Leveraging multimodal MRI-based radiomics analysis with diverse machine learning models to evaluate lymphovascular invasion in clinically node-negative breast cancer

Jiang, Yihong; Zeng, Ying; Zuo, Zhichao; Yang, Xiuqi; Liu, Haibo; Zhou, Yingjun; Fan, Xiaohong

doi:10.1016/j.heliyon.2023.e23916

Heliyon

2024

DOI: 10.1016/j.heliyon.2023.e23916

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Leveraging multimodal MRI-based radiomics analysis with diverse machine learning models to evaluate lymphovascular invasion in clinically node-negative breast cancer

Yihong Jiang,

Ying Zeng,

Zhichao Zuo

et al.

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Cited by 4 publications

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Leveraging multimodal MRI-based radiomics analysis with diverse machine learning models to evaluate lymphovascular invasion in clinically node-negative breast cancer

Cited by 4 publications

References 24 publications

Exploring habitats-based spatial distributions: improving predictions of lymphovascular invasion in invasive breast cancer

Exploring habitats-based spatial distributions: improving predictions of lymphovascular invasion in invasive breast cancer

Multiphases DCE-MRI Radiomics Nomogram for Preoperative Prediction of Lymphovascular Invasion in Invasive Breast Cancer

Machine learning based on optimal VOI of multi-sequence MR images to predict lymphovascular invasion in invasive breast cancer

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