Breast Density Impact on Mammographic Screening for Breast Cancer Diagnosis

NCT07415499 | Recruiting

• 1 other identifier: SINATRA
• Study Type: observational
• Target: 3,300
• Locations: 1 country
• Sites: 1

Brief Summary

This retrospective, observational study aims to evaluate how breast density affects the accuracy and outcomes of mammographic screening for breast cancer within the regional screening program "Prevenzione Serena". Breast density is an important factor because dense breast tissue can make it more difficult to detect breast cancer on a mammogram. Dense tissue and tumors both appear white on a mammogram, which may hide abnormalities and lead to missed cancers or false-positive results. Women aged 45 to 75 years who underwent routine mammographic screening at ASL CN2 between September 2023 and May 2024 will be included. Breast density will be classified using the BI-RADS system (categories A-D), and the study will assess whether women with dense breasts (categories C and D) experience higher rates of recalls for second-level examinations such as ultrasound, MRI, etc). The study also includes an internal validation of Insight BD, an automated breast-density measurement software used at ASL CN2. The software will be evaluated using a mammography phantom (to verify technical accuracy) and by comparing its BI-RADS density classifications with readings from two radiologists (one expert and one less experienced). This will help determine whether the software can support radiologists, especially in evaluating dense breast tissue. Additional factors such as menopausal status, family history of breast cancer, and hormone therapy will also be examined to understand how they relate to breast density and screening outcomes. The study aims to quantify the frequency of false-positive recalls-cases in which additional tests are recommended but cancer is not found-because these events can increase patient anxiety and healthcare workload. Ultimately, this research seeks to provide evidence that may inform future screening guidelines and support more personalized approaches, particularly for women with dense breasts.

Conditions

Breast Cancer Screening; Breast Cancer Screening and Diagnosis

Keywords

Breast cancer; Breast cancer screening; BRADS; Mammography; Dense breast; Breast imaging

Outcome Measures

Primary Outcomes (1)

• Recall Rate for Second-Level Examinations by BI-RADS Breast Density Category

  Percentage of women recalled for second-level diagnostic examinations among all women undergoing mammographic screening, calculated separately for each BI-RADS density category (A, B, C, D) and for the dichotomized groups A-B versus C-D.

  September 2023 - May 2024

Secondary Outcomes (4)

• Technical Performance of Insight BD: Accuracy and Repeatability on Breast Density Phantom

  September 2023 - May 2024

• Concordance Between Insight BD BI-RADS Classification and Radiologist Assessment

  September 2023 - May 2024

• Recall Rate for Second-Level Examinations in Negative Mammographic Screens by BI-RADS Density

  September 2023 - May 2024

• Distribution of Breast Density by Menopausal Status, Hormonal Therapy, and Family History

  September 2023 - May 2024

Study Arms (1)

Screening cohort

Women aged 45-75 who underwent routine mammographic screening within the "Prevenzione Serena" program at ASL CN2 between September 2023 and May 2024. Breast density (BI-RADS A-D) will be evaluated, along with recall rates, false-positive findings, and comparisons of automated breast-density assessment (Insight BD) with radiologist readings. No interventions are administered; data are collected retrospectively from clinical records.

Other: No intervention is administered.

Interventions

No intervention is administered. OTHER

No intervention is administered.

Screening cohort

Eligibility Criteria

• Age: 45 Years - 75 Years
• Sex: female
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study will include women aged 45 to 75 years who participated in the "Prevenzione Serena" mammography screening program and underwent screening mammography at ASL CN2 using a Siemens MAMMOMAT Revelation mammography system between September 25, 2023, and May 3, 2024.

You may qualify if:

• Women aged 45 to 75 years who participated in the "Prevenzione Serena" mammography screening program and underwent screening mammography at ASL CN2 between September 25, 2023, and May 3, 2024.
• Signed informed consent or equivalent substitute declaration, when applicable.

You may not qualify if:

• Women with a history of mastectomy.
• Women with breast implants.
• Women with cardiac implantable devices, such as pacemakers or loop recorders.
• Cases in which the Insight BD software cannot be applied due to compression thickness below 15 mm.

Sponsors & Collaborators

• Azienda Sanitaria Locale CN2 Alba-Bra: lead

Study Sites (1)

SSD Fisica Sanitaria - Ospedale Michele e Pietro Ferrero di Verduno (CN) - ASL CN2

Verduno, Italy/CN, 12060, Italy

RECRUITING

Related Publications (11)

• Ray KM, Price ER, Joe BN. Breast density legislation: mandatory disclosure to patients, alternative screening, billing, reimbursement. AJR Am J Roentgenol. 2015 Feb;204(2):257-60. doi: 10.2214/AJR.14.13558.

