Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set.

Journal: Breast cancer research and treatment

PMID: 30328048

Abstract

PURPOSE: To determine whether a multivariate machine learning-based model using computer-extracted features of pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer patients.

Authors

Elizabeth Hope Cain

Department of Radiology, Duke University School of Medicine, 2301 Erwin Road, Durham, NC, 27705, USA. Elizabeth.cain@duke.edu.
Ashirbani Saha

Department of Radiology, Duke University School of Medicine, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA. ashirbani.saha@duke.edu.
Michael R Harowicz

Department of Radiology, Duke University School of Medicine, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA.
Jeffrey R Marks

Department of Surgery, Duke University School of Medicine, Durham, North Carolina.
P Kelly Marcom

Department of Medicine, Duke University School of Medicine, 2301 Erwin Road, Durham, NC, 27705, USA.
Maciej A Mazurowski

Department of Radiology, Duke University School of Medicine, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA.

Keywords

Adult Aged Antineoplastic Combined Chemotherapy Protocols Breast Feasibility Studies Female Humans Image Processing, Computer-Assisted Machine Learning Magnetic Resonance Imaging Mastectomy, Segmental Middle Aged Neoadjuvant Therapy Neoplasm Staging Receptor, ErbB-2 Retrospective Studies ROC Curve Treatment Outcome Triple Negative Breast Neoplasms

External Resources

View on PubMed Access via DOI PubMed (30328048)

Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set.

Abstract

Authors

Keywords

External Resources

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