Radiomics Analysis Based on Automatic Image Segmentation of DCE-MRI for Predicting Triple-Negative and Nontriple-Negative Breast Cancer.

Journal: Computational and mathematical methods in medicine

PMID: 34422088

Abstract

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).

Authors

Mingming Ma

Department of Diagnostic Ultrasound and Echocardiography, Sir Run Run Shaw Hospital, Zhejiang University College of Medicine, Hangzhou, People's Republic of China.
Liangyu Gan

Breast Disease Center, Peking University First Hospital, Beijing, China.
Yuan Jiang

Beijing Smart Tree Medical Technology co. Ltd., Beijing, China.
Naishan Qin

Beijing Smart Tree Medical Technology co. Ltd., Beijing, China.
Changxin Li

Beijing Smart Tree Medical Technology co. Ltd., Beijing, China.
Yaofeng Zhang

Beijing Smart Tree Medical Technology co. Ltd., Beijing, China.
XiaoYing Wang

Keywords

Adult Aged Breast Neoplasms China Cohort Studies Computational Biology Contrast Media Deep Learning Female Humans Image Interpretation, Computer-Assisted Magnetic Resonance Imaging Middle Aged Principal Component Analysis Retrospective Studies ROC Curve Software Triple Negative Breast Neoplasms

External Resources

View on PubMed Access via DOI PubMed (34422088)

Radiomics Analysis Based on Automatic Image Segmentation of DCE-MRI for Predicting Triple-Negative and Nontriple-Negative Breast Cancer.

Abstract

Authors

Keywords

External Resources

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