Tumor Heterogeneity and Permeability as Measured on the CT Component of PET/CT Predict Survival in Patients with Non–Small Cell Lung Cancer

Thida Win,Kenneth A Miles,Balaji Ganeshan,Simon Wan,Manu Shastry,Peter J Ell,Irfan Kayani,Raymondo Endozo,Ashley M Groves,Marie Meagher,Sam M Janes,Robert I Shortman

doi:10.1158/1078-0432.ccr-12-1307

Abstract

We prospectively examined the role of tumor textural heterogeneity on positron emission tomography/computed tomography (PET/CT) in predicting survival compared with other clinical and imaging parameters in patients with non-small cell lung cancer (NSCLC). The feasibility study consisted of 56 assessed consecutive patients with NSCLC (32 males, 24 females; mean age 67 ± 9.7 years) who underwent combined fluorodeoxyglucose (FDG) PET/CT. The validation study population consisted of 66 prospectively recruited consecutive consenting patients with NSCLC (37 males, 29 females; mean age, 67.5 ± 7.8 years) who successfully underwent combined FDG PET/CT-dynamic contrast-enhanced (DCE) CT. Images were used to derive tumoral PET/CT textural heterogeneity, DCE CT permeability, and FDG uptake (SUVmax). The mean follow-up periods were 22.6 ± 13.3 months and 28.5± 13.2 months for the feasibility and validation studies, respectively. Optimum threshold was determined for clinical stage and each of the above biomarkers (where available) from the feasibility study population. Kaplan-Meier analysis was used to assess the ability of the biomarkers to predict survival in the validation study. Cox regression determined survival factor independence. Univariate analysis revealed that tumor CT-derived heterogeneity (P < 0.001), PET-derived heterogeneity (P = 0.003), CT-derived permeability (P = 0.002), and stage (P < 0.001) were all significant survival predictors. The thresholds used in this study were derived from a previously conducted feasibility study. Tumor SUVmax did not predict survival. Using multivariable analysis, tumor CT textural heterogeneity (P = 0.021), stage (P = 0.001), and permeability (P < 0.001) were independent survival predictors. These predictors were independent of patient treatment. Tumor stage and CT-derived textural heterogeneity were the best predictors of survival in NSCLC. The use of CT-derived textural heterogeneity should assist the management of many patients with NSCLC.

Highlights

Non–small cell lung cancer (NSCLC) is a common malignancy with a poor prognosis
Tumor stage and computed tomography (CT)-derived textural heterogeneity were the best predictors of survival in non–small cell lung cancer (NSCLC)
The use of CT-derived textural heterogeneity should assist the management of many patients with NSCLC

Summary

Introduction

Non–small cell lung cancer (NSCLC) is a common malignancy with a poor prognosis. Predictive factors are needed to refine the management of these patients, for example, to guide the use of surgical adjuncts and help determine patients who are at risk of early reoccurrence and require intense monitoring and follow-up [1,2,3,4]. Multiple factors such as performance status [5, 6] and staging [7] Other factors, such as tumor metabolism and vascularity, have been proposed as prognostic indicators [8, 9]. Imaging patients with NSCLC with positron emission tomography (PET) and computed tomography (CT) provides important staging information. These imaging techniques can be used to derive tumor glucose metabolism and vascularity [8, 9]. These functional data may in turn be used to predict patient outcome

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Conclusion