Faculty, Staff and Student Publications
Publication Date
10-2-2023
Journal
Cancers
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
DOI
10.3390/cancers15194829
PMID
37835523
PMCID
PMC10571741
PubMedCentral® Posted Date
October 2023
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Medical Sciences Commons, Oncology Commons
Comments
Supplementary Material
PMID: 37835523