  PMID: 25615746 BACKGROUND

• Dehkordy SF, Carlos RC. Dense Breast Legislation in the United States: State of the States. J Am Coll Radiol. 2016 Nov;13(11S):R53-R57. doi: 10.1016/j.jacr.2016.09.027.

  PMID: 27814815 BACKGROUND

• Damases CN, Brennan PC, Mello-Thoms C, McEntee MF. Mammographic Breast Density Assessment Using Automated Volumetric Software and Breast Imaging Reporting and Data System (BIRADS) Categorization by Expert Radiologists. Acad Radiol. 2016 Jan;23(1):70-7. doi: 10.1016/j.acra.2015.09.011. Epub 2015 Oct 26.

  PMID: 26514436 BACKGROUND

• Pesce K, Tajerian M, Chico MJ, Swiecicki MP, Boietti B, Frangella MJ, Benitez S. Interobserver and intraobserver variability in determining breast density according to the fifth edition of the BI-RADS(R) Atlas. Radiologia (Engl Ed). 2020 Nov-Dec;62(6):481-486. doi: 10.1016/j.rx.2020.04.006. Epub 2020 May 31. English, Spanish.

  PMID: 32493654 BACKGROUND

• Sheehan J. Brain fragments: Leksell's autobiography newly translated to English. J Neurooncol. 2022 Apr;157(2):383. doi: 10.1007/s11060-022-03968-y. Epub 2022 Mar 29. No abstract available.

  PMID: 35348987 BACKGROUND

• Martinez-Navarro B, Sanchis R, Asedegbega-Nieto E, Solsona B, Ivars-Barcelo F. (Ag)Pd-Fe3O4 Nanocomposites as Novel Catalysts for Methane Partial Oxidation at Low Temperature. Nanomaterials (Basel). 2020 May 21;10(5):988. doi: 10.3390/nano10050988.

  PMID: 32455643 BACKGROUND

• Zimri K, Hesseling AC, Godfrey-Faussett P, Schaaf HS, Seddon JA. Why do child contacts of multidrug-resistant tuberculosis not come to the assessment clinic? Public Health Action. 2012 Sep 21;2(3):71-5. doi: 10.5588/pha.12.0024.

  PMID: 26392955 BACKGROUND

• Oliver A, Tortajada M, Llado X, Freixenet J, Ganau S, Tortajada L, Vilagran M, Sentis M, Marti R. Breast Density Analysis Using an Automatic Density Segmentation Algorithm. J Digit Imaging. 2015 Oct;28(5):604-12. doi: 10.1007/s10278-015-9777-5.

  PMID: 25720749 BACKGROUND

• Eng A, Gallant Z, Shepherd J, McCormack V, Li J, Dowsett M, Vinnicombe S, Allen S, dos-Santos-Silva I. Digital mammographic density and breast cancer risk: a case-control study of six alternative density assessment methods. Breast Cancer Res. 2014 Sep 20;16(5):439. doi: 10.1186/s13058-014-0439-1.

  PMID: 25239205 BACKGROUND

• Sartor H, Lang K, Rosso A, Borgquist S, Zackrisson S, Timberg P. Measuring mammographic density: comparing a fully automated volumetric assessment versus European radiologists' qualitative classification. Eur Radiol. 2016 Dec;26(12):4354-4360. doi: 10.1007/s00330-016-4309-3. Epub 2016 Mar 24.

  PMID: 27011371 BACKGROUND

• Jeffers AM, Sieh W, Lipson JA, Rothstein JH, McGuire V, Whittemore AS, Rubin DL. Breast Cancer Risk and Mammographic Density Assessed with Semiautomated and Fully Automated Methods and BI-RADS. Radiology. 2017 Feb;282(2):348-355. doi: 10.1148/radiol.2016152062. Epub 2016 Sep 5.

  PMID: 27598536 BACKGROUND

MeSH Terms

Conditions

Disease; Breast Neoplasms

Condition Hierarchy (Ancestors)

Pathologic Processes; Pathological Conditions, Signs and Symptoms; Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases

Central Study Contacts

Francesco Lucio, Principal Investigator

CONTACT

+39.0172.1408486; flucio@aslcn2.it

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 10, 2026
• First Posted: February 17, 2026
• Study Start: January 12, 2026
• Primary Completion (Estimated): June 1, 2026
• Study Completion (Estimated): September 1, 2026
• Last Updated: February 17, 2026

Record last verified: 2026-02

Locations

IT (1)