Artificial Intelligence in Breast Cancer Screening Programs in Córdoba (AITIC)

Study Overview
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Brief Summary
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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.

Official Title
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New Strategies Based on Artificial Intelligence in Breast Cancer Screening Programs in Córdoba With Digital Mammography and Digital Breast Tomosynthesis. A Prospective Evaluation.

Conditions
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Breast Cancer

Intervention / Treatment
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* Diagnostic Test: Mammograms


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Any woman between the ages of 50 and 69, from the hospital area of the Reina Sofía University Hospital, invited to the Breast Cancer Early Detection Program, summoned in rooms 2024 and 2001 and who agrees to participate in the study by signing the consent informed. Women studied in the program in the set period and who have previously participated. Women who are studied in the program for the first time in the set period. Exclusion Criteria: Women invited to the program who do not agree to enter the research study by signing the informed consent. Women with breast prostheses. Women with symptoms or signs of suspected breast cancer.

Ages Eligible for Study
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Minimum Age: 50 Years
Maximum Age: 69 Years

Sexes Eligible for Study
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Female

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Intervention Model: Single Group Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Double reading of all cases with and without Transpara software<br>Double reading of all cases with and without Transpara software | Diagnostic Test: Mammograms<br>* 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®.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Assessment of Workload of each strategy | 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. | In the middle of the study, at 1 year. | 
| Assessment of Workload of each strategy | 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. | At the end of the study, at 2 years. | 
| Detection rate | Proportion of women diagnosed with breast cancer among those screened. | In the middle of the study, at 1 year. | 
| Detection rate | Proportion of women diagnosed with breast cancer among those screened. | At the end of the study, at 2 years. | 
| Recall or referral rate | Proportion of women who, after the screening test, are referred to the breast diagnosis unit. | In the middle of the study, at 1 year. | 
| Recall or referral rate | Proportion of women who, after the screening test, are referred to the breast diagnosis unit. | At the end of the study, at 2 years. | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Positive predictive value of referrals | Proportion of women diagnosed with breast cancer among those referred to the hospital. | In the middle of the study, at 1 year. | 
| Positive predictive value of referrals | Proportion of women diagnosed with breast cancer among those referred to the hospital. | At the end of the study, at 2 years. | 
| Positive predictive value of biopsies | Proportion of women with breast cancer among all women undergoing biopsy. | In the middle of the study, at 1 year. | 
| Positive predictive value of biopsies | Proportion of women with breast cancer among all women undergoing biopsy. | At the end of the study, at 2 years. | 
| Positive predictive value of Transpara® scores | Proportion of breast cancers diagnosed among women with a given score. | In the middle of the study, at 1 year. | 
| Positive predictive value of Transpara® scores | Proportion of breast cancers diagnosed among women with a given score. | At the end of the study, at 2 years. | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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breast cancer, artificial intelligence