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Research ArticleExperimental Studies

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

FRANCESCO BIANCONI, MARIO LUCA FRAVOLINI, RAQUEL BELLO-CEREZO, MATTEO MINESTRINI, MICHELE SCIALPI and BARBARA PALUMBO
Anticancer Research April 2018, 38 (4) 2155-2160;
FRANCESCO BIANCONI
1Department of Engineering, University of Perugia, Perugia, Italy
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  • For correspondence: bianco@ieee.org
MARIO LUCA FRAVOLINI
1Department of Engineering, University of Perugia, Perugia, Italy
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RAQUEL BELLO-CEREZO
1Department of Engineering, University of Perugia, Perugia, Italy
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MATTEO MINESTRINI
2Department of Surgical and Biomedical Sciences, University of Perugia, Perugia, Italy
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MICHELE SCIALPI
2Department of Surgical and Biomedical Sciences, University of Perugia, Perugia, Italy
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BARBARA PALUMBO
2Department of Surgical and Biomedical Sciences, University of Perugia, Perugia, Italy
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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.

  • Computed tomography
  • non-small-cell lung cancer
  • shape
  • texture
  • radiomics
  • Received January 24, 2018.
  • Revision received February 15, 2018.
  • Accepted February 26, 2018.
  • Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved
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Anticancer Research: 38 (4)
Anticancer Research
Vol. 38, Issue 4
April 2018
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Evaluation of Shape and Textural Features from CT as Prognostic Biomarkers in Non-small Cell Lung Cancer
FRANCESCO BIANCONI, MARIO LUCA FRAVOLINI, RAQUEL BELLO-CEREZO, MATTEO MINESTRINI, MICHELE SCIALPI, BARBARA PALUMBO
Anticancer Research Apr 2018, 38 (4) 2155-2160;

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Evaluation of Shape and Textural Features from CT as Prognostic Biomarkers in Non-small Cell Lung Cancer
FRANCESCO BIANCONI, MARIO LUCA FRAVOLINI, RAQUEL BELLO-CEREZO, MATTEO MINESTRINI, MICHELE SCIALPI, BARBARA PALUMBO
Anticancer Research Apr 2018, 38 (4) 2155-2160;
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Keywords

  • computed tomography
  • Non-small-cell lung cancer
  • shape
  • texture
  • radiomics
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