Contrast-Enhanced CT Texture Analysis: a New Set of Predictive Factors for Small Cell Lung Cancer



The purpose of this study was to investigate the relationship between x-ray computed tomography (CT) texture features of small cell lung cancer (SCLC) and the survival of the patients.


Eighty-eight patients with unresectable SCLCs (extended stage, 57; limited stage, 31) underwent platinum-based chemotherapy at our institution between January 2010 and 2015. All the patients were followed up for at least 18 months or until death. The CT texture features of tumor tissue were extracted from contrast-enhanced CT images taken before antitumor treatment. Receiver operating characteristic (ROC) curve analysis was used to calculate the optimal cutoff values of each texture parameter, based on which the patients were dichotomized into two subgroups to evaluate the prognostic value of each feature. Kaplan–Meier survival analysis and the log rank test were performed to compare the differences of 18-month overall survival (OS) and 6-month event-free survival (EFS) in dichotomized subgroups. Multivariate Cox regression analysis was performed to determine if the features could be taken as independent prognostic factors.


A total number of 35 CT texture features were extracted from six matrixes. Four of them (GLCM-Contrast, GLCM-Dissimilarity, Histo-Energy, and Histo-Entropy) were shown to be significantly related to 18-month OS, and two (GLCM-Energy and GLCM-Entropy) were shown to be significantly related to 6-month EFS. Cox regression suggested that GLCM-Dissimilarity was independently associated with OS, while GLCM-Energy were independently associated with EFS.


The texture features of contrast-enhanced computed tomography image could potentially serve as radiological prognostic biomarkers for SCLC patients.

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Correspondence to Xuelei Ma.

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Chen, C., Ou, X., Li, H. et al. Contrast-Enhanced CT Texture Analysis: a New Set of Predictive Factors for Small Cell Lung Cancer. Mol Imaging Biol 22, 745–751 (2020).

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Key words

  • CT
  • Texture analysis
  • Small cell lung cancer
  • Survival
  • Biomarker