What question did this study set out to answer?

This research aims to evaluate the effectiveness of an AI algorithm in predicting breast cancer risk among women with benign breast disease.

February 19, 2026

Abstract PD4-02: An AI algorithm for breast cancer detection improves future BC risk prediction among women with benign breast disease

Key Result

An AI breast cancer detection algorithm predicted future breast cancer risk with HR 1.14 per score unit and improved C-statistic by 0.024 beyond benign breast disease severity and density.

Key Points

This research aims to evaluate the effectiveness of an AI algorithm in predicting breast cancer risk among women with benign breast disease.
Examined women without prior breast cancer with a benign breast disease biopsy between 2002-2013 at Mayo Clinic.
Identified incident breast cancer occurrences using the Mayo Tumor Registry and follow-up questionnaires.
Evaluated BBD severity and assessed AI malignancy scores from mammograms and volumetric percent density.
Among 3,125 women, 250 developed incident breast cancer over a median follow-up of 12.9 years.
Higher breast cancer risk associated with increased BBD severity, with an HR of 3.18 for atypical hyperplasia.
The AI malignancy score showed comparable discriminatory accuracy to traditional risk factors.

Structured PICO

Does an AI cancer detection algorithm improve future breast cancer risk prediction among women with benign breast disease?

Population

3,125 women without prior breast cancer who had an initial benign breast disease (BBD) biopsy at Mayo Clinic, Rochester, between 2002 and 2013, with a full field digital mammogram at least 2 years prior to BC diagnosis.

Intervention

AI malignancy scores derived from the Transpara detection algorithm

Comparator

Established risk factors (BBD severity and volumetric percent density)

Outcome

Incident breast cancer occurring at least two years after the mammogramhard clinical

An AI breast cancer detection algorithm achieved similar performance in predicting future breast cancer risk compared to established risk factors among women with benign breast disease.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

Abstract Background: Artificial intelligence (AI) algorithms based on deep learning approaches show promise in improving breast cancer (BC) detection on mammography and may also improve prediction of future BC risk compared with clinical risk prediction models. Historical clinical risk prediction models underperform among women with BBD; however, AI models for BC detection on mammography have not been tested among women at elevated BC risk due to benign breast disease (BBD). We evaluated if an AI cancer detection algorithm can aid BC risk prediction among women with BBD. Methods: We examined women without a prior BC who had an initial BBD biopsy at Mayo Clinic, Rochester, between 2002 and 2013. Incident BC occurring after BBD was identified using the Mayo Tumor Registry and supplemented by follow-up questionnaires. Only women with a full field digital mammogram at least 2 years prior to BC diagnosis were eligible. A breast pathologist evaluated BBD according to increasing severity, as non-proliferative (NP), proliferative disease without atypia (PDWA) or atypical hyperplasia (AH). AI malignancy scores derived from the Transpara detection algorithm (1-10) and volumetric percent density (VPD) from Volpara (per one standard deviation, SD) were assessed from mammograms close to BBD diagnosis. Cox proportional hazards regression models were applied to estimate hazard ratios (HRs) with 95% Confidence Intervals (CIs), adjusted for age and BMI. C-statistics were estimated to assess the contribution of each factor to BC risk prediction. Likelihood ratio tests (LRTs) and bootstrapping methods were used to compare model performance with the addition of AI malignancy score. Results: The BBD cohort with mammography consisted of 3,125 women followed for a median 12.9 years, with 250 incident BC. Of these, 221 BC occurred at least two years after the mammogram and were used in analyses. As expected, increased BC risk was associated with BBD severity for AH HR=3.18 (95%CI: 2.04, 4.95) and for PDWA HR=1.59 (95%CI: 1.17, 2.14) compared to NP and with higher VPD HR=1.24 per SD (95% CI: 1.05, 1.45) (Table ). The AI-malignancy score alone was also associated with BC risk HR=1.16 per 1 unit score (95%CI: 1.09,1.23) and showed similar discriminatory accuracy C-statistic=0.626 (95%CI: 0.586, 0.665) as the model with BBD severity and VPD combined C-statistic=0.627, (95%CI: 0.585, 0.669) (Table ). In models with BBD severity and VPD, the AI-malignancy score was an independent risk factor for BC HR=1.14 (95%CI: 1.07, 1.21); PLRT0.001 but only achieved a marginally significant improvement in discriminatory accuracy C-statistic=0.651 (0.609, 0.693), ΔC-statistic=0.024 (95% CI: 0.000, 0.047). Conclusion: In this preliminary study, an AI BC detection algorithm achieved similar performance in predicting future BC risk compared to established risk factors. Citation Format: C. M. Vachon, S. Winham, M. Jensen, D. Hursh, L. Pacheco-Spann, A. Norman, J. Fischer, S. Schrup, L. Seymour, D. Gehling, S. Nyante, M. Troester, N. Karssemeijer, R. Vierkant, D. Radisky, C. Scott, S. I. Maimone, A. Degnim, M. Sherman. An AI algorithm for breast cancer detection improves future BC risk prediction among women with benign breast disease abstract. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PD4-02.

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