Correlation Between Tumor Metabolism and Semiquantitative Perfusion MRI Metrics in Non–small Cell Lung Cancer

Abstract

To correlate semiquantitative parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) and 18F-fluorodeoxyglucose (FDG)-PET for non-small cell lung cancer (NSCLC). A total of 24 NSCLC patients who underwent pretreatment FDG-PET and DCE-MRI were analyzed. SUVmax was measured from FDG-PET. DCE-MRI was obtained on 3T MRI scanner using a 4D T1-weighted high resolution imaging method with volume excitation (4D THRIVE) sequence during free breathing to achieve high temporal resolution. A total of 130 dynamic sequences were obtained over a period of 4.3 minutes with 30 coronal slices, 2.5 mm slice thickness, TR/TE of 10/2.0 ms, FA of 15°, in-plane resolution of up to 2 mm, and temporal resolution of 2 seconds. A bolus injection of 0.2 mL/kg of Gd-DTPA (Magnevist) followed by saline flush was used. DCE-MRI parameters consisting of mean, median, standard deviation (SD) and median absolute deviation (MAD) of peak enhancement, wash-in-slope (WIS), wash-out-slope (WOS), time-to-peak (TTP), time-to-half-peak (TTHP), initial gradient, wash-out gradient, signal enhancement ratio and initial area under the relative signal enhancement curve before 30 seconds, 60 seconds, 90 seconds, 120 seconds, 150 seconds, 180 seconds, TTP and TTHP (IAUCtthp) were calculated for each lesion. Univariate analysis (UVA) was performed using Spearman correlation. A linear regression model to predict SUVmax from DCE-MRI parameters was developed by multivariate analysis (MVA) using least absolute shrinkage selection operator (LASSO) in combination with leave-one-out cross-validation (LOOCV). In UVA, mean(WOS) (r = -0.456, P = 0.025), mean(IAUCtthp) (r = -0.439, P = 0.032), median(IAUCtthp) (r = -0.543, P = 0.006), and MAD(IAUCtthp) (r = -0.557, P = 0.005) were statistically significant; all these parameters were negatively correlated with SUVmax. In MVA, a linear combination of SD(WIS), SD(TTP), MAD(TTHP), and MAD(IAUCtthp) was statistically significant for predicting SUVmax (LOOCV-based adjusted R2 = 0.298, P = 0.0006). Decrease in SD(WIS), MAD(TTHP), and MAD(IAUCtthp), and increase in SD(TTP) were associated with significant increase in SUVmax. Association was found between SUVmax of FDG-PET and SD and MAD of DCE-MRI metrics derived during Gd-DTPA uptake phase in NSCLC, reflecting that intratumoral heterogeneity of angiogenesis in wash-in phase is associated with tumor metabolism. MAD(IAUCtthp) was consistently significant in both UVA and MVA, suggesting that lower spatial heterogeneity in intensity and rapidity of contrast wash-in is associated with higher metabolic rate in NSCLC. This study will facilitate better understanding of complex relationship between tumor vascularization and metabolism, and eventually help in guiding targeted therapy.