Predicting short- to long-term breast cancer risk from longitudinal mammographic screening history

Breast cancer (BC) risk assessment aims to enhance individualized screening and prevention strategies. While recent deep learning (DL) models based on mammography have shown promise in short-term risk prediction, they primarily rely on single-time-point (STP) exams, ignoring temporal changes in breast tissue from sequence exams. We present the Multi-Time Point Breast Cancer Risk Model (MTP-BCR), a novel DL approach that integrates traditional risk factors and longitudinal mammography data to capture subtle tissue changes indicative of future malignancy. Using a large in-house dataset with 171,168 mammograms from 9133 women, MTP-BCR achieved superior performance in 10-year risk prediction, with an AUC of 0.80 (95% CI, 0.78-0.82) at the patient level, outperforming STP-based and traditional risk models. External validation on the CSAW-CC dataset confirmed its robustness. Further analysis demonstrates the advantages of the MTP-BCR method in diverse populations. MTP-BCR also excels in risk stratification and offers heatmaps to enhance clinical interpretability.

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Publication Details

Journal
npj Breast Cancer
Published
2025-10-29
DOI
https://doi.org/10.1038/s41523-025-00831-x
Citations
2
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
4.96

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article

Predicting short- to long-term breast cancer risk from longitudinal mammographic screening history

Marjanka K. Schmidt, Luyi Han, Yuan Gao, Ruisheng Su et al.
2 citations
npj Breast Cancer
AI in cancer detection
4.96
article

Predicting short- to long-term breast cancer risk from longitudinal mammographic screening history

Marjanka K. Schmidt, Luyi Han, Yuan Gao, Ruisheng Su, Anna D’Angelo, Caroline A. Drukker, Xin Wang, Ritse M. Mann, Jaap Kroes, Tianyu Zhang, Tao Tan, Regina G. H. Beets‐Tan, Jonas Teuwen, Nico Karssemeijer
article en
2 citations

Abstract

Breast cancer (BC) risk assessment aims to enhance individualized screening and prevention strategies. While recent deep learning (DL) models based on mammography have shown promise in short-term risk prediction, they primarily rely on single-time-point (STP) exams, ignoring temporal changes in breast tissue from sequence exams. We present the Multi-Time Point Breast Cancer Risk Model (MTP-BCR), a novel DL approach that integrates traditional risk factors and longitudinal mammography data to capture subtle tissue changes indicative of future malignancy. Using a large in-house dataset with 171,168 mammograms from 9133 women, MTP-BCR achieved superior performance in 10-year risk prediction, with an AUC of 0.80 (95% CI, 0.78-0.82) at the patient level, outperforming STP-based and traditional risk models. External validation on the CSAW-CC dataset confirmed its robustness. Further analysis demonstrates the advantages of the MTP-BCR method in diverse populations. MTP-BCR also excels in risk stratification and offers heatmaps to enhance clinical interpretability.

npj Breast CancerVol. 11(1)
Radboud University Nijmegen (NL), Erasmus MC (NL), Agostino Gemelli University Polyclinic (IT), Radboud University Medical Center (NL), The Netherlands Cancer Institute (NL), Maastricht University (NL), Oncode Institute (NL), Istituti di Ricovero e Cura a Carattere Scientifico (IT), Macao Polytechnic University (MO)
China Scholarship Council
Openalex Percentile: Top 4%
AI in cancer detection
4.96
DOIOpen Access PDFOpenAlex
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