Texture analysis using machine learning–based 3-T magnetic resonance imaging for predicting recurrence in breast cancer patients treated with neoadjuvant chemotherapy | Zendy

Na Lae Eun | Zendy; Daesung Kang | Zendy; Eun Ju Son | Zendy; Ji Hyun Youk | Zendy; JeongAh Kim | Zendy; Hye Mi Gweon | Zendy

Open Access

Texture analysis using machine learning–based 3-T magnetic resonance imaging for predicting recurrence in breast cancer patients treated with neoadjuvant chemotherapy

Author(s) -

Na Lae Eun,

Daesung Kang,

Eun Ju Son,

Ji Hyun Youk,

JeongAh Kim,

Hye Mi Gweon

Publication year - 2021

Publication title -

european radiology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.606

H-Index - 149

eISSN - 1432-1084

pISSN - 0938-7994

DOI - 10.1007/s00330-021-07816-x

Subject(s) - medicine , magnetic resonance imaging , neuroradiology , receiver operating characteristic , breast cancer , random forest , radiology , cancer , nuclear medicine , artificial intelligence , computer science , neurology , psychiatry

To determine whether texture analysis for magnetic resonance imaging (MRI) can predict recurrence in patients with breast cancer treated with neoadjuvant chemotherapy (NAC).

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