Correlation between tumor metabolism and semiquantitative perfusion magnetic resonance imaging metrics in non-small cell lung cancer Journal Article

Authors: Lee, S. H.; Rimner, A.; Gelb, E.; Deasy, J. O.; Hunt, M. A.; Humm, J. L.; Tyagi, N.
Article Title: Correlation between tumor metabolism and semiquantitative perfusion magnetic resonance imaging metrics in non-small cell lung cancer
Abstract: Purpose: To correlate semiquantitative parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) for non-small cell lung cancer (NSCLC). Methods and Materials: Twenty-four NSCLC patients who underwent pretreatment 18F-FDG-PET and DCE-MRI were analyzed. The maximum standardized uptake value (SUVmax) was measured from 18F-FDG-PET. Dynamic contrast-enhanced MRI was obtained on a 3T MRI scanner using 4-dimensional T1-weighted high-resolution imaging with a volume excitation sequence. The DCE-MRI parameters, consisting of mean, median, standard deviation (SD), and median absolute deviation (MAD) of peak enhancement, time to peak (TTP), time to half peak (TTHP), wash-in slope (WIS), wash-out slope (WOS), initial gradient, wash-out gradient, signal enhancement ratio, and initial area under the relative signal enhancement curve taken up to 30, 60, 90, 120, 150, and 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 in combination with leave-one-out cross-validation (LOOCV). Results: In UVA, mean(WOS) (ρ = −0.456, P =.025), mean(IAUCtthp) (ρ = −0.439, P =.032), median(IAUCtthp) (ρ = −0.543, P =.006), and MAD(IAUCtthp) (ρ = −0.557, P =.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 =.0006). A decrease in SD(WIS), MAD(TTHP), and MAD(IAUCtthp) and an increase in SD(TTP) were associated with a significant increase in SUVmax. Conclusion: An association was found between SUVmax, the SD, and MAD of DCE-MRI metrics derived during contrast uptake in NSCLC, reflecting that intratumoral heterogeneity in wash-in contrast kinetics is associated with tumor metabolism. Although MAD(IAUCtthp) was a significant feature in both UVA and MVA, the LASSO-based multivariate regression model yielded better predictability of SUVmax than a univariate regression model using MAD(IAUCtthp). This study will facilitate understanding of the complex relationship between tumor vascularization and metabolism and eventually help in guiding targeted therapy. © 2018 Elsevier Inc.
Keywords: scanning; positron emission tomography; magnetic resonance imaging; metabolism; tumors; multivariant analysis; biological organs; diseases; non small cell lung cancer; linear regression; dynamic contrast enhanced magnetic resonance imaging; maximum standardized uptake values; dynamic contrast enhanced mri; leave-one-out cross-validation (loocv); linear regression models; median absolute deviation; multivariate regression models
Journal Title: International Journal of Radiation Oncology, Biology, Physics
Volume: 102
Issue: 4
ISSN: 0360-3016
Publisher: Elsevier Inc.  
Date Published: 2018-11-15
Start Page: 718
End Page: 726
Language: English
DOI: 10.1016/j.ijrobp.2018.02.031
PROVIDER: scopus
PMCID: PMC6119645
PUBMED: 29680254
Notes: Article -- Export Date: 1 November 2018 -- Source: Scopus
Citation Impact
MSK Authors
  1. Andreas Rimner
    314 Rimner
  2. John Laurence Humm
    366 Humm
  3. Joseph Owen Deasy
    341 Deasy
  4. Margie A Hunt
    264 Hunt
  5. Neelam Tyagi
    76 Tyagi
  6. Emily Helene Gelb
    20 Gelb
  7. Sang Ho Lee
    5 Lee