Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data

 

 Permissions and Reprints

Abstract

Nuclear imaging techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) in combination with computed tomography (CT) are established imaging modalities in clinical practice, particularly for oncological problems. Due to a multitude of manufacturers, different measurement protocols, local demographic or clinical workflow variations as well as various available reconstruction and analysis software, very heterogeneous datasets are generated. This review article examines the current state of interoperability and harmonisation of image data and related clinical data in the field of nuclear medicine. Various approaches and standards to improve data compatibility and integration are discussed. These include, for example, structured clinical history, standardisation of image acquisition and reconstruction as well as standardised preparation of image data for evaluation. Approaches to improve data acquisition, storage and analysis will be presented. Furthermore, approaches are presented to prepare the datasets in such a way that they become usable for projects applying artificial intelligence (AI) (machine learning, deep learning, etc.). This review article concludes with an outlook on future developments and trends related to AI in nuclear medicine, including a brief research of commercial solutions.

Zusammenfassung

Die Verwendung nuklearmedizinischer Bildgebungsverfahren wie der Positronen-Emissions-Tomografie (PET) und der Single-Photonen-Emissions-Computertomografie (SPECT) in Kombination mit der Computertomografie (CT) hat sich in der klinischen Praxis vor allem bei onkologischen Fragestellungen etabliert. Aufgrund einer Vielzahl an Herstellern, möglicher Messprotokolle sowie verfügbarer Rekonstruktions- und Auswertungssoftware werden dabei teils sehr heterogene Datensätze generiert. Der vorliegende Übersichtsartikel untersucht den aktuellen Stand der Interoperabilität und Harmonisierung von Bilddaten und der damit verbundenen klinischen Daten im Bereich der Nuklearmedizin. Es werden verschiedene Ansätze und Standards zur Verbesserung der Datenkompatibilität und -integration diskutiert. Dazu gehören beispielsweise die strukturierte klinische Anamnese, die Vereinheitlichung der Bildakquisition und -rekonstruktion sowie eine standardisierte Aufbereitung der Bilddaten für die Auswertung. Es werden Lösungsansätze zur Verbesserung der Datenerfassung, -speicherung und -analyse aufgezeigt. Weiterhin werden Möglichkeiten vorgestellt, die Datensätze so vorzubereiten, dass sie für Projekte, die künstliche Intelligenz (KI) anwenden (Machine Learning, Deep Learning etc.), nutzbar werden. Der Übersichtsartikel schließt mit einem Ausblick auf zukünftige Entwicklungen und Trends im Zusammenhang mit KI in der Nuklearmedizin, inkl. einer kurzen Marktrecherche.

Publication History

Received: 05 September 2023

Accepted: 21 September 2023

Article published online:
31 October 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

• References

• 1 Kurz FT, Schlemmer HP. Imaging in translational cancer research. Cancer Biol Med 2022; 19: 1565-1585 DOI: 10.20892/J.ISSN.2095-3941.2022.0677. (PMID: 36476372)
  CrossrefPubMedGoogle Scholar
• 2 Zhang Y, Feng Y, Zhang Y. et al. Is it the time for personalized imaging protocols in cancer radiation therapy?. Int J Radiat Oncol Biol Phys 2015; 91: 659-660 DOI: 10.1016/j.ijrobp.2014.10.044. (PMID: 25680605)
  CrossrefPubMedGoogle Scholar
• 3 Schons M, Pilgram L, Reese JP. et al. The German National Pandemic Cohort Network (NAPKON): rationale, study design and baseline characteristics. Eur J Epidemiol 2022; 37: 849-870 DOI: 10.1007/S10654-022-00896-Z/TABLES/5. (PMID: 35904671)
  CrossrefPubMedGoogle Scholar
• 4 Heyder R, Kroemer HK, Wiedmann S. et al. The German Network of University Medicine: technical and organizational approaches for research data platforms. Bundesgesundheitsblatt – Gesundheitsforsch – Gesundheitsschutz 2023; 66: 114-125 DOI: 10.1007/S00103-022-03649-1.
