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Purpose <p>To present the prevalence screening results of the RIsk-Based Breast Screening (RIBBS) study (ClinicalTrials.gov NCT05675085), a quasi-experimental population-based study evaluating a personalized screening model for women aged 45–49. This model uses digital breast tomosynthesis (DBT) and stratifies participants by risk and breast density, incorporating tailored screening intervals with or without supplemental imaging (ultrasound, US, and breast MRI), with the goal of reducing advanced breast cancer (BC) incidence compared to annual digital mammography (DM).</p> Materials and methods <p>An interventional cohort of 10,269 women aged 45 was enrolled (January 2020–December 2021. Participants underwent DBT and completed a BC risk questionnaire. Volumetric breast density and lifetime risk were used to assign five subgroups to tailored screening regimens: low-risk low-density (LR–LD), low-risk high-density (LR–HD), intermediate-risk low-density (IR–LD), intermediate-risk high-density (IR–HD), and high-risk (HR). Screening performance was compared with an observational control cohort of 43,838 women undergoing annual DM.</p> Results <p>Compared to LR–LD, intermediate-risk groups showed a 4.9- (IR–LD) and 4.6-fold (IR–HD) higher prevalence of BC, driven by a 7.1- and 7.1-fold higher prevalence of pT1c tumors. The interventional cohort had lower recall rate (rate ratio, 0.5), higher surgery rate (1.9) and increased prevalence of DCIS (2.9), pT1c (2.3) and grade 3 tumors (2.4), compared to controls.</p> Conclusion <p>The prevalence screening demonstrated the feasibility of using DBT and&#xa0;—in high-density subgroups—&#xa0;supplemental US. The stratification criteria effectively identified subpopulations with different BC prevalence. Increasing the detection rate of pT1c tumors is not sufficient but necessary to achieve a reduction in advanced BC incidence.</p>

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Personalized screening based on risk and density: prevalence data from the RIBBS study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月21日

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  • Francesca Caumo,
  • Gisella Gennaro,
  • Alessandra Ravaioli,
  • Enrica Baldan,
  • Elisabetta Bezzon,
  • Silvia Bottin,
  • Paolo Carlevaris,
  • Lina Ciampani,
  • Alessandro Coran,
  • Chiara Dal Bosco,
  • Sara Del Genio,
  • Alessia Dalla Pietà,
  • Fabio Falcini,
  • Federico Maggetto,
  • Giuseppe Manco,
  • Tiziana Masiero,
  • Maria Petrioli,
  • Ilaria Polico,
  • Tiziana Pisapia,
  • Martina Zemella,
  • Manuel Zorzi,
  • Stefania Zovato,
  • Lauro Bucchi

摘要

Purpose

To present the prevalence screening results of the RIsk-Based Breast Screening (RIBBS) study (ClinicalTrials.gov NCT05675085), a quasi-experimental population-based study evaluating a personalized screening model for women aged 45–49. This model uses digital breast tomosynthesis (DBT) and stratifies participants by risk and breast density, incorporating tailored screening intervals with or without supplemental imaging (ultrasound, US, and breast MRI), with the goal of reducing advanced breast cancer (BC) incidence compared to annual digital mammography (DM).

Materials and methods

An interventional cohort of 10,269 women aged 45 was enrolled (January 2020–December 2021. Participants underwent DBT and completed a BC risk questionnaire. Volumetric breast density and lifetime risk were used to assign five subgroups to tailored screening regimens: low-risk low-density (LR–LD), low-risk high-density (LR–HD), intermediate-risk low-density (IR–LD), intermediate-risk high-density (IR–HD), and high-risk (HR). Screening performance was compared with an observational control cohort of 43,838 women undergoing annual DM.

Results

Compared to LR–LD, intermediate-risk groups showed a 4.9- (IR–LD) and 4.6-fold (IR–HD) higher prevalence of BC, driven by a 7.1- and 7.1-fold higher prevalence of pT1c tumors. The interventional cohort had lower recall rate (rate ratio, 0.5), higher surgery rate (1.9) and increased prevalence of DCIS (2.9), pT1c (2.3) and grade 3 tumors (2.4), compared to controls.

Conclusion

The prevalence screening demonstrated the feasibility of using DBT and —in high-density subgroups— supplemental US. The stratification criteria effectively identified subpopulations with different BC prevalence. Increasing the detection rate of pT1c tumors is not sufficient but necessary to achieve a reduction in advanced BC incidence.