September 12, 2025European RadiologyOpen Access

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

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Authors

RTRaquel TenderoCentro Tecnológico de Investigación, Desarrollo e Innovación en tecnologías de la Información y las Comunicaciones (TIC)ALAndrés LarrozaCentro Tecnológico de Investigación, Desarrollo e Innovación en tecnologías de la Información y las Comunicaciones (TIC)FPFrancisco Javier Pérez‐BenitoCentro Tecnológico de Investigación, Desarrollo e Innovación en tecnologías de la Información y las Comunicaciones (TIC)

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Implication

Retrospective case-control study shows hybrid model improves breast cancer risk prediction using clinical data, suggesting better screening strategies for high-risk women.

Key Points

Integrating artificial intelligence with clinical and mammographic data boosts breast cancer risk prediction accuracy.
The hybrid model achieved an AUC of 0.75, significantly outperforming traditional models like CNN and ERTpd + im.
Robust performance across breast density quartiles and better accuracy for screen-detected cancers highlight the model's reliability.
Findings suggest personalized screening strategies could be significantly improved for high-risk women.

Cite This Study

Tendero et al. (2025) studied this question.

synapsesocial.com/papers/68d418d4713b0b5dfea646eb https://doi.org/10.1007/s00330-025-11980-9

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Implication

Retrospective case-control study shows hybrid model improves breast cancer risk prediction using clinical data, suggesting better screening strategies for high-risk women.

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

Discussion

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Implication

Key Points

Cite This Study

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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