  CrossrefPubMedGoogle Scholar
• 5 Rorie DA, Flynn RWV, Grieve K. et al. Electronic case report forms and electronic data capture within clinical trials and pharmacoepidemiology. Br J Clin Pharmacol 2017; 83: 1880 DOI: 10.1111/BCP.13285.
  CrossrefPubMedGoogle Scholar
• 6 Griffon N, Pereira H, Djadi-Prat J. et al. Performances of a Solution to Semi-Automatically Fill eCRF with Data from the Electronic Health Record: Protocol for a Prospective Individual Participant Data Meta-Analysis. Stud Health Technol Inform 2020; 270: 367-371 DOI: 10.3233/SHTI200184.
  CrossrefPubMedGoogle Scholar
• 7 Cheng AC, Banasiewicz MK, Johnson JD. et al. Evaluating automated electronic case report form data entry from electronic health records. J Clin Transl Sci 2023; 7: e29 DOI: 10.1017/CTS.2022.514.
  CrossrefPubMedGoogle Scholar
• 8 Nordo AH, Eisenstein EL, Hawley J. et al. A comparative effectiveness study of eSource used for data capture for a clinical research registry. Int J Med Inform 2017; 103: 89-94 DOI: 10.1016/J.IJMEDINF.2017.04.015. (PMID: 28551007)
  CrossrefPubMedGoogle Scholar
• 9 Vattikola A, Dai H, Buckley M. et al. Direct Data Extraction and Exchange of Local Labs for Clinical Research Protocols: A Partnership with Sites, Biopharmaceutical Firms, and Clinical Research Organizations. J Soc Clin Data Manag 2021; 1 DOI: 10.47912/JSCDM.21.
  CrossrefGoogle Scholar
• 10 Williams E, Kienast M, Medawar E. et al. A Standardized Clinical Data Harmonization Pipeline for Scalable AI Application Deployment (FHIR-DHP): Validation and Usability Study. JMIR Med informatics 2023; 11 DOI: 10.2196/43847. (PMID: 36943344)
  CrossrefPubMedGoogle Scholar
• 11 Ayaz M, Pasha MF, Alzahrani MY. et al. The Fast Health Interoperability Resources (FHIR) Standard: Systematic Literature Review of Implementations, Applications, Challenges and Opportunities. JMIR Med Informatics 2021; 9 DOI: 10.2196/21929. (PMID: 34328424)
  CrossrefPubMedGoogle Scholar
• 12 Semler SC, Wissing F, Heyder R. German Medical Informatics Initiative. Methods Inf Med 2018; 57: e50-e56 DOI: 10.3414/ME18-03-0003. (PMID: 30016818)
  Article in Thieme ConnectPubMedGoogle Scholar
• 13 Tran-Gia J, Denis-Bacelar AM, Ferreira KM. et al. A multicentre and multi-national evaluation of the accuracy of quantitative Lu-177 SPECT/CT imaging performed within the MRTDosimetry project. EJNMMI Phys 2021; 8: 1-17 DOI: 10.1186/S40658-021-00397-0/TABLES/8.
