Breast Cancer, Screening
Conditions
Keywords
breast cancer, artificial intelligence, Digital breast tomosynthesis, Mammography, Mass screening
Brief summary
The use of artificial intelligence software in breast screening (Transpara®) makes it possible to identify studies with a very low probability of cancer. The hypothesis raised in this work is that reading strategies based on artificial intelligence (single or double reading only of cases with a score\> 7 with Transpara®), allow reducing the workload of a screening program by more than 50 % with respect to the standard reading of the program (double reading of all cases without Transpara®), without presenting inferiority in terms of detection rates and recalls of the program, both with the use of 2D digital mammography and with the use of tomosynthesis or 3D mammogram.
Interventions
DIAGNOSTIC_TESTMammograms
In the women participating in the study, two strategies for reading mammograms will be carried out: Strategy 1: Standard reading of the program. Double independent and non-consensual reading of all cases, without any artificial intelligence system (standard strategy). Strategy 2: Reading strategy based on the global Score granted by Transpara® (strategy based on artificial intelligence): * In studies with a Score \<8 (studies with a low probability of cancer): They will not be evaluated by any radiologist. * In studies with a Score\> 7 (studies with a high probability of cancer): double reading will be carried out, assisted by Transpara®.
Sponsors
Maimónides Biomedical Research Institute of Córdoba
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
FEMALE
Age
50 Years to 71 Years
Healthy volunteers
Yes
Inclusion criteria
All women between 50 and 71 years of age (including women who reach that age in the year of appointment), in the Reina Sofía University Hospital district, invited to participate in the Breast Cancer Early Detection Program, that have been randomly assigned in the Hologic equipment (DM or DBT), and who agree to participate in the study by signing the informed consent form. 1. Women studied in the program during the established period and who have previously participated. 2. Women studied in the program for the first time in the established period.
Exclusion criteria
1. Women invited to the program who do not agree to participate in the research study by signing the informed consent form. 2. Women with breast prostheses. 3. Women with signs or symptoms of suspected breast cancer.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Assessment of Workload of each strategy | In the middle of the study, at 1 year. | The workload of each strategy shall be assessed by multiplying the average time for a reading of that strategy by the total number of readings of that strategy. The average reading time of a case in each strategy shall be calculated from the measurement of the individual reading time in a sample of 500 cases in each strategy. |
| Detection rate | In the middle of the study, at 1 year. | Proportion of women diagnosed with breast cancer among those screened. |
| Recall or referral rate | In the middle of the study, at 1 year. | Proportion of women who, after the screening test, are referred to the breast diagnosis unit. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Positive predictive value of referrals | In the middle of the study, at 1 year. | Proportion of women diagnosed with breast cancer among those referred to the hospital. |
| Positive predictive value of biopsies | In the middle of the study, at 1 year. | Proportion of women with breast cancer among all women undergoing biopsy. |
| Positive predictive value of Transpara® scores | In the middle of the study, at 1 year. | Proportion of breast cancers diagnosed among women with a given score. |
Countries
Spain
Outcome results
None listed