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

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.

Author information

1
Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center (MUMC +), Maastricht, the Netherlands.
2
Institute of Radiooncology-OncoRay, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.
3
Department of Radiotherapy and Radiation Oncology, Medical Faculty and University Hospital Carl Gustav Carus of Technische Universität Dresden, Dresden, Germany.
4
OncoRay, National Centre for Radiation Research in Oncology, Medical Faculty and University Hospital Carl Gustav Carus of Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.
5
Department of Radiology and Nuclear Medicine, Radboud UMC, Nijmegen, the Netherlands.
6
Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.
7
Biomedical Photonic Imaging Group, MIRA Institute, University of Twente, Enschede, the Netherlands.
8
Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.

Abstract

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.

PMID:
29494598
PMCID:
PMC5832210
DOI:
10.1371/journal.pone.0192859
[Indexed for MEDLINE]
Free PMC Article

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