Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ.

Journal: Computers in biology and medicine

Published Date: Oct 16, 2019

Abstract

PURPOSE: To determine whether deep learning-based algorithms applied to breast MR images can aid in the prediction of occult invasive disease following the diagnosis of ductal carcinoma in situ (DCIS) by core needle biopsy.

Authors

Zhe Zhu
Michael Harowicz

Department of Radiology, Duke University, Durham, NC, 27705, USA. Electronic address: michael.harowicz@gmail.com.
Jun Zhang

First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.
Ashirbani Saha

Department of Radiology, Duke University School of Medicine, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA. ashirbani.saha@duke.edu.
Lars J Grimm
E Shelley Hwang

Department of Surgery, Duke University School of Medicine, Durham, North Carolina.
Maciej A Mazurowski

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

Keywords

Adult Aged Biopsy, Fine-Needle Breast Neoplasms Carcinoma, Ductal, Breast Deep Learning Female Humans Magnetic Resonance Imaging Middle Aged Predictive Value of Tests Retrospective Studies Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (31698241)

Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ.

Abstract

Authors

Keywords

External Resources

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