  CrossrefPubMedGoogle Scholar
• 14 Dickson JC, Armstrong IS, Gabiña PM. et al. EANM practice guideline for quantitative SPECT-CT. Eur J Nucl Med Mol Imaging 2023; 50: 980-995 DOI: 10.1007/S00259-022-06028-9/FIGURES/5. (PMID: 36469107)
  CrossrefGoogle Scholar
• 15 Koole M, Armstrong I, Krizsan AK. et al. EANM guidelines for PET-CT and PET-MR routine quality control. Z Med Phys 2023; 33: 103-113 DOI: 10.1016/J.ZEMEDI.2022.08.003. (PMID: 36167600)
  CrossrefPubMedGoogle Scholar
• 16 Marquis H, Willowson KP, Bailey DL. Partial volume effect in SPECT & PET imaging and impact on radionuclide dosimetry estimates. Asia Ocean J Nucl Med Biol 2023; 11: 44-54 DOI: 10.22038/AOJNMB.2022.63827.1448. (PMID: 36619190)
  CrossrefPubMedGoogle Scholar
• 17 Zimmerman BE, Pibida L, King LE. et al. Development of a calibration methodology for large-volume, solid 68Ge phantoms for traceable measurements in positron emission tomography. Appl Radiat Isot 2014; 87: 5-9 DOI: 10.1016/J.APRADISO.2013.11.049. (PMID: 24332342)
  CrossrefPubMedGoogle Scholar
• 18 Lasnon C, Houdu B, Kammerer E. et al. Patient’s weight: a neglected cause of variability in SUV measurements? A survey from an EARL accredited PET centre in 513 patients. Eur J Nucl Med Mol Imaging 2016; 43: 197-199 DOI: 10.1007/S00259-015-3214-3.
  CrossrefPubMedGoogle Scholar
• 19 Boellaard R, Sera T, Kaalep A. et al. Updating PET/CT performance standards and PET/CT interpretation criteria should go hand in hand. EJNMMI Res 2019; 9 DOI: 10.1186/S13550-019-0565-Y.
  CrossrefPubMedGoogle Scholar
• 20 Kaalep A, Sera T, Oyen W. et al. EANM/EARL FDG-PET/CT accreditation – summary results from the first 200 accredited imaging systems. Eur J Nucl Med Mol Imaging 2018; 45: 412-422 DOI: 10.1007/S00259-017-3853-7.
  CrossrefPubMedGoogle Scholar
• 21 Houdu B, Lasnon C, Licaj I. et al. Why harmonization is needed when using FDG PET/CT as a prognosticator: demonstration with EARL-compliant SUV as an independent prognostic factor in lung cancer. Eur J Nucl Med Mol Imaging 2019; 46: 421-428 DOI: 10.1007/S00259-018-4151-8.
  CrossrefPubMedGoogle Scholar
• 22 Tixier F, Hatt M, Le Rest CC. et al. Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET. J Nucl Med 2012; 53: 693-700 DOI: 10.2967/JNUMED.111.099127.
  CrossrefPubMedGoogle Scholar
• 23 Leijenaar RTH, Carvalho S, Velazquez ER. et al. Stability of FDG-PET Radiomics features: An integrated analysis of test-retest and inter-observer variability. Acta Oncol (Madr) 2013; 52: 1391-1397 DOI: 10.3109/0284186X.2013.812798. (PMID: 24047337)
  CrossrefPubMedGoogle Scholar
• 24 Zwanenburg A, Leger S, Agolli L. et al. Assessing robustness of radiomic features by image perturbation. Sci Rep 2019; 9 DOI: 10.1038/S41598-018-36938-4. (PMID: 30679599)
  CrossrefPubMedGoogle Scholar
• 25 Galavis PE, Hollensen C, Jallow N. et al. Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters. Acta Oncol (Madr) 2010; 49: 1012-1016 DOI: 10.3109/0284186X.2010.498437. (PMID: 20831489)
  CrossrefPubMedGoogle Scholar
• 26 Yan J, Chu-Shern JL, Loi HY. et al. Impact of image reconstruction settings on texture features in 18F-FDG PET. J Nucl Med 2015; 56: 1667-1673 DOI: 10.2967/JNUMED.115.156927. (PMID: 26229145)
  CrossrefPubMedGoogle Scholar
• 27 Pfaehler E, Beukinga RJ, de Jong JR. et al. Repeatability of 18F-FDG PET radiomic features: A phantom study to explore sensitivity to image reconstruction settings, noise, and delineation method. Med Phys 2019; 46: 665-678 DOI: 10.1002/MP.13322.
  CrossrefPubMedGoogle Scholar
• 28 Papp L, Rausch I, Grahovac M. et al. Optimized feature extraction for radiomics analysis of 18F-FDG PET imaging. J Nucl Med 2019; 60: 864-872 DOI: 10.2967/JNUMED.118.217612.
