Longitudinal analysis of micro-calcifications features for breast cancer risk prediction with the Mirai model

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

doi:10.1117/12.3027024

29 May 2024 Longitudinal analysis of micro-calcifications features for breast cancer risk prediction with the Mirai model

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

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Proceedings Volume 13174, 17th International Workshop on Breast Imaging (IWBI 2024); 131740P (2024) https://doi.org/10.1117/12.3027024
Event: 17th International Workshop on Breast Imaging (IWBI 2024), 2024, Chicago, IL, United States

Abstract

Complementary relationship between computer-aided detection (CAD) and risk prediction has been identified. To understand the factors triggering either cancer detection or risk prediction, we previously studied the performance of the deep learning (DL)-based risk prediction model, Mirai, using a feature-centric explainable AI (XAI) approach. A total of 16 calcification features were identified from Mirai as major risk factor contributors. Several studies have revealed the existence of early detection signs on prior mammograms of screen-detected and interval cancers. Accordingly, the longitudinal behavior of calcifications may further improve the understanding of the causal relationship between Mirai calcification features and elevated risk. In this study, we hypothesize that the calcification features from Mirai have the ability to capture early suspicious signs, which may be important for the risk prediction of breast cancer development. Thus, we tracked the Mirai calcification features across two screening rounds using the breast polar coordinate system. Subsequently, we assessed the ability to predict the current Breast Imaging-Reporting and Data System (BI-RADS) assessment from prior mammograms. The results show that calcification features were able to capture early suspicious signs on prior mammograms at the same location with an average polar angle difference of 13 degrees compared to the current mammograms. In addition, the calcification features were able to classify the current BI-RADS assessment with an area under the receiver operating characteristic curve (AUC) of 0.74 using prior mammograms. In conclusion, the predictive power of calcification features in short-term risk prediction may arise from their ability to detect early suspicious signs.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Y. K. Wang, Z. Klanecek, T. Wagner, L. Cockmartin, N. W. Marshall, A. Studen, R. Jeraj, and H. Bosmans "Longitudinal analysis of micro-calcifications features for breast cancer risk prediction with the Mirai model", Proc. SPIE 13174, 17th International Workshop on Breast Imaging (IWBI 2024), 131740P (29 May 2024); https://doi.org/10.1117/12.3027024

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KEYWORDS

Mammography

Breast

Breast cancer

Cancer detection

Feature extraction

Tumor growth modeling

Computer aided detection

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