The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge, Part II.

This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate Ktrans (volume transfer rate constant), ve (extravascular, extracellular volume fraction), kep (efflux rate constant), and τi (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T1 values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for Ktrans, ve, kep, and τi, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in Ktrans and ve (wCV = 0.50 and 0.10, respectively), but had smaller effects on kep and τi (wCV = 0.39 and 0.22, respectively). kep is less sensitive to AIF variation than Ktrans, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τi parameter may have advantages over the conventional PK parameters in a longitudinal study.

DCE-MRI of the prostate using shutter-speed vs. Tofts model for tumor characterization and assessment of aggressiveness.

PURPOSE: To quantify Tofts model (TM) and shutter-speed model (SSM) perfusion parameters in prostate cancer (PCa) and noncancerous peripheral zone (PZ) and to compare the diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to Prostate Imaging and Reporting and Data System (PI-RADS) classification for the assessment of PCa aggressiveness. MATERIALS AND METHODS: Fifty PCa patients (mean age 60 years old) who underwent MRI at 3.0T followed by prostatectomy were included in this Institutional Review Board-approved retrospective study. DCE-MRI parameters (Ktrans , ve , kep [TM&SSM] and intracellular water molecule lifetime τi [SSM]) were determined in PCa and PZ. Differences in DCE-MRI parameters between PCa and PZ, and between models were assessed using Wilcoxon signed-rank tests. Receiver operating characteristic (ROC) analysis for differentiation between PCa and PZ was performed for individual and combined DCE-MRI parameters. Diagnostic performance of DCE-MRI parameters for identification of aggressive PCa (Gleason ≥8, grade group [GG] ≥3 or pathology stage pT3) was assessed using ROC analysis and compared with PI-RADSv2 scores. RESULTS: DCE-MRI parameters were significantly different between TM and SSM and between PZ and PCa (P < 0.037). Diagnostic performances of TM and SSM for differentiation of PCa from PZ were similar (highest AUC TM: Ktrans +kep 0.76, SSM: τi +kep 0.80). PI-RADS outperformed TM and SSM DCE-MRI for identification of Gleason ≥8 lesions (AUC PI-RADS: 0.91, highest AUC DCE-MRI: Ktrans +τi SSM 0.61, P = 0.002). The diagnostic performance of PI-RADS and DCE-MRI for identification of GG ≥3 and pT3 PCa was not significantly different (P > 0.213). CONCLUSION: SSM DCE-MRI did not increase the diagnostic performance of DCE-MRI for PCa characterization. PI-RADS outperformed both TM and SSM DCE-MRI for identification of aggressive cancer. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:837-849.

Integrative analysis of diffusion-weighted MRI and genomic data to inform treatment of glioblastoma.

Gene expression profiling from glioblastoma (GBM) patients enables characterization of cancer into subtypes that can be predictive of response to therapy. An integrative analysis of imaging and gene expression data can potentially be used to obtain novel biomarkers that are closely associated with the genetic subtype and gene signatures and thus provide a noninvasive approach to stratify GBM patients. In this retrospective study, we analyzed the expression of 12,042 genes for 558 patients from The Cancer Genome Atlas (TCGA). Among these patients, 50 patients had magnetic resonance imaging (MRI) studies including diffusion weighted (DW) MRI in The Cancer Imaging Archive (TCIA). We identified the contrast enhancing region of the tumors using the pre- and post-contrast T1-weighted MRI images and computed the apparent diffusion coefficient (ADC) histograms from the DW-MRI images. Using the gene expression data, we classified patients into four molecular subtypes, determined the number and composition of genes modules using the gap statistic, and computed gene signature scores. We used logistic regression to find significant predictors of GBM subtypes. We compared the predictors for different subtypes using Mann-Whitney U tests. We assessed detection power using area under the receiver operating characteristic (ROC) analysis. We computed Spearman correlations to determine the associations between ADC and each of the gene signatures. We performed gene enrichment analysis using Ingenuity Pathway Analysis (IPA). We adjusted all p values using the Benjamini and Hochberg method. The mean ADC was a significant predictor for the neural subtype. Neural tumors had a significantly lower mean ADC compared to non-neural tumors ([Formula: see text]), with mean ADC of [Formula: see text] and [Formula: see text] for neural and non-neural tumors, respectively. Mean ADC showed an area under the ROC of 0.75 for detecting neural tumors. We found eight gene modules in the GBM cohort. The mean ADC was significantly correlated with the gene signature related with dendritic cell maturation ([Formula: see text], [Formula: see text]). Mean ADC could be used as a biomarker of a gene signature associated with dendritic cell maturation and to assist in identifying patients with neural GBMs, known to be resistant to aggressive standard of care.

