Deep learning radiomic nomogram outperforms the clinical model in distinguishing intracranial solitary fibrous tumors from angiomatous meningiomas and can predict patient prognosis.
| Publicado en: | European Radiology Vol. 35; no. 5; pp. 2670 - 2681 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2025
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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=184707005&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184707005 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: May2025 vid: 35 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184707005 180292330 10.1007/s00330-024-11082-y 184707005 ppf: 2670 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep learning radiomic nomogram outperforms the clinical model in distinguishing intracranial solitary fibrous tumors from angiomatous meningiomas and can predict patient prognosis. aug: au: Liang, Xiaohong Ke, Xiaoai Hu, Wanjun Jiang, Jian Li, Shenglin Xue, Caiqiang Liu, Xianwang Dend, Juan Yan, Cheng Gao, Mingzi Zhao, Liqin Zhou, Junlin affil: https://ror.org/013xs5b60 Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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