  CrossrefPubMedGoogle Scholar
• 29 Park JE, Park SY, Kim HJ. et al. Reproducibility and generalizability in radiomics modeling: Possible strategies in radiologic and statistical perspectives. Korean J Radiol 2019; 20: 1124-1137 DOI: 10.3348/KJR.2018.0070. (PMID: 31270976)
  CrossrefPubMedGoogle Scholar
• 30 Zwanenburg A. Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis. Eur J Nucl Med Mol Imaging 2019; 46: 2638-2655 DOI: 10.1007/S00259-019-04391-8. (PMID: 31240330)
  CrossrefPubMedGoogle Scholar
• 31 Vallières M, Zwanenburg A, Badic B. et al. Responsible radiomics research for faster clinical translation. J Nucl Med 2018; 59: 189-193 DOI: 10.2967/JNUMED.117.200501. (PMID: 29175982)
  CrossrefPubMedGoogle Scholar
• 32 Lambin P, Leijenaar RTH, Deist TM. et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 2017; 14: 749-762 DOI: 10.1038/NRCLINONC.2017.141.
  CrossrefPubMedGoogle Scholar
• 33 Lambin P, Rios-Velazquez E, Leijenaar R. et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012; 48: 441-446 DOI: 10.1016/J.EJCA.2011.11.036.
  CrossrefPubMedGoogle Scholar
• 34 Pfaehler E, Van Sluis J, Merema BBJ. et al. Experimental Multicenter and Multivendor Evaluation of the Performance of PET Radiomic Features Using 3-Dimensionally Printed Phantom Inserts. J Nucl Med 2020; 61: 469-476 DOI: 10.2967/JNUMED.119.229724. (PMID: 31420497)
  CrossrefPubMedGoogle Scholar
• 35 Gillett D, Marsden D, Ballout S. et al. 3D printing 18F radioactive phantoms for PET imaging. EJNMMI Phys 2021; 8 DOI: 10.1186/S40658-021-00383-6. (PMID: 33909154)
  CrossrefPubMedGoogle Scholar
• 36 Läppchen T, Meier LP, Fürstner M. et al. 3D printing of radioactive phantoms for nuclear medicine imaging. EJNMMI Phys 2020; 7 DOI: 10.1186/S40658-020-00292-0. (PMID: 32323035)
  CrossrefPubMedGoogle Scholar
• 37 Da-Ano R, Visvikis D, Hatt M. Harmonization strategies for multicenter radiomics investigations. Phys Med Biol 2020; 65 DOI: 10.1088/1361-6560/ABA798. (PMID: 32688357)
  CrossrefPubMedGoogle Scholar
• 38 Papadimitroulas P, Brocki L, Christopher Chung N. et al. Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization. Phys Medica 2021; 83: 108-121 DOI: 10.1016/J.EJMP.2021.03.009.
  CrossrefPubMedGoogle Scholar
• 39 Hatt M, Le Rest CC, Tixier F. et al. Radiomics: Data are also images. J Nucl Med 2019; 60: 38S-44S DOI: 10.2967/JNUMED.118.220582.
  CrossrefPubMedGoogle Scholar
• 40 Kaalep A, Sera T, Rijnsdorp S. et al. Feasibility of state of the art PET/CT systems performance harmonisation. Eur J Nucl Med Mol Imaging 2018; 45: 1344-1361 DOI: 10.1007/S00259-018-3977-4. (PMID: 29500480)
  CrossrefPubMedGoogle Scholar
• 41 Whybra P, Parkinson C, Foley K. et al. Assessing radiomic feature robustness to interpolation in 18F-FDG PET imaging. Sci Rep 2019; 9 DOI: 10.1038/S41598-019-46030-0.