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge.

Dynamic contrast-enhanced MRI (DCE-MRI) has been widely used in tumor detection and therapy response evaluation. Pharmacokinetic analysis of DCE-MRI time-course data allows estimation of quantitative imaging biomarkers such as Ktrans(rate constant for plasma/interstitium contrast reagent (CR) transfer) and ve (extravascular and extracellular volume fraction). However, the use of quantitative DCE-MRI in clinical prostate imaging islimited, with uncertainty in arterial input function (AIF, i.e., the time rate of change of the concentration of CR in the blood plasma) determination being one of the primary reasons. In this multicenter data analysis challenge to assess the effects of variations in AIF quantification on estimation of DCE-MRI parameters, prostate DCE-MRI data acquired at one center from 11 prostate cancer patients were shared among nine centers. Each center used its site-specific method to determine the individual AIF from each data set and submitted the results to the managing center. Along with a literature population averaged AIF, these AIFs and their reference-tissue-adjusted variants were used by the managing center to perform pharmacokinetic analysis of the DCE-MRI data sets using the Tofts model (TM). All other variables including tumor region of interest (ROI) definition and pre-contrast T1 were kept the same to evaluate parameter variations caused by AIF variations only. Considerable pharmacokinetic parameter variations were observed with the within-subject coefficient of variation (wCV) of Ktrans obtained with unadjusted AIFs as high as 0.74. AIF-caused variations were larger in Ktrans than ve and both were reduced when reference-tissue-adjusted AIFs were used. The parameter variations were largely systematic, resulting in nearly unchanged parametric map patterns. The CR intravasation rate constant, kep (= Ktrans/ve), was less sensitive to AIF variation than Ktrans (wCV for unadjusted AIFs: 0.45 for kepvs. 0.74 for Ktrans), suggesting that it might be a more robust imaging biomarker of prostate microvasculature than Ktrans.

Hepatocellular carcinoma: IVIM diffusion quantification for prediction of tumor necrosis compared to enhancement ratios.

PURPOSE: To correlate intra voxel incoherent motion (IVIM) diffusion parameters of liver parenchyma and hepatocellular carcinoma (HCC) with degree of liver/tumor enhancement and necrosis; and to assess the diagnostic performance of diffusion parameters vs. enhancement ratios (ER) for prediction of complete tumor necrosis. PATIENTS AND METHODS: In this IRB approved HIPAA compliant study, we included 46 patients with HCC who underwent IVIM diffusion-weighted (DW) MRI in addition to routine sequences at 3.0 T. True diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (PF) and apparent diffusion coefficient (ADC) were quantified in tumors and liver parenchyma. Tumor ER were calculated using contrast-enhanced imaging, and degree of tumor necrosis was assessed using post-contrast image subtraction. IVIM parameters and ER were compared between HCC and background liver and between necrotic and viable tumor components. ROC analysis for prediction of complete tumor necrosis was performed. RESULTS: 79 HCCs were assessed (mean size 2.5 cm). D, PF and ADC were significantly higher in HCC vs. liver (p < 0.0001). There were weak significant negative/positive correlations between D/PF and ER, and significant correlations between D/PF/ADC and tumor necrosis (for D, r 0.452, p < 0.001). Among diffusion parameters, D had the highest area under the curve (AUC 0.811) for predicting complete tumor necrosis. ER outperformed diffusion parameters for prediction of complete tumor necrosis (AUC > 0.95, p < 0.002). CONCLUSION: D has a reasonable diagnostic performance for predicting complete tumor necrosis, however lower than that of contrast-enhanced imaging.

Non-invasive prediction of portal pressures using CT and MRI in chronic liver disease.

