Prognostic impact of deep learning–based quantification in clinical stage 0-I lung adenocarcinoma.
| Publicado en: | European Radiology Vol. 33; no. 12; pp. 8542 - 8554 |
|---|---|
| Autores principales: | , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2023
|
| 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=173805916&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173805916 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Dec2023 vid: 33 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 173805916 164867964 10.1007/s00330-023-09845-0 173805916 ppf: 8542 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Prognostic impact of deep learning–based quantification in clinical stage 0-I lung adenocarcinoma. aug: au: Zhu, Ying Chen, Li-Li Luo, Ying-Wei Zhang, Li Ma, Hui-Yun Yang, Hao-Shuai Liu, Bao-Cong Li, Lu-Jie Zhang, Wen-Biao Li, Xiang-Min Xie, Chuan-Miao Yang, Jian-Cheng Wang, De-ling Li, Qiong affil: Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, 510080, Guangzhou, Province Guangdong, People's Republic of China sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
|---|