ReviewJournal of personalized medicine2021
Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods.
Review in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 126 papers, 6 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
126 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Performance of Machine Learning Models Based on Medical Imaging in Predicting Pathological Grade of Clear Cell Renal Cell Carcinoma.Cancer medicine · 2026Pooled it
- Magnetic Resonance Imaging-Based Artificial Intelligence in Predicting Prostate Cancer Biochemical Recurrence: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Image Quality Standardization in Radiomics: A Systematic Review of Artifacts, Variability, and Feature Stability.Sensors (Basel, Switzerland) · 2026Pooled it
- Mastering CT-based radiomic research in lung cancer: a practical guide from study design to critical appraisal.The British journal of radiology · 2025Guideline
- Systematic Review, Meta-Analysis and Radiomics Quality Score Assessment of CT Radiomics-Based Models Predicting Tumor EGFR Mutation Status in Patients with Non-Small-Cell Lung Cancer.International journal of molecular sciences · 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
- CT radiomics showed no improvement beyond volume dynamics for early lesion-level size response to immunotherapy in metastatic melanoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Observational
- Transparent, Reproducible Text-Based Phenotyping of Lumbar Intervertebral Disc Degeneration from 500 Consecutive MRI Reports, with a Pre-Specified Image Analysis Framework.Diagnostics (Basel, Switzerland) · 2026Article
- Diagnostic Accuracy of Artificial Intelligence Models for Detecting Odontogenic Keratocytes on CBCT Imaging: A Systematic Review and Meta-Analysis.Health science reports · 2026Review
- The cult of the external validation set: undertraining is only half of the radiomics validation crisis.European radiology · 2026Article
- Improving the robustness of radiomic features to patient size variations in CBCT imaging for radiotherapy.Journal of X-ray science and technology · 2026Article
- Imaging the hallmarks of cancer.Nature reviews. Cancer · 2026Review
- Association between preoperative MRI radiomic features and methylation-defined phenotypes in non-functioning pituitary adenomas.Pituitary · 2026Article
- Article
- MRI analysis of undifferentiated pleomorphic sarcoma: correlating imaging features with histological grade.Quantitative imaging in medicine and surgery · 2026Article
- Radiomics in Gastric Cancer: Advancing Precision Medicine.Journal of gastric cancer · 2026Review
- A pre-trained foundation model framework for multiplanar MRI classification of extramural vascular invasion and mesorectal fascia invasion in rectal cancer.Insights into imaging · 2026Article
- Artificial Intelligence in Oncology: A Comprehensive Cross-Cancer Translational Readiness Analysis Across 18 Malignancies.Cancers · 2026Review
- Amide proton transfer and arterial spin labeling for non-invasive molecular stratification of glioma: a multi-dataset imaging biomarker study.Neuroradiology · 2026Article
- Radiomic analysis of medical imaging for classification in oncology and recommendations for clinically initiated research: a literature review.Quantitative imaging in medicine and surgery · 2026Review
66 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
9 authors.
Funding
Abstract
Radiomics converts medical images into mineable data via a high-throughput extraction of quantitative features used for clinical decision support. However, these radiomic features are susceptible to variation across scanners, acquisition protocols, and reconstruction settings. Various investigations have assessed the reproducibility and validation of radiomic features across these discrepancies. In this narrative review, we combine systematic keyword searches with prior domain knowledge to discuss various harmonization solutions to make the radiomic features more reproducible across various scanners and protocol settings. Different harmonization solutions are discussed and divided into two main categories: image domain and feature domain. The image domain category comprises methods such as the standardization of image acquisition, post-processing of raw sensor-level image data, data augmentation techniques, and style transfer. The feature domain category consists of methods such as the identification of reproducible features and normalization techniques such as statistical normalization, intensity harmonization, ComBat and its derivatives, and normalization using deep learning. We also reflect upon the importance of deep learning solutions for addressing variability across multi-centric radiomic studies especially using generative adversarial networks (GANs), neural style transfer (NST) techniques, or a combination of both. We cover a broader range of methods especially GANs and NST methods in more detail than previous reviews.
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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.