Dynamic Contrast-Enhanced Perfusion MRI of High Grade Brain Gliomas Obtained with Arterial or Venous Waveform Input Function

Reproducibility and reliability of pancreatic pharmacokinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging

BACKGROUND

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with an extended Tofts linear (ETL) model for tissue and tumor evaluation has been established, but its effectiveness in evaluating the pancreas remains uncertain.

PURPOSE

To understand the pharmacokinetics of normal pancreas and serve as a reference for future studies of pancreatic diseases.

MATERIAL AND METHODS

Pancreatic pharmacokinetic parameters of 54 volunteers were calculated using DCE-MRI with the ETL model. First, intra- and inter-observer reliability was assessed through the use of the intra-class correlation coefficient (ICC) and coefficient of variation (CoV). Second, a subgroup analysis of the pancreatic DCE-MRI pharmacokinetic parameters was carried out by dividing the 54 individuals into three groups based on the pancreatic region, three groups based on age, and two groups based on sex.

RESULTS

There was excellent agreement and low variability of intra- and inter-observer to pancreatic DCE-MRI pharmacokinetic parameters. The intra- and inter-observer ICCs of Ktrans, kep, ve, and vp were 0.971, 0.952, 0.959, 0.944 and 0.947, 0.911, 0.978, 0.917, respectively. The intra- and inter-observer CoVs of Ktrans, kep, ve, vp were 9.98%, 5.99%, 6.47%, 4.76% and 10.15%, 5.22%, 6.28%, 5.40%, respectively. Only the pancreatic ve of the older group was higher than that of the young and middle-aged groups (P = 0.042, 0.001), and the vp of the pancreatic head was higher than that of the pancreatic body and tail (P = 0.014, 0.043).

CONCLUSION

The application of DCE-MRI with an ETL model provides a reliable, robust, and reproducible means of non-invasively quantifying pancreatic pharmacokinetic parameters.

Delay of Aortic Arterial Input Function Time Improves Detection of Malignant Vertebral Body Lesions on Dynamic Contrast-Enhanced MRI Perfusion

Dynamic contrast-enhanced MRI (DCE) is an emerging modality in the study of vertebral body malignancies. DCE-MRI analysis relies on a pharmacokinetic model, which assumes that contrast uptake is simultaneous in the feeding of arteries and tissues of interest. While true in the highly vascularized brain, the perfusion of the spine is delayed. This delay of contrast reaching vertebral body lesions can affect DCE-MRI analyses, leading to misdiagnosis for the presence of active malignancy in the bone marrow. To overcome the limitation of delayed contrast arrival to vertebral body lesions, we shifted the arterial input function (AIF) curve over a series of phases and recalculated the plasma volume values (V_p) for each phase shift. We hypothesized that shifting the AIF tracer curve would better reflect actual contrast perfusion, thereby improving the accuracy of V_p maps in metastases. We evaluated 18 biopsy-proven vertebral body metastases in which standard DCE-MRI analysis failed to demonstrate the expected increase in V_p. We manually delayed the AIF curve for multiple phases, defined as the scan-specific phase temporal resolution, and analyzed DCE-MRI parameters with the new AIF curves. All patients were found to require at least one phase-shift delay in the calculated AIF to better visualize metastatic spinal lesions and improve quantitation of V_p. Average normalized V_p values were 1.78 ± 1.88 for zero phase shifts (P0), 4.72 ± 4.31 for one phase shift (P1), and 5.59 ± 4.41 for two phase shifts (P2). Mann-Whitney U tests obtained p-values = 0.003 between P0 and P1, and 0.0004 between P0 and P2. This study demonstrates that image processing analysis for DCE-MRI in patients with spinal metastases requires a careful review of signal intensity curve, as well as a possible adjustment of the phase of aortic AIF to increase the accuracy of V_p.

