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

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2018 Mar 2

PMID 29494598

Citations 31

Authors

Sara Carvalho

Ralph T H Leijenaar

Esther G C Troost

Janna E van Timmeren

Cary Oberije

Wouter van Elmpt

Lioe-Fee de Geus-Oei

Johan Bussink

Philippe Lambin

Affiliations

Soon will be listed here.

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.

Citing Articles

Does FDG PET-Based Radiomics Have an Added Value for Prediction of Overall Survival in Non-Small Cell Lung Cancer?.

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The effect of data resampling methods in radiomics.

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Coefficient of Variation in Metastatic Lymph Nodes Determined by F-FDG PET/CT in Patients with Advanced NSCLC: Combination with Coefficient of Variation in Primary Tumors.

Pellegrino S, Fonti R, Vallone C, Morra R, Matano E, De Placido S Cancers (Basel). 2024; 16(2).

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From Head and Neck Tumour and Lymph Node Segmentation to Survival Prediction on PET/CT: An End-to-End Framework Featuring Uncertainty, Fairness, and Multi-Region Multi-Modal Radiomics.

Salahuddin Z, Chen Y, Zhong X, Woodruff H, Mohammadian Rad N, Mali S Cancers (Basel). 2023; 15(7).

PMID: 37046593 PMC: 10093277. DOI: 10.3390/cancers15071932.

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