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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) (? = -0.456, P = .025), mean(IAUCtthp) (? = -0.439, P = .032), median(IAUCtthp) (? = -0.543, P = .006), and MAD(IAUCtthp) (? = -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.

SUBMITTER: Lee SH

PROVIDER: S-EPMC6119645 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Correlation Between Tumor Metabolism and Semiquantitative Perfusion Magnetic Resonance Imaging Metrics in Non-Small Cell Lung Cancer.

Lee Sang Ho SH Rimner Andreas A Gelb Emily E Deasy Joseph O JO Hunt Margie A MA Humm John L JL Tyagi Neelam N

International journal of radiation oncology, biology, physics 20180302 4

<h4>Purpose</h4>To correlate semiquantitative parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and <sup>18</sup>F-fluorodeoxyglucose positron emission tomography (<sup>18</sup>F-FDG-PET) for non-small cell lung cancer (NSCLC).<h4>Methods and materials</h4>Twenty-four NSCLC patients who underwent pretreatment <sup>18</sup>F-FDG-PET and DCE-MRI were analyzed. The maximum standardized uptake value (SUVmax) was measured from 18F-FDG-PET. Dynamic contrast-enhance ...[more]

PMID: 29680254

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Project description:BackgroundRadiologists currently subjectively examine multi-parametric magnetic resonance imaging (MP-MRI) to determine prostate tumor aggressiveness using the Prostate Imaging Reporting and Data System scoring system (PI-RADS). Recent studies showed that modified signal to clutter ratio (SCR), tumor volume, and eccentricity (elongation or roundness) of prostate tumors correlated with Gleason score (GS). No previous studies have combined the prostate tumor's shape, SCR, tumor volume, in order to predict potential tumor aggressiveness and GS.MethodsMP-MRI (T1, T2, diffusion, dynamic contrast-enhanced images) were obtained, resized, translated, and stitched to form spatially registered multi-parametric cubes. Multi-parametric signatures that characterize prostate tumors were inserted into a target detection algorithm [adaptive cosine estimator (ACE)]. Pixel-based blobbing, and labeling were applied to the threshold ACE images. Eccentricity calculation used moments of inertia from the blobs. Tumor volume was computed by counting pixels within multi parametric MRI blobs and tumor outlines based on pathologist assessment of whole mount histology. Pathology assessment of GS was performed on whole mount prostatectomy. The covariance matrix and mean of normal tissue background was computed from normal prostate. Using signatures and normal tissue statistics, the z-score, noise corrected SCR [principal component (PC), modified regularization] from each patient was computed. Eccentricity, tumor volume, and SCR were fitted to GS. Analysis of variance assesses the relationship among the variables.ResultsA multivariate analysis generated correlation coefficient (0.60 to 0.784) and P value (0.00741 to <0.0001) from fitting two sets of independent variates, namely, tumor eccentricity (the eccentricity for the largest blob, weighted average for the eccentricity) and SCR (removing 3 PCs, removing 4 PCs, modified regularization, and z-score) to GS. The eccentricity t-statistic exceeded the SCR t-statistic. The three-variable fit to GS using tumor volume (histology, MRI) yielded correlation coefficients ranging from 0.724 to 0.819 (P value <<0.05). Tumor volumes generated from histology yielded higher correlation coefficients than MRI volumes. Adding volume to eccentricity and SCR adds little improvement for fitting GS due to higher correlation coefficients among independent variables and little additional, independent information.ConclusionsCombining prostate tumors eccentricity with SCR relatively highly correlates with GS.

Project description:BackgroundStress cardiac magnetic resonance imaging (CMR) has demonstrated excellent diagnostic and prognostic value in single-center studies.ObjectivesThis study sought to investigate the prognostic value of stress CMR and downstream costs from subsequent cardiac testing in a retrospective multicenter study in the United States.MethodsIn this retrospective study, consecutive patients from 13 centers across 11 states who presented with a chest pain syndrome and were referred for stress CMR were followed for a target period of 4 years. The authors associated CMR findings with a primary outcome of cardiovascular death or nonfatal myocardial infarction using competing risk-adjusted regression models and downstream costs of ischemia testing using published Medicare national payment rates.ResultsIn this study, 2,349 patients (63 ± 11 years of age, 47% female) were followed for a median of 5.4 years. Patients with no ischemia or late gadolinium enhancement (LGE) by CMR, observed in 1,583 patients (67%), experienced low annualized rates of primary outcome (<1%) and coronary revascularization (1% to 3%), across all years of study follow-up. In contrast, patients with ischemia+/LGE+ experienced a >4-fold higher annual primary outcome rate and a >10-fold higher rate of coronary revascularization during the first year after CMR. Patients with ischemia and LGE both negative had low average annual cost spent on ischemia testing across all years of follow-up, and this pattern was similar across the 4 practice environments of the participating centers.ConclusionsIn a multicenter U.S. cohort with stable chest pain syndromes, stress CMR performed at experienced centers offers effective cardiac prognostication. Patients without CMR ischemia or LGE experienced a low incidence of cardiac events, little need for coronary revascularization, and low spending on subsequent ischemia testing. (Stress CMR Perfusion Imaging in the United States [SPINS]: A Society for Cardiovascular Resonance Registry Study; NCT03192891).

Dataset Information

Correlation Between Tumor Metabolism and Semiquantitative Perfusion Magnetic Resonance Imaging Metrics in Non-Small Cell Lung Cancer.

Publications

Correlation Between Tumor Metabolism and Semiquantitative Perfusion Magnetic Resonance Imaging Metrics in Non-Small Cell Lung Cancer.

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