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.
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.
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DOI：
10.1007/s00259-022-06001-6
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发表时间：
2023-01
影响因子：
9.1
通讯作者：
Visvikis, D.
中科院分区：
文献类型：
--
作者：
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.
本指南的目的是为手工制作和基于深度学习的方法提供关于强大放射组学分析最佳实践的全面信息。在EANM和SNMMI之间的合作努力中，我们就放射组学分析相关方面的当前最佳实践和建议达成一致，包括研究设计，质量保证，数据收集，采集和重建的影响，检测和分割，特征标准化和实施，以及适当的建模方案，模型评估和解释。我们还对未来前景进行了展望。 放射组学是一个发展非常迅速的研究领域。本指南侧重于已确定的发现以及基于最新技术水平的建议。虽然本指南承认手工制作和基于深度学习的放射组学方法，但由于该领域更加成熟，因此主要侧重于前者。一旦更多的研究和结果有助于提高关于放射组学深度学习方法应用的共识，该指南将更新。尽管本文中的方法学建议适用于大多数医学图像模式，但我们在此重点关注核医学，并在必要时对PET/CT、PET/MR和定量SPECT提出具体建议。在线版本包含补充材料，可通过10.1007/s 00259 -022-06001-6获得。
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. 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. 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. The online version contains supplementary material available at 10.1007/s00259-022-06001-6.
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影响因子：
10.6
作者：
Cirujeda, Pol;Cid, Yashin Dicente;Depeursinge, Adrien
通讯作者：
Depeursinge, Adrien
影响因子：
105.7
作者：
Collins, Gary S.;Reitsma, Johannes B.;Moons, Karel G. M.
通讯作者：
Moons, Karel G. M.
影响因子：
3.8
作者：
Apte, Aditya P.;Iyer, Aditi;Deasy, Joseph O.
通讯作者：
Deasy, Joseph O.
影响因子：
3.8
作者：
Avanzo M;Wei L;Stancanello J;Vallières M;Rao A;Morin O;Mattonen SA;El Naqa I
通讯作者：
El Naqa I
影响因子：
9.3
作者：
Bradshaw, Tyler J.;Boellaard, Ronald;Buvat, Irene
通讯作者：
Buvat, Irene