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

Abstract

BackgroundLymph 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).MethodsTwo 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).ResultsCommon 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.ConclusionCombining imaging information based on FDG-PET-Radiomics features from tumors and LNs is desirable to achieve a higher prognostic discriminative power for NSCLC.

Combined CT radiomics of primary tumor and metastatic lymph nodes improves prediction of loco-regional control in head and neck cancer

“…Carvalho et al . 25 observed improved prediction of overall survival in lung cancer when lymph node PET radiomic features were incorporated. However, the performance of obtained models was generally low, c-index 0.56–0.59.…”

Exploring Tumor Heterogeneity Using PET Imaging: The Big Picture

“…Keeping in mind these limitations, the evidence supporting the additional value of advanced image features from FDG-PET continues to expand year after year. Several of the most recent studies have used techniques such as external cohort validation [95,96,97], or even machine-learning technique [98,99] and concluded in the usefulness of textural analysis regarding patient management. Several method were also proposed to minimize the effect of inter-center variability related to textural features computation [95,100,101] with encouraging results both on a methodological and prognostic level.…”

Development of a Radiomics Prediction Model for Histological Type Diagnosis in Solitary Pulmonary Nodules: The Combination of CT and FDG PET

“…Many studies have shown that radiomics features have great potential to be the maker for tumor phenotype (8)(9)(10)(11)(12)(13)(14)(15)(16)(17), and found Adc can be differentiated from Sqc by radiomics (17)(18)(19)(20)(21)(22)(23). However, The data sets of those studies only included Adc and Sqc, that is to say, the accuracy of those models will be affected by other histological subtypes of lung cancer.…”