How to use CT texture analysis for prognostication of non-small cell lung cancer

Cancer Imaging. 2016 Apr 11:16:10. doi: 10.1186/s40644-016-0065-5.

Abstract

Patients with non-small cell lung cancer frequently demonstrate differing clinical courses, even when they express the same tumour stage. Additional markers of prognostic significance could allow further stratification of treatment for these patients. By generating quantitative information about tumour heterogeneity as reflected by the distribution of pixel values within the tumour, CT texture analysis (CTTA) can provide prognostic information for patients with NSCLC. In addition to describing the practical application of CTTA to NSCLC, this article discusses a range of issues that need to be addressed when CTTA is included as part of routine clinical care as opposed to its use in a research setting. The use of quantitative imaging to provide prognostic information is a new and exciting development within cancer imaging that can expand the imaging specialist's existing role in tumour evaluation. Derivation of prognostic information through the application of image processing techniques such as CTTA, to images acquired as part of routine care can help imaging specialists make best use of the technologies they deploy for the benefit of patients with cancer.

MeSH terms

  • Biomarkers, Tumor / analysis
  • Carcinoma, Non-Small-Cell Lung / diagnostic imaging*
  • Carcinoma, Non-Small-Cell Lung / genetics
  • Carcinoma, Non-Small-Cell Lung / therapy
  • Clinical Audit / standards
  • Data Display
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Lung Neoplasms / diagnostic imaging*
  • Lung Neoplasms / therapy
  • MAP Kinase Signaling System / genetics
  • Multimodal Imaging / methods
  • Neoplasm Staging
  • Patient Care Planning
  • Positron-Emission Tomography / methods
  • Prognosis
  • Quality Assurance, Health Care / standards
  • Radiology Information Systems
  • Survival Rate
  • Tomography, X-Ray Computed / methods*
  • Tomography, X-Ray Computed / standards
  • Workflow

Substances

  • Biomarkers, Tumor