technical development of Automated Low Dose Risk Assessment Mammography (ALDRAM) in women attending for mammography through a family history clinic

Status

Active, not recruiting

Phases

Unknown

Study type

Interventional

Source

ISRCTN

Registry ID

ISRCTN55983830

Enrollment

600

Registered

2021-11-02

Start date

2019-03-28

Completion date

Unknown

Last updated

2022-03-28

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Risk assessment of young women for breast cancer using automated low dose risk assessment mammography Cancer

Interventions

Participants will be approached for participation at the Breast Cancer Family History, Risk and Prevention Clinic at Wythenshawe Hospital. If a participant agrees to take part in this study, they will

Eligibility

Sex/Gender

Female

Inclusion criteria

Inclusion criteria: 1. Women aged between 30 and 45 years 2. Moderate to high risk of developing breast cancer 3. Capable of providing informed consent to a participant information sheet written in English

Exclusion criteria

Exclusion criteria: 1. Prior breast cancer 2. Prior breast augmentation or reduction (this does not include those who have had breast surgery for a benign condition) 3. Participation in the TARA-Prev study which included an additional mammogram and thus radiation dose

Design outcomes

Primary

Measure	Time frame
Correlation between Mammographic density assessed as predicted VAS (visual assessment score) on the full dose and low dose mammograms measured using an artificial intelligence-driven algorithm for automated measurement on Full Field Digital Mammography (FFDM) and low dose counterpart from the raw mammography image files at a single time point	—

Secondary

Measure	Time frame
1. To refine machine learning methods for ALDRAM vs FFDM using mammogram files collected at a single timepoint. The files will be analysed to give a predicted Visual Assessment Score (pVAS), a machine learning derived method for assessing breast density, calculated from FFDM and low dose image data and determine the correlation between the two across the whole study population and in subgroups based on age (30-34; 35-39; and 40-44). 2. To determine the view (CC versus MLO versus both) to take forward in a prospective clinical cohort if the approach is successful, using mammogram files collected at a single timepoint. Correlations of the averaged pVAS values from the four FFDM exposures (this is the standard for density analysis) and the individual CC and MLO and the averaged CC and MLO exposures from ALDRAM will be performed. This will be used to determine the best correlation and root mean squared error values and will determine which approach to take forward to subsequent studies.	—

Measure

Time frame

1. To refine machine learning methods for ALDRAM vs FFDM using mammogram files collected at a single timepoint. The files will be analysed to give a predicted Visual Assessment Score (pVAS), a machine learning derived method for assessing breast density, calculated from FFDM and low dose image data and determine the correlation between the two across the whole study population and in subgroups based on age (30-34; 35-39; and 40-44). 2. To determine the view (CC versus MLO versus both) to take forward in a prospective clinical cohort if the approach is successful, using mammogram files collected at a single timepoint. Correlations of the averaged pVAS values from the four FFDM exposures (this is the standard for density analysis) and the individual CC and MLO and the averaged CC and MLO exposures from ALDRAM will be performed. This will be used to determine the best correlation and root mean squared error values and will determine which approach to take forward to subsequent studies.

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Countries

England, United Kingdom

Outcome results

None listed

Source: ISRCTN (via WHO ICTRP) · Data processed: Feb 4, 2026