  CrossrefPubMedGoogle Scholar
• 42 ADNI |  PET Acquisition. Accessed July 12, 2023 at: https://adni.loni.usc.edu/methods/pet-acquisition/
  Google Scholar
• 43 Hognon C, Tixier F, Gallinato O. et al. Standardization of Multicentric Image Datasets with Generative Adversarial Networks. 2019
  PubMedGoogle Scholar
• 44 Choe J, Lee SM, Do KH. et al. Deep Learning–based Image Conversion of CT Reconstruction Kernels Improves Radiomics Reproducibility for Pulmonary Nodules or Masses. Radiology 2019; 292: 365-373 DOI: 10.1148/RADIOL.2019181960. (PMID: 31210613)
  CrossrefPubMedGoogle Scholar
• 45 Modanwal G, Vellal A, Buda M. et al. MRI image harmonization using cycle-consistent generative adversarial network. 2020; 36 DOI: 10.1117/12.2551301.
  CrossrefGoogle Scholar
• 46 Zhong J, Wang Y, Li J. et al. Inter-site harmonization based on dual generative adversarial networks for diffusion tensor imaging: Application to neonatal white matter development. Biomed Eng Online 2020; 19 DOI: 10.1186/S12938-020-0748-9. (PMID: 31941515)
  CrossrefPubMedGoogle Scholar
• 47 Apostolopoulos ID, Papathanasiou ND, Apostolopoulos DJ. et al. Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review. Eur J Nucl Med Mol Imaging 2022; 49: 3717-3739 DOI: 10.1007/S00259-022-05805-W. (PMID: 35451611)
  CrossrefPubMedGoogle Scholar
• 48 Bashyam VM, Doshi J, Erus G. et al. Deep Generative Medical Image Harmonization for Improving Cross-Site Generalization in Deep Learning Predictors. J Magn Reson Imaging 2022; 55: 908-916 DOI: 10.1002/JMRI.27908. (PMID: 34564904)
  CrossrefPubMedGoogle Scholar
• 49 Zhu JY, Park T, Isola P. et al. Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. Proc IEEE Int Conf Comput Vis 2017; 10: 2242-2251 DOI: 10.1109/ICCV.2017.244.
  CrossrefGoogle Scholar
• 50 Isola P, Zhu JY, Zhou T. et al. Image-to-image translation with conditional adversarial networks. Proc – 30th IEEE Conf Comput Vis Pattern Recognition. CVPR 2017 2017; 5967-5976 DOI: 10.1109/CVPR.2017.632.
  CrossrefGoogle Scholar
• 51 Kazerouni A, Aghdam EK, Heidari M. et al. Diffusion models in medical imaging: A comprehensive survey. Med Image Anal 2023; 88: 102846 DOI: 10.1016/J.MEDIA.2023.102846. (PMID: 37295311)
  CrossrefPubMedGoogle Scholar
• 52 Lyu Q, Wang G. Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models. 2022
  PubMedGoogle Scholar
• 53 Meng X, Gu Y, Pan Y. et al. A Novel Unified Conditional Score-based Generative Framework for Multi-modal Medical Image Completion. 2022
  PubMedGoogle Scholar
• 54 Leijenaar RTH, Nalbantov G, Carvalho S. et al. The effect of SUV discretization in quantitative FDG-PET Radiomics: The need for standardized methodology in tumor texture analysis. Sci Rep 2015; 5 DOI: 10.1038/SREP11075. (PMID: 26242464)
  CrossrefPubMedGoogle Scholar
• 55 Zwanenburg A, Leger S, Vallières M. et al. Image biomarker standardisation initiative. Radiology 2016; 295: 328-338 DOI: 10.1148/radiol.2020191145.
  CrossrefPubMedGoogle Scholar
• 56 Parmar C, Velazquez ER, Leijenaar R. et al. Robust radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One 2014; 9 DOI: 10.1371/JOURNAL.PONE.0102107.
  CrossrefPubMedGoogle Scholar
• 57 Pavic M, Bogowicz M, Würms X. et al. Influence of inter-observer delineation variability on radiomics stability in different tumor sites. Acta Oncol (Madr) 2018; 57: 1070-1074 DOI: 10.1080/0284186X.2018.1445283. (PMID: 29513054)
  CrossrefPubMedGoogle Scholar
• 58 Che F, Shi R, Wu J. et al. Dynamic radiomics: a new methodology to extract quantitative time-related features from tomographic images. Appl Intell 2020; 52: 11827-11845 DOI: 10.1007/s10489-021-03053-3.
