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

Francesco Giganti, S Antunes, A Salerno, A Ambrosi, Paolo Marra, R Nicoletti, E Orsenigo, D Chiari, L Albarello, Carlo Staudacher, A Esposito, A Del Maschio, F De Cobelli

Research output: Contribution to journalArticlepeer-review

Abstract

Objectives: To investigate the association between preoperative texture analysis from multidetector computed tomography (MDCT) and overall survival in patients with gastric cancer. Methods: 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. Results: 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). Conclusions: 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. © 2016 European Society of Radiology
Original languageEnglish
Pages (from-to)1831-1839
Number of pages9
JournalEuropean Radiology
Volume27
Issue number5
DOIs
Publication statusPublished - 2017

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