ReviewQuantitative imaging in medicine and surgery2021
Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review.
Review in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 113 papers, 5 of them syntheses that pooled it.
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Who cites it
113 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Mastering CT-based radiomic research in lung cancer: a practical guide from study design to critical appraisal.The British journal of radiology · 2025Guideline
- Application of radiomics-based prediction model to predict preoperative lymph node metastasis in prostate cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Role of radiomics as a predictor of disease recurrence in ovarian cancer: a systematic review.Abdominal radiology (New York) · 2024Pooled it
- Repeatability of radiomics studies in colorectal cancer: a systematic review.BMC gastroenterology · 2023Pooled it
- 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.European journal of nuclear medicine and molecular imaging · 2023Guideline
- Prediction of the mitotic index and preoperative risk stratification of gastrointestinal stromal tumors with CT radiomic features.La Radiologia medica · 2023Trial
- CT radiomics and topology-based intratumoral heterogeneity for preoperative prediction of nuclear β-catenin expression in hepatocellular carcinoma.Abdominal radiology (New York) · 2026Article
- B-mode ultrasound radiomics of the masseter muscle for differentiating individuals with and without clinically defined probable bruxism.Oral radiology · 2026Article
- Moran's index habitat and peritumoral radiomics based on digital breast tomosynthesis for predicting Ki-67 status in breast cancer.Quantitative imaging in medicine and surgery · 2026Article
- Detection of myocardial infarction by postmortem CT combined with radiomics-based machine-learning analysis.European radiology experimental · 2026Article
- Psychometric Properties of the Persian Version of the Delirium Care Self-Efficacy Scale for Intensive Care Unit Nurses: A Methodological Study.Nursing open · 2026Article
- Can radiomics outperform CT morphological features in diagnosing ovarian clear cell carcinoma? A multicenter study.Abdominal radiology (New York) · 2026Article
- Pretreatment Prediction of Tumor Recurrence in Breast Cancer After Neoadjuvant Systemic Therapy Using Machine Learning With Clinical and CT Radiomics Features.The Kaohsiung journal of medical sciences · 2026Article
- Radiomics-based quantitative assessment of the mandibular cortical index on panoramic radiographs.Scientific reports · 2026Article
- Radiomics-based MRI model for predicting the severity of rotator cuff tears.Orthopadie (Heidelberg, Germany) · 2026Article
- Why Radiomics Rarely Reaches the Clinic: Reproducibility, Validation, and Evidence Gap-A Critical Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Inter-Observer Reproducibility of [18F]FDG PET/CT Radiomic Features in Primary Breast Carcinoma.Journal of imaging · 2026Article
- Multimodal MRI-based nomogram integrating clinical-radiological, radiomic, and habitat features to discriminate solitary fibrous tumors from atypical meningiomas.Scientific reports · 2026Article
- An MRI-based radiomics model for precision subtyping of hereditary spastic paraplegia: discriminating SPG4 from SPG5.Neuroradiology · 2026Article
- Interpretable MRI Radiomics for Preoperative Meningioma Consistency Prediction.Journal of imaging informatics in medicine · 2026Article
53 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiomics research is rapidly growing in recent years, but more concerns on radiomics reliability are also raised. This review attempts to update and overview the current status of radiomics reliability research in the ever expanding medical literature from the perspective of a single reliability metric of intraclass correlation coefficient (ICC). To conduct this systematic review, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. After literature search and selection, a total of 481 radiomics studies using CT, PET, or MRI, covering a wide range of subject and disease types, were included for review. In these highly heterogeneous studies, feature reliability to image segmentation was much more investigated than reliability to other factors, such as image acquisition, reconstruction, post-processing, and feature quantification. The reported ICCs also suggested high radiomics feature reliability to image segmentation. Image acquisition was found to introduce much more feature variability than image segmentation, in particular for MRI, based on the reported ICC values. Image post-processing and feature quantification yielded different levels of radiomics reliability and might be used to mitigate image acquisition-induced variability. Some common flaws and pitfalls in ICC use were identified, and suggestions on better ICC use were given. Due to the extremely high study heterogeneities and possible risks of bias, the degree of radiomics feature reliability that has been achieved could not yet be safely synthesized or derived in this review. More future researches on radiomics reliability are warranted.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.