  CrossrefGoogle Scholar
• 59 Fave X, Zhang L, Yang J. et al. Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer. Sci Rep 2017; 7 DOI: 10.1038/S41598-017-00665-Z. (PMID: 28373718)
  CrossrefPubMedGoogle Scholar
• 60 Nasief H, Zheng C, Schott D. et al. A machine learning based delta-radiomics process for early prediction of treatment response of pancreatic cancer. npj Precis Oncol 2019; 3 DOI: 10.1038/S41698-019-0096-Z. (PMID: 31602401)
  CrossrefPubMedGoogle Scholar
• 61 Jung J, Hong H, Lee H. et al. Uni- and Multi-Modal Radiomic Features for the Predicting Prostate Cancer Aggressiveness. Proc – Int Symp Biomed Imaging 2020; 1343-1346 DOI: 10.1109/ISBI45749.2020.9098375.
  CrossrefGoogle Scholar
• 62 Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images are more than pictures, they are data. Radiology 2016; 278: 563-577 DOI: 10.1148/RADIOL.2015151169. (PMID: 26579733)
  CrossrefPubMedGoogle Scholar
• 63 Mayerhoefer ME, Materka A, Langs G. et al. Introduction to radiomics. J Nucl Med 2020; 61: 488-495 DOI: 10.2967/JNUMED.118.222893. (PMID: 32060219)
  CrossrefPubMedGoogle Scholar
• 64 Escobar T, Vauclin S, Orlhac F. et al. Voxel-wise supervised analysis of tumors with multimodal engineered features to highlight interpretable biological patterns. Med Phys 2022; 49: 3816-3829 DOI: 10.1002/MP.15603.
  CrossrefPubMedGoogle Scholar
• 65 Peng H, Dong D, Fang MJ. et al. Prognostic value of deep learning PET/CT-based radiomics: Potential role for future individual induction chemotherapy in advanced nasopharyngeal carcinoma. Clin Cancer Res 2019; 25: 4271-4279 DOI: 10.1158/1078-0432.CCR-18-3065. (PMID: 30975664)
  CrossrefPubMedGoogle Scholar
• 66 Oliveira C, Amstutz F, Vuong D. et al. Preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine FDG-PET imaging. EJNMMI Res 2021; 11 DOI: 10.1186/S13550-021-00809-3.
  CrossrefPubMedGoogle Scholar
• 67 Orlhac F, Boughdad S, Philippe C. et al. A postreconstruction harmonization method for multicenter radiomic studies in PET. J Nucl Med 2018; 59: 1321-1328 DOI: 10.2967/JNUMED.117.199935. (PMID: 29301932)
  CrossrefPubMedGoogle Scholar
• 68 Da-Ano R, Lucia F, Masson I. et al. A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets. PLoS One 2021; 16 DOI: 10.1371/JOURNAL.PONE.0253653. (PMID: 34197503)
  CrossrefPubMedGoogle Scholar
• 69 Ligero M, Jordi-Ollero O, Bernatowicz K. et al. Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis. Eur Radiol 2021; 31: 1460-1470 DOI: 10.1007/S00330-020-07174-0. (PMID: 32909055)
  CrossrefPubMedGoogle Scholar
• 70 Orlhac F, Eertink JJ, Cottereau AS. et al. A guide to ComBat harmonization of imaging biomarkers in multicenter studies. J Nucl Med 2022; 63 DOI: 10.2967/JNUMED.121.262464. (PMID: 34531263)
  CrossrefPubMedGoogle Scholar
• 71 Zounek AJ, Albert NL, Holzgreve A. et al. Feasibility of radiomic feature harmonization for pooling of [18F]FET or [18F]GE-180 PET images of gliomas. Z Med Phys 2023; 33: 91-102 DOI: 10.1016/J.ZEMEDI.2022.12.005. (PMID: 36710156)
  CrossrefPubMedGoogle Scholar
• 72 Da-ano R, Masson I, Lucia F. et al. Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies. Sci Rep 2020; 10 DOI: 10.1038/S41598-020-66110-W. (PMID: 32581221)
  CrossrefPubMedGoogle Scholar
• 73 Orlhac F, Lecler A, Savatovski J. et al. How can we combat multicenter variability in MR radiomics? Validation of a correction procedure. Eur Radiol 2021; 31: 2272-2280 DOI: 10.1007/S00330-020-07284-9. (PMID: 32975661)
  CrossrefPubMedGoogle Scholar
• 74 Barry N, Rowshanfarzad P, Francis RJ. et al. Repeatability of image features extracted from FET PET in application to post-surgical glioblastoma assessment. Phys Eng Sci Med 2021; 44: 1131-1140 DOI: 10.1007/S13246-021-01049-4.
