A comprehensive approach for evaluating lymphovascular invasion in invasive breast cancer: Leveraging multimodal MRI findings, radiomics, and deep learning analysis of intra- and peritumoral regions.

Journal: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

Published Date: Jul 8, 2024

Abstract

PURPOSE: To evaluate lymphovascular invasion (LVI) in breast cancer by comparing the diagnostic performance of preoperative multimodal magnetic resonance imaging (MRI)-based radiomics and deep-learning (DL) models.

Authors

Wen Liu

Department of Dermatology, Air Force General Hospital, PLA Beijing 100142, China.
Li Li

Department of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
Jiao Deng

Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha 410013, China.
Wei Li

Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.

Keywords

Adult Aged Breast Neoplasms Deep Learning Female Humans Lymphatic Metastasis Magnetic Resonance Imaging Middle Aged Multimodal Imaging Neoplasm Invasiveness Radiomics Retrospective Studies

External Resources

View on PubMed Access via DOI PubMed (39032451)

A comprehensive approach for evaluating lymphovascular invasion in invasive breast cancer: Leveraging multimodal MRI findings, radiomics, and deep learning analysis of intra- and peritumoral regions.

Abstract

Authors

Keywords

External Resources

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