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Aim <p>The AI case malignancy score (AI-CMS) represents the AI algorithm’s confidence (from 0 to 100%) that a mammography exam is malignant. This work aims to retrospectively evaluate, through simulation on real-world data, a strategy that integrates AI-CMS into a standard screening scenario to reduce the radiologists' workload.</p> Methods <p>A total of 89176 consecutive screening exams from the 2023–2024 Reggio Emilia Breast Screening Program (REBSP) were retrospectively considered, which included 479 biopsy-proven cancers (interval cancers were only partially available, therefore false negatives beyond those detected in the real screening workflow could not be assessed). In the proposed strategy, computer-aided detection (CAD) acts as a reader (CR), recalling women with an AI-CMS greater than a predefined threshold (ranging from 5 to 25%). If the first radiologist (HR1) disagrees with CR, the case goes to a second radiologist (HR2) and, in case of human disagreement, to a third radiologist (HR3). For each threshold, final recall rate (RR), cancer detection rate (CDR), number of detected cancers (DC), predictive positive value (PPV) of recalls, false positive rate (FPR), human reading workload, and economic impact were estimated.</p> Results <p>At AI-CMS thresholds of 5%, 8%, 10%, 15%, 20%, and 25%, human workload decrease ranged from 13.4% to 36.1%. The final RR decreased between 4.3% and 4.0%, slightly lower than the current 4.4% with human double reading. The PPV ranged from 12.6% to 13.3%, higher than the current PPV of 12.2%. The FPR ranged from 3.8% to 3.5%, down from the current 3.9%. With thresholds up to 5%, no true positive cases were missed, maintaining the CDR of 5.4‰ of those detected by current double reading. Considering CAD payback periods of either 6 or 8&#xa0;years, financial savings from our strategy ranged from approximately 17800 to over 590,000€.</p> Conclusion <p>Integrating AI-CMS support into a standard screening scenario could substantially reduce the screen-reading workload and slightly reduce unnecessary ascertainments without affecting the cancer detection rate. This approach, although limited by its retrospective simulation design and the partial availability of interval cancer data, has also proven to be economically sustainable.</p>

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Adding artificial intelligence case malignancy scoring to reduce screen-reading workload in breast screening program: results of the retrospective REAI program

  • 期刊论文

  • 发表日期：

    2025年11月26日

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  • Andrea Nitrosi,
  • Paolo Giorgi Rossi,
  • Laura Verzellesi,
  • Martina Creola,
  • Cinzia Campari,
  • Rita Vacondio,
  • Chiara Coriani,
  • Valentina Iotti,
  • Pierpaolo Pattacini,
  • Giulia Besutti,
  • Valeria Trojani,
  • Marco Bertolini,
  • Giulia Paolani,
  • Mauro Iori

摘要

Aim

The AI case malignancy score (AI-CMS) represents the AI algorithm’s confidence (from 0 to 100%) that a mammography exam is malignant. This work aims to retrospectively evaluate, through simulation on real-world data, a strategy that integrates AI-CMS into a standard screening scenario to reduce the radiologists' workload.

Methods

A total of 89176 consecutive screening exams from the 2023–2024 Reggio Emilia Breast Screening Program (REBSP) were retrospectively considered, which included 479 biopsy-proven cancers (interval cancers were only partially available, therefore false negatives beyond those detected in the real screening workflow could not be assessed). In the proposed strategy, computer-aided detection (CAD) acts as a reader (CR), recalling women with an AI-CMS greater than a predefined threshold (ranging from 5 to 25%). If the first radiologist (HR1) disagrees with CR, the case goes to a second radiologist (HR2) and, in case of human disagreement, to a third radiologist (HR3). For each threshold, final recall rate (RR), cancer detection rate (CDR), number of detected cancers (DC), predictive positive value (PPV) of recalls, false positive rate (FPR), human reading workload, and economic impact were estimated.

Results

At AI-CMS thresholds of 5%, 8%, 10%, 15%, 20%, and 25%, human workload decrease ranged from 13.4% to 36.1%. The final RR decreased between 4.3% and 4.0%, slightly lower than the current 4.4% with human double reading. The PPV ranged from 12.6% to 13.3%, higher than the current PPV of 12.2%. The FPR ranged from 3.8% to 3.5%, down from the current 3.9%. With thresholds up to 5%, no true positive cases were missed, maintaining the CDR of 5.4‰ of those detected by current double reading. Considering CAD payback periods of either 6 or 8 years, financial savings from our strategy ranged from approximately 17800 to over 590,000€.

Conclusion

Integrating AI-CMS support into a standard screening scenario could substantially reduce the screen-reading workload and slightly reduce unnecessary ascertainments without affecting the cancer detection rate. This approach, although limited by its retrospective simulation design and the partial availability of interval cancer data, has also proven to be economically sustainable.