ReviewRadiology2022
Radiomic Analysis: Study Design, Statistical Analysis, and Other Bias Mitigation Strategies.
Review in Radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
42 citing papers in PubMed, 3 syntheses or guidelines pooled it, 77 citations in OpenAlex.
- Pretreatment CT Texture Analysis for Predicting Survival Outcomes in Advanced Nonsmall Cell Lung Cancer Patients Receiving Immunotherapy: A Systematic Review and Meta-Analysis.Thoracic cancer · 2025Pooled it
- Research progress of MRI-based radiomics in hepatocellular carcinoma.Frontiers in oncology · 2025Pooled it
- A systematic review of radiomics in giant cell tumor of bone (GCTB): the potential of analysis on individual radiomics feature for identifying genuine promising imaging biomarkers.Journal of orthopaedic surgery and research · 2023Pooled it
- Performance of magnetic-resonance imaging radiomics in prediction of response after neoadjuvant chemotherapy in head and neck squamous cell carcinoma: A systematic review and meta-analysis.European journal of radiology open · 2026Article
- Temporalis muscle biomarkers from routine brain MRI and risk of dementia in two independent cohorts.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Review
- Transferability of radiomics models between deep learning and conventional CT reconstruction algorithms: A task-based assessment for stratifying acute pancreatitis severity.Journal of applied clinical medical physics · 2026Article
- Early prediction of periventricular leukomalacia from MRI changes: a machine learning approach for risk stratification.European radiology · 2026Article
- Development and validation of a prognostic prediction model for lumbar-disc herniation based on machine learning and fusion of clinical text data and radiomic features.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Review
- CT radiomics-histopathological correlation for mediastinal lymph node staging in non-small cell lung cancer.PloS one · 2026Article
- A single MRI scan contains sufficient imaging information for accurate prediction of meningioma growth risk.NeuroImage. Clinical · 2026Article
- Mining tumor and surrounding tissue information using artificial intelligence to predict responses to EGFR-targeted therapies and immunotherapy in lung cancer: a multicenter attribution analysis.La Radiologia medica · 2025Article
- Application value of radiomics features based on PSMA PET/CT in diagnosis of clinically significant prostate cancer: a comparison between manual and automatic segmentation.BMC medical imaging · 2025Article
- Measured intrapatient radiomic variability as a predictor of treatment response in multi-metastatic soft tissue sarcoma patients.Scientific reports · 2025Article
- Reproducibility and interpretability in radiomics: a critical assessmentDiagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- Radiomics applications in the modern management of esophageal squamous cell carcinoma.Medical oncology (Northwood, London, England) · 2025Review
- Cerebral morphometric alterations predict the outcome of migraine diagnosis and subtyping: a radiomics analysis.BMC medical imaging · 2025Article
- Early prediction of progression-free survival of patients with locally advanced nasopharyngeal carcinoma using multi-parametric MRI radiomics.BMC cancer · 2025Article
- Overlooked and underpowered: a meta-research addressing sample size in radiomics prediction models for binary outcomes.European radiology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Rapid advances in automated methods for extracting large numbers of quantitative features from medical images have led to tremendous growth of publications reporting on radiomic analyses. Translation of these research studies into clinical practice can be hindered by biases introduced during the design, analysis, or reporting of the studies. Herein, the authors review biases, sources of variability, and pitfalls that frequently arise in radiomic research, with an emphasis on study design and statistical analysis considerations. Drawing on existing work in the statistical, radiologic, and machine learning literature, approaches for avoiding these pitfalls are described.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.