ArticleTranslational oncology2014
Exploring Variability in CT Characterization of Tumors: A Preliminary Phantom Study.
Article in Translational oncology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 77 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
77 citing papers in PubMed, 1 synthesis or guideline pooled it, 139 citations in OpenAlex.
- Repeatability and Reproducibility of Radiomic Features: A Systematic Review.International journal of radiation oncology, biology, physics · 2018Pooled it
- CT radiomics-based long-term survival prediction for locally advanced non-small cell lung cancer patients treated with concurrent chemoradiotherapy using features from tumor and tumor organismal environment.Radiation oncology (London, England) · 2022Trial
- Detection of Confounders and Potential Confounders in Computed Tomography Lung Datasets.Journal of imaging informatics in medicine · 2026Article
- Evaluating consistency of radiomic features derived from CT images: A cross-center phantom study.Journal of applied clinical medical physics · 2026Article
- Radiomics-based kidney lesion classification: Mitigating batch effect with nested combat harmonization.Medical physics · 2025Article
- Significance of Image Reconstruction Parameters for Future Lung Cancer Risk Prediction Using Low-Dose Chest Computed Tomography and the Open-Access Sybil Algorithm.Investigative radiology · 2025Article
- Robust radiomics: a review of guidelines for radiomics in medical imaging.Frontiers in radiology · 2025Review
- Article
- 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
- Enhancing Super-Resolution Network Efficacy in CT Imaging: Cost-Effective Simulation of Training Data.IEEE open journal of engineering in medicine and biology · 2025Article
- Annotated test-retest dataset of lung cancer CT scan images reconstructed at multiple imaging parameters.Scientific data · 2024Article
- Technical and clinical considerations of a physical liver phantom for CT radiomics analysis.Journal of applied clinical medical physics · 2024Article
- Prediction of Surgical Outcome by Tumor Volume Doubling Time via Stereo Imaging Software in Early Non-Small Cell Lung Cancer.Cancers · 2023Article
- Phantom-based radiomics feature test-retest stability analysis on photon-counting detector CT.European radiology · 2023Article
- The Promise and Future of Radiomics for Personalized Radiotherapy Dosing and Adaptation.Seminars in radiation oncology · 2023Review
- Clinical validation of deep learning algorithms for radiotherapy targeting of non-small-cell lung cancer: an observational study.The Lancet. Digital health · 2022Observational
- Comparison Study of Myocardial Radiomics Feature Properties on Energy-Integrating and Photon-Counting Detector CT.Diagnostics (Basel, Switzerland) · 2022Article
- Correction for Systematic Bias in Radiomics Measurements Due to Variation in Imaging Protocols.Academic radiology · 2022Article
- Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies.Tomography (Ann Arbor, Mich.) · 2021Article
- Article
17 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
- Erratum issued
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo explore the effects of computed tomography (CT) slice thickness and reconstruction algorithm on quantification of image features to characterize tumors using a chest phantom. MATERIALS AND
methodsTwenty-two phantom lesions of known sizes (10 and 20 mm), shapes (spherical, elliptical, lobulated, and spiculated), and densities [-630, -10, and +100 Hounsfield Unit (HU)] were inserted into an anthropomorphic thorax phantom and scanned three times with relocations. The raw data were reconstructed using six imaging settings, i.e., a combination of three slice thicknesses of 1.25, 2.5, and 5 mm and two reconstruction kernels of lung and standard. Lesions were segmented and 14 image features representing lesion size, shape, and texture were calculated. Differences in the measured image features due to slice thickness and reconstruction algorithm were compared using linear regression method by adjusting three confounding variables (size, density, and shape).
resultsAll 14 features were significantly different between 1.25 and 5 mm slice images. The 1.25 and 2.5 mm slice thicknesses were better than 5 mm for volume, density mean, density SD gray-level co-occurrence matrix (GLCM) energy and homogeneity. As for the reconstruction algorithm, there was no significant difference in uni-dimension, volume, shape index 9, and compactness. Lung reconstruction was better for density mean, whereas standard reconstruction was better for density SD.
conclusionsCT slice thickness and reconstruction algorithm can significantly affect the quantification of image features. Thinner (1.25 and 2.5 mm) and thicker (5 mm) slice images should not be used interchangeably. Sharper and smoother reconstructions significantly affect the density-based features.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.