Candidate biomarkers of extravascular extracellular space: a direct comparison of apparent diffusion coefficient and dynamic contrast-enhanced MR imaging--derived measurement of the volume of the extravascular extracellular space in glioblastoma multiforme

MR Imaging Techniques for Microenvironment Mapping of the Glioma Tumors: A Systematic Review

RATIONALE AND OBJECTIVES

The tumor microenvironment (TME) is a critical regulator of cancer progression, metastasis, and treatment response. Currently, various imaging approaches exist to assess the pathophysiological features of the TME. This systematic review provides an overview of magnetic resonance imaging (MRI) methods used in clinical practice to characterize the pathophysiological features of the gliomas TME.

METHODS

This review involved a systematic comprehensive search of original open-access articles reporting the clinical use of MR imaging in glioma patients of all ages in the PubMed, Scopus, and Web of Science databases between January 2010 and December 2023. We restricted our research to papers published in the English language.

RESULTS

A total of 1137 studies were preliminarily identified through electronic database searches. After duplicate studies were removed, 44 studies met the eligibility criteria. The glioma TME was accompanied by alterations in metabolism, pH, vascularity, oxygenation, and extracellular matrix components, including tumor-associated macrophages, and sodium concentration.

CONCLUSION

Multiparametric MRI is capable of noninvasively assessing the pathophysiological features and tumor-supportive niches of the TME, which is in line with its application in personalized medicine.

Dynamic contrast-enhanced and diffusion-weighted MRI of cervical carcinoma: Correlations with Ki-67 proliferation status

OBJECTIVES

To investigate the association of quantitative parameter (apparent diffusion coefficient [ADC]) from diffusion-weighted imaging (DWI) and various quantitative and semiquantitative parameters from dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) with Ki-67 proliferation index (PI) in cervical carcinoma (CC).

METHODS

A total of 102 individuals with CC who received 3.0 T MRI examination (DWI and DCE MRI) between October 2016 and December 2022 were enrolled in our investigation. Two radiologists separately assessed the ADC parameter and various quantitative and semiquantitative parameters including (volume transfer constant [Ktrans], rate constant [kep], extravascular extracellular space volume fraction [ve], volume fraction of plasma [vp], time to peak [TTP], maximum concentration [MaxCon], maximal slope [MaxSlope] and area under curve [AUC]) for each tumor. Their association with Ki-67 PI was analyzed by Spearman association analysis. The discrepancy between low-proliferation and high-proliferation groups was subsequently analyzed. The receiver operating characteristic (ROC) curve analysis utilized to identify optimal cut-off points for significant parameters.

RESULTS

Both ADC (ρ = -0.457, p < 0.001) and Ktrans (ρ = -0.467, p < 0.001) indicated a strong negative association with Ki-67 PI. Ki-67 PI showed positive correlations with TTP, MaxCon, MaxSlope and AUC (ρ = 0.202, 0.231, 0.309, 0.235, respectively; all p values<0.05). Compared with the low-proliferation group, high-Ki-67 group presented a significantly lower ADC (0.869 ± 0.125 × 10-3 mm2/s vs. 1.149 ± 0.318 × 10-3 mm2/s; p < 0.001) and Ktrans (1.314 ± 1.162 min-1vs. 0.391 ± 0.390 min-1; p < 0.001), also significantly higher MaxCon values (0.756 ± 0.959 vs. 0.422 ± 0.341; p < 0.05) and AUC values (2.373 ± 3.012 vs. 1.273 ± 1.000; p < 0.05). The cut-offs of ADC, Ktrans, MaxCon and AUC for discrimating low- and high-Ki-67 groups were 0.920 × 10-3 mm2/s, 0.304 min-1, 0.209 and 1.918, respectively.

CONCLUSIONS

ADC, Ktrans, TTP, MaxCon, MaxSlope and AUC are associated with Ki-67 PI. ADC and Ktrans exhibited high performance to discriminate low and high Ki-67 status of CC.

