A machine learning-based radiomics model for the prediction of axillary lymph-node metastasis in breast cancer.

Journal: Breast cancer (Tokyo, Japan)

Published Date: Jan 17, 2021

Abstract

OBJECTIVE: The aim of this study was to develop and validate machine learning-based radiomics model for predicting axillary lymph-node (ALN) metastasis in invasive ductal breast cancer (IDC) using F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT).

Authors

Bong-Il Song

Department of Nuclear Medicine, Keimyung University Dongsan Hospital, Keimyung University School of Medicine, 1095 Dalgubeol-daero, Dalseo-gu, Daegu, 42601, Republic of Korea. song@dsmc.or.kr.

Keywords

Breast Neoplasms Carcinoma, Ductal, Breast Female Fluorodeoxyglucose F18 Humans Lymphatic Metastasis Machine Learning Positron Emission Tomography Computed Tomography Radiopharmaceuticals Sensitivity and Specificity

External Resources

View on PubMed Access via DOI PubMed (33454875)

A machine learning-based radiomics model for the prediction of axillary lymph-node metastasis in breast cancer.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals