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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Double reading of all cases with and without Transpara software | Experimental | Double reading of all cases with and without Transpara software |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Mammograms | Diagnostic Test | 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):
|
| 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. |
| 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 |
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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.
Exclusion Criteria:
Women participating in the regular Breast Cancer Early Detection Program in Cordoba
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| Name | Affiliation | Role |
|---|---|---|
| Esperanza Elias Cabot, MD | Hospital Universitario Reina Sofia de Cordoba | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Hospital Universitario Reina Sofia | Córdoba | Córdoba | 14004 | Spain |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 33944627 | Background | Raya-Povedano JL, Romero-Martin S, Elias-Cabot E, Gubern-Merida A, Rodriguez-Ruiz A, Alvarez-Benito M. AI-based Strategies to Reduce Workload in Breast Cancer Screening with Mammography and Tomosynthesis: A Retrospective Evaluation. Radiology. 2021 Jul;300(1):57-65. doi: 10.1148/radiol.2021203555. Epub 2021 May 4. | |
| 30834436 | Background |
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The database and the protocol Will be shared after the trial is published.
After the trial is published.
Upon request to the principal investigator.
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| ID | Term |
|---|---|
| D001943 | Breast Neoplasms |
| ID | Term |
|---|---|
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D001941 | Breast Diseases |
| D012871 | Skin Diseases |
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| ID | Term |
|---|---|
| D008327 | Mammography |
| ID | Term |
|---|---|
| D011859 | Radiography |
| D003952 | Diagnostic Imaging |
| D019937 | Diagnostic Techniques and Procedures |
| D003933 | Diagnosis |
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|
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. |
| Rodriguez-Ruiz A, Lang K, Gubern-Merida A, Broeders M, Gennaro G, Clauser P, Helbich TH, Chevalier M, Tan T, Mertelmeier T, Wallis MG, Andersson I, Zackrisson S, Mann RM, Sechopoulos I. Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. J Natl Cancer Inst. 2019 Sep 1;111(9):916-922. doi: 10.1093/jnci/djy222. |
| 30993432 | Background | Rodriguez-Ruiz A, Lang K, Gubern-Merida A, Teuwen J, Broeders M, Gennaro G, Clauser P, Helbich TH, Chevalier M, Mertelmeier T, Wallis MG, Andersson I, Zackrisson S, Sechopoulos I, Mann RM. Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study. Eur Radiol. 2019 Sep;29(9):4825-4832. doi: 10.1007/s00330-019-06186-9. Epub 2019 Apr 16. |
| 30457482 | Background | Rodriguez-Ruiz A, Krupinski E, Mordang JJ, Schilling K, Heywang-Kobrunner SH, Sechopoulos I, Mann RM. Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System. Radiology. 2019 Feb;290(2):305-314. doi: 10.1148/radiol.2018181371. Epub 2018 Nov 20. |
| 31385754 | Background | Yala A, Schuster T, Miles R, Barzilay R, Lehman C. A Deep Learning Model to Triage Screening Mammograms: A Simulation Study. Radiology. 2019 Oct;293(1):38-46. doi: 10.1148/radiol.2019182908. Epub 2019 Aug 6. |
| 32052311 | Background | Sasaki M, Tozaki M, Rodriguez-Ruiz A, Yotsumoto D, Ichiki Y, Terawaki A, Oosako S, Sagara Y, Sagara Y. Artificial intelligence for breast cancer detection in mammography: experience of use of the ScreenPoint Medical Transpara system in 310 Japanese women. Breast Cancer. 2020 Jul;27(4):642-651. doi: 10.1007/s12282-020-01061-8. Epub 2020 Feb 12. |
| 30898381 | Background | Le EPV, Wang Y, Huang Y, Hickman S, Gilbert FJ. Artificial intelligence in breast imaging. Clin Radiol. 2019 May;74(5):357-366. doi: 10.1016/j.crad.2019.02.006. Epub 2019 Mar 18. |
| D017437 |
| Skin and Connective Tissue Diseases |