DA, K. KK. Diagnostic Utility of Integration of Dynamic Contrast-Enhanced and Dynamic Susceptibility Contrast MR Perfusion Employing Split Bolus Technique in Differentiating High-Grade Glioma

Background : Despite documented correlation between glioma grades and dynamic contrast-enhanced (DCE) magnetic resonance (MR) perfusion-derived parameters, and its inherent advantages over dynamic susceptibility contrast (DSC) perfusion, the former remains underutilized in clinical practice. Given the inherent spatial heterogeneity in high-grade diffuse glioma (HGG) and assessment of different perfusion parameters by DCE (extravascular extracellular space volume [Ve] and volume transfer constant in unit time [k-trans]) and DSC (rCBV), integration of the two into a protocol could provide a holistic assessment. Considering therapeutic and prognostic implications of differentiating WHO grade 3 from 4, we analyzed the two grades based on a combined DCE and DSC perfusion. Methods : Perfusion sequences were performed on 3-T MR. Cumulative dose of 0.1 mmol/kg of gadodiamide, split into two equal boluses, was administered with an interval of 6 minutes between the DCE and DSC sequences. DCE data were analyzed utilizing commercially available GenIQ software. Results : Of the 41 cases of diffuse gliomas analyzed, 24 were WHO grade III and 17 grade IV gliomas (2016 WHO classification). To differentiate grade III and IV gliomas, Ve cut-off value of 0.178 provided the best combination of sensitivity (88.24%) and specificity (87.50%; AUC: 0.920; p  < 0.001). A relative cerebral blood volume (rCBV) of value 3.64 yielded a sensitivity of 70.59% and specificity of 62.50% ( p  = 0.018). The k-trans value, although higher in grade III than in grade IV gliomas, did not reach statistical significance ( p  = 0.108). Conclusion : Uniqueness of employed combined perfusion technique, treatment naïve patients at imaging, user-friendly postprocessing software utilization, and ability of Ve and rCBV to differentiate between grade III and IV gliomas ( p  < 0.05) are the strengths of the present study, contributing to the existing literature and moving a step closer to achieving accurate MR perfusion-based glioma grading.

Influence of scan duration on dynamic contrast -enhanced magnetic resonance imaging pharmacokinetic parameters for brain lesions

OBJECTIVE

Gadolinium-based contrast agent needs time to leak into the extravascular-extracellular space, leak back into the vascular space, and reach an equilibrium state. For this reason, acquisition times of <10 min may cause inaccurate estimation of pharmacokinetic parameters. Since no studies have been conducted on the influence of long scan times on DCE-MRI parameters in brain tumors, the aim of this study is to investigate the variation of DCE-MRI-derived kinetic parameters as a function of acquisition time, from 5 to 10 min in brain tumors.

MATERIALS AND METHODS

Fifty-two patients with histologically confirmed brain tumors were enrolled in this retrospective study, and examination at 3 T, DCE-MRI, with scan duration of 10 min, was used for retrospective generation of 6 sets of quantitative DCE-MRI maps (Ktrans, Ve and Kep) from 5 to 10 min. Features were extracted from the DCE-MRI maps in contrast enhancement (CE) volumes. Kruskal-Wallis with post-hoc correction and coefficient of variation (CoV) were used as statistical test to compare DCE-MRI maps obtained from 6 data sets.

SIGNIFICANCE

p < 0.05.

RESULTS

No differences in Ktrans features in CE volumes between different scan durations. Ve, Kep features in CE volumes were influenced by different data length. The highest number of significantly different Ve and Kep features in CE volumes were between 5 min and 10 min (p < 0.013), 5 min and 9 min (p < 0.044), 6 min and 10 min (p < 0.040). CoV of Kep was reduced from 5 min to 10 min, going from highly variable (CoV = 0.70) to mildly variable (CoV = 0.42).

CONCLUSION

Kep and Ve were time-dependent in brain tumors, so a longer scan time is needed to obtain reliable parameter values. Ktrans was found to be time-independent, as it remains the same in all 6 acquisition times and is the only reliable parameter with short acquisition times.

