Data Partitioning and Statistical Considerations for Association of Radiomic Features to Biological Underpinnings: What Is Needed

HomeRadiologyVol. 307, No. 1 PreviousNext Reviews and CommentaryEditorialData Partitioning and Statistical Considerations for Association of Radiomic Features to Biological Underpinnings: What Is NeededMichael A. Jacobs Michael A. Jacobs Author AffiliationsFrom the Department of Diagnostic and Interventional Imaging, McGovern Medical School at The University of Texas Health Science Center at Houston (UTHealth Houston), 6431 Fannin St, Room R172, Houston, TX 77030; and The Russell H. Morgan Department of Radiology and Radiological Science and Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University, Baltimore, Md.Address correspondence to the author (email: [email protected]).Michael A. Jacobs Published Online:Dec 20 2022https://doi.org/10.1148/radiol.223007MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In References1. Parekh V, Jacobs MA. Radiomics: a new application from established techniques. Expert Rev Precis Med Drug Dev 2016;1(2):207–226. Crossref, Medline, Google Scholar2. 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 Scholar3. Kumar V, Gu Y, Basu S, et al. Radiomics: the process and the challenges. Magn Reson Imaging 2012;30(9):1234–1248. Crossref, Medline, Google Scholar4. 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 Scholar5. Kontos D, Winham SJ, Oustimov A, et al. Radiomic Phenotypes of Mammographic Parenchymal Complexity: Toward Augmenting Breast Density in Breast Cancer Risk Assessment. Radiology 2019;290(1):41–49. Link, Google Scholar6. Parekh VS, Jacobs MA. Multiparametric radiomics methods for breast cancer tissue characterization using radiological imaging. Breast Cancer Res Treat 2020;180(2):407–421. Crossref, Medline, Google Scholar7. 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 Scholar8. Soliman MAS, Kelahan LC, Magnetta M, et al. A Framework for Harmonization of Radiomics Data for Multicenter Studies and Clinical Trials. JCO Clin Cancer Inform 2022;6(6):e2200023. Crossref, Medline, Google Scholar9. Gidwani M, Chang K, Patel JB, et al. Inconsistent partitioning and unproductive feature associations yield idealized radiomic models. Radiology 2023;307(1):e220715. Google Scholar10. Moskowitz CS, Welch ML, Jacobs MA, Kurland BF, Simpson AL. Radiomic Analysis: Study Design, Statistical Analysis, and Other Bias Mitigation Strategies. Radiology 2022;304(2):265–273. Link, Google ScholarArticle HistoryReceived: Nov 21 2022Revision requested: Nov 22 2022Revision received: Nov 26 2022Accepted: Nov 30 2022Published online: Dec 20 2022 FiguresReferencesRelatedDetailsAccompanying This ArticleInconsistent Partitioning and Unproductive Feature Associations Yield Idealized Radiomic ModelsDec 20 2022RadiologyRecommended Articles Surveillance Imaging for Head and Neck Cancer: Some Much-needed Proof of EffectivenessRadiology2023Volume: 307Issue: 2Interrater Reliability of NI-RADS on Posttreatment PET/Contrast-enhanced CT Scans in Head and Neck Squamous Cell CarcinomaRadiology: Imaging Cancer2021Volume: 3Issue: 3Normalized Dynamic Contrast-enhanced Perfusion and Diffusion-weighted MRI Parameters Distinguish Head and Neck Cancer Recurrence from Posttreatment ChangesRadiology: Imaging Cancer2022Volume: 4Issue: 6Can Amide Proton Transfer MRI Distinguish Benign and Malignant Head and Neck Tumors?Radiology2018Volume: 288Issue: 3pp. 791-792Using Deep Learning for MRI to Identify Responders to Chemoradiotherapy in Rectal CancerRadiology2020Volume: 296Issue: 1pp. 65-66See More RSNA Education Exhibits CT And MRI Evaluation Of Head And Neck Cancer Treated With Chemoradiotherapy: Basics And State-of-the-artDigital Posters2021Outcome Prediction For Head And Neck Cancer Patients: How Can We Improve Further?Digital Posters2021Imaging Biomarkers in Targeted Therapies: From Quantitative Imaging to RadiomicsDigital Posters2019 RSNA Case Collection Cytotoxic lesion of the corpus callosum RSNA Case Collection2021Creutzfeldt-Jakob DiseaseRSNA Case Collection2021Metronidazole induced encephalopathyRSNA Case Collection2022 Vol. 307, No. 1 Metrics Altmetric Score PDF download