Enhancing Breast Cancer Risk Prediction by Incorporating Prior Images

• URL: http://arxiv.org/abs/2303.15699v2
• Date: Mon, 28 Aug 2023 04:46:01 GMT
• Title: Enhancing Breast Cancer Risk Prediction by Incorporating Prior Images
• Authors: Hyeonsoo Lee, Junha Kim, Eunkyung Park, Minjeong Kim, Taesoo Kim, Thijs Kooi
• Abstract summary: We present a new method, PRIME+, for breast cancer risk prediction that leverages prior mammograms using a transformer decoder. We validate our approach on a dataset with 16,113 exams and demonstrate that it effectively captures patterns of changes from prior mammograms. Experimental results show that our model achieves a statistically significant improvement over the state-of-the-art based model, with a C-index increase from 0.68 to 0.73.
• Score: 8.756888862171195
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recently, deep learning models have shown the potential to predict breast cancer risk and enable targeted screening strategies, but current models do not consider the change in the breast over time. In this paper, we present a new method, PRIME+, for breast cancer risk prediction that leverages prior mammograms using a transformer decoder, outperforming a state-of-the-art risk prediction method that only uses mammograms from a single time point. We validate our approach on a dataset with 16,113 exams and further demonstrate that it effectively captures patterns of changes from prior mammograms, such as changes in breast density, resulting in improved short-term and long-term breast cancer risk prediction. Experimental results show that our model achieves a statistically significant improvement in performance over the state-of-the-art based model, with a C-index increase from 0.68 to 0.73 (p < 0.05) on held-out test sets.

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