A numerical human brain phantom for dynamic glucose-enhanced (DGE) MRI: On the influence of head motion at 3T

PURPOSE

Dynamic glucose-enhanced (DGE) MRI relates to a group of exchange-based MRI techniques where the uptake of glucose analogues is studied dynamically. However, motion artifacts can be mistaken for true DGE effects, while motion correction may alter true signal effects. The aim was to design a numerical human brain phantom to simulate a realistic DGE MRI protocol at 3T that can be used to assess the influence of head movement on the signal before and after retrospective motion correction.

METHODS

MPRAGE data from a tumor patient were used to simulate dynamic Z-spectra under the influence of motion. The DGE responses for different tissue types were simulated, creating a ground truth. Rigid head movement patterns were applied as well as physiological dilatation and pulsation of the lateral ventricles and head-motion-induced B0 -changes in presence of first-order shimming. The effect of retrospective motion correction was evaluated.

RESULTS

Motion artifacts similar to those previously reported for in vivo DGE data could be reproduced. Head movement of 1 mm translation and 1.5 degrees rotation led to a pseudo-DGE effect on the order of 1% signal change. B0 effects due to head motion altered DGE changes due to a shift in the water saturation spectrum. Pseudo DGE effects were partly reduced or enhanced by rigid motion correction depending on tissue location.

CONCLUSION

DGE MRI studies can be corrupted by motion artifacts. Designing post-processing methods using retrospective motion correction including B0 correction will be crucial for clinical implementation. The proposed phantom should be useful for evaluation and optimization of such techniques.

A three-component multi-b-value diffusion-weighted imaging might be a useful biomarker for detecting microstructural features in gliomas with differences in malignancy and IDH-1 mutation status

OBJECTIVES

The purpose of the study was to explore the performance of a three-component diffusion model in evaluating the degree of malignancy and isocitrate dehydrogenase 1 (IDH-1) gene type of gliomas.

METHODS

Overall, 60 patients with gliomas were enrolled. The intermediate and perfusion-related diffusion coefficients (D_int and D_p) and fractions of strictly limited, intermediate, and perfusion-related diffusion (F_very-slow, F_int, and F_p) were obtained with a three-component diffusion model. Parameters were also obtained from a diffusion kurtosis model and mono- and biexponential models. All parameters were compared between different tumor grades and IDH-1 gene types. Diagnostic performance and logistic regression analyses were performed.

RESULTS

High-grade gliomas (HGGs) had significantly higher F_int, F_very-slow, and D_p values but significantly lower F_p and D_int values than low-grade gliomas (LGGs), and F_int and F_p differed significantly among grade II, III, and IV gliomas (p < 0.05 for all). F_int achieved the highest AUC of 0.872 in differentiating between LGGs and HGGs. Logistic regression analysis revealed that in each model, F_int, diffusion coefficient (D), apparent diffusion coefficient (ADC), mean diffusivity (MD), and mean kurtosis (MK) were associated with glioma grading. After multiple regression analysis, F_int remained the only differentiator. Additionally, F_int and F_p showed significant differences between IDH-1 mutated and IDH-1 wild-type gliomas (p = 0.007 and 0.01, respectively).

CONCLUSIONS

The three-component DWI model served as a useful biomarker for detecting microstructural features in gliomas with different grades and IDH-1 mutation statuses.

KEY POINTS

• The three-component model enables the estimation of an intermediate diffusion component. • The three-component model performed better than the other models in glioma grading and genotyping. • F_int was useful in evaluating the grade and genotype of gliomas.

Diagnostic Performance of Dynamic Contrast-Enhanced MRI and 18F-FDG PET/CT for Evaluation of Soft Tissue Tumors and Correlation with Pathology Parameters

RATIONALE AND OBJECTIVES

To assess the diagnostic performance of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and fluorine-18-fluorodeoxyglucose (¹⁸F-FDG) positron-emission tomography/computed tomography (PET/CT) parameters in evaluating the biological behavior of soft tissue tumors.

