Machine learning model to preoperatively predict T2/T3 staging of laryngeal and hypopharyngeal cancer based on the CT radiomic signature.
| Published in: | European Radiology Vol. 34; no. 8; pp. 5349 - 5360 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Aug2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=178483257&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178483257 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Aug2024 vid: 34 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 178483257 174730269 10.1007/s00330-023-10557-8 178483257 ppf: 5349 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine learning model to preoperatively predict T2/T3 staging of laryngeal and hypopharyngeal cancer based on the CT radiomic signature. aug: au: Liu, Qianhan Liu, Shengdan Mao, Yu Kang, Xuefeng Yu, Mingling Chen, Guangxiang affil: https://ror.org/0014a0n68 Department of Radiology, The Affiliated Hospital of Southwest Medical University, No. 23 Tai Ping Street, 646000, Luzhou, Sichuan, China sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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