Robust Cross-vendor Mammographic Texture Models Using Augmentation-based
Domain Adaptation for Long-term Breast Cancer Risk
- URL: http://arxiv.org/abs/2212.13439v1
- Date: Tue, 27 Dec 2022 10:37:02 GMT
- Title: Robust Cross-vendor Mammographic Texture Models Using Augmentation-based
Domain Adaptation for Long-term Breast Cancer Risk
- Authors: Andreas D. Lauritzen, My Catarina von Euler-Chelpin, Elsebeth Lynge,
Ilse Vejborg, Mads Nielsen, Nico Karssemeijer, and Martin Lillholm
- Abstract summary: Mammography-based risk models should remain robust to domain shifts caused by different populations and mammographic devices.
We developed a robust, cross-vendor model for long-term risk assessment.
- Score: 0.5284541478311979
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The future of population-based breast cancer screening is likely personalized
strategies based on clinically relevant risk models. Mammography-based risk
models should remain robust to domain shifts caused by different populations
and mammographic devices. Modern risk models do not ensure adaptation across
vendor-domains and are often conflated to unintentionally rely on both
precursors of cancer and systemic/global mammographic information associated
with short- and long-term risk, respectively, which might limit performance. We
developed a robust, cross-vendor model for long-term risk assessment. An
augmentation-based domain adaption technique, based on flavorization of
mammographic views, ensured generalization to an unseen vendor-domain. We
trained on samples without diagnosed/potential malignant findings to learn
systemic/global breast tissue features, called mammographic texture, indicative
of future breast cancer. However, training so may cause erratic convergence. By
excluding noise-inducing samples and designing a case-control dataset, a robust
ensemble texture model was trained. This model was validated in two independent
datasets. In 66,607 Danish women with flavorized Siemens views, the AUC was
0.71 and 0.65 for prediction of interval cancers within two years (ICs) and
from two years after screening (LTCs), respectively. In a combination with
established risk factors, the model's AUC increased to 0.68 for LTCs. In 25,706
Dutch women with Hologic-processed views, the AUCs were not different from the
AUCs in Danish women with flavorized views. The results suggested that the
model robustly estimated long-term risk while adapting to an unseen processed
vendor-domain. The model identified 8.1% of Danish women accounting for 20.9%
of ICs and 14.2% of LTCs.
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