ArticleThe British journal of radiology2021
Impact of CT convolution kernel on robustness of radiomic features for different lung diseases and tissue types.
Article in The British journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 31 citations in OpenAlex.
- Diagnostic accuracy of CT-based radiomics for predicting occult lymph node metastasis in early-stage lung adenocarcinoma: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Mastering CT-based radiomic research in lung cancer: a practical guide from study design to critical appraisal.The British journal of radiology · 2025Guideline
- Comparative analysis of radiomics and deep-learning algorithms for survival prediction in hepatocellular carcinoma.Scientific reports · 2024Trial
- Assessing the influence of kernel selection on chest computed tomography image quality across varying dose levels using TrueFidelity reconstruction.Radiation protection dosimetry · 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
- Impact of reconstruction kernel variability on segmentation consistency in low-dose thoracic CT.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Multipath cycleGAN for harmonization of paired and unpaired low-dose lung computed tomography reconstruction kernels.Medical physics · 2025Article
- Effect of slice thickness on quantitative analysis of interstitial lung disease: a retrospective volumetric chest CT study.La Radiologia medica · 2025Article
- The textures of sarcoidosis: quantifying lung disease through variograms.Physics in medicine and biology · 2025Article
- From images to clinical insights: an educational review on radiomics in lung diseases.Breathe (Sheffield, England) · 2025Review
- Enhancing the Clinical Utility of Radiomics: Addressing the Challenges of Repeatability and Reproducibility in CT and MRI.Diagnostics (Basel, Switzerland) · 2024Review
- The textures of sarcoidosis: quantifying lung disease through variograms.medRxiv : the preprint server for health sciences · 2024Article
- Inter-vendor harmonization of CT reconstruction kernels using unpaired image translation.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Transfer learning approach in pre-treatment CT images to predict therapeutic response in advanced malignant pleural mesothelioma.Frontiers in oncology · 2024Article
- Repeatability of computed tomography liver radiomic features in a nonhuman primate model of diet-induced steatosis.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling.Military Medical Research · 2023Review
- Deep learning-based harmonization of CT reconstruction kernels towards improved clinical task performance.European radiology · 2023Article
- Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer.Translational lung cancer research · 2022Article
- Toward the determination of sensitive and reliable whole-lung computed tomography features for robust standard radiomics and delta-radiomics analysis in a nonhuman primate model of coronavirus disease 2019.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- The Impact of Image Acquisition Parameters and ComBat Harmonization on the Predictive Performance of Radiomics: A Renal Cell Carcinoma Model.Applied sciences (Basel, Switzerland) · 2022Article
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15 authors at 3 institutions in 1 country.
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Abstract
objectivesIn this study, we aimed to assess the impact of different CT reconstruction kernels on the stability of radiomic features and the transferability between different diseases and tissue types. Three lung diseases were evaluated,
methodsPre-treatment non-contrast enhanced CT scans from 23 NSCLC, 10 MPM and 12 SSc-ILD patients were collected retrospectively. For each patient, CT scans were reconstructed using smooth and sharp kernel in filtered back projection. The regions of interest (ROIs) were contoured on the smooth kernel-based CT and transferred to the sharp kernel-based CT. The voxels were resized to the largest voxel dimension of each cohort. In total, 1386 features were analyzed. Feature stability was assessed using the intraclass correlation coefficient. Features above the stability threshold >0.9 were considered stable.
resultsWe observed a strong impact of the reconstruction method on stability of the features (at maximum 26% of the 1386 features were stable). Intensity features were the most stable followed by texture and wavelet features. The wavelet features showed a positive correlation between percentage of stable features and size of the ROI (R2 = 0.79,
conclusionThe robustness of radiomic features is strongly affected by different reconstruction kernels. The effect is largely influenced by the tissue type and less by the disease type. ADVANCES IN KNOWLEDGE: The study presents to our knowledge the most complete analysis on the impact of convolution kernel on the robustness of CT-based radiomics for four relevant tissue types in three different lung diseases. .
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