ReviewEuropean radiology2021
Why did European Radiology reject my radiomic biomarker paper? How to correctly evaluate imaging biomarkers in a clinical setting.
Review in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 5 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
38 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Citation and adherence to TRIPOD guidelines by published radiological prognostic models: systematic review.The British journal of radiology · 2025Pooled it
- Diagnostic accuracy of a machine learning-based radiomics approach of MR in predicting IDH mutations in glioma patients: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled 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
- Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative.European radiology · 2023Pooled it
- The differential diagnosis value of radiomics-based machine learning in Parkinson's disease: a systematic review and meta-analysis.Frontiers in aging neuroscience · 2023Pooled it
- Radiomics machine learning models for lung cancer early diagnosis in heterogenous multicentre chest CT data: LIBRA study results.European radiology experimental · 2026Article
- Why Radiomics Rarely Reaches the Clinic: Reproducibility, Validation, and Evidence Gap-A Critical Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- APT MRI Signature for Risk Stratification of Pediatric Medulloblastoma.Chemical & biomedical imaging · 2026Article
- Artificial intelligence prognostication of liver disease using imaging.The British journal of radiology · 2026Review
- Deep learning-based CT radiomics for ALK rearrangement status prediction in lung adenocarcinoma.BMC cancer · 2026Article
- Methodological review of the level of statistical support declared in radiological research articles.The British journal of radiology · 2026Review
- Habitat-Derived Radiomic Features of Planning Target Volume to Determine the Local Recurrence After Radiotherapy in Patients with Gliomas: A Feasibility Study.Journal of imaging informatics in medicine · 2026Article
- Texture analysis of [European journal of nuclear medicine and molecular imaging · 2026Observational
- Better risk stratification of colon cancer is needed, but is radiogenomics the answer?European radiology · 2025Article
- Radiomic imaging models for predicting breast cancer prognosis based on Interleukin-18 (IL18).BMC medical imaging · 2025Article
- Dosiomic and radiomic features within radiotherapy target volume for predicting the treatment response in patients with glioma after radiotherapy.BMC medical imaging · 2025Article
- Quantum integration in swin transformer mitigates overfitting in breast cancer screening.Scientific reports · 2025Article
- The current state of imaging biomarker development and evaluation.The British journal of radiology · 2025Review
- MRI-based radiomics model for predicting endometrial cancer with high tumor mutation burden.Abdominal radiology (New York) · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This review explains in simple terms, accessible to the non-statistician, general principles regarding the correct research methods to develop and then evaluate imaging biomarkers in a clinical setting, including radiomic biomarkers. The distinction between diagnostic and prognostic biomarkers is made and emphasis placed on the need to assess clinical utility within the context of a multivariable model. Such models should not be restricted to imaging biomarkers and must include relevant disease and patient characteristics likely to be clinically useful. Biomarker utility is based on whether its addition to the basic clinical model improves diagnosis or prediction. Approaches to both model development and evaluation are explained and the need for adequate amounts of representative data stressed so as to avoid underpowering and overfitting. Advice is provided regarding how to report the research correctly. KEY POINTS: • Imaging biomarker research is common but methodological errors are encountered frequently that may mean the research is not clinically useful. • The clinical utility of imaging biomarkers is best assessed by their additive effect on multivariable models based on clinical factors known to be important. • The data used to develop such models should be sufficient for the number of variables investigated and the model should be evaluated, preferably using data unrelated to development.
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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.