  CrossrefPubMedGoogle Scholar
• 75 Gutsche R, Scheins J, Kocher M. et al. Evaluation of fet pet radiomics feature repeatability in glioma patients. Cancers (Basel) 2021; 13: 1-15 DOI: 10.3390/CANCERS13040647. (PMID: 33562803)
  CrossrefPubMedGoogle Scholar
• 76 Lucia F, Visvikis D, Vallières M. et al. External validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer patients treated with chemoradiotherapy. Eur J Nucl Med Mol Imaging 2019; 46: 864-877 DOI: 10.1007/S00259-018-4231-9.
  CrossrefPubMedGoogle Scholar
• 77 Buvat I, Orlhac F. The dark side of radiomics: On the paramount importance of publishing negative results. J Nucl Med 2019; 60: 1543-1544 DOI: 10.2967/JNUMED.119.235325. (PMID: 31541033)
  CrossrefPubMedGoogle Scholar
• 78 Lohmann P, Kocher M, Ruge MI. et al. PET/MRI Radiomics in Patients With Brain Metastases. Front Neurol 2020; 11 DOI: 10.3389/FNEUR.2020.00001.
  CrossrefPubMedGoogle Scholar
• 79 Lohmann P, Galldiks N, Kocher M. et al. Radiomics in neuro-oncology: Basics, workflow, and applications. Methods 2021; 188: 112-121 DOI: 10.1016/J.YMETH.2020.06.003. (PMID: 32522530)
  CrossrefPubMedGoogle Scholar
• 80 Arabi H, AkhavanAllaf A, Sanaat A. et al. The promise of artificial intelligence and deep learning in PET and SPECT imaging. Phys Medica 2021; 83: 122-137 DOI: 10.1016/J.EJMP.2021.03.008. (PMID: 33765602)
  CrossrefPubMedGoogle Scholar
• 81 Zenker S, Strech D, Ihrig K. et al. Data protection-compliant broad consent for secondary use of health care data and human biosamples for (bio)medical research: Towards a new German national standard. J Biomed Inform 2022; 131 DOI: 10.1016/J.JBI.2022.104096. (PMID: 35643273)
  CrossrefPubMedGoogle Scholar
• 82 Tamborero D, Dienstmann R, Rachid MH. et al. The Molecular Tumor Board Portal supports clinical decisions and automated reporting for precision oncology. Nat Cancer 2022; 3: 251-261 DOI: 10.1038/S43018-022-00332-X. (PMID: 35221333)
  CrossrefPubMedGoogle Scholar
• 83 Krämer M, Ingwersen M, Teichgräber U. et al. Added value of chest CT in a machine learning-based prediction model to rule out COVID-19 before inpatient admission: A retrospective university network study. Eur J Radiol 2023; 163 DOI: 10.1016/J.EJRAD.2023.110827.
