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.
CITATION STYLE
Bianconi, F., Fravolini, M. L., Bello-Cerezo, R., Minestrini, M., Scialpi, M., & Palumbo, B. (2018). Evaluation of shape and textural features from CT as prognostic biomarkers in non-small cell lung cancer. Anticancer Research, 38(4), 2155–2160. https://doi.org/10.21873/anticanres.12456
Mendeley helps you to discover research relevant for your work.