ArticleCancer imaging : the official publication of the International Cancer Imaging Society2010
Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 161 papers, 2 of them syntheses that pooled it.
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Who cites it
161 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Clinical applications of textural analysis in non-small cell lung cancer.The British journal of radiology · 2018Pooled it
- Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review.PloS one · 2014Pooled it
- Limits of radiomic-based entropy as a surrogate of tumor heterogeneity: ROI-area, acquisition protocol and tissue site exert substantial influence.Scientific reports · 2017Trial
- Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer?Medical physics · 2015Trial
- Noninvasive image texture analysis differentiates K-ras mutation from pan-wildtype NSCLC and is prognostic.PloS one · 2014Trial
- Preliminary clinical investigation ofEuropean journal of nuclear medicine and molecular imaging · 2026Article
- Pre-treatment prediction of microsatellite instability in colon cancer: a nomogram model combining clinicopathological features and pre-treatment CT-based radiomics.BMC medical imaging · 2026Article
- Performance Comparison of Machine Learning Using Radiomic Features and CNN-Based Deep Learning in Benign and Malignant Classification of Vertebral Compression Fractures Using CT Scans.Journal of imaging informatics in medicine · 2026Article
- Comparing subjective and objective sonographic heterogeneity in predicting long-term testicular non-viability in cases of testicular torsion.Singapore medical journal · 2026Article
- Hybrid Approach to Classifying Histological Subtypes of Non-small Cell Lung Cancer (NSCLC): Combining Radiomics and Deep Learning Features from CT Images.Journal of imaging informatics in medicine · 2025Article
- Predicting pathological staging of non-small cell lung cancer using a multi-task radiomics model integrating intratumoral and peritumoral features.Oncology letters · 2025Article
- Application of Fractal Radiomics and Machine Learning for Differentiation of Non-Small Cell Lung Cancer Subtypes on PET/MR Images.Journal of clinical medicine · 2025Article
- Integrating peritumor and tumor CT radiomics features in predicting local control after SBRT in patients with pulmonary oligometastases.Radiation oncology (London, England) · 2025Article
- Patch-Based Texture Feature Extraction Towards Improved Clinical Task Performance.Bioengineering (Basel, Switzerland) · 2025Article
- Texture analysis of CT colonography to develop a novel imaging biomarker for the management of colorectal cancer.Annals of gastroenterological surgery · 2025Article
- CT-based radiomics and clinical characteristics for predicting bone metastasis in lung adenocarcinoma patients.Translational lung cancer research · 2024Article
- Prediction of transformation in the histopathological growth pattern of colorectal liver metastases after chemotherapy using CT-based radiomics.Clinical & experimental metastasis · 2024Article
- Observational
- An MRI-based radiomics nomogram for preoperative prediction of Ki-67 index in nasopharyngeal carcinoma: a two-center study.Frontiers in oncology · 2024Article
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Abstract
The aim was to undertake an initial study of the relationship between texture features in computed tomography (CT) images of non-small cell lung cancer (NSCLC) and tumour glucose metabolism and stage. This retrospective pilot study comprised 17 patients with 18 pathologically confirmed NSCLC. Non-contrast-enhanced CT images of the primary pulmonary lesions underwent texture analysis in 2 stages as follows: (a) image filtration using Laplacian of Gaussian filter to differentially highlight fine to coarse textures, followed by (b) texture quantification using mean grey intensity (MGI), entropy (E) and uniformity (U) parameters. Texture parameters were compared with tumour fluorodeoxyglucose (FDG) uptake (standardised uptake value (SUV)) and stage as determined by the clinical report of the CT and FDG-positron emission tomography imaging. Tumour SUVs ranged between 2.8 and 10.4. The number of NSCLC with tumour stages I, II, III and IV were 4, 4, 4 and 6, respectively. Coarse texture features correlated with tumour SUV (E: r = 0.51, p = 0.03; U: r = -0.52, p = 0.03), whereas fine texture features correlated with tumour stage (MGI: rs = 0.71, p = 0.001; E: rs = 0.55, p = 0.02; U: rs = -0.49, p = 0.04). Fine texture predicted tumour stage with a kappa of 0.7, demonstrating 100% sensitivity and 87.5% specificity for detecting tumours above stage II ( p = 0.0001). This study provides initial evidence for a relationship between texture features in NSCLC on non-contrast-enhanced CT and tumour metabolism and stage. Texture analysis warrants further investigation as a potential method for obtaining prognostic information for patients with NSCLC undergoing CT.
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