Tumor Heterogeneity in Lung Cancer: Assessment with Dynamic Contrast-enhanced MR Imaging.

Purpose To evaluate histogram and texture parameters on pretreatment dynamic contrast material-enhanced (DCE) magnetic resonance (MR) images in lung cancer in terms of temporal change, optimal time for analysis, and prognostic potential. Materials and Methods This retrospective study was approved by the institutional review board, and the requirement to obtain informed consent was waived. Thirty-eight patients with pathologically proved lung cancer undergoing standard pretreatment DCE MR imaging were included. A fat-suppressed, T1-weighted, volume-interpolated breath-hold MR sequence was performed every 30 seconds for 300 and 480 seconds after contrast material administration. A region of interest was manually drawn in the largest cross-sectional area of the tumor on DCE MR images to extract semiquantitative perfusion, histogram, and texture parameters. Predictability of 2-year progression-free survival (PFS) was analyzed by using the Kaplan-Meier method and Cox regression analysis. Results MR histogram and texture parameters increased rapidly 30-60 seconds after contrast material administration. Standard deviation and entropy then plateaued, whereas skewness and kurtosis rapidly decreased. Univariate Cox regression analysis revealed that standard deviation and entropy were significant predictors of survival; their statistical significance was preserved from 60 to 300 seconds, with the smallest P values (P ≤ .001) occurring from 120 to 180 seconds. At multivariate Cox regression analysis, entropy was the sole significant predictor of 2-year PFS (hazard ratio at 180 seconds, 10.098 [95% confidence interval: 1.579, 64.577], P = .015; hazard ratio at 120 seconds: 11.202 [95% confidence interval: 1.761, 71.260], P = .010). Conclusion Histogram and texture parameter changes varied after contrast material injection. The 120-180-second window after contrast material injection was optimal for MR imaging-derived texture parameter and entropy at DCE MR imaging. (©) RSNA, 2016 Online supplemental material is available for this article.