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Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

To evaluate the ability of CT and PET radiomics features to classify lung lesions as primary or metastatic, and secondly to differentiate histological subtypes of primary lung cancers.

Methods

A cohort of 534 patients with lung lesions were retrospectively studied. Radiomics texture features were extracted using the LIFEx package from semiautomatically segmented PET and CT images. Histology data were recorded in all patients. The patient cohort was divided into a training and a validation group and linear discriminant analysis (LDA) was performed to classify the lesions using both direct and backward stepwise methods. The robustness of the procedure was tested by repeating the entire process 100 times with different assignments to the training and validation groups. Scoring metrics included analysis of the receiver operating characteristic curves in terms of area under the curve (AUC), sensitivity, specificity and accuracy.

Results

Radiomics features extracted from CT and PET datasets were able to differentiate primary tumours from metastases in both the training and the validation group (AUCs 0.79 ± 0.03 and 0.70 ± 0.04, respectively, from the CT dataset; AUCs 0.92 ± 0.01 and 0.91 ± 0.03, respectively, from the PET dataset). The AUC cut-off thresholds identified by LDA using direct and backward elimination strategies were −0.79 ± 0.06 and −0.81 ± 0.08, respectively (CT dataset) and −0.69 ± 0.05 and −0.68 ± 0.04, respectively (PET dataset). For differentiation between primary subgroups based on CT features, the AUCs in the training and validation groups were 0.81 ± 0.02 and 0.69 ± 0.04 for adenocarcinoma (Adc) vs. squamous cell carcinoma (Sqc) or “Other”, 0.85 ± 0.02 and 0.70 ± 0.05 for Sqc vs. Adc or Other, and 0.77 ± 0.03 and 0.57 ± 0.05 for Other vs. Adc or Sqc. The same analyses for the PET data revealed AUCs of 0.90 ± 0.10 and 0.80 ± 0.04, 0.80 ± 0.02 and 0.61 ± 0.06, and 0.97 ± 0.01 and 0.88 ± 0.04, respectively.

Conclusion

PET radiomics features were able to differentiate between primary and metastatic lung lesions and showed the potential to identify primary lung cancer subtypes.

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Acknowledgments

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. Tommaso Cozzi is acknowledged for support in the processing of radiomics features. M.K. is supported by an AIRC (Italian Association for Cancer Research) scholarship funded by a grant won by A.C. (IG-2016-18585).

Author information

Author notes

  1. Margarita Kirienko and Luca Cozzi contributed equally to this work.

Authors and Affiliations

  1. Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20090, Milan, Italy

    Margarita Kirienko, Luca Cozzi, Alexia Rossi, Arturo Chiti & Martina Sollini

  2. Radiotherapy and Radiosurgery, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy

    Luca Cozzi & Antonella Fogliata

  3. Radiology, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy

    Alexia Rossi

  4. Thoracic Surgery, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy

    Emanuele Voulaz

  5. Nuclear Medicine, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy

    Lidija Antunovic & Arturo Chiti

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  1. Margarita Kirienko
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Contributions

M.S., M.K. and A.C. conceived the idea of the study; L.C. 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 the radiomics 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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Conflicts of interest

A. Chiti received speaker honoraria from General Electric and Sirtex Medical System, acted as scientific advisor to Blue Earth Diagnostics and Advanced Accelerator Applications, 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 an AIRC (Italian Association for Cancer Research) scholarship funded by a grant won by A.C. (IG-2016-18,585).

All other authors have no conflicts of interest.

Ethical approval

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 principles of the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

Informed consent

For this type of study (i.e. retrospective), formal consent was not required.

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Kirienko, M., Cozzi, L., Rossi, A. et al. Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions. Eur J Nucl Med Mol Imaging 45, 1649–1660 (2018). https://doi.org/10.1007/s00259-018-3987-2

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  • DOI: https://doi.org/10.1007/s00259-018-3987-2

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