Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery
- 1.4k Downloads
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.
KeywordsRadiomics Lung cancer Prognosis CT PET/CT Texture analysis
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).
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.
Compliance with ethical standards
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.
For this type of study formal consent was not required.
- 1.EUCAN | Home page [Internet]. Available from: http://eco.iarc.fr/EUCAN/Default.aspx.
- 8.Sollini M, Cozzi L, Antunovic L, Chiti A, Kirienko M. PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology. Sci Rep. 2017;7:358.Google Scholar
- 13.Orlhac F, Nioche C, Buvat I. Technical Appendix — Local Image Features Extraction — — LIFEx —. 2016.Google Scholar
- 17.Velez-Cubian FO, Rodriguez KL, Thau MR, Moodie CC, Garrett JR, Fontaine JP, et al. Efficacy of lymph node dissection during robotic-assisted lobectomy for non-small cell lung cancer: retrospective review of 159 consecutive cases. J Thorac Dis. 2016;8:2454–63.CrossRefPubMedPubMedCentralGoogle Scholar
- 19.Brundage MD. Prognostic factors in non-small cell lung cancer: a decade of progress. Chest. 2002;122:1037–57.Google Scholar
- 28.Yuan M, Zhang Y-D, Pu X-H, Zhong Y, Li H, Wu J-F, et al. Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival. Eur Radiol. 2017;In press.Google Scholar
- 31.Huynh E, Coroller TP, Narayan V, Agrawal V, Hou Y, Romano J, et al. CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer. Int J Radiat. 2016;120:258–66.Google Scholar
- 32.Li Q, Kim J, Balagurunathan Y, Liu Y, Latifi K, Stringfield O, et al. Imaging features from pre-treatment CT scans are associated with clinical outcomes in non-small-cell lung cancer patients treated with stereotactic body radiotherapy. Med Phys. 2017;44:4341–9.Google Scholar
- 33.Van Velden FHP, Kramer GM, Frings V, Nissen IA, Mulder ER, De Langen AJ, et al. Repeatability of radiomic features in non-small-cell lung cancer [(18F)]FDG-PET/CT studies: impact of reconstruction and delineation. Mol Imaging Biol. 2016;18:788–95.Google Scholar