Deep learning and radiomics integration of photoacoustic/ultrasound imaging for non-invasive prediction of luminal and non-luminal breast cancer subtypes.
| Publicado en: | Breast Cancer Research Vol. 27; no. 1; pp. 1 - 16 |
|---|---|
| Autores principales: | , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
9/24/2025
|
| 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=188240314&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188240314 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14655411 8UYJ jtl: Breast Cancer Research issn: 14655411 maglogo: N pubinfo: dt: 9/24/2025 vid: 27 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 188240314 10.1186/s13058-025-02113-7 188240314 ppf: 1 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep learning and radiomics integration of photoacoustic/ultrasound imaging for non-invasive prediction of luminal and non-luminal breast cancer subtypes. aug: au: Wang, Mengyun Mo, Sijie Li, Guoqiu Zheng, Jing Wu, Huaiyu Tian, Hongtian Chen, Jing Tang, Shuzhen Chen, Zhijie Xu, Jinfeng Huang, Zhibin Dong, Fajin affil: https://ror.org/02xe5ns62 Department of Ultrasound, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, Shenzhen, Guangdong, China sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
|---|