ArticlePloS one2016
Impact of Reconstruction Algorithms on CT Radiomic Features of Pulmonary Tumors: Analysis of Intra- and Inter-Reader Variability and Inter-Reconstruction Algorithm Variability.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 1 of them a synthesis that pooled it.
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Who cites it
70 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Repeatability of radiomics studies in colorectal cancer: a systematic review.BMC gastroenterology · 2023Pooled it
- Radiomics signature for predicting postoperative disease-free survival of patients with gastric cancer: development and validation of a predictive nomogram.Diagnostic and interventional radiology (Ankara, Turkey) · 2022Trial
- CT radiomics showed no improvement beyond volume dynamics for early lesion-level size response to immunotherapy in metastatic melanoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Observational
- Initial experience of deep learning reconstruction algorithm in lung kernel: clinical usefulness for lung nodules at ultra-low-dose protocol.European radiology · 2026Article
- Evaluating consistency of radiomic features derived from CT images: A cross-center phantom study.Journal of applied clinical medical physics · 2026Article
- Multimodality and temporal analysis of cervical cancer treatment response.Quantitative imaging in medicine and surgery · 2026Article
- Deep Learning-Based Image Reconstruction in Musculoskeletal MRI.Journal of the Korean Society of Radiology · 2025Review
- Developing a Reproducible Radiomics Model for Diagnosis of Active Crohn's Disease on CT Enterography Across Annotation Variations and Acquisition Differences.Journal of imaging informatics in medicine · 2025Article
- Kernel Conversion Improves the Correlation between the Extent of Emphysema and Clinical Parameters in Chronic Obstructive Pulmonary Disease: A Multicenter Cohort Study.Tuberculosis and respiratory diseases · 2025Article
- Reproducibility of the CT radiomic features of pulmonary nodules: the effects of the CT reconstruction algorithm, radiation dose, and contrast agent.Quantitative imaging in medicine and surgery · 2025Article
- The potential of photon-counting CT for the improved precision of lung nodule radiomics.Physics in medicine and biology · 2025Article
- Texture analysis improves lung-tissue segmentation on high-resolution computed tomography in COVID-19.Frontiers in radiology · 2025Article
- Image-Based Generative Artificial Intelligence in Radiology: Comprehensive Updates.Korean journal of radiology · 2024Review
- State of the art: radiomics and radiomics-related artificial intelligence on the road to clinical translation.BJR open · 2024Review
- Correlation analysis of clinical, pathological, imaging and genetic features of ground-glass nodule featured lung adenocarcinomas between high-risk and non-high-risk individuals.European journal of medical research · 2023Article
- Radiomics in the evaluation of ovarian masses - a systematic review.Insights into imaging · 2023Article
- Stability of Multi-Parametric Prostate MRI Radiomic Features to Variations in Segmentation.Journal of personalized medicine · 2023Article
- Impact of slice thickness on reproducibility of CT radiomic features of lung tumors.F1000Research · 2023Article
- Impact of deep learning image reconstruction algorithms on CT radiomic features in patients with liver tumors.Frontiers in oncology · 2023Article
- Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology.Biomedicines · 2022Review
10 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
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Abstract
purposeTo identify the impact of reconstruction algorithms on CT radiomic features of pulmonary tumors and to reveal and compare the intra- and inter-reader and inter-reconstruction algorithm variability of each feature.
methodsForty-two patients (M:F = 19:23; mean age, 60.43±10.56 years) with 42 pulmonary tumors (22.56±8.51mm) underwent contrast-enhanced CT scans, which were reconstructed with filtered back projection and commercial iterative reconstruction algorithm (level 3 and 5). Two readers independently segmented the whole tumor volume. Fifteen radiomic features were extracted and compared among reconstruction algorithms. Intra- and inter-reader variability and inter-reconstruction algorithm variability were calculated using coefficients of variation (CVs) and then compared.
resultsAmong the 15 features, 5 first-order tumor intensity features and 4 gray level co-occurrence matrix (GLCM)-based features showed significant differences (p<0.05) among reconstruction algorithms. As for the variability, effective diameter, sphericity, entropy, and GLCM entropy were the most robust features (CV≤5%). Inter-reader variability was larger than intra-reader or inter-reconstruction algorithm variability in 9 features. However, for entropy, homogeneity, and 4 GLCM-based features, inter-reconstruction algorithm variability was significantly greater than inter-reader variability (p<0.013).
conclusionsMost of the radiomic features were significantly affected by the reconstruction algorithms. Inter-reconstruction algorithm variability was greater than inter-reader variability for entropy, homogeneity, and GLCM-based features.
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