Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery



Radiomic features derived from the texture analysis of different imaging modalities e show promise in lesion characterisation, response prediction, and prognostication in lung cancer patients. The present study aimed to identify an images-based radiomic signature capable of predicting disease-free survival (DFS) in non-small cell lung cancer (NSCLC) patients undergoing surgery.


A cohort of 295 patients was selected. Clinical parameters (age, sex, histological type, tumour grade, and stage) were recorded for all patients. The endpoint of this study was DFS. Both computed tomography (CT) and fluorodeoxyglucose positron emission tomography (PET) images generated from the PET/CT scanner were analysed. Textural features were calculated using the LifeX package. Statistical analysis was performed using the R platform. The datasets were separated into two cohorts by random selection to perform training and validation of the statistical models. Predictors were fed into a multivariate Cox proportional hazard regression model and the receiver operating characteristic (ROC) curve as well as the corresponding area under the curve (AUC) were computed for each model built.


The Cox models that included radiomic features for the CT, the PET, and the PET+CT images resulted in an AUC of 0.75 (95%CI: 0.65–0.85), 0.68 (95%CI: 0.57–0.80), and 0.68 (95%CI: 0.58–0.74), respectively. The addition of clinical predictors to the Cox models resulted in an AUC of 0.61 (95%CI: 0.51–0.69), 0.64 (95%CI: 0.53–0.75), and 0.65 (95%CI: 0.50–0.72) for the CT, the PET, and the PET+CT images, respectively.


A radiomic signature, for either CT, PET, or PET/CT images, has been identified and validated for the prediction of disease-free survival in patients with non-small cell lung cancer treated by surgery.

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We thank Elena Vanni for support in patient selection; Paola Bossi and Dahoud Rahal for collaboration in pathological analyses; Marco Alloisio, Giulia Veronesi and the Thoracic Surgery Unit for close collaboration in patient selection and follow-up; Lorenzo Leonardi for image processing; and Riccardo Muglia, Nicolò Gennaro and Orazio Giuseppe Santonocito for their help in patient selection.

M.K. is supported by the AIRC (Italian Association for Cancer Research) scholarship funded by the grant won by A.C. (IG-2016-18585).

Author information




M.S., M.K. and A.C. conceived the idea of the study; L.C., L.L. and A.F. performed the statistical analysis; E.V. collected the data and selected the patients; M.S., M.K. and L.A. reviewed and segmented the images; L.C. performed image analysis; M.S., M.K. and L.C. wrote the manuscript; A.R. edited and reviewed the manuscript.

All the authors discussed the results and commented on the manuscript.

Corresponding author

Correspondence to Martina Sollini.

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Disclosure of potential

A. Chiti received speaker honoraria from General Electric and Sirtex Medical System, acted as scientific advisor for Blue Earth Diagnostics and benefited from an unconditional grant from Sanofi to Humanitas University. All honoraria and grants are outside the scope of the submitted work.

L. Cozzi acts as Scientific Advisor to Varian Medical Systems. All honoraria are outside the scope of the submitted work.

M. Kirienko is supported by the AIRC (Italian Association for Cancer Research) scholarship funded by the grant won by A.C. (IG-2016-18,585).

Conflict of interest

All other authors have no conflicts of interest.

Research involving human participants

The study was approved by the institutional Ethics Committee. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

For this type of study formal consent was not required.

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Kirienko, M., Cozzi, L., Antunovic, L. et al. Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery. Eur J Nucl Med Mol Imaging 45, 207–217 (2018).

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  • Radiomics
  • Lung cancer
  • Prognosis
  • CT
  • PET/CT
  • Texture analysis