Assessing breast cancer volume alterations post-neoadjuvant chemotherapy through DenseNet-201 deep learning analysis on DCE-MRI-Reference-Cited by-同舟云学术

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Assessing breast cancer volume alterations post-neoadjuvant chemotherapy through DenseNet-201 deep learning analysis on DCE-MRI

Published:2024-09 Issue:3 Volume:17 Page:100971
ISSN:1687-8507
Container-title:Journal of Radiation Research and Applied Sciences
language:en
Short-container-title:Journal of Radiation Research and Applied Sciences

Author:

Gu Zhenqi,Huang Jing,Zhou Changyu,Wang Qi,Kong Jianchun,You Xiongbin,Zhang Ziwei,Zhao Hong^ORCID

Funder

Zhejiang Province Natural Science Foundation

Publisher

Elsevier BV

Reference39 articles.

1. AJCC cancer staging manual;Amin,2017

2. Breast cancer Risk prediction using deep learning;Bae;Radiology,2021

3. Classification of hypervascularized lesions in CE MR imaging of the breast;Baum;European Radiology,2002

4. Radiomic signatures derived from multiparametric MRI for the pretreatment prediction of response to neoadjuvant chemotherapy in breast cancer;Bian;British Journal of Radiology,2020

5. Customizing local and systemic therapies for women with early breast cancer: The St. Gallen International Consensus Guidelines for treatment of early breast cancer 2021;Burstein;Annals of Oncology,2021

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