18F-fluorodeoxyglucose positron-emission tomography (FDG-PET)-Radiomics of metastatic lymph nodes and primary tumor in non-small cell lung cancer (NSCLC) - A prospective externally validated study

PLoS One. 2018 Mar 1;13(3):e0192859. doi: 10.1371/journal.pone.0192859. eCollection 2018.


Background: Lymph node stage prior to treatment is strongly related to disease progression and poor prognosis in non-small cell lung cancer (NSCLC). However, few studies have investigated metabolic imaging features derived from pre-radiotherapy 18F-fluorodeoxyglucose (FDG) positron-emission tomography (PET) of metastatic hilar/mediastinal lymph nodes (LNs). We hypothesized that these would provide complementary prognostic information to FDG-PET descriptors to only the primary tumor (tumor).

Methods: Two independent cohorts of 262 and 50 node-positive NSCLC patients were used for model development and validation. Image features (i.e. Radiomics) including shape and size, first order statistics, texture, and intensity-volume histograms (IVH) (http://www.radiomics.io/) were evaluated by univariable Cox regression on the development cohort. Prognostic modeling was conducted with a 10-fold cross-validated least absolute shrinkage and selection operator (LASSO), automatically selecting amongst FDG-PET-Radiomics descriptors from (1) tumor, (2) LNs or (3) both structures. Performance was assessed with the concordance-index. Development data are publicly available at www.cancerdata.org and Dryad (doi:10.5061/dryad.752153b).

Results: Common SUV descriptors (maximum, peak, and mean) were significantly related to overall survival when extracted from LNs, as were LN volume and tumor load (summed tumor and LNs' volumes), though this was not true for either SUV metrics or tumor's volume. Feature selection exclusively from imaging information based on FDG-PET-Radiomics, exhibited performances of (1) 0.53 -external 0.54, when derived from the tumor, (2) 0.62 -external 0.56 from LNs, and (3) 0.62 -external 0.59 from both structures, including at least one feature from each sub-category, except IVH.

Conclusion: Combining imaging information based on FDG-PET-Radiomics features from tumors and LNs is desirable to achieve a higher prognostic discriminative power for NSCLC.

Publication types

  • Clinical Trial
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Carcinoma, Non-Small-Cell Lung / diagnostic imaging*
  • Carcinoma, Non-Small-Cell Lung / pathology
  • Female
  • Fluorodeoxyglucose F18 / analysis
  • Humans
  • Lung / diagnostic imaging*
  • Lung / pathology
  • Lung Neoplasms / diagnostic imaging*
  • Lung Neoplasms / pathology
  • Lymph Nodes / diagnostic imaging*
  • Lymph Nodes / pathology
  • Lymphatic Metastasis / diagnostic imaging*
  • Lymphatic Metastasis / pathology
  • Male
  • Middle Aged
  • Positron-Emission Tomography / methods*
  • Prognosis
  • Prospective Studies


  • Fluorodeoxyglucose F18

Associated data

  • Dryad/10.5061/dryad.52153b

Grants and funding

Authors acknowledge financial support from ERC advanced grant (ERC-ADG-2015, n° 694812 - Hypoximmuno) and the QuIC-ConCePT project, which is partly funded by EFPI A companies and the Innovative Medicine Initiative Joint Undertaking (IMI JU) under Grant Agreement No. 115151. This research is also supported by the Dutch technology Foundation STW (grant n° 10696 DuCAT & n° P14-19 Radiomics STRaTegy), which is the applied science division of NWO, and the Technology Programme of the Ministry of Economic Affairs. Authors also acknowledge financial support from the EU 7th framework program (ARTFORCE - n° 257144, REQUITE - n° 601826), SME Phase 2 (EU proposal 673780 – RAIL), EUROSTARS (SeDI, CloudAtlas, DART), the European Program H2020-2015-17 (BD2Decide - PHC30-689715 and ImmunoSABR - n° 733008), Interreg V-A Euregio Meuse-Rhine (“Euradiomics”), Kankeronderzoekfonds Limburg from the Health Foundation Limburg, Alpe d’HuZes-KWF (DESIGN), the Zuyderland-MAASTRO grant and the Dutch Cancer Society.