ArticleScientific reports2016
Reproducibility of radiomics for deciphering tumor phenotype with imaging.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 310 papers, 4 of them syntheses that pooled it.
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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
310 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Image Quality Standardization in Radiomics: A Systematic Review of Artifacts, Variability, and Feature Stability.Sensors (Basel, Switzerland) · 2026Pooled it
- Repeatability of radiomics studies in colorectal cancer: a systematic review.BMC gastroenterology · 2023Pooled it
- Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy.Seminars in cancer biology · 2022Pooled it
- The impact of radiomics for human papillomavirus status prediction in oropharyngeal cancer: systematic review and radiomics quality score assessment.Neuroradiology · 2022Pooled it
- Automated Imaging as an Adjunct to Serum and Clinical Biomarkers: A New Validated Prediction Tool for Metastatic Castration-Resistant Prostate Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Trial
- Radiomics model for preoperative prediction of 3-year survival-based CT image biomarkers in esophageal cancer.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2022Trial
- Radiomics signature for predicting postoperative disease-free survival of patients with gastric cancer: development and validation of a predictive nomogram.Diagnostic and interventional radiology (Ankara, Turkey) · 2022Trial
- Impact of inter-reader contouring variability on textural radiomics of colorectal liver metastases.European radiology experimental · 2020Trial
- Bridging radiomics and histopathology: cross-modality consistency of radiomic features from CT to micro-CT in NSCLC.European radiology experimental · 2026Article
- CT texture phantom dataset with paired image quality assessments for quantitative imaging.Medical physics · 2026Article
- Article
- An open-source CT texture phantom for radiomic imaging studies: Design, manufacture, and initial evaluation.Medical physics · 2026Article
- CT-based interpretable delta-radiomics model for risk stratification of pulmonary ground-glass nodules: a multicentre study.Insights into imaging · 2026Article
- Machine Learning for Classification in Lung Cancer Using Routine Clinical and Laboratory Data.Annals of surgical oncology · 2026Article
- Influence of CT harmonization in longitudinal radiomics for NSCLC immunotherapy response prediction.Computers in biology and medicine · 2026Article
- Predicting neoadjuvant chemotherapy response in osteosarcoma using T2-based MRI delta-radiomics: a retrospective study.BMC medical imaging · 2026Article
- Assessing the impact of CT reconstruction kernel on radiomic features extracted from normal and fibrotic tissue in patients with diffuse lung disease.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Evaluation and implementation of an independent Kilovoltage X-ray-based imaging platform for carbon ion radiotherapy.Journal of applied clinical medical physics · 2026Article
- Article
- Integration of multi-scale radiomics and deep learning for Ki-67 prediction in clear cell renal carcinoma.NPJ precision oncology · 2025Article
250 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
Abstract
Radiomics (radiogenomics) characterizes tumor phenotypes based on quantitative image features derived from routine radiologic imaging to improve cancer diagnosis, prognosis, prediction and response to therapy. Although radiomic features must be reproducible to qualify as biomarkers for clinical care, little is known about how routine imaging acquisition techniques/parameters affect reproducibility. To begin to fill this knowledge gap, we assessed the reproducibility of a comprehensive, commonly-used set of radiomic features using a unique, same-day repeat computed tomography data set from lung cancer patients. Each scan was reconstructed at 6 imaging settings, varying slice thicknesses (1.25 mm, 2.5 mm and 5 mm) and reconstruction algorithms (sharp, smooth). Reproducibility was assessed using the repeat scans reconstructed at identical imaging setting (6 settings in total). In separate analyses, we explored differences in radiomic features due to different imaging parameters by assessing the agreement of these radiomic features extracted from the repeat scans reconstructed at the same slice thickness but different algorithms (3 settings in total). Our data suggest that radiomic features are reproducible over a wide range of imaging settings. However, smooth and sharp reconstruction algorithms should not be used interchangeably. These findings will raise awareness of the importance of properly setting imaging acquisition parameters in radiomics/radiogenomics research.
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