  CrossrefPubMedGoogle Scholar
• 84 RACOON: Das Radiological Cooperative Network zur Beantwortung der großen Fragen in der Radiologie. Rofo 2022; 194: 95 DOI: 10.1055/A-1544-2240/BIB. (PMID: 34911092)
  CrossrefGoogle Scholar
• 85 Kush RD, Warzel D, Kush MA. et al. FAIR data sharing: The roles of common data elements and harmonization. J Biomed Inform 2020; 107 DOI: 10.1016/J.JBI.2020.103421. (PMID: 32407878)
  CrossrefPubMedGoogle Scholar
• 86 Isensee F, Jaeger PF, Kohl SAA. et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 2021; 18: 203-211 DOI: 10.1038/S41592-020-01008-Z. (PMID: 33288961)
  CrossrefPubMedGoogle Scholar
• 87 Müller D, Hartmann D, Soto-Rey I. et al. Abstract: AUCMEDI. Bildverarbeitung für die Medizin 2023; 253-253 DOI: 10.1007/978-3-658-41657-7_55.
  CrossrefGoogle Scholar
• 88 Zaharchuk G, Davidzon G. Artificial Intelligence for Optimization and Interpretation of PET/CT and PET/MR Images. Semin Nucl Med 2021; 51: 134-142 DOI: 10.1053/J.SEMNUCLMED.2020.10.001. (PMID: 33509370)
  CrossrefPubMedGoogle Scholar
• 89 Saboury B, Bradshaw T, Boellaard R. et al. Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem. J Nucl Med 2023; 64: 188 DOI: 10.2967/JNUMED.121.263703. (PMID: 36522184)
  CrossrefPubMedGoogle Scholar
• 90 Bera K, Braman N, Gupta A. et al. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol 2022; 19: 132 DOI: 10.1038/S41571-021-00560-7.
  CrossrefPubMedGoogle Scholar
• 91 Union PO of the E. Regulation (EU) 2017/745 of the European Parliament and of the Council of 5 April 2017 on medical devices, amending Directive 2001/83/EC, Regulation (EC) No 178/2002 and Regulation (EC) No 1223/2009 and repealing Council Directives 90/385/EEC and 93/42/EEC (Text with EEA relevance.). 2017
  PubMedGoogle Scholar
• 92 Tjoa E, Guan C. A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI. IEEE Trans Neural Networks Learn Syst 2021; 32: 4793-4813 DOI: 10.1109/TNNLS.2020.3027314. (PMID: 33079674)
  CrossrefPubMedGoogle Scholar
• 93 Beckers R, Kwade Z, Zanca F. The EU medical device regulation: Implications for artificial intelligence-based medical device software in medical physics. Phys Medica 2021; 83: 1-8 DOI: 10.1016/j.ejmp.2021.02.011. (PMID: 33657513)
  CrossrefPubMedGoogle Scholar
• 94 Lambert B, Forbes F, Tucholka A. et al. Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis. 2022
  PubMedGoogle Scholar
• 95 Omoumi P, Ducarouge A, Tournier A. et al. To buy or not to buy—evaluating commercial AI solutions in radiology (the ECLAIR guidelines). Eur Radiol 2021; 31: 3786-3796 DOI: 10.1007/S00330-020-07684-X. (PMID: 33666696)
  CrossrefPubMedGoogle Scholar
• 96 Fink M, Akra B. Comparison of the international regulations for medical devices–USA versus Europe. Injury 2023; 110908 DOI: 10.1016/J.INJURY.2023.110908. (PMID: 37365092)
  CrossrefPubMedGoogle Scholar
• 97 Benjamens S, Dhunnoo P, Meskó B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. npj Digit Med 2020; 3: 1-8 DOI: 10.1038/s41746-020-00324-0. (PMID: 32984550)
  CrossrefPubMedGoogle Scholar
• 98 Currie G, Rohren E. Social Asymmetry, Artificial Intelligence and the Medical Imaging Landscape. Semin Nucl Med 2022; 52: 498-503 DOI: 10.1053/J.SEMNUCLMED.2021.11.011. (PMID: 34972549)
  CrossrefPubMedGoogle Scholar