MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer

MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer

Background: To use clinical and MRI radiomic features coupled with machine learning to assess HER2 expression level and predict pathologic response (pCR) in HER2 overexpressing breast cancer patients receiving neoadjuvant chemotherapy (NAC). Methods: This retrospective study included 311 patients. p...

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Bibliographic Details
Main Authors: Almir G.V. Bitencourt, Peter Gibbs, Carolina Rossi Saccarelli, Isaac Daimiel, Roberto Lo Gullo, Michael J. Fox, Sunitha Thakur, Katja Pinker, Elizabeth A. Morris, Monica Morrow, Maxine S. Jochelson
Format: Article
Language:English
Published: Elsevier 2020-11-01
Series:EBioMedicine
Subjects:
Magnetic resonance imaging
Breast invasive ductal carcinoma
HER2
ErbB-2 receptor
Neoadjuvant therapy
Machine learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2352396420304187

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http://www.sciencedirect.com/science/article/pii/S2352396420304187

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