PURPOSE: To assess the diagnostic value of a fast scoring system based on non-invasive cross-sectional imaging to predict portal hypertension (PH) in patients with liver disease. METHODS: In this retrospective study, we included patients who underwent contrast-enhanced CT or MRI within 3 months of hepatic venous pressure gradient (HVPG) measurements. Two independent observers provided an imaging-based scoring system (max of 9): number of variceal sites, volume of ascites, and spleen size. ROC analysis was performed to predict the presence of PH (HVPG ≥ 5 mmHg) and clinically significant PH (HVPG ≥ 10 mmHg). RESULTS: Our cohort consists of 143 patients with mean HVPG of 13.1 ± 2.0 mmHg. Mean PH scores from the two observers were 3.9 ± 2.7 and 3.2 ± 2.5. There was a significant correlation between PH score and HVPG (r = 0.58, p < 0.001 for both observers) with high inter-observer agreement (kappa 0.71). AUCs of 0.78-0.76 and 0.83-0.81 were observed for diagnosing HVPG ≥ 5 mmHg and HVPG ≥ 10 mmHg, respectively, for observers 1 and 2. CONCLUSIONS: We have developed a fast PH imaging-based composite score, which could be used for non-invasive detection of clinically significant PH.

DCE-MRI of hepatocellular carcinoma: perfusion quantification with Tofts model versus shutter-speed model--initial experience.

OBJECTIVE: To quantify hepatocellular carcinoma (HCC) perfusion and flow with the fast exchange regime-allowed Shutter-Speed model (SSM) compared to the Tofts model (TM). MATERIALS AND METHODS: In this prospective study, 25 patients with HCC underwent DCE-MRI. ROIs were placed in liver parenchyma, portal vein, aorta and HCC lesions. Signal intensities were analyzed employing dual-input TM and SSM models. ART (arterial fraction), K (trans) (contrast agent transfer rate constant from plasma to extravascular extracellular space), ve (extravascular extracellular volume fraction), kep (contrast agent intravasation rate constant), and τi (mean intracellular water molecule lifetime) were compared between liver parenchyma and HCC, and ART, K (trans), v e and k ep were compared between models using Wilcoxon tests and limits of agreement. Test-retest reproducibility was assessed in 10 patients. RESULTS: ART and v e obtained with TM; ART, ve, ke and τi obtained with SSM were significantly different between liver parenchyma and HCC (p < 0.04). Parameters showed variable reproducibility (CV range 14.7-66.5% for both models). Liver K (trans) and ve; HCC ve and kep were significantly different when estimated with the two models (p < 0.03). CONCLUSION: Our results show differences when computed between the TM and the SSM. However, these differences are smaller than parameter reproducibilities and may be of limited clinical significance.

Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge.

Pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time-course data allows estimation of quantitative parameters such as K (trans) (rate constant for plasma/interstitium contrast agent transfer), v e (extravascular extracellular volume fraction), and v p (plasma volume fraction). A plethora of factors in DCE-MRI data acquisition and analysis can affect accuracy and precision of these parameters and, consequently, the utility of quantitative DCE-MRI for assessing therapy response. In this multicenter data analysis challenge, DCE-MRI data acquired at one center from 10 patients with breast cancer before and after the first cycle of neoadjuvant chemotherapy were shared and processed with 12 software tools based on the Tofts model (TM), extended TM, and Shutter-Speed model. Inputs of tumor region of interest definition, pre-contrast T1, and arterial input function were controlled to focus on the variations in parameter value and response prediction capability caused by differences in models and associated algorithms. Considerable parameter variations were observed with the within-subject coefficient of variation (wCV) values for K (trans) and v p being as high as 0.59 and 0.82, respectively. Parameter agreement improved when only algorithms based on the same model were compared, e.g., the K (trans) intraclass correlation coefficient increased to as high as 0.84. Agreement in parameter percentage change was much better than that in absolute parameter value, e.g., the pairwise concordance correlation coefficient improved from 0.047 (for K (trans)) to 0.92 (for K (trans) percentage change) in comparing two TM algorithms. Nearly all algorithms provided good to excellent (univariate logistic regression c-statistic value ranging from 0.8 to 1.0) early prediction of therapy response using the metrics of mean tumor K (trans) and k ep (=K (trans)/v e, intravasation rate constant) after the first therapy cycle and the corresponding percentage changes. The results suggest that the interalgorithm parameter variations are largely systematic, which are not likely to significantly affect the utility of DCE-MRI for assessment of therapy response.