ArticleApplied sciences (Basel, Switzerland)2024
Reproducibility in Radiomics: A Comparison of Feature Extraction Methods and Two Independent Datasets.
Article in Applied sciences (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Training set augmentation and biology-aware harmonization improve radiomic models for lung cancer prediction in indeterminate nodules.Scientific reports · 2026Article
- Insights into radiomics: a comprehensive review for beginners.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Review
- Training Set Augmentation and Harmonization Enables Radiomic Models to Detect Early Onset of Lung Cancer.Research square · 2025Article
- Impact of a deep learning image reconstruction algorithm on the robustness of abdominal computed tomography radiomics features using standard and low radiation doses.Quantitative imaging in medicine and surgery · 2025Article
- ChatGPT-4-Driven Liver Ultrasound Radiomics Analysis: Diagnostic Value and Drawbacks in a Comparative Study.JMIR AI · 2025Article
- Deep learning and radiomics-based system for early diagnosis of hip synovitis in juvenile idiopathic arthritis.Frontiers in immunology · 2025Article
- Machine learning-based radiomics for differentiating lung cancer subtypes in brain metastases using CE-T1WI.Frontiers in oncology · 2025Article
- Radiomics-based machine learning for differentiating lung squamous cell carcinoma and adenocarcinoma using T1-enhanced MRI of brain metastases.Frontiers in oncology · 2025Article
- Prospects and challenges of deep learning in gynecologic malignancies.Frontiers in oncology · 2025Review
- Non-invasive CT radiomic biomarkers predict microsatellite stability status in colorectal cancer: a multicenter validation study.European radiology experimental · 2024Article
- The Application of Radiomics and AI to Molecular Imaging for Prostate Cancer.Journal of personalized medicine · 2024Review
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14 authors.
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Abstract
Radiomics involves the extraction of information from medical images that are not visible to the human eye. There is evidence that these features can be used for treatment stratification and outcome prediction. However, there is much discussion about the reproducibility of results between different studies. This paper studies the reproducibility of CT texture features used in radiomics, comparing two feature extraction implementations, namely the MATLAB toolkit and Pyradiomics, when applied to independent datasets of CT scans of patients: (i) the open access RIDER dataset containing a set of repeat CT scans taken 15 min apart for 31 patients (RIDER Scan 1 and Scan 2, respectively) treated for lung cancer; and (ii) the open access HN1 dataset containing 137 patients treated for head and neck cancer. Gross tumor volume (GTV), manually outlined by an experienced observer available on both datasets, was used. The 43 common radiomics features available in MATLAB and Pyradiomics were calculated using two intensity-level quantization methods with and without an intensity threshold. Cases were ranked for each feature for all combinations of quantization parameters, and the Spearman's rank coefficient,
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