التفاصيل البيبلوغرافية
| العنوان: |
Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer. |
| المؤلفون: |
Xu, Zhan, Rauch, David E., Mohamed, Rania M., Pashapoor, Sanaz, Zhou, Zijian, Panthi, Bikash, Son, Jong Bum, Hwang, Ken-Pin, Musall, Benjamin C., Adrada, Beatriz E., Candelaria, Rosalind P., Leung, Jessica W. T., Le-Petross, Huong T. C., Lane, Deanna L., Perez, Frances, White, Jason, Clayborn, Alyson, Reed, Brandy, Chen, Huiqin, Sun, Jia |
| المصدر: |
Cancers; Oct2023, Vol. 15 Issue 19, p4829, 14p |
| مصطلحات موضوعية: |
DEEP learning, DIGITAL image processing, MATHEMATICAL models, MAGNETIC resonance imaging, CANCER patients, TUMOR classification, THEORY, RESEARCH funding, SENSITIVITY & specificity (Statistics), BREAST tumors |
| مستخلص: |
Simple Summary: Quantitative image analysis of cancers requires accurate tumor segmentation that is often performed manually. In this study, we developed a deep learning model with a self-configurable nnU-Net for fully automated tumor segmentation on serially acquired dynamic contrast-enhanced MRI images of triple-negative breast cancer. In an independent testing dataset, our nnU-Net-based deep learning model performed automated tumor segmentation with a Dice similarity coefficient of 93% and a sensitivity of 96%. 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. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
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