Assessing the reproducibility of high temporal and spatial resolution dynamic contrast-enhanced magnetic resonance imaging in patients with gliomas

Temporal and spatial resolution of dynamic contrast-enhanced MR imaging (DCE-MRI) is critical to reproducibility, and the reproducibility of high-resolution (HR) DCE-MRI was evaluated. Thirty consecutive patients suspected to have brain tumors were prospectively enrolled with written informed consent. All patients underwent both HR-DCE (voxel size, 1.1 × 1.1 × 1.1 mm³; scan interval, 1.6 s) and conventional DCE (C-DCE; voxel size, 1.25 × 1.25 × 3.0 mm³; scan interval, 4.0 s) MRI. Regions of interests (ROIs) for enhancing lesions were segmented twice in each patient with glioblastoma (n = 7) to calculate DCE parameters (K^trans, V_p, and V_e). Intraclass correlation coefficients (ICCs) of DCE parameters were obtained. In patients with gliomas (n = 25), arterial input functions (AIFs) and DCE parameters derived from T2 hyperintense lesions were obtained, and DCE parameters were compared according to WHO grades. ICCs of HR-DCE parameters were good to excellent (0.84–0.95), and ICCs of C-DCE parameters were moderate to excellent (0.66–0.96). Maximal signal intensity and wash-in slope of AIFs from HR-DCE MRI were significantly greater than those from C-DCE MRI (31.85 vs. 7.09 and 2.14 vs. 0.63; p < 0.001). Both 95^th percentile K^trans and V_e from HR-DCE and C-DCE MRI could differentiate grade 4 from grade 2 and 3 gliomas (p < 0.05). In conclusion, HR-DCE parameters generally showed better reproducibility than C-DCE parameters, and HR-DCE MRI provided better quality of AIFs.

Sources of systematic error in DCE-MRI estimation of low-level blood-brain barrier leakage

PURPOSE

Dynamic contrast-enhanced (DCE) -MRI with Patlak model analysis is increasingly used to quantify low-level blood-brain barrier (BBB) leakage in studies of pathophysiology. We aimed to investigate systematic errors due to physiological, experimental, and modeling factors influencing quantification of the permeability-surface area product PS and blood plasma volume v_p , and to propose modifications to reduce the errors so that subtle differences in BBB permeability can be accurately measured.

METHODS

Simulations were performed to predict the effects of potential sources of systematic error on conventional PS and v_p quantification: restricted BBB water exchange, reduced cerebral blood flow, arterial input function (AIF) delay and B 1 + error. The impact of targeted modifications to the acquisition and processing were evaluated, including: assumption of fast versus no BBB water exchange, bolus versus slow injection of contrast agent, exclusion of early data from model fitting and B 1 + correction. The optimal protocol was applied in a cohort of recent mild ischaemic stroke patients.

RESULTS

Simulation results demonstrated substantial systematic errors due to the factors investigated (absolute PS error ≤ 4.48 × 10^-4 min^-1 ). However, these were reduced (≤0.56 × 10^-4 min^-1 ) by applying modifications to the acquisition and processing pipeline. Processing modifications also had substantial effects on in-vivo normal-appearing white matter PS estimation (absolute change ≤ 0.45 × 10^-4 min^-1 ).

CONCLUSION

Measuring subtle BBB leakage with DCE-MRI presents unique challenges and is affected by several confounds that should be considered when acquiring or interpreting such data. The evaluated modifications should improve accuracy in studies of neurodegenerative diseases involving subtle BBB breakdown.

Differentiation between glioblastoma and primary CNS lymphoma: application of DCE-MRI parameters based on arterial input function obtained from DSC-MRI

OBJECTIVE

This study aimed to evaluate whether arterial input functions (AIFs) obtained from dynamic susceptibility contrast (DSC)-MRI (AIF_DSC) improve the reliability and diagnostic accuracy of dynamic contrast-enhanced (DCE)-derived pharmacokinetic (PK) parameters for differentiating glioblastoma from primary CNS lymphoma (PCNSL) compared with AIFs derived from DCE-MRI (AIF_DCE).

METHODS

This retrospective study included 172 patients with glioblastoma (n = 147) and PCNSL (n = 25). All patients had undergone preoperative DSC- and DCE-MRI. The volume transfer constant (K^trans), volume of the vascular plasma space (v_p), and volume of the extravascular extracellular space (v_e) were acquired using AIF_DSC and AIF_DCE. The relative cerebral blood volume (rCBV) was obtained from DSC-MRI. Intraclass correlation coefficients (ICC) and ROC curves were used to assess the reliability and diagnostic accuracy of individual parameters.

