Tumor heterogeneity and permeability as measured on the CT component of PET/CT predict survival in patients with non-small cell lung cancer

Abstract

Purpose: 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).

Experimental design: 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.

Results: 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.

Conclusions: 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.