Evaluation of Shape and Textural Features from CT as Prognostic Biomarkers in Non-small Cell Lung Cancer

Anticancer Res. 2018 Apr;38(4):2155-2160. doi: 10.21873/anticanres.12456.

Abstract

Background/aim: We retrospectively investigated the prognostic potential (correlation with overall survival) of 9 shape and 21 textural features from non-contrast-enhanced computed tomography (CT) in patients with non-small-cell lung cancer.

Materials and methods: We considered a public dataset of 203 individuals with inoperable, histologically- or cytologically-confirmed NSCLC. Three-dimensional shape and textural features from CT were computed using proprietary code and their prognostic potential evaluated through four different statistical protocols.

Results: Volume and grey-level run length matrix (GLRLM) run length non-uniformity were the only two features to pass all four protocols. Both features correlated negatively with overall survival. The results also showed a strong dependence on the evaluation protocol used.

Conclusion: Tumour volume and GLRLM run-length non-uniformity from CT were the best predictor of survival in patients with non-small-cell lung cancer. We did not find enough evidence to claim a relationship with survival for the other features.

Keywords: Computed tomography; non-small-cell lung cancer; radiomics; shape; texture.

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Biomarkers, Tumor / analysis*
  • Carcinoma, Non-Small-Cell Lung / diagnosis*
  • Carcinoma, Non-Small-Cell Lung / pathology
  • Female
  • Humans
  • Lung Neoplasms / diagnosis*
  • Lung Neoplasms / pathology
  • Male
  • Middle Aged
  • Predictive Value of Tests
  • Prognosis
  • Retrospective Studies
  • Tomography, X-Ray Computed*
  • Tumor Burden*

Substances

  • Biomarkers, Tumor