RESULTS

The mean K^trans, v_p, and v_e values revealed better ICCs with AIF_DSC than with AIF_DCE (K^trans, 0.911 vs 0.355; v_p, 0.766 vs 0.503; v_e, 0.758 vs 0.657, respectively). For differentiating all glioblastomas from PCNSL, the mean rCBV (AUC = 0.856) was more accurate than the AIF_DSC-driven mean K^trans, which had the largest AUC (0.711) among the DCE-derived parameters (p = 0.02). However, for glioblastomas with low rCBV (≤ 75th percentile of PCNSL; n = 30), the AIF_DSC-driven mean K^trans and v_p were more accurate than rCBV (AUC: K^trans, 0.807 vs rCBV, 0.515, p = 0.004; v_p, 0.715 vs rCBV, p = 0.045).

CONCLUSION

DCE-derived PK parameters using the AIF_DSC showed improved reliability and diagnostic accuracy for differentiating glioblastoma with low rCBV from PCNSL.

KEY POINTS

• An accurate differential diagnosis of glioblastoma and PCNSL is crucial because of different therapeutic strategies. • In contrast to the rCBV from DSC-MRI, another perfusion imaging technique, the DCE parameters for the differential diagnosis have been limited because of the low reliability of AIFs from DCE-MRI. • When we analyzed DCE-MRI data using AIFs from DSC-MRI (AIF_DSC), AIF_DSC-driven DCE parameters showed improved reliability and better diagnostic accuracy than rCBV for differentiating glioblastoma with low rCBV from PCNSL.

Effects of artery input function on dynamic contrast-enhanced MRI for determining grades of gliomas

OBJECTIVE

To evaluate the effect of artery input function (AIF) derived from different arteries for pharmacokinetic modeling on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters in the grading of gliomas.

METHODS

49 patients with pathologically confirmed gliomas were recruited and underwent DCE-MRI. A modified Tofts model with different AIFs derived from anterior cerebral artery (ACA), ipsilateral and contralateral middle cerebral artery (MCA) and posterior cerebral artery (PCA) was used to estimate quantitative parameters such as K^trans (volume transfer constant) and V_e (fractional extracellular-extravascular space volume) for distinguishing the low grade glioma from high grade glioma. The K^trans and V_e were compared between different arteries using Two Related Samples Tests (TRST) (i.e. Wilcoxon Signed Ranks Test). In addition, these parameters were compared between the low and high grades as well as between the grade II and III using the Mann-Whitney U-test. A p-value of less than 0.05 was regarded as statistically significant.

RESULTS

All the patients completed the DCE-MRI successfully. Sharp wash-in and wash-out phases were observed in all AIFs derived from the different arteries. The quantitative parameters (K^trans and V_e) calculated from PCA were significant higher than those from ACA and MCA for low and high grades, respectively (p < 0.05). Despite the differences of quantitative parameters derived from ACA, MCA and PCA, the K^trans and V_e from any AIFs could distinguish between low and high grade, however, only K^trans from any AIFs could distinguish grades II and III. There was no significant correlation between parameters and the distance from the artery, which the AIF was extracted, to the tumor.

CONCLUSION

Both quantitative parameters K^trans and V_e calculated using any AIF of ACA, MCA, and PCA can be used for distinguishing the low- from high-grade gliomas, however, only K^trans can distinguish grades II and III.

ADVANCES IN KNOWLEDGE

We sought to assess the effect of AIF on DCE-MRI for determining grades of gliomas. Both quantitative parameters K^trans and V_e calculated using any AIF of ACA, MCA, and PCA can be used for distinguishing the low- from high-grade gliomas.

Shutter-Speed DCE-MRI Analyses of Human Glioblastoma Multiforme (GBM) Data

BACKGROUND

The shutter-speed model dynamic contrast-enhanced (SSM-DCE) MRI pharmacokinetic analysis adds a metabolic dimension to DCE-MRI. This is of particular interest in cancers, since abnormal metabolic activity might happen.

PURPOSE

To develop a DCE-MRI SSM analysis framework for glioblastoma multiforme (GBM) cases considering the heterogeneous tissue found in GBM.

STUDY TYPE

Prospective.

SUBJECTS

Ten GBM patients.

FIELD STRENGTH/SEQUENCE

3T MRI with DCE-MRI.

ASSESSMENTS

The corrected Akaike information criterion (AIC_c ) was used to automatically separate DCE-MRI data into proper SSM versions based on the contrast agent (CA) extravasation in each pixel. The supra-intensive parameters, including the vascular water efflux rate constant (k_bo ), the cellular efflux rate constant (k_io ), and the CA vascular efflux rate constant (k_pe ), together with intravascular and extravascular-extracellular water mole fractions (p_b and p_o , respectively) were determined. Further error analyses were also performed to eliminate unreliable estimations on k_io and k_bo .

