[18F]FET PET/MR and machine learning in the evaluation of glioma.
This article discusses the use of [18F]FET PET/MR imaging and machine learning in the evaluation of glioma, the most common malignant tumors of the central nervous system. The article highlights the importance of molecular diagnostics in tumor characterization and the potential of radiomics, which a...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 51; no. 3; pp. 797 - 800 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Feb2024
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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=174879063&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 174879063 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Feb2024 vid: 51 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 174879063 173550076 10.1007/s00259-023-06505-9 174879063 ppf: 797 ppct: 3 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: [18F]FET PET/MR and machine learning in the evaluation of glioma. aug: au: Piscopo, Leandra Zampella, Emilia Klain, Michele affil: https://ror.org/05290cv24 Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy sug: ab: This article discusses the use of [18F]FET PET/MR imaging and machine learning in the evaluation of glioma, the most common malignant tumors of the central nervous system. The article highlights the importance of molecular diagnostics in tumor characterization and the potential of radiomics, which analyzes and quantifies imaging data, to provide valuable biological information. The study presented in the article focuses on the role of MR multiparametric radiomics in predicting tumor residual derived from [18F]FET PET/MR imaging data in patients with glioma. The results show that the radiomics models, particularly the nomogram, have the capacity to predict tumor residual and can potentially improve the prognosis and treatment of glioma patients. The article also mentions other studies that have explored the relationship between [18F]FET PET and MR imaging in glioma patients using machine learning techniques. Overall, this research suggests that the combination of hybrid imaging methods and machine learning has the potential to enhance the prognostic, diagnostic, and therapeutic outcomes for glioma patients. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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