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29 May 2024 Added value of feature uncertainty in a radiomic analysis of contrast-enhanced digital mammography boosted by deep learning
Ricardo Montoya-del-Angel, Jorge Patricio Castillo-Lopez, Robert Martí, Vyanka Eunice Sanchez-Goytia, Liliana Moreno-Astudillo, Yolanda VIllaseñor-Navarro, Maria-Ester Brandan
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Proceedings Volume 13174, 17th International Workshop on Breast Imaging (IWBI 2024); 1317403 (2024) https://doi.org/10.1117/12.3027034
Event: 17th International Workshop on Breast Imaging (IWBI 2024), 2024, Chicago, IL, United States
Abstract
Deep learning models are the state-of-the-art for most medical imaging application, including mammography. However, large amounts of data are generally required for their usage. Radiomic analysis has shown the potential to improve clinical decision support systems for small datasets. One of the challenges facing the clinical implementation of radiomics is reproducibility. Our goal is to show that assessing radiomic features uncertainty can improve the robustness and performance of radiomic-based prediction models for contrast-enhanced digital mammographic images. Additionally, we propose the use of a pretrained tomosynthesis (DBT) lesion detection model as feature extractor for the boosting of the prediction framework. The prediction goal was the immunohistochemical status of breast cancer in 33 patients. We assessed two sources of uncertainty: misalignment between the subtracted images and region-of-interest delineation variability. Including uncertainties in the training step improved the performance of the prediction models, and the use of the DBT lesion detection model to boost the prediction improved the overall radiomic model performance for PR, ER and Ki67 receptors.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Ricardo Montoya-del-Angel, Jorge Patricio Castillo-Lopez, Robert Martí, Vyanka Eunice Sanchez-Goytia, Liliana Moreno-Astudillo, Yolanda VIllaseñor-Navarro, and Maria-Ester Brandan "Added value of feature uncertainty in a radiomic analysis of contrast-enhanced digital mammography boosted by deep learning", Proc. SPIE 13174, 17th International Workshop on Breast Imaging (IWBI 2024), 1317403 (29 May 2024); https://doi.org/10.1117/12.3027034
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KEYWORDS
Radiomics
Deep learning
Feature extraction
Digital breast tomosynthesis
Image processing
Mammography
Image registration
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