European Radiology

, Volume 27, Issue 5, pp 1831–1839 | Cite as

Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker

  • Francesco Giganti
  • Sofia Antunes
  • Annalaura Salerno
  • Alessandro Ambrosi
  • Paolo Marra
  • Roberto Nicoletti
  • Elena Orsenigo
  • Damiano Chiari
  • Luca Albarello
  • Carlo Staudacher
  • Antonio Esposito
  • Alessandro Del Maschio
  • Francesco De Cobelli



To investigate the association between preoperative texture analysis from multidetector computed tomography (MDCT) and overall survival in patients with gastric cancer.


Institutional review board approval and informed consent were obtained. Fifty-six patients with biopsy-proved gastric cancer were examined by MDCT and treated with surgery. Image features from texture analysis were quantified, with and without filters for fine to coarse textures. The association with survival time was assessed using Kaplan–Meier and Cox analysis.


The following parameters were significantly associated with a negative prognosis, according to different thresholds: energy [no filter] – Logarithm of relative risk (Log RR): 3.25; p = 0.046; entropy [no filter] (Log RR: 5.96; p = 0.002); entropy [filter 1.5] (Log RR: 3.54; p = 0.027); maximum Hounsfield unit value [filter 1.5] (Log RR: 3.44; p = 0.027); skewness [filter 2] (Log RR: 5.83; p = 0.004); root mean square [filter 1] (Log RR: - 2.66; p = 0.024) and mean absolute deviation [filter 2] (Log RR: - 4.22; p = 0.007).


Texture analysis could increase the performance of a multivariate prognostic model for risk stratification in gastric cancer. Further evaluations are warranted to clarify the clinical role of texture analysis from MDCT.

Key points

Textural analysis from computed tomography can be applied in gastric cancer.

Preoperative non-invasive texture features are related to prognosis in gastric cancer.

Texture analysis could help to evaluate the aggressiveness of this tumour.


Gastric cancer Multidetector computed tomography Prognosis Survival Medical oncology 


Log RR

Logarithm of relative risk


Random survival forest


Variable importance


Akaike information criteria



The authors are indebted to all the patients, families, and health care assistants (nurses and radiographers) who greatly contributed to the realisation of this study.

The scientific guarantor of this publication is Prof. Francesco De Cobelli. The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article. The authors state that this work has not received any funding. One of the authors has significant statistical expertise. Institutional review board approval was obtained. Written informed consent was obtained from all subjects (patients) in this study. The publication did not include animals. Some study subjects or cohorts have been previously reported in Giganti F, Orsenigo E, Esposito A et al. (2015) Prognostic Role of Diffusion-weighted MR Imaging for Resectable Gastric Cancer. 276(2):444–52. Doi:  10.1148/radiol.15141900. Methodology: retrospective, diagnostic or prognostic study, performed at one institution.

Supplementary material

330_2016_4540_MOESM1_ESM.doc (94 kb)
ESM 1 (DOC 94.5 kb)


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Copyright information

© European Society of Radiology 2016

Authors and Affiliations

  • Francesco Giganti
    • 1
    • 2
  • Sofia Antunes
    • 3
  • Annalaura Salerno
    • 1
    • 2
  • Alessandro Ambrosi
    • 2
  • Paolo Marra
    • 1
    • 2
  • Roberto Nicoletti
    • 1
  • Elena Orsenigo
    • 4
  • Damiano Chiari
    • 2
    • 4
  • Luca Albarello
    • 5
  • Carlo Staudacher
    • 2
    • 4
  • Antonio Esposito
    • 1
    • 2
  • Alessandro Del Maschio
    • 1
    • 2
  • Francesco De Cobelli
    • 1
    • 2
  1. 1.Department of Radiology and Centre for Experimental Imaging San Raffaele Scientific InstituteMilanItaly
  2. 2.San Raffaele Vita-Salute UniversityMilanItaly
  3. 3.Centre for Experimental ImagingSan Raffaele Scientific InstituteMilanItaly
  4. 4.Department of SurgerySan Raffaele Scientific InstituteMilanItaly
  5. 5.Pathology UnitSan Raffaele Scientific InstituteMilanItaly

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