Joint EANM/SNMMI guideline on radiomics in nuclear medicine: Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council.
Purpose: The purpose of this guideline is to provide comprehensive information on best practices for robust radiomics analyses for both hand-crafted and deep learning-based approaches. Methods: In a cooperative effort between the EANM and SNMMI, we agreed upon current best practices and recommendati...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 50; no. 2; pp. 352 - 376 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jan2023
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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=161159503&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161159503 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: Jan2023 vid: 50 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 161159503 160015077 10.1007/s00259-022-06001-6 161159503 ppf: 352 ppct: 24 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Joint EANM/SNMMI guideline on radiomics in nuclear medicine: Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council. aug: au: Hatt, M. Krizsan, A. K. Rahmim, A. Bradshaw, T. J. Costa, P. F. Forgacs, A. Seifert, R. Zwanenburg, A. El Naqa, I. Kinahan, P. E. Tixier, F. Jha, A. K. Visvikis, D. affil: LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France sug: ab: Purpose: The purpose of this guideline is to provide comprehensive information on best practices for robust radiomics analyses for both hand-crafted and deep learning-based approaches. Methods: In a cooperative effort between the EANM and SNMMI, we agreed upon current best practices and recommendations for relevant aspects of radiomics analyses, including study design, quality assurance, data collection, impact of acquisition and reconstruction, detection and segmentation, feature standardization and implementation, as well as appropriate modelling schemes, model evaluation, and interpretation. We also offer an outlook for future perspectives. Conclusion: Radiomics is a very quickly evolving field of research. The present guideline focused on established findings as well as recommendations based on the state of the art. Though this guideline recognizes both hand-crafted and deep learning-based radiomics approaches, it primarily focuses on the former as this field is more mature. This guideline will be updated once more studies and results have contributed to improved consensus regarding the application of deep learning methods for radiomics. Although methodological recommendations in the present document are valid for most medical image modalities, we focus here on nuclear medicine, and specific recommendations when necessary are made for PET/CT, PET/MR, and quantitative SPECT. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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