ArticleJournal of digital imaging2014
Test-retest reproducibility analysis of lung CT image features.
Article in Journal of digital imaging, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 142 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
142 citing papers in PubMed, 2 syntheses or guidelines pooled it, 264 citations in OpenAlex.
- Repeatability of radiomics studies in colorectal cancer: a systematic review.BMC gastroenterology · 2023Pooled it
- Clinical applications of textural analysis in non-small cell lung cancer.The British journal of radiology · 2018Pooled it
- Radiomics signature based on robust features derived from diffusion data for differentiation between benign and malignant solitary pulmonary lesions.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Trial
- Revealing Tumor Habitats from Texture Heterogeneity Analysis for Classification of Lung Cancer Malignancy and Aggressiveness.Scientific reports · 2019Trial
- Bridging tumor biology and radiomics through an ultra-large physics-driven vascular model.npj biomedical innovations · 2026Article
- Deep learning-based imaging model to predict early hematoma enlargement and hospital mortality in spontaneous intracerebral hemorrhage.World journal of radiology · 2026Article
- Stability of dynamic radiomics features in cardiac MRI under noise.European heart journal. Imaging methods and practice · 2026Article
- Assessing the multi-software robustness of radiomic biomarkers: a three-tool evaluation.Frontiers in oncology · 2026Article
- The study of dual-phaseScientific reports · 2025Article
- Role of Radiomics to Predict Malignant Transformation of Sinonasal Inverted Papilloma: A Systematic Review.Cancers · 2025Review
- Article
- Extracellular Microvesicle MicroRNAs and Imaging Metrics Improve the Detection of Aggressive Prostate Cancer: A Pilot Study.Cancers · 2025Article
- Annotated test-retest dataset of lung cancer CT scan images reconstructed at multiple imaging parameters.Scientific data · 2024Article
- Stability of Radiomic Features against Variations in Lesion Segmentations Computed on Apparent Diffusion Coefficient Maps of Breast Lesions.Diagnostics (Basel, Switzerland) · 2024Article
- Protocol selection formalism for minimizing detectable differences in morphological radiomics features of lung lesions in repeated CT acquisitions.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- State of the art: radiomics and radiomics-related artificial intelligence on the road to clinical translation.BJR open · 2024Review
- A CT texture-based nomogram for predicting futile reperfusion in patients with intraparenchymal hyperdensity after endovascular thrombectomy for acute anterior circulation large vessel occlusion.Frontiers in neurology · 2024Article
- Impact of Image Enhancement on the Radiomics Stability of Diffusion-Weighted MRI Images of Cervical Cancer.Cureus · 2024Article
- Radiomic features of PET/CT imaging of large B cell lymphoma lesions predicts CAR T cell therapy efficacy.Frontiers in oncology · 2024Article
- Predicting tumor deposits in rectal cancer: a combined deep learning model using T2-MR imaging and clinical features.Insights into imaging · 2023Article
82 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 3 institutions in 2 countries.
Funding
Abstract
Quantitative size, shape, and texture features derived from computed tomographic (CT) images may be useful as predictive, prognostic, or response biomarkers in non-small cell lung cancer (NSCLC). However, to be useful, such features must be reproducible, non-redundant, and have a large dynamic range. We developed a set of quantitative three-dimensional (3D) features to describe segmented tumors and evaluated their reproducibility to select features with high potential to have prognostic utility. Thirty-two patients with NSCLC were subjected to unenhanced thoracic CT scans acquired within 15 min of each other under an approved protocol. Primary lung cancer lesions were segmented using semi-automatic 3D region growing algorithms. Following segmentation, 219 quantitative 3D features were extracted from each lesion, corresponding to size, shape, and texture, including features in transformed spaces (laws, wavelets). The most informative features were selected using the concordance correlation coefficient across test-retest, the biological range and a feature independence measure. There were 66 (30.14%) features with concordance correlation coefficient ≥ 0.90 across test-retest and acceptable dynamic range. Of these, 42 features were non-redundant after grouping features with R (2) Bet ≥ 0.95. These reproducible features were found to be predictive of radiological prognosis. The area under the curve (AUC) was 91% for a size-based feature and 92% for the texture features (runlength, laws). We tested the ability of image features to predict a radiological prognostic score on an independent NSCLC (39 adenocarcinoma) samples, the AUC for texture features (runlength emphasis, energy) was 0.84 while the conventional size-based features (volume, longest diameter) was 0.80. Test-retest and correlation analyses have identified non-redundant CT image features with both high intra-patient reproducibility and inter-patient biological range. Thus making the case that quantitative image features are informative and prognostic biomarkers for NSCLC.
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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.