18F] FDG Positron Emission Tomography (PET) Tumor and Penumbra Imaging Features Predict Recurrence in Non-Small Cell Lung Cancer.

Sarah A Mattonen,Guido A Davidzon,Viswam S Nair,Shaimaa Bakr,Sandy Napel,George Horng,Minal Vasanawala,Ann N.C Leung,Sebastian Echegaray

doi:10.18383/j.tom.2018.00026

Abstract

We identified computational imaging features on 18F-fluorodeoxyglucose positron emission tomography (PET) that predict recurrence/progression in non–small cell lung cancer (NSCLC). We retrospectively identified 291 patients with NSCLC from 2 prospectively acquired cohorts (training, n = 145; validation, n = 146). We contoured the metabolic tumor volume (MTV) on all pretreatment PET images and added a 3-dimensional penumbra region that extended outward 1 cm from the tumor surface. We generated 512 radiomics features, selected 435 features based on robustness to contour variations, and then applied randomized sparse regression (LASSO) to identify features that predicted time to recurrence in the training cohort. We built Cox proportional hazards models in the training cohort and independently evaluated the models in the validation cohort. Two features including stage and a MTV plus penumbra texture feature were selected by LASSO. Both features were significant univariate predictors, with stage being the best predictor (hazard ratio [HR] = 2.15 [95% confidence interval (CI): 1.56–2.95], P < .001). However, adding the MTV plus penumbra texture feature to stage significantly improved prediction (P = .006). This multivariate model was a significant predictor of time to recurrence in the training cohort (concordance = 0.74 [95% CI: 0.66–0.81], P < .001) that was validated in a separate validation cohort (concordance = 0.74 [95% CI: 0.67–0.81], P < .001). A combined radiomics and clinical model improved NSCLC recurrence prediction. FDG PET radiomic features may be useful biomarkers for lung cancer prognosis and add clinical utility for risk stratification.

Highlights

Lung cancer remains the most common cause of cancer death worldwide, and the 5-year survival rates of non–small cell lung cancer (NSCLC) remain quite poor despite advances in diagnosis and treatment [1, 2]
We investigated the potential of FDG-positron emission tomography (PET) radiomics to predict recurrence in NSCLC by [1] assessing the variability in radiomic feature extraction from PET images and [2] building and validating a radiomics model to predict time to recurrence
The training cohort consisted of subjects from a pool of patients with early-stage NSCLC referred for surgical treatment at 2 local medical centers between 2008 and 2012 with preoperative PET/computed tomography (CT) performed before surgery (n ϭ 145)

Summary

Introduction

Lung cancer remains the most common cause of cancer death worldwide, and the 5-year survival rates of non–small cell lung cancer (NSCLC) remain quite poor despite advances in diagnosis and treatment [1, 2]. More accurate clinical, imaging, and molecular biomarkers will be extremely useful for stratifying patients who are at a higher risk of recurrence and who might benefit from adjuvant or more aggressive treatment options [6]. Maximum standardized uptake value (SUVmax) on fluorine-18F fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET) imaging has been shown to predict recurrence or death in NSCLC [7]. This is a singlevoxel metric; we hypothesized that applying a radiomics approach to extract more complex information (eg, texture) from standard medical images could provide additional prognostic information [8, 9]

Methods

Results

Conclusion