Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?
By Fergus Davnall, C. Yip, Gunnar Ljungqvist, M. Selmi, Francesca Ng, B. Sanghera, B. Ganeshan, K. Miles, G. Cook, V. Goh. Insights into Imaging, 2012
884 citations recorded by the source corpus.
BackgroundTumor spatial heterogeneity is an important prognostic factor, which may be reflected in medical imagesMethodsImage texture analysis is an approach of quantifying heterogeneity that may not be appreciated by the naked eye. Different methods can be applied including statistical-, model-, and transform-based methods.ResultsEarly evidence suggests that texture analysis has the potential to augment diagnosis and characterization as well as improve tumor staging and therapy response assessment in oncological practice.ConclusionThis review provides an overview of the application of texture analysis with different imaging modalities, CT, MRI, and PET, to date and describes the technical challenges that have limited its widespread clinical implementation so far. With further efforts to refine its application, image texture analysis has the potential to develop into a valuable clinical tool for oncologic imaging.Teaching Points• Tumor spatial heterogeneity is an important prognostic factor.• Image texture analysis is an approach of quantifying heterogeneity.• Different methods can be applied, including statistical-, model-, and transform-based methods.• Texture analysis could improve the diagnosis, tumor staging, and therapy response assessment.
Subfields: Cancer Research, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/a4ad66f3046d34f63c0a9ea20dcaef633036ea14
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cancer Research
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- CT texture analysis using the filtration-histogram method: what do the measurements mean? (2013, background): “Texture analysis is emerging as a useful technique for assessing heterogeneity in tumour images that are acquired in routine clinical practice [1,2] .”
- Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine (2018, methodology): “Indeed, data derived from radiomics investigation, such as intensity, shape, texture, wavelength, etc., can be extracted from medical images [23, 27 – 31] and extracted by or integrated in ML approaches, providing valuable information for the prediction of treatment response, differentiating benign…”
- CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. (2015, background): “Biomarkers based on quantitative features have demonstrated strong prognostic performance across a range of cancer types and investigators have reported that these features are associated with clinical outcomes and underlying genomic patterns [13–26].”
- Characterization of PET/CT images using texture analysis: the past, the present… any future? (2016, methodology): “As a multimodal imaging modality, PET/CT is a promising tool for noninvasive exploration of intratumour heterogeneity at the macroscopic scale in both the anatomical and functional dimensions [2, 3].”