{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T08:54:45Z","timestamp":1773305685630,"version":"3.50.1"},"reference-count":121,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T00:00:00Z","timestamp":1667779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020 research and innovation programme","award":["945175"],"award-info":[{"award-number":["945175"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Radiomics analysis is a powerful tool aiming to provide diagnostic and prognostic patient information directly from images that are decoded into handcrafted features, comprising descriptors of shape, size and textural patterns. Although radiomics is gaining momentum since it holds great promise for accelerating digital diagnostics, it is susceptible to bias and variation due to numerous inter-patient factors (e.g., patient age and gender) as well as inter-scanner ones (different protocol acquisition depending on the scanner center). A variety of image and feature based harmonization methods has been developed to compensate for these effects; however, to the best of our knowledge, none of these techniques has been established as the most effective in the analysis pipeline so far. To this end, this review provides an overview of the challenges in optimizing radiomics analysis, and a concise summary of the most relevant harmonization techniques, aiming to provide a thorough guide to the radiomics harmonization process.<\/jats:p>","DOI":"10.3390\/jimaging8110303","type":"journal-article","created":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T10:53:52Z","timestamp":1667904832000},"page":"303","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Harmonization Strategies in Multicenter MRI-Based Radiomics"],"prefix":"10.3390","volume":"8","author":[{"given":"Elisavet","family":"Stamoulou","sequence":"first","affiliation":[{"name":"Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology\u2014Hellas (FORTH), 700 13 Heraklion, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7830-1255","authenticated-orcid":false,"given":"Constantinos","family":"Spanakis","sequence":"additional","affiliation":[{"name":"Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology\u2014Hellas (FORTH), 700 13 Heraklion, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3396-0644","authenticated-orcid":false,"given":"Georgios C.","family":"Manikis","sequence":"additional","affiliation":[{"name":"Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology\u2014Hellas (FORTH), 700 13 Heraklion, Greece"},{"name":"Department of Oncology-Pathology, Karolinska Institutet, 171 77 Stockholm, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9478-0375","authenticated-orcid":false,"given":"Georgia","family":"Karanasiou","sequence":"additional","affiliation":[{"name":"Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 451 10 Ioannina, Greece"}]},{"given":"Grigoris","family":"Grigoriadis","sequence":"additional","affiliation":[{"name":"Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 451 10 Ioannina, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8952-9987","authenticated-orcid":false,"given":"Theodoros","family":"Foukakis","sequence":"additional","affiliation":[{"name":"Department of Oncology-Pathology, Karolinska Institutet, 171 77 Stockholm, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8454-1450","authenticated-orcid":false,"given":"Manolis","family":"Tsiknakis","sequence":"additional","affiliation":[{"name":"Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology\u2014Hellas (FORTH), 700 13 Heraklion, Greece"},{"name":"Department of Electrical & Computer Engineering, Hellenic Mediterranean University, 714 10 Heraklion, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7362-5082","authenticated-orcid":false,"given":"Dimitrios I.","family":"Fotiadis","sequence":"additional","affiliation":[{"name":"Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 451 10 Ioannina, Greece"},{"name":"Department of Biomedical Research, Institute of Molecular Biology and Biotechnology\u2014FORTH, University Campus of Ioannina, 451 15 Ioannina, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3783-5223","authenticated-orcid":false,"given":"Kostas","family":"Marias","sequence":"additional","affiliation":[{"name":"Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology\u2014Hellas (FORTH), 700 13 Heraklion, Greece"},{"name":"Department of Electrical & Computer Engineering, Hellenic Mediterranean University, 714 10 Heraklion, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: Images Are More than Pictures, They Are Data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/s13244-020-00887-2","article-title":"Radiomics in Medical Imaging\u2014\u201cHow-to\u201d Guide and Critical Reflection","volume":"11","author":"Cester","year":"2020","journal-title":"Insights Imaging"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"488","DOI":"10.2967\/jnumed.118.222893","article-title":"Introduction to Radiomics","volume":"61","author":"Mayerhoefer","year":"2020","journal-title":"J. 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