ReviewRadiology. Artificial intelligence2024
Radiomics Beyond the Hype: A Critical Evaluation Toward Oncologic Clinical Use.
Review in Radiology. Artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 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
71 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Image Quality Standardization in Radiomics: A Systematic Review of Artifacts, Variability, and Feature Stability.Sensors (Basel, Switzerland) · 2026Pooled it
- Evaluating the impact of the Radiomics Quality Score: a systematic review and meta-analysis.European radiology · 2025Pooled it
- Diagnostic Value ofTechnology in cancer research & treatmentPooled it
- DCE-MRI and Mammography integrated radiomic analysis in triple negative ductal invasive breast cancer patients. Comparison between BRCA and not BRCA mutated patients: preliminary results.European journal of radiology open · 2026Article
- Artificial Intelligence for the Preoperative Molecular Characterization of Adult-Type Diffuse High-Grade Gliomas: A Systematic Review and Meta-Analysis.Journal of imaging informatics in medicine · 2026Review
- Externally validated yet undertrained: sample size deficits in machine learning-based radiomics.European radiology · 2026Article
- A predictive model for fusion gene positivity in treatment-naive advanced non-small cell lung cancer.Translational lung cancer research · 2026Article
- U-Swing: An Adaptive U-Net and Swin Fusion for WB-MRI Whole Spine Bone Marrow Segmentation.Journal of imaging informatics in medicine · 2026Article
- Multidimensional deep learning for grading and prognostic assessment of intrahepatic mass-forming cholangiocarcinoma.Insights into imaging · 2026Article
- Why Radiomics Rarely Reaches the Clinic: Reproducibility, Validation, and Evidence Gap-A Critical Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Enhancing the preoperative differentiation of ameloblastoma and odontogenic keratocyst using pathomics-guided radiomics: a pilot study.BMC oral health · 2026Article
- MRI-based quantification of intratumoral heterogeneity for predicting recurrence risk in ER+/HER2- breast cancer.Insights into imaging · 2026Article
- [A bidirectional feature mapping classification model based on multi-constrained latent representation learning for differential diagnosis of pneumonia].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- Associations between CT radiomics analyses and kidney biopsy in patients with kidney disease.BMC nephrology · 2026Article
- Radiomics and Back Pain.Global spine journal · 2026Review
- Influence of image preprocessing on reproducibility and longitudinal repeatability analysis of radiomics features in magnetic resonance image-guided accelerator imaging.Quantitative imaging in medicine and surgery · 2026Article
- Challenges underlying radiology's research problem.Insights into imaging · 2026Review
- Interpretable machine learning models based on CT radiomics for predicting chemoradiotherapy response in rectal cancer.BMC medical imaging · 2026Article
- Integration of Conventional and Radiomic Features From Fluorine-18 Fluorodeoxyglucose Positron Emission Tomography/Magnetic Resonance Imaging for Multimodal Prediction of Symptomatic Carotid Atherosclerotic Plaques.Journal of the American Heart Association · 2026Article
- Predicting intrahepatic recurrence of colorectal cancer liver metastases after curative hepatectomy using a machine learning model with data integration of ultrasound radiomics and clinicopathological parameters.Insights into imaging · 2026Article
11 more citing papers are in PubMed but not listed here.
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
Abstract
Radiomics is a promising and fast-developing field within oncology that involves the mining of quantitative high-dimensional data from medical images. Radiomics has the potential to transform cancer management, whereby radiomics data can be used to aid early tumor characterization, prognosis, risk stratification, treatment planning, treatment response assessment, and surveillance. Nevertheless, certain challenges have delayed the clinical adoption and acceptability of radiomics in routine clinical practice. The objectives of this report are to (
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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.