Literature DB >> 29599334

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

Francesco Bianconi1, Mario Luca Fravolini2, Raquel Bello-Cerezo2, Matteo Minestrini3, Michele Scialpi3, Barbara Palumbo3.   

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. Copyright
© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Entities:  

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

Mesh:

Substances:

Year:  2018        PMID: 29599334     DOI: 10.21873/anticanres.12456

Source DB:  PubMed          Journal:  Anticancer Res        ISSN: 0250-7005            Impact factor:   2.480


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