What a Radiologist Needs to Know About Radiomics, Standardization, and Reproducibility

HomeRadiologyVol. 310, No. 2 PreviousNext Reviews and CommentaryEditorialWhat a Radiologist Needs to Know About Radiomics, Standardization, and ReproducibilityMerel Huisman , Tugba Akinci D'AntonoliMerel Huisman , Tugba Akinci D'AntonoliAuthor AffiliationsFrom the Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands (M.H.); and Institute of Radiology and Nuclear Medicine, Cantonal Hospital Baselland, Liestal, Switzerland (T.A.D.).Address correspondence to M.H. (email: [email protected]).Merel Huisman Tugba Akinci D'AntonoliPublished Online:Feb 6 2024https://doi.org/10.1148/radiol.232459See also the article by Whybra and Zwanenburg et al in this issue.MoreSectionsFull textPDF ToolsAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookXLinked In References1. 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(4):441–446. Crossref, Medline, Google Scholar2. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology 2016;278(2):563–577. Link, Google Scholar3. Akinci D'Antonoli T, Cuocolo R, Baessler B, Pinto Dos Santos D. Towards reproducible radiomics research: introduction of a database for radiomics studies. Eur Radiol 2023. https://doi.org/10.1007/s00330-023-10095-3. Published online August 12, 2023. Accessed September 11, 2023. Crossref, Google Scholar4. 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(13):2638–2655. Crossref, Medline, Google Scholar5. Ioannidis JPA. Why most published research findings are false. PLoS Med 2005;2(8):e124. [Published correction appears in PLoS Med 2022;19(8):e1004085.] Crossref, Medline, Google Scholar6. Bennett CM, Miller MB, Wolford GL. Neural correlates of interspecies perspective taking in the post-mortem Atlantic Salmon: an argument for multiple comparisons correction. Neuroimage 2009;47:S125. Crossref, Google Scholar7. Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 2017;14(12):749–762. Crossref, Medline, Google Scholar8. Kocak B, Baessler B, Bakas S, et al. CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII. Insights Imaging 2023;14(1):75. Crossref, Medline, Google Scholar9. Whybra P, Zwanenburg A, Andrearczyk V, et al. The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights. Radiology 2024;310(2):e231319. Link, Google Scholar10. Zwanenburg A, Vallières M, Abdalah MA, et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology 2020;295(2):328–338. Link, Google ScholarArticle HistoryReceived: Sept 13 2023Revision requested: Sept 21 2023Revision received: Sept 24 2023Accepted: Sept 25 2023Published online: Feb 06 2024 FiguresReferencesRelatedDetailsAccompanying This ArticleThe Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical InsightsFeb 6 2024RadiologyRecommended Articles The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical InsightsRadiology2024Volume: 310Issue: 2Identification of the Somatomotor Network from Language Task–based fMRI Compared with Resting-State fMRI in Patients with Brain LesionsRadiology2021Volume: 301Issue: 1pp. 178-184Deep Learning: A Primer for RadiologistsRadioGraphics2017Volume: 37Issue: 7pp. 2113-2131RadioGraphics International Team InitiativesRadioGraphics2022Volume: 42Issue: 5pp. E142-E144Current Applications and Future Impact of Machine Learning in RadiologyRadiology2018Volume: 288Issue: 2pp. 318-328See More RSNA Education Exhibits Deep-Brain: A Cutting-edge Concept for Outstanding Functional Resolution in fMRIDigital Posters2020Imaging and Radiomics in Hepatocellular Carcinoma (HCC): Current Update and Future PerspectivesDigital Posters2021Radiomics Analysis Using The Image Biomarker Standardization Initiative (IBSI): Benchmarks And GuidelinesDigital Posters2021 RSNA Case Collection SilicosisRSNA Case Collection2020Multiple System Atrophy (MSA)RSNA Case Collection2021Mesial temporal sclerosisRSNA Case Collection2020 Vol. 310, No. 2 Metrics Altmetric Score PDF download