MATERIALS AND METHODS

We retrospectively analyzed DCE-MRI and ¹⁸F-FDG PET/CT parameters in 78 patients with pathology-confirmed soft tissue tumors. A total of 78 patients had undergone DCE-MRI examination, while 24 patients with malignant soft tissue tumor had undergone ¹⁸F-FDG PET/CT examination. Microvessel density (MVD) and the Ki-67 labeling index (LI) were detected using immunohistochemistry. Differences in parameters (Ktrans, Kep, Ve, MVD, and Ki-67 LI) between benign and malignant tumors were compared. Differences in parameters (Ktrans, Kep, Ve, MVD, and SUV_max) between high- and low-proliferation malignant tumors (grouped by Ki-67 LI) were compared. Correlation of the DCE-MRI and ¹⁸F-FDG PET/CT parameters with MVD and Ki-67 LI was analyzed.

RESULTS

Only the Ktrans, Kep, MVD, and Ki-67 LI differed significantly between the benign and malignant soft tissue tumors (all p < 0.001). Only Kep (p = 0.033) and SUV_max (p = 0.001) differed significantly between high- and low-proliferation malignant soft tissue tumors. Ktrans, Kep, and SUV_max correlated positively with MVD (r = 0.805, 0.778, 0.730, respectively; all p < 0.001), and with Ki-67 LI (r = 0.721, 0.685, 0.655, respectively; all p < 0.001).

CONCLUSION

DCE-MRI and ¹⁸F-FDG PET/CT parameters indicate soft tissue tumor biological behavior and can be used to differentiate between benign and malignant soft tissue tumors and between high- and low-proliferation malignant soft tissue tumors.

Contrast Pediatric Brain Perfusion: Dynamic Susceptibility Contrast and Dynamic Contrast-Enhanced MR Imaging

Magnetic resonance (MR) perfusion is a robust imaging technique that assesses the passage of blood through the cerebral vascular network using a variety of techniques. The applications of MR perfusion have been expanded and is well suited to investigate cerebrovascular diseases and cerebral neoplastic processes in pediatric patients. Assessment of brain perfusion can augment the information obtained on conventional MR imaging and provides additional information on the biological and physiologic features of pediatric brain tumors. Similarly, MR perfusion can help guide the management of a variety of pediatric cerebrovascular diseases, including acute ischemic stroke and Moyamoya syndrome.

Diffusion-weighted and dynamic contrast-enhanced magnetic resonance imaging after radiation therapy for bone metastases in patients with hepatocellular carcinoma

The objectives of this study were to assess changes in apparent diffusion coefficient (ADC) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) parameters after radiation therapy (RT) for bone metastases from hepatocellular carcinoma (HCC) and to evaluate their prognostic value. This prospective study was approved by the Institutional Review Board. Fourteen patients with HCC underwent RT (30 Gy in 10 fractions once daily) for bone metastases. The ADC and DCE-MRI parameters and the volume of the target lesions were measured before (baseline) and one month after RT (post-RT). The Wilcoxon signed-rank test was used to compare the parameters between the baseline and post-RT MRI. The parameters were compared between patients with or without disease progression in RT fields using the Mann–Whitney test. Intraclass correlation coefficients were used to evaluate the interobserver agreement. The medians of the ADC, rate constant [k_ep], and volume fraction of the extravascular extracellular matrix [v_e] in the baseline and post-RT MRI were 0.67 (range 0.61–0.72) and 0.75 (range 0.63–1.43) (× 10^–3 mm²/s) (P = 0.027), 836.33 (range 301.41–1082.32) and 335.80 (range 21.86–741.87) (× 10^–3/min) (P = 0.002), and 161.54 (range 128.38–410.13) and 273.99 (range 181.39–1216.95) (× 10^–3) (P = 0.027), respectively. The medians of the percent change in the ADC of post-RT MRI in patients with progressive disease and patients without progressive disease were − 1.35 (range − 6.16 to 6.79) and + 46.71 (range 7.71–112.81) (%) (P = 0.011), respectively. The interobserver agreements for all MRI parameters were excellent (intraclass correlation coefficients > 0.8). In conclusion, the ADC, k_ep, and v_e of bone metastases changed significantly after RT. The percentage change in the ADC was closely related to local tumor progression.

