European Radiology

, Volume 29, Issue 2, pp 915–923 | Cite as

Prognostic value of radiomic analysis of iodine overlay maps from dual-energy computed tomography in patients with resectable lung cancer

  • Jooae Choe
  • Sang Min LeeEmail author
  • Kyung-Hyun Do
  • Jung Bok Lee
  • Sang Min Lee
  • June-Goo Lee
  • Joon Beom Seo



To investigate whether radiomics on iodine overlay maps from dual-energy computed tomography (DECT) can predict survival outcomes in patients with resectable lung cancer.


Ninety-three lung cancer patients eligible for curative surgery were examined with DECT at the time of diagnosis. The median follow-up was 60.4 months. Radiomic features of the entire primary tumour were extracted from iodine overlay maps generated by DECT. A Cox proportional hazards regression model was used to determine independent predictors of overall survival (OS) and disease-free survival (DFS), respectively.


Forty-two patients (45.2%) had disease recurrence and 39 patients (41.9%) died during the follow-up period. The mean DFS was 49.8 months and OS was 55.2 months. Univariate analysis revealed that significant predictors of both OS and DFS were stage and radiomic parameters, including histogram energy, histogram entropy, grey-level co-occurrence matrix (GLCM) angular second moment, GLCM entropy and homogeneity. The multivariate analysis identified stage and entropy as independent risk factors predicting both OS (stage, hazard ratio (HR) = 2.020 [95% CI 1.014–4.026], p = 0.046; entropy, HR = 1.543 [95% CI 1.069–2.228], p = 0.021) and DFS (stage, HR = 2.132 [95% CI 1.060–4.287], p = 0.034; entropy, HR = 1.497 [95% CI 1.031–2.173], p = 0.034). The C-index showed that adding entropy improved prediction of OS compared to stage only (0.720 and 0.667, respectively; p = 0.048).


Radiomic features extracted from iodine overlay map reflecting heterogeneity of tumour perfusion can add prognostic information for patients with resectable lung cancer.

Key Points

• Radiomic feature (histogram entropy) from DECT iodine overlay maps was an independent risk factor predicting both overall survival and disease-free survival.

• Adding histogram entropy to clinical stage improved prediction of overall survival compared to stage only (0.720 and 0.667, respectively; p = 0.048).

• DECT can be a good option for comprehensive pre-operative evaluation in cases of resectable lung cancer.


Lung neoplasms Prognosis Multidetector computed tomography Diagnostic imaging 



Angular second moment


Computed tomography


Dual-energy computed tomography


Grey level co-occurrence matrix


Absolute gradient


Histogram analysis


Interclass correlation coefficient.


Overall survival


Positron emission tomography


Run-length encoding


Receiver operating characteristic


Maximal standard uptake values



This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (grant number: NRF-2016R1A2B1016355).

Compliance with ethical standards


The scientific guarantor of this publication is Dr. Joon Beom Seo.

Conflict of interest

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.

Statistics and biometry

One of the authors (Jung Bok Lee, PhD) has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.


• Retrospective

• Prognostic study

• Performed at one institution


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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of Radiology and Research Institute of RadiologyUniversity of Ulsan College of Medicine, Asan Medical CenterSeoulKorea
  2. 2.Department of Medical StatisticsAsan Medical Center, University of Ulsan College of MedicineSeoulKorea
  3. 3.Department of Convergence MedicineUniversity of Ulsan College of Medicine, Asan Medical CenterSeoulKorea

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