Dataset Information

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:BackgroundAccurately distinguishing between pleomorphic adenoma (PA) and Warthin tumor (WT) is beneficial for their respective management. Preoperative magnetic resonance imaging (MRI) can provide valuable information due to its excellent soft tissue contrast. This study explored the value of semiquantitative contrast-enhanced MRI parameters in the differential diagnosis of PA and WT.MethodsData from 106 patients, 62 with PA and 44 with WT (confirmed by histopathology) were retrospectively and consecutively analyzed. The tumor-to-spinal cord contrast ratios (TSc-CR) based on the mean, maximum, and minimum signal intensity (T1-mean TSc-CR, T1-max TSc-CR, and T1-min TSc-CR, respectively) in the early and delayed phases were calculated on contrast-enhanced T1-weighted images as semiquantitative parameters, and then compared between PA and WT. Receiver operating characteristic (ROC) curve analysis and areas under the curve (AUCs) were used to determine the performance of these parameters in the differential diagnosis of PA from WT.ResultsExcept T1-min TSc-CR in the early phase, all semiquantitative MRI parameters differed significantly between PA and WT (all P<0.05). T1-max TSc-CR showed higher sensitivity {70.45% [95% confidence interval (CI): 0.548-0.832]} and specificity [70.97% (95% CI: 0.581-0.818)] and had a higher AUC [0.707 (95% CI: 0.610-0.791)] in the early phase when using a cutoff value of 1.89. T1-max TSc-CR showed higher sensitivity [88.64% (95% CI: 0.754-0.962)], specificity [72.58% (95% CI: 0.598-0.831)], and AUC [0.854 (95% CI: 0.772-0.915)] in the delayed phase when using a cutoff value of 2.33. The sensitivity, specificity, and AUC were improved to 90.91% (95% CI: 0.783-0.975), 93.55% (95% CI: 0.843-0.982), and 0.960 (95% CI: 0.903-0.988), respectively, after combination of all semiquantitative parameters in the early and delayed phases. The two radiologists had excellent interobserver agreement on TSc-CRs [all interclass correlation coefficient (ICC) >0.75].ConclusionsSemiquantitative parameters using TSc-CR are valuable in distinguishing PA from WT, and a combination of these parameters can improve the differential diagnostic efficiency.

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Dataset Information

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

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

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