Faculty, Staff and Student Publications
Publication Date
10-2-2023
Journal
Cancers
DOI
10.3390/cancers15194829
PMID
37835523
PMCID
PMC10571741
PubMedCentral® Posted Date
October 2023
PubMedCentral® Full Text Version
Post-print
Abstract
Accurate tumor segmentation is required for quantitative image analyses, which are increasingly used for evaluation of tumors. We developed a fully automated and high-performance segmentation model of triple-negative breast cancer using a self-configurable deep learning framework and a large set of dynamic contrast-enhanced MRI images acquired serially over the patients' treatment course. Among all models, the top-performing one that was trained with the images across different time points of a treatment course yielded a Dice similarity coefficient of 93% and a sensitivity of 96% on baseline images. The top-performing model also produced accurate tumor size measurements, which is valuable for practical clinical applications.
Keywords
deep learning, tumor segmentation, triple-negative breast cancer
Published Open-Access
yes
Recommended Citation
Xu, Zhan; Rauch, David E; Mohamed, Rania M; et al., "Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer" (2023). Faculty, Staff and Student Publications. 1151.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/1151
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