ArticleScientific reports2021
Radiomics feature stability of open-source software evaluated on apparent diffusion coefficient maps in head and neck cancer.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 30 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.
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Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Mastering CT-based radiomic research in lung cancer: a practical guide from study design to critical appraisal.The British journal of radiology · 2025Guideline
- CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Four MRI-Based Radiomics Models for Diagnosis of Lumbar Intervertebral Disc Degeneration.Journal of imaging informatics in medicine · 2026Article
- [MP-MRI in the evaluation of non-operative treatment response, for residual and recurrent tumor detection in head and neck cancer].Magyar onkologia · 2025Review
- Long-axial field-of-view PET/CT improves radiomics feature reliability.European journal of nuclear medicine and molecular imaging · 2025Article
- The impact of Inter-observation variation on radiomic features of pulmonary nodules.Frontiers in oncology · 2025Article
- Article
- Decoding Radiomics: A Step-by-Step Guide to Machine Learning Workflow in Hand-Crafted and Deep Learning Radiomics Studies.Diagnostics (Basel, Switzerland) · 2024Review
- Development of a Secure Web-Based Medical Imaging Analysis Platform: The AWESOMME Project.Journal of imaging informatics in medicine · 2024Article
- Impact of quantitative CT texture analysis on the outcome of CT-guided bone biopsy.Journal of bone oncology · 2024Article
- Utilization of texture features of volumetric ADC maps in differentiating between serous cystadenoma and intraductal papillary neoplasms.Abdominal radiology (New York) · 2024Article
- Developments and future prospects of personalized medicine in head and neck squamous cell carcinoma diagnoses and treatments.Cancer reports (Hoboken, N.J.) · 2024Review
- Review
- Spatially-aware clustering improves AJCC-8 risk stratification performance in oropharyngeal carcinomas.Oral oncology · 2023Article
- A Statistical Approach to Assess the Robustness of Radiomics Features in the Discrimination of Mammographic Lesions.Journal of personalized medicine · 2023Article
- Phantom-based radiomics feature test-retest stability analysis on photon-counting detector CT.European radiology · 2023Article
- Radiomics in Cardiac Computed Tomography.Diagnostics (Basel, Switzerland) · 2023Review
- Feature selection methods and predictive models in CT lung cancer radiomics.Journal of applied clinical medical physics · 2023Review
- Radiomics in Abdominopelvic Solid-Organ Oncologic Imaging: Current Status.AJR. American journal of roentgenology · 2022Review
- Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors.
Funding
Abstract
Radiomics is a promising technique for discovering image based biomarkers of therapy response in cancer. Reproducibility of radiomics features is a known issue that is addressed by the image biomarker standardisation initiative (IBSI), but it remains challenging to interpret previously published radiomics signatures. This study investigates the reproducibility of radiomics features calculated with two widely used radiomics software packages (IBEX, MaZda) in comparison to an IBSI compliant software package (PyRadiomics). Intensity histogram, shape and textural features were extracted from 334 diffusion weighted magnetic resonance images of 59 head and neck cancer (HNC) patients from the PREDICT-HN observational radiotherapy study. Based on name and linear correlation, PyRadiomics shares 83 features with IBEX and 49 features with MaZda, a sub-set of well correlated features are considered reproducible (IBEX: 15 features, MaZda: 18 features). We explore the impact of including non-reproducible radiomics features in a HNC radiotherapy response model. It is possible to classify equivalent patient groups using radiomic features from either software, but only when restricting the model to reliable features using a correlation threshold method. This is relevant for clinical biomarker validation trials as it provides a framework to assess the reproducibility of reported radiomic signatures from existing trials.
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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.