STATISTICAL TESTS

Student's t-test.

RESULTS

For tumor pixels of all subjects, 88% show lower AIC_c with SSM than with the Tofts model. Compared to normal-appearing white matter (NAWM), tumor tissue showed significantly larger p_b (0.045 vs. 0.011, P < 0.001) and higher k_pe (3.0 × 10^-2 s^-1 vs. 6.1 × 10^-4 s^-1 , P < 0.001). In the contrast, significant k_bo reduction was observed from NAWM to GBM tumor tissue (2.8 s^-1 vs. 1.0 s^-1 , P < 0.001). In addition, k_bo is four orders and two orders of magnitude greater than k_pe in the NAWM and GBM tumor, respectively. These results indicate that CA and water molecule have different transmembrane pathways. The mean tumor k_io of all subjects was 0.57 s^-1 .

DATA CONCLUSION

We demonstrate the feasibility of applying SSM models in GBM cases. Within the proposed SSM analysis framework, k_io and k_bo could be estimated, which might be useful biomarkers for GBM diagnosis and survival prediction in future.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: Stage 1 J. Magn. Reson. Imaging 2020;52:850-863.

Radiomic features of Pk-DCE MRI parameters based on the extensive Tofts model in application of breast cancer

To explore radiomic features of pharmacokinetic dynamic contrast-enhanced (Pk-DCE) MRI on the extensive Tofts model to diagnose breast cancer and predict molecular phenotype. Breast lesions enrolled must undergo Pk-DCE MRI before treatment or puncture, and be identified as primary lesions by pathology. K^trans, K_ep, V_e and V_p were generated on the extensive Tofts model. Radiomic features (histogram, geometry and texture features) were extracted from parametric maps and selected by LASSO. The subjects were divided into training and validation cohort with a ratio of 4:1 to construct model in diagnosis of breast cancer. Feature analysis was made to predict the molecular phenotype. Area under curve (AUC), sensitivity, specificity and accuracy were used to evaluate radiomic features. DeLong's test was performed to compare AUC values. 228 breast lesions met the criteria were used to discrimination and 126 malignant lesions were used to study molecular phenotypes. The number of training cohort and validation cohort were 182 and 46, respectively. The AUC of K^trans, K_ep, V_e, and V_p was 0.95, 0.93, 0.89, and 0.96, and their accuracy was 85%, 89%, 89%, 94% respectively in diagnosis of breast lesions, while their AUC was 0.71 to 0.77, 0.61 to 0.68, and 0.67 to 0.74 to predict ER/PR, Her-2, and Ki-67. There was no significant difference among parameters (P > 0.05). Radiomic features based on Pk-DCE MRI have an advantage to diagnose breast cancer and less ability to predict molecular phenotypes, which are beneficial to guide clinical treatment of breast lesions in some extent.

Pharmacokinetic modeling of dynamic contrast-enhanced MRI using a reference region and input function tail

PURPOSE

Quantitative analysis of dynamic contrast-enhanced MRI (DCE-MRI) requires an arterial input function (AIF) which is difficult to measure. We propose the reference region and input function tail (RRIFT) approach which uses a reference tissue and the washout portion of the AIF.

METHODS

RRIFT was evaluated in simulations with 100 parameter combinations at various temporal resolutions (5-30 s) and noise levels (σ = 0.01-0.05 mM). RRIFT was compared against the extended Tofts model (ETM) in 8 studies from patients with glioblastoma multiforme. Two versions of RRIFT were evaluated: one using measured patient-specific AIF tails, and another assuming a literature-based AIF tail.

RESULTS

RRIFT estimated the transfer constant K trans and interstitial volume v e with median errors within 20% across all simulations. RRIFT was more accurate and precise than the ETM at temporal resolutions slower than 10 s. The percentage error of K trans had a median and interquartile range of -9 ± 45% with the ETM and -2 ± 17% with RRIFT at a temporal resolution of 30 s under noiseless conditions. RRIFT was in excellent agreement with the ETM in vivo, with concordance correlation coefficients (CCC) of 0.95 for K trans , 0.96 for v e , and 0.73 for the plasma volume v p using a measured AIF tail. With the literature-based AIF tail, the CCC was 0.89 for K trans , 0.93 for v e and 0.78 for v p .

CONCLUSIONS

Quantitative DCE-MRI analysis using the input function tail and a reference tissue yields absolute kinetic parameters with the RRIFT method. This approach was viable in simulation and in vivo for temporal resolutions as low as 30 s.

Blood-brain barrier permeability measured using dynamic contrast-enhanced magnetic resonance imaging: a validation study

KEY POINTS

The blood-brain barrier (BBB) is an important and dynamic structure which contributes to homeostasis in the central nervous system. BBB permeability changes occur in health and disease but measurement of BBB permeability in humans is not straightforward. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can be used to model the movement of gadolinium contrast into the brain, expressed as the influx constant Ki . Here evidence is provided that Ki as measured by DCE-MRI behaves as expected for a marker of overall BBB leakage. These results support the use of DCE-MRI for in vivo studies of human BBB permeability in health and disease.

ABSTRACT

Blood-brain barrier (BBB) leakage can be measured using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as the influx constant Ki . To validate this method we compared measured Ki with biological expectations, namely (1) higher Ki in healthy individual grey matter (GM) versus white matter (WM), (2) GM/WM cerebral blood volume (CBV) ratio close to the histologically established GM/WM vascular density ratio, (3) higher Ki in visibly enhancing multiple sclerosis (MS) lesions versus MS normal appearing white matter (NAWM), and (4) higher Ki in MS NAWM versus healthy individual NAWM. We recruited 13 healthy individuals and 12 patients with MS and performed whole-brain 3D DCE-MRI at 3 T. Ki and CBV were calculated using Patlak modelling for manual regions of interest (ROI) and segmented tissue masks. Ki was higher in control GM versus WM (P = 0.001). CBV was higher in GM versus WM (P = 0.005, mean ratio 1.9). Ki was higher in visibly enhancing MS lesions versus MS NAWM (P = 0.002), and in MS NAWM versus controls (P = 0.014). Bland-Altman analysis showed no significant difference between ROI and segmentation methods (P = 0.638) and an intra-class correlation coefficient showed moderate single measure consistency (0.610). Ki behaves as expected for a compound marker of permeability and surface area. The GM/WM CBV ratio measured by this technique is in agreement with the literature. This adds evidence to the validity of Ki measured by DCE-MRI as a marker of overall BBB leakage.

An extended reference region model for DCE-MRI that accounts for plasma volume

The reference region model (RRM) for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides pharmacokinetic parameters without requiring the arterial input function. A limitation of the RRM is that it assumes that the blood plasma volume in the tissue of interest is zero, but this is often not true in highly vascularized tissues, such as some tumours. This study proposes an extended reference region model (ERRM) to account for tissue plasma volume. Furthermore, ERRM was combined with a two-fit approach to reduce the number of fitting parameters, and this was named the constrained ERRM (CERRM). The accuracy and precision of RRM, ERRM and CERRM were evaluated in simulations covering a range of parameters, noise and temporal resolutions. These models were also compared with the extended Tofts model (ETM) on in vivo glioblastoma multiforme data. In simulations, RRM overestimated K^trans by over 10% at v_p  = 0.01 under noiseless conditions. In comparison, ERRM and CERRM were both accurate, with CERRM showing better precision when noise was included. On in vivo data, CERRM provided maps that had the highest agreement with ETM, whilst also being robust at temporal resolutions as poor as 30 s. ERRM can provide pharmacokinetic parameters without an arterial input function in tissues with non-negligible v_p where RRM provides inaccurate estimates. The two-fit approach, named CERRM, further improves on the accuracy and precision of ERRM.

Grading of pancreatic neuroendocrine neoplasms using pharmacokinetic parameters derived from dynamic contrast-enhanced MRI

The present study aimed to evaluate the diagnostic efficacy of pharmacokinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in prospective evaluation of pancreatic neuroendocrine neoplasms (pNENs) grading. A total of 25 histologically proven patients with pNENs (30 lesions in total) who underwent DCE-MRI were enrolled. Lesions were divided into G1, G2 neuroendocrine tumor (NET) and G3 NET/neuroendocrine carcinoma (NEC) groups based on their histological findings according to 2017 World Health Organization Neuroendocrine Tumor Classification Guideline. In addition, the same numbers of tumor-free regions were selected using as normal control group. For each group, pharmacokinetic DCE parameters: volume transfer constant (K^trans); contrast transfer rate constant (k_ep); extravascular extracellular space volume fraction (v_e); and plasma volume fraction (v_p) were calculated with Extended Tofts Linear model. Receiver operator characteristics analysis was conducted to assess the diagnostic efficacy of these parameters in pNENs grading. There were significant differences of K^trans, k_ep, v_e and v_p between tumor-free areas and G1, G2 NET (P<0.001). The K^trans and k_ep of G1 NET were significantly lower compared with those of G2 ones (P<0.005). The area under the curve of K^trans and k_ep in differentiating G2 from G1 NET were 0.767 and 0.846, respectively. When K^trans was >0.667 and k_ep >1.644, the sensitivity of diagnosing G2 NET was the lowest (53.85%), but the specificity was the highest (93.75%). When K^trans was >0.667 or k_ep >1.644, the sensitivity of diagnosing G2 NET was 92.31%, but the specificity was 75.00%. Pharmacokinetic parameters of DCE-MRI, particularly the quantitative values of K^trans and k_ep, are helpful for differentiating G2 NET from G1 ones.

Differentiation of High-Grade from Low-Grade Astrocytoma: Improvement in Diagnostic Accuracy and Reliability of Pharmacokinetic Parameters from DCE MR Imaging by Using Arterial Input Functions Obtained from DSC MR Imaging

Purpose To evaluate whether arterial input functions (AIFs) derived from dynamic susceptibility-contrast (DSC) magnetic resonance (MR) imaging, or AIF_DSC values, improve diagnostic accuracy and reliability of the pharmacokinetic (PK) parameters of dynamic contrast material-enhanced (DCE) MR imaging for differentiating high-grade from low-grade astrocytomas, compared with AIFs obtained from DCE MR imaging (AIF_DCE). Materials and Methods This retrospective study included 226 patients (138 men, 88 women; mean age, 52.27 years ± 15.17; range, 24-84 years) with pathologically confirmed astrocytomas (World Health Organization grade II = 21, III = 53, IV = 152; isocitrate dehydrogenase mutant, 11.95% [27 of 226]; 1p19q codeletion 0% [0 of 226]). All patients underwent both DSC and DCE MR imaging before surgery, and AIF_DSC and AIF_DCE were obtained from each image. Volume transfer constant (K^trans), volume of vascular plasma space (v_p), and volume of extravascular extracellular space (v_e) were processed by using postprocessing software with two AIFs. The diagnostic accuracies of individual parameters were compared by using receiver operating characteristic curve (ROC) analysis. Intraclass correlation coefficients (ICCs) and the Bland-Altman method were used to assess reliability. Results The AIF_DSC-driven mean K^trans and v_e were more accurate for differentiating high-grade from low-grade astrocytoma than those derived by using AIF_DCE (area under the ROC curve: mean K^trans, 0.796 vs 0.645, P = .038; mean v_e, 0.794 vs 0.658, P = .020). All three parameters had better ICCs with AIF_DSC than with AIF_DCE (K^trans, 0.737 vs 0.095; v_p, 0.848 vs 0.728; v_e, 0.875 vs 0.581, respectively). In AIF analysis, maximal signal intensity (0.837 vs 0.524) and wash-in slope (0.800 vs 0.432) demonstrated better ICCs with AIF_DSC than AIF_DCE. Conclusion AIF_DSC-driven DCE MR imaging PK parameters showed better diagnostic accuracy and reliability for differentiating high-grade from low-grade astrocytoma than those derived from AIF_DCE. ^© RSNA, 2017 Online supplemental material is available for this article.

Quantitative analysis of permeability for glioma grading using dynamic contrast-enhanced magnetic resonance imaging

The objective of the present study was to quantitatively analyze the permeability of tumor entity and peritumor edema in glioma grading, using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). In the present retrospective study, 80 patients underwent T1-weighted DCE-MRI examination at 3.0 T and the pathological results (including astrocytoma and oligodendroglioma) were obtained between January 2012 and June 2015. All cases were surgically validated as grade I–IV gliomas. The original DCE-MRI data were analyzed using dual compartment modified Tofts model. The forward volume transfer constant (K^trans), backflux rate (k_ep) and fractional volume (v_e) were calculated with the region of interest selected on the highest permeability area of the tumor entity and peritumor edema. Analysis of variance with the Bonferroni correction was used to compare the values of K^trans, k_ep, and v_e of the tumor entity and peritumor edema in different glioma grades. The results of the present study revealed that the K^trans, k_ep, and v_e values in each stage were associated with the pathological grading (r=0.951, 0.804 and 0.766, respectively). There were significant differences identified between different tumor grades in K^trans, k_ep, with the exception being between grades II and III in k_ep. In addition, there was a significant difference revealed between grade I/II and grade III/IV in v_e. Receiver operator characteristics curve analysis was used to evaluate the diagnosis accuracies of permeability parameters. K^trans was demonstrated to exhibit the highest sensitivity and specificity for evaluating the tumor grade. With the threshold values of 0.160, 0.420 and 0.935 in K^trans on tumor, glioma grades I vs. II, II vs III and III vs. IV may be differentiated with sensitivities of 0.900, 0.950 and 0.950, and specificities of 0.950, 0.950 and 0.850, respectively. Furthermore, associations were observed between the K^trans, k_ep and v_e values of peritumor edema and the pathological grading in glioma (K^trans r=0.438, P<0.001; K_ep r=0.385, P<0.001; V_e r=0.397, P<0.001, respectively). K^trans values in peritumoral edema revealed significant differences between low-grade and high-grade glioma. The sensitivity and specificity for K^trans of peritumor edema were 0.975 and 0.950, with a threshold value of 0.007. Therefore, the DCE-MRI parameters of K^trans of tumor entity and peritumor edema in gliomas may be used to accurately differentiate glioma grades.

Radiomic Phenotyping in Brain Cancer to Unravel Hidden Information in Medical Images

Radiomics is a new area of research in the field of imaging with tremendous potential to unravel the hidden information in digital images. The scope of radiology has grown exponentially over the last two decades; since the advent of radiomics, many quantitative imaging features can now be extracted from medical images through high-throughput computing, and these can be converted into mineable data that can help in linking imaging phenotypes with clinical data, genomics, proteomics, and other "omics" information. In cancer, radiomic imaging analysis aims at extracting imaging features embedded in the imaging data, which can act as a guide in the disease or cancer diagnosis, staging and planning interventions for treating patients, monitor patients on therapy, predict treatment response, and determine patient outcomes.

Can Dynamic Contrast-Enhanced MRI (DCE-MRI) and Diffusion-Weighted MRI (DW-MRI) Evaluate Inflammation Disease: A Preliminary Study of Crohn's Disease

The aim of the study was to investigate diagnosis efficacy of dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DW-MRI) in Crohn's disease (CD). To find out the correlations between functional MRI parameters including K, Kep, Ve, Vp, and apparent diffusion coefficient (ADC) with a serologic biomarker. The relationships between pharmacokinetic parameters and ADC were also studied.Thirty-two patients with CD (22 men, 10 women; mean age: 30.5 years) and 18 healthy volunteers without any inflammatory disease (10 men, 8 women; mean age, 34.11 years) were enrolled into this approved prospective study. Pearson analysis was used to evaluate the correlation between K, Kep, Ve, Vp, and C-reactive protein (CRP), ADC, and CRP respectively. The diagnostic efficacy of the functional MRI parameters in terms of sensitivity and specificity were analyzed by receiver operating characteristic (ROC) curve analyses. Optimal cut-off values of each functional MRI parameters for differentiation of inflammatory from normal bowel were determined according to the Youden criterion.Mean value of K in the CD group was significantly higher than that of normal control group. Similar results were observed for Kep and Ve. On the contrary, the ADC value was lower in the CD group than that in the control group. K and Ve were shown to be correlated with CRP (r = 0.725, P < 0.001; r = 0.533, P = 0.002), meanwhile ADC showed negative correlation with CRP (r = -0.630, P < 0.001). There were negative correlations between the pharmacokinetic parameters and ADC, such as K to ADC (r = -0.856, P < 0.001), and Ve to ADC (r = -0.451, P = 0.01). The area under the curve (AUC) was 0.994 for K (P < 0.001), 0.905 for ADC (P < 0.001), 0.806 for Ve (P < 0.001), and 0.764 for Kep (P = 0.002). The cut-off point of the K was found to be 0.931 min. This value provided the best trade-off between sensitivity (93.8%) and specificity (100%). The best cut-off point of ADC was 1.11 × 10 mm/s. At this level, sensitivity was 100% and specificity was 68.8%.DCE-MRI and DW-MRI were helpful in the diagnosis of CD. Quantitative MRI parameters could be used to assess the severity of inflammation. The relationships between pharmacokinetic parameters (K and Ve) and ADC reflected microstructure and microcirculation of CD to some extent.