Cargando…
Radiomics Analysis Based on Automatic Image Segmentation of DCE-MRI for Predicting Triple-Negative and Nontriple-Negative Breast Cancer
PURPOSE: To investigate whether quantitative radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) could be used to differentiate triple-negative breast cancer (TNBC) and nontriple-negative breast cancer (non-TNBC). MATERIALS AND METHODS: This retrospective...
Autores principales: | Ma, Mingming, Gan, Liangyu, Jiang, Yuan, Qin, Naishan, Li, Changxin, Zhang, Yaofeng, Wang, Xiaoying |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371618/ https://www.ncbi.nlm.nih.gov/pubmed/34422088 http://dx.doi.org/10.1155/2021/2140465 |
Ejemplares similares
-
Epidermal Growth Factor Receptor Expression in Triple Negative and Nontriple Negative Breast Carcinomas
por: Changavi, Arathi A, et al.
Publicado: (2015) -
A Radiomics Model for Preoperative Predicting Sentinel Lymph Node Metastasis in Breast Cancer Based on Dynamic Contrast-Enhanced MRI
por: Ma, Mingming, et al.
Publicado: (2022) -
Appearance Constrained Semi-Automatic Segmentation from DCE-MRI is Reproducible and Feasible for Breast Cancer Radiomics: A Feasibility Study
por: Veeraraghavan, Harini, et al.
Publicado: (2018) -
A Clinical–Radiomics Model for Predicting Axillary Pathologic Complete Response in Breast Cancer With Axillary Lymph Node Metastases
por: Gan, Liangyu, et al.
Publicado: (2021) -
Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI
por: Jiao, Han, et al.
Publicado: (2020)