Objectives This study evaluates whether integrating clinical data with mammographic features using artificial intelligence (AI) improves 2-year breast cancer risk prediction compared to using either data type alone. Materials and methods This retrospective nested case-control study included 2193 women (mean age, 59 ± 5 years) screened at Hospital del Mar, Spain (2013–2020), with 418 cases (mammograms taken 2 years before diagnosis) and 1775 controls (cancer-free for ≥ 2 years). Three models were evaluated: (1) ERTpd + im, based on Extremely Randomized Trees (ERT), split into sub-models for personal data (ERTpd) and image features (ERTim); (2) an image-only model (CNN); and (3) a hybrid model (ERTpd + im + CNN). Five-fold cross-validation, area under the receiver operating characteristic curve (AUC), bootstrapping for confidence intervals, and DeLong tests for paired data assessed performance. Robustness was evaluated across breast density quartiles and detection type (screen-detected vs. interval cancers). Results The hybrid model achieved an AUC of 0.75 (95% CI: 0.71–0.76), significantly outperforming the CNN model (AUC, 0.74; 95% CI: 0.70–0.75; p < 0.05) and slightly surpassing ERTpd + im (AUC, 0.74; 95% CI: 0.70–0.76). Sub-models ERTpd and ERTim had AUCs of 0.59 and 0.73, respectively. The hybrid model performed consistently across breast density quartiles (p > 0.05) and better for screen-detected (AUC, 0.79) than interval cancers (AUC, 0.59; p < 0.001). Conclusions This study shows that integrating clinical and mammographic data with AI improves 2-year breast cancer risk prediction, outperforming single-source models. The hybrid model demonstrated higher accuracy and robustness across breast density quartiles, with better performance for screen-detected cancers. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Current breast cancer risk models have limitations in accuracy. Can integrating clinical and mammographic data using artificial intelligence (AI) improve short-term risk prediction? <Emphasis Type="BoldItalic">Findings</Emphasis> A hybrid model combining clinical and imaging data achieved the highest accuracy in predicting 2-year breast cancer risk, outperforming models using either data type alone. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> Integrating clinical and mammographic data with AI improves breast cancer risk prediction. This approach enables personalized screening strategies and supports early detection. It helps identify high-risk women and optimizes the use of additional assessments within screening programs. Graphical Abstract

Breast cancer risk assessment for screening: a hybrid artificial intelligence approach

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2025年9月11日

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Raquel Tendero,
Andrés Larroza,
Francisco Javier Pérez-Benito,
Juan Carlos Perez-Cortes,
Marta Román,
Rafael Llobet

摘要

Objectives

This study evaluates whether integrating clinical data with mammographic features using artificial intelligence (AI) improves 2-year breast cancer risk prediction compared to using either data type alone.

Materials and methods

This retrospective nested case-control study included 2193 women (mean age, 59 ± 5 years) screened at Hospital del Mar, Spain (2013–2020), with 418 cases (mammograms taken 2 years before diagnosis) and 1775 controls (cancer-free for ≥ 2 years). Three models were evaluated: (1) ERTpd + im, based on Extremely Randomized Trees (ERT), split into sub-models for personal data (ERTpd) and image features (ERTim); (2) an image-only model (CNN); and (3) a hybrid model (ERTpd + im + CNN). Five-fold cross-validation, area under the receiver operating characteristic curve (AUC), bootstrapping for confidence intervals, and DeLong tests for paired data assessed performance. Robustness was evaluated across breast density quartiles and detection type (screen-detected vs. interval cancers).

Results

The hybrid model achieved an AUC of 0.75 (95% CI: 0.71–0.76), significantly outperforming the CNN model (AUC, 0.74; 95% CI: 0.70–0.75; p < 0.05) and slightly surpassing ERTpd + im (AUC, 0.74; 95% CI: 0.70–0.76). Sub-models ERTpd and ERTim had AUCs of 0.59 and 0.73, respectively. The hybrid model performed consistently across breast density quartiles (p > 0.05) and better for screen-detected (AUC, 0.79) than interval cancers (AUC, 0.59; p < 0.001).

Conclusions

This study shows that integrating clinical and mammographic data with AI improves 2-year breast cancer risk prediction, outperforming single-source models. The hybrid model demonstrated higher accuracy and robustness across breast density quartiles, with better performance for screen-detected cancers.

Key Points

Question Current breast cancer risk models have limitations in accuracy. Can integrating clinical and mammographic data using artificial intelligence (AI) improve short-term risk prediction?

Findings A hybrid model combining clinical and imaging data achieved the highest accuracy in predicting 2-year breast cancer risk, outperforming models using either data type alone.

Clinical relevance Integrating clinical and mammographic data with AI improves breast cancer risk prediction. This approach enables personalized screening strategies and supports early detection. It helps identify high-risk women and optimizes the use of additional assessments within screening programs.

Graphical Abstract