ArticleScientific reports2022
The impact of inter-observer variation in delineation on robustness of radiomics features in non-small cell lung cancer.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Post-radiotherapy stage III/IV non-small cell lung cancer radiomics research: a systematic review and comparison of CLEAR and RQS frameworks.European radiology · 2024Pooled it
- MR-Guided Adaptive Radiotherapy in Lung Cancer: Current Landscape and Future Directions.Cancers · 2026Review
- AI-driven radiomics in vascular disease: a narrative review of image analysis and clinical translation.Cardiovascular diagnosis and therapy · 2026Review
- Segmentation for pelvic malignancies in radiation oncology practice: a systematic review and meta-analysis protocol.Systematic reviews · 2026Article
- Radiomic analysis of medical imaging for classification in oncology and recommendations for clinically initiated research: a literature review.Quantitative imaging in medicine and surgery · 2026Review
- Comparison of Manual, Semi-Automatic, and Automatic CT-Based Methods for Liver Volume Segmentation.Diagnostics (Basel, Switzerland) · 2026Article
- Cancer classification with radiomics in controlled preclinical models.Scientific reports · 2026Article
- AI-Based 3D Liver Segmentation and Volumetric Analysis in Living Donor Data.Journal of imaging informatics in medicine · 2025Article
- Segmentation variability and radiomics stability for predicting triple-negative breast cancer subtype using magnetic resonance imaging.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Integrated Hyperparameter Optimization with Dimensionality Reduction and Clustering for Radiomics: A Bootstrapped Approach.Multimodal technologies and interaction · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- From images to clinical insights: an educational review on radiomics in lung diseases.Breathe (Sheffield, England) · 2025Review
- The impact of Inter-observation variation on radiomic features of pulmonary nodules.Frontiers in oncology · 2025Article
- Mitigating Interobserver Variability in Radiomics with ComBat: A Feasibility Study.Journal of imaging · 2024Article
- Interobserver delineation variability of computed tomography-based radiomic features of the parotid gland.Radiation oncology journal · 2024Article
- A clinically relevant computed tomography (CT) radiomics strategy for intracranial rodent brain tumour monitoring.Scientific reports · 2024Article
- Prediction ofCancers · 2023Article
- Assessment of Variabilities in Lung-Contouring Methods on CBCT Preclinical Radiomics Outputs.Cancers · 2023Article
- Clinical validation of a semi-automated segmentation algorithm for target volume definition on planning CT and CBCT in stereotactic body radiotherapy (SBRT) for peripheral lung lesions.Journal of medical radiation sciences · 2023Article
- The role of artificial intelligence based on PET/CT radiomics in NSCLC: Disease management, opportunities, and challenges.Frontiers in oncology · 2023Review
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence and radiomics have the potential to revolutionise cancer prognostication and personalised treatment. Manual outlining of the tumour volume for extraction of radiomics features (RF) is a subjective process. This study investigates robustness of RF to inter-observer variation (IOV) in contouring in lung cancer. We utilised two public imaging datasets: 'NSCLC-Radiomics' and 'NSCLC-Radiomics-Interobserver1' ('Interobserver'). For 'NSCLC-Radiomics', we created an additional set of manual contours for 92 patients, and for 'Interobserver', there were five manual and five semi-automated contours available for 20 patients. Dice coefficients (DC) were calculated for contours. 1113 RF were extracted including shape, first order and texture features. Intraclass correlation coefficient (ICC) was computed to assess robustness of RF to IOV. Cox regression analysis for overall survival (OS) was performed with a previously published radiomics signature. The median DC ranged from 0.81 ('NSCLC-Radiomics') to 0.85 ('Interobserver'-semi-automated). The median ICC for the 'NSCLC-Radiomics', 'Interobserver' (manual) and 'Interobserver' (semi-automated) were 0.90, 0.88 and 0.93 respectively. The ICC varied by feature type and was lower for first order and gray level co-occurrence matrix (GLCM) features. Shape features had a lower median ICC in the 'NSCLC-Radiomics' dataset compared to the 'Interobserver' dataset. Survival analysis showed similar separation of curves for three of four RF apart from 'original_shape_Compactness2', a feature with low ICC (0.61). The majority of RF are robust to IOV, with first order, GLCM and shape features being the least robust. Semi-automated contouring improves feature stability. Decreased robustness of a feature is significant as it may impact upon the features' prognostic capability.
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