The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: Preliminary evidence of an association with tumour metabolism, stage, and survival
Open accessAbstract
AIM To undertake a pilot study assessing whether tumour heterogeneity evaluated using computed tomography texture analysis (CTTA) has the potential to provide a marker of tumour aggression and prognosis in oesophageal cancer. MATERIALS AND METHODS In 21 patients, unenhanced CT images of the primary oesophageal lesion obtained using positron-emission tomography (PET)-CT examinations underwent CTTA. CTTA was carried out using a software algorithm that selectively filters and extracts textures at different anatomical scales between filter values 1.0 (fine detail) and 2.5 (coarse features) with quantification as entropy and uniformity (measures image heterogeneity). Texture parameters were correlated with average tumour 2-[(18)F]-fluoro-2-deoxy-d-glucose (FDG) uptake [standardized uptake values (SUV(mean) and SUV(max))] and clinical staging as determined by endoscopic ultrasound (nodal involvement) and PET-CT (distant metastases). The relationship between tumour stage, FDG uptake, and texture with survival was assessed using Kaplan-Meier analysis. RESULTS Tumour heterogeneity correlated with SUV(max) and SUV(mean). The closest correlations were found for SUV(mean) measured as uniformity and entropy with coarse filtration (r=-0.754, p<0.0001; and r=0.748, p=0.0001 respectively). Heterogeneity was also significantly greater in patients with clinical stage III or IV for filter values between 1.0 and 2.0 (maximum difference at filter value 1.5: entropy: p=0.027; uniformity p=0.032). The median (range) survival was 21 (4-34) months. Tumour heterogeneity assessed by CTTA (coarse uniformity) was an independent predictor of survival [odds ratio (OR)=4.45 (95% CI: 1.08, 18.37); p=0.039]. CONCLUSION CTTA assessment of tumour heterogeneity has the potential to identify oesophageal cancers with adverse biological features and provide a prognostic indicator of survival.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
HIF-1 and tumor progression: pathophysiology and therapeutics
Semenza, G. L. (2002) · Trends in Molecular Medicine · doi:10.1016/s1471-4914(02)02317-1 · Cited by 1,060
Cited by
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Aerts, H. J. W. L., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., et al. (2014) · Nature Communications · doi:10.1038/ncomms5006 · Cited by 5,132
Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?
Davnall, F., Yip, C. S. P., Ljungqvist, G., Selmi, M., Ng, F., Sanghera, B., Ganeshan, B., Miles, K. A., Cook, G. J., & Goh, V. (2012) · Insights into Imaging · doi:10.1007/s13244-012-0196-6 · Cited by 878
Self-citationCT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges
Lubner, M. G., Smith, A. D., Sandrasegaran, K., Sahani, D. V., & Pickhardt, P. J. (2017) · RadioGraphics · doi:10.1148/rg.2017170056 · Cited by 773
Ng, F., Ganeshan, B., Kozarski, R., Miles, K. A., & Goh, V. (2013) · Radiology · doi:10.1148/radiol.12120254 · Cited by 432
Self-citationPermuth, J. B., Collaborative, T. F. P., Choi, J., Balarunathan, Y., Kim, J., Chen, D.-T., Chen, L., Orcutt, S., Doepker, M. P., Gage, K., et al. (2016) · Oncotarget · doi:10.18632/oncotarget.11768 · Cited by 127
Metadata
- Authors
- Ganeshan, B., Skogen, K., Pressney, I., Coutroubis, D., & Miles, K.
- Venue
- Clinical Radiology
- Publisher
- Elsevier BV
- Published
- 2012-02
- Volume
- 67
- Issue
- 2
- Pages
- 157-164
- ISSN
- 0009-9260 (ISSN-L)
- OpenAlex
- W1965797467
- Cited by
- 310
- References
- 1
- FWCI
- 8.04
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: Preliminary evidence of an association with tumour metabolism, stage, and survival. https://app.cochat.ai/papers/10.1016/j.crad.2011.08.012
- The paper
- Ganeshan, B., Skogen, K., Pressney, I., Coutroubis, D., & Miles, K. (2012). Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: Preliminary evidence of an association with tumour metabolism, stage, and survival. Clinical Radiology. https://doi.org/10.1016/j.crad.2011.08.012
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.