ArticleJCO clinical cancer informatics2022
A Framework for Harmonization of Radiomics Data for Multicenter Studies and Clinical Trials.
Article in JCO clinical cancer informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
What it found
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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
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Precision or Paradox? AI-Driven Adiposity Imaging in Women With Overweight and Obesity: A Systematic Review and Meta-Analysis.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026Pooled it
- Image Quality Standardization in Radiomics: A Systematic Review of Artifacts, Variability, and Feature Stability.Sensors (Basel, Switzerland) · 2026Pooled it
- Multi-Parametric Ultrasound Radiomic Kinetics with Machine Learning Ensemble for Early Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer.Diagnostics (Basel, Switzerland) · 2026Article
- Physics-Informed Multiscale Decoding of Tissue Microstructure: The Gray Level Affinity Metrics (GLAM) Framework.Journal of imaging informatics in medicine · 2026Article
- A Predictive MRI Radiomics Model for Histologic Differentiation in Soft Tissue Sarcomas.Cancers · 2026Article
- Influence of CT harmonization in longitudinal radiomics for NSCLC immunotherapy response prediction.Computers in biology and medicine · 2026Article
- Crohn's disease: research progress in decoding pathogenic multi-network and precision management of artificial intelligence radiomics.Frontiers in immunology · 2026Review
- Beyond Radiomics Alone: Enhancing Prostate Cancer Classification with ADC Ratio in a Multicenter Benchmarking Study.Diagnostics (Basel, Switzerland) · 2025Article
- Radiomics Models to Predict Tumor Response and Pneumonitis in Non-Small Cell Lung Cancer Patients Treated with Immunotherapy.Journal of clinical medicine · 2025Article
- Kernel Conversion Improves the Correlation between the Extent of Emphysema and Clinical Parameters in Chronic Obstructive Pulmonary Disease: A Multicenter Cohort Study.Tuberculosis and respiratory diseases · 2025Article
- Delta Radiomics and Tumor Size: A New Predictive Radiomics Model for Chemotherapy Response in Liver Metastases from Breast and Colorectal Cancer.Tomography (Ann Arbor, Mich.) · 2025Article
- Comparing quantitative image parameters between animal and clinical CT-scanners: a translational phantom study analysis.Frontiers in medicine · 2024Article
- Automating Clinical Trial Matches Via Natural Language Processing of Synthetic Electronic Health Records and Clinical Trial Eligibility Criteria.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Artificial Intelligence-Assisted Processing of Anterior Segment OCT Images in the Diagnosis of Vitreoretinal Lymphoma.Diagnostics (Basel, Switzerland) · 2023Article
- Robustness of radiomics features of virtual unenhanced and virtual monoenergetic images in dual-energy CT among different imaging platforms and potential role of CT number variability.Insights into imaging · 2023Article
- Data Partitioning and Statistical Considerations for Association of Radiomic Features to Biological Underpinnings: What Is Needed.Radiology · 2023Article
- Artificial Intelligence in Emergency Radiology: Where Are We Going?Diagnostics (Basel, Switzerland) · 2022Review
- Early Diagnosis of Pancreatic Cancer: Clinical Premonitions, Timely Precursor Detection and Increased Curative-Intent Surgery.Cancer control : journal of the Moffitt Cancer CenterReview
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeVariability in computed tomography images intrinsic to individual scanners limits the application of radiomics in clinical and research settings. The development of reproducible and generalizable radiomics-based models to assess lesions requires harmonization of data. The purpose of this study was to develop, test, and analyze the efficacy of a radiomics data harmonization model. MATERIALS AND
methodsRadiomic features from biopsy-proven untreated hepatic metastasis (N = 380) acquired from 167 unique patients with pancreatic, colon, and breast cancers were analyzed. Radiomic features from volume-match 551 samples of normal liver tissue and 188 hepatic cysts were included as references. A novel linear mixed effect model was used to identify effects associated with lesion size, tissue type, and scanner model. Six separate machine learning models were then used to test the effectiveness of radiomic feature harmonization using multivariate analysis.
resultsProposed model identifies and removes scanner-associated effects while preserving cancer-specific functional dependence of radiomic features on the tumor size. Data harmonization improves the performance of classification models by reducing the scanner-associated variability. For example, the multiclass logistic regression model, LogitBoost, demonstrated the improvement in sensitivity in the range from 15% to 40% for each type of liver metastasis, whereas the overall model accuracy and the kappa coefficient increased by 5% and 8% accordingly.
conclusionThe model removed scanner-associated effects while preserving cancer-specific functional dependence of radiomic features.
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