Incorporating longitudinal screening data into image-based breast cancer risk assessment

T. Wagner; Z. Klanecek; Y. K. Wang; L. Cockmartin; N. Marshall; A. Studen; R. Jeraj; H. Bosmans

doi:10.1117/12.3026995

29 May 2024 Incorporating longitudinal screening data into image-based breast cancer risk assessment

T. Wagner, Z. Klanecek, Y. K. Wang, L. Cockmartin, N. Marshall, A. Studen, R. Jeraj, H. Bosmans

Proceedings Volume 13174, 17th International Workshop on Breast Imaging (IWBI 2024); 131740T (2024) https://doi.org/10.1117/12.3026995
Event: 17th International Workshop on Breast Imaging (IWBI 2024), 2024, Chicago, IL, United States

Abstract

This study proposes a method to use longitudinal breast cancer screening data to develop a 1- to 4-year breast cancer risk prediction model. It uses transfer learning from an open-source breast cancer detection model, an Autoencoder to perform dimensionality reduction as well as an LSTM network to incorporate the sequential data. The study utilizes a labelled dataset of 846 patients with up to five different mammography screening exams. The exams were taken on three systems from the vendor Siemens and the images are of the “FOR PRESENTATION” type. In this dataset there are 423 low risk cases and 423 high risk cases. A breast cancer detection model was used to obtain a latent representation of features extracted from the screening images. Dimensionality reduction was performed on the latent space using an Autoencoder architecture. The reduced latent space was then mapped to 1- to 4-year breast cancer risk with an LSTM model. The model achieved an AUC of 0.74 for differentiating high and low risk cases, outperforming the Tyrer-Cuzick model. At the reference specificity operating point of 85.4% from the Tyrer-Cuzick model, the longitudinal model achieves a sensitivity of 60%, outperforming a similar model trained by only seeing a single exam of a given patient. The incorporation of longitudinal data into breast cancer risk assessment models can increase the sensitivity to underlying patterns that are correlated to breast cancer and therefore improve breast cancer screening strategies.

Citation Download Citation

T. Wagner, Z. Klanecek, Y. K. Wang, L. Cockmartin, N. Marshall, A. Studen, R. Jeraj, and H. Bosmans "Incorporating longitudinal screening data into image-based breast cancer risk assessment", Proc. SPIE 13174, 17th International Workshop on Breast Imaging (IWBI 2024), 131740T (29 May 2024); https://doi.org/10.1117/12.3026995

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KEYWORDS

Data modeling

Breast cancer

Education and training

Risk assessment

Cancer

Performance modeling

Cancer detection

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