Texture analysis using machine learning–based 3-T magnetic resonance imaging for predicting recurrence in breast cancer patients treated with neoadjuvant chemotherapy.
| Publicado en: | European Radiology Vol. 31; no. 9; pp. 6916 - 6929 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152014447&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152014447 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Sep2021 vid: 31 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 152014447 149166973 10.1007/s00330-021-07816-x 152014447 ppf: 6916 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Texture analysis using machine learning–based 3-T magnetic resonance imaging for predicting recurrence in breast cancer patients treated with neoadjuvant chemotherapy. aug: au: Eun, Na Lae Kang, Daesung Son, Eun Ju Youk, Ji Hyun Kim, Jeong-Ah Gweon, Hye Mi affil: Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, 06273, Seoul, Republic of Korea sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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