Deep learning-based radiomics does not improve residual cancer burden prediction post-chemotherapy in LIMA breast MRI trial.
| Publicado en: | European Radiology Vol. 36; no. 2; pp. 850 - 863 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Feb2026
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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=192011560&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 192011560 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Feb2026 vid: 36 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 192011560 187159493 10.1007/s00330-025-11801-z 192011560 ppf: 850 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep learning-based radiomics does not improve residual cancer burden prediction post-chemotherapy in LIMA breast MRI trial. aug: au: Janse, Markus H. A. Janssen, Liselore M. Wolters-van der Ben, Elian J. M. Moman, Maaike R. Viergever, Max A. van Diest, Paul J. Gilhuijs, Kenneth G. A. affil: https://ror.org/04pp8hn57 Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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