ArticleScientific reports2021
Robustness of radiomic features in CT images with different slice thickness, comparing liver tumour and muscle.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Enhanced prediction of microvascular invasion in hepatocellular carcinoma: a comparative study of intraoperative ultrasound, CT imaging and radiomic.La Radiologia medica · 2026Article
- Radiomics-Based Machine Learning for Sarcopenia Detection in Abdominal and Low-Dose CT.Diagnostics (Basel, Switzerland) · 2026Article
- Radiomics-Based Assessment of Portal Hypertension Severity and Risk Stratification of Cirrhotic Patients Using Routine CT Scans.Liver international : official journal of the International Association for the Study of the Liver · 2026Article
- Voxel Normalization in LDCT Imaging: Its Significance in Texture Feature Selection for Pulmonary Nodule Malignancy Classification: Insights from Two Centers.Diagnostics (Basel, Switzerland) · 2026Article
- Interpretable and reproducible machine learning model for coronary calcification and segment-level stenoses stratification on computed tomography angiography.BMC medicine · 2025Article
- Cross-Software Radiomic Feature Robustness Assessed by Hierarchical Clustering and Composite Index Analysis: A Multi-Cancer Study on Colorectal and Liver Lesions.Bioengineering (Basel, Switzerland) · 2025Article
- Radiomic analysis of the deltoid and scapula: identification of computed tomography-image based measurements predictive of pain, motion, and function before and after shoulder arthroplasty.JSES international · 2025Article
- Imaging-based assessment of response to olaparib in platinum-sensitive relapsed ovarian cancer patients.Frontiers in oncology · 2025Article
- Robust radiomics: a review of guidelines for radiomics in medical imaging.Frontiers in radiology · 2025Review
- Finding Reproducible and Prognostic Radiomic Features in Variable Slice Thickness Contrast Enhanced CT of Colorectal Liver Metastases.The journal of machine learning for biomedical imaging · 2025Article
- Establishment of a CT-based radiomic feature robustness databank for OPC patients via image perturbation in a multi-institutional study: a practical method to safeguard model generalizability.Frontiers in oncology · 2025Article
- On the robustness of [18F]-FDG-PET radiomic features to variations in image acquisition and reconstruction settings: A phantom study.PloS one · 2025Article
- Enhancing Radiomics Reproducibility: Deep Learning-Based Harmonization of Abdominal Computed Tomography (CT) Images.Bioengineering (Basel, Switzerland) · 2024Article
- Decoding Radiomics: A Step-by-Step Guide to Machine Learning Workflow in Hand-Crafted and Deep Learning Radiomics Studies.Diagnostics (Basel, Switzerland) · 2024Review
- Radiomics for differentiation of somaticJournal of medical imaging (Bellingham, Wash.) · 2024Article
- A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease.Tomography (Ann Arbor, Mich.) · 2024Article
- Article
- Mitigating the impact of image processing variations on tumour [Scientific reports · 2024Article
- Robustness and classification capabilities of MRI radiomic features in identifying carotid plaque vulnerability.The British journal of radiology · 2024Article
- Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence.Scientific reports · 2024Article
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6 authors.
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Abstract
Radiomic image features are becoming a promising non-invasive method to obtain quantitative measurements for tumour classification and therapy response assessment in oncological research. However, despite its increasingly established application, there is a need for standardisation criteria and further validation of feature robustness with respect to imaging acquisition parameters. In this paper, the robustness of radiomic features extracted from computed tomography (CT) images is evaluated for liver tumour and muscle, comparing the values of the features in images reconstructed with two different slice thicknesses of 2.0 mm and 5.0 mm. Novel approaches are presented to address the intrinsic dependencies of texture radiomic features, choosing the optimal number of grey levels and correcting for the dependency on volume. With the optimal values and corrections, feature values are compared across thicknesses to identify reproducible features. Normalisation using muscle regions is also described as an alternative approach. With either method, a large fraction of features (75-90%) was found to be highly robust (< 25% difference). The analyses were performed on a homogeneous CT dataset of 43 patients with hepatocellular carcinoma, and consistent results were obtained for both tumour and muscle tissue. Finally, recommended guidelines are included for radiomic studies using variable slice thickness.
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