Soft tissue sarcoma: can dynamic contrast-enhanced (DCE) MRI be used to predict the histological grade?

OBJECTIVE

To determine if dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) parameters reflect histological grade of soft tissue sarcoma (STS) MATERIALS AND METHODS: The medical records of 50 patients diagnosed with pathologically confirmed STS were retrospectively reviewed. Each STS was assessed with conventional contrast-enhanced MRI and DCE-MRI using a 3.0-T MRI system. The conventional MRI characteristics of low-grade (grade 1) and high-grade (grade 2 and grade 3) tumors were analyzed. Semi-quantitative parameters, including iAUC and TTP, and quantitative parameters, including K^trans, K_ep, and V_e, were derived from DCE-MRI. The diagnostic performances and optimal thresholds of various combinations of DCE-MRI parameters for predicting histological grades of STS were investigated using receiver operator characteristic (ROC) curves.

RESULTS

On conventional MRI, high-grade STSs were significantly larger (≥ 5 cm) and more likely to show a heterogeneous signal intensity on T₂WI (> 75%), peritumoral hyperintensity on T₂WI, or tumor necrosis (> 50%) compared with low-grade STS. On DCE-MRI, iAUC, TTP, K^trans, and K_ep were significant predictors of STS histological grade. K^trans had a high diagnostic value for differentiating between high-grade and low-grade STSs. The combination of iAUC, TTP, and K^trans yielded a higher AUC value (0.841) than the other models.

CONCLUSION

High-grade STSs were usually larger than low-grade STSs, had unclear boundaries, a heterogeneous signal intensity on T2-weighted image (T₂WI), and extensive necrosis. On DCE-MRI, iAUC, TTP, K^trans, and K_ep could differentiate between high-grade and low-grade STSs. The combination of iAUC, TTP, and K^trans had a high diagnostic performance for differentiating between STS histological grades.

Quantitative perfusion parameters of benign inflammatory breast pathologies: A descriptive study

PURPOSE

With this study, we evaluated the perfusion magnetic resonance imaging (MRI) features of benign inflammatory breast lesions for the first time and compared their K_trans, K_ep, V_e values and contrast kinetic curves to benign masses and invasive ductal carcinoma (IDC).

MATERIALS AND METHODS

Perfusion MRIs of the benign masses (n = 42), inflammatory lesions (n = 25), and IDCs (n = 16) were evaluated retrospectively in terms of K_trans, K_ep, V_e values and contrast kinetic curves and compared by the Kruskal-Wallis, Mann-Whitney U, chi-square tests statistically. Cronbach α test was used to measure intraobserver and interobserver reliability.

RESULTS

Mean K_trans values were 0.052 for benign masses, 0.086 for inflammatory lesions and 0.101 for IDC (p < 0.001). Mean K_ep values were 0.241 for benign masses, 0.435 for inflammatory lesions and 0.530 for IDC (p < 0.001). Mean V_e values were 0.476 for benign masses, 0.318 for inflammatory lesions and 0.310 for IDC (p = 0.067). For inflammatory and IDC lesions, K_trans and K_ep values were found to be higher and V_e values were lower than benign masses (p = 0.001 for K_trans, p = 0.001 for K_ep, p = 0.045 for V_e). There were excellent or good intra-interobserver reliabilities. For the kinetic curve pattern, most of the benign lesions showed progressive (81%), inflammatory lesions progressive (64%) and IDC lesions plateau (75%) patterns (p < 0.001).

CONCLUSIONS

On T1 perfusion MRI, similar to IDC lesions, inflammatory lesions demonstrate higher K_trans and K_ep and lower V_e values than benign masses. Quantitative perfusion parameters are not helpful in differentiating them from IDC lesions.

Value of quantitative multiparametric MRI in differentiating pleomorphic adenomas from malignant epithelial tumors in lacrimal gland

PURPOSE

To evaluate the diagnostic performance of the quantitative parameters derived from diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI in differentiating lacrimal gland pleomorphic adenomas