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Related Concept Videos

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Related Experiment Video

Updated: May 12, 2026

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
07:54

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

Quantifying tumour heterogeneity with CT.

Balaji Ganeshan1, Kenneth A Miles

  • 1Institute of Nuclear Medicine, University College London, Eustace Road, London, UK. b.ganeshan@ucl.ac.uk

Cancer Imaging : the Official Publication of the International Cancer Imaging Society
|April 3, 2013
PubMed
Summary
This summary is machine-generated.

Computed tomography (CT) texture analysis quantifies tumor heterogeneity, offering a non-invasive imaging biomarker. This method aids in cancer characterization, prognosis, and treatment response prediction.

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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging Analysis

Background:

  • Tumor heterogeneity is a hallmark of malignancy linked to poor prognosis.
  • Non-invasive imaging biomarkers are crucial for assessing tumor biology.
  • Computed tomography (CT) texture analysis offers a method to quantify this heterogeneity.

Purpose of the Study:

  • To explore the potential of CT texture analysis as a non-invasive imaging biomarker for quantifying tumor heterogeneity.
  • To investigate the relationship between CT texture features and underlying tumor biology.
  • To assess the utility of CT texture analysis in oncologic imaging for characterization, prognosis, and treatment response.

Main Methods:

  • Texture analysis applied to unenhanced, contrast-enhanced, and perfusion CT images.
  • Image transformation techniques to derive subimages based on spatial and frequency components.
  • Quantification methods including structural, model-based (fractal dimensions), statistical, and frequency-based approaches.

Main Results:

  • CT texture analysis extracts quantitative spatial information from CT images, potentially revealing imperceptible heterogeneity.
  • Texture features may correlate with tumor biology, including hypoxia and angiogenesis.
  • Emerging studies indicate CT texture analysis's potential as an adjunct tool in oncology.

Conclusions:

  • CT texture analysis is a promising technique for quantifying tumor heterogeneity non-invasively.
  • It has the potential to provide valuable insights into tumor characterization, prognosis, and treatment prediction.
  • Further research supports its role as an adjunct in clinical oncologic imaging.