Data from: 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

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.