Observational studyEuropean radiology2019
Adaptive statistical iterative reconstruction (ASIR) affects CT radiomics quantification in primary colorectal cancer.
Observational study in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 31 citations in OpenAlex.
- Reducing contrast medium dose with low photon energy images in renal dual-energy spectral CT angiography and adaptive statistical iterative reconstruction (ASIR).The British journal of radiology · 2021Trial
- Noninvasive Characterization of Tumor Heterogeneity in HNSCC: From Clinical Utility to Biological Correlates.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Impact of deep learning based reconstruction algorithms on CT radiomic features of carotid plaques.Journal of applied clinical medical physics · 2025Article
- Impact of deep learning image reconstruction algorithms on CT radiomic features in patients with liver tumors.Frontiers in oncology · 2023Article
- Quality Assessment of Computed Tomography Images using a Channelized Hoteling Observer: Optimization of Protocols in Clinical Practice.Advanced biomedical research · 2023Article
- Primary Tumor Radiomic Model for Identifying Extrahepatic Metastasis of Hepatocellular Carcinoma Based on Contrast Enhanced Computed Tomography.Diagnostics (Basel, Switzerland) · 2022Article
- Deep learning-based reconstruction on cardiac CT yields distinct radiomic features compared to iterative and filtered back projection reconstructions.Scientific reports · 2022Article
- Predictive Value ofDiagnostics (Basel, Switzerland) · 2022Article
- Reproducibility of radiomic features in CT images of NSCLC patients: an integrative analysis on the impact of acquisition and reconstruction parameters.European radiology experimental · 2022Article
- Effect of adaptive statistical iterative reconstruction-V (ASiR-V) levels on ultra-low-dose CT radiomics quantification in pulmonary nodules.Quantitative imaging in medicine and surgery · 2021Article
- Influence of Adaptive Statistical Iterative Reconstructions on CT Radiomic Features in Oncologic Patients.Diagnostics (Basel, Switzerland) · 2021Article
- Primary Gastro-Intestinal Lymphoma and Gastro-Intestinal Adenocarcinoma: An Initial Study of CT Texture Analysis as Quantitative Biomarkers for Differentiation.Life (Basel, Switzerland) · 2021Article
- Chest Computed Tomography Images in Neonatal Bronchial Pneumonia under the Adaptive Statistical Iterative Reconstruction Algorithm.Journal of healthcare engineering · 2021Article
- CT Image Features under Reconstruction Algorithm in Analysis of the Effect of Probiotics Combined with Ursodeoxycholic Acid in Treatment of Intrahepatic Cholestasis of Pregnancy.Journal of healthcare engineering · 2021Article
- Outcome prediction in resectable lung adenocarcinoma patients: value of CT radiomics.European radiology · 2020Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesTo investigate whether adaptive statistical iterative reconstruction (ASIR), a hybrid iterative CT image reconstruction algorithm, affects radiomics feature quantification in primary colorectal cancer compared to filtered back projection. Additionally, to establish whether radiomics from single-slice analysis undergo greater change than those from multi-slice analysis.
methodsFollowing review board approval, contrast-enhanced CT studies from 32 prospective primary colorectal cancer patients were reconstructed with 20% ASIR level increments, from 0 to 100%. Radiomics analysis was applied to single-slice and multi-slice regions of interest outlining the tumour: 70 features, including statistical (first-, second- and high-order) and fractal radiomics, were generated per dataset. The effect of ASIR was calculated by means of multilevel linear regression.
resultsTwenty-eight CT datasets were suitable for analysis. Incremental ASIR levels determined a significant change (p < 0.001) in most statistical radiomics features, best described by a simple linear relationship. First-order statistical features, including mean, standard deviation, skewness, kurtosis, energy and entropy, underwent a relatively small change in both single-slice and multi-slice analysis (median standardised effect size B = 0.08). Second-order statistical features, including grey-level co-occurrence and difference matrices, underwent a greater change in single-slice analysis (median B = 0.36) than in multi-slice analysis (median B = 0.13). Fractal features underwent a significant change only in single-slice analysis (median B = 0.49).
conclusionsIncremental levels of ASIR affect significantly CT radiomics quantification in primary colorectal cancer. Second-order statistical and fractal features derived from single-slice analysis undergo greater change than those from multi-slice analysis. KEY POINTS: • Incremental levels of ASIR determine a significant change in most statistical (first-, second- and high-order) CT radiomics features measured in primary colorectal cancer, best described by a linear relationship. • First-order statistical features undergo a small change, both from single-slice and multi-slice radiomics analyses. • Most second-order statistical features undergo a greater change in single-slice analysis than in multi-slice analysis. Fractal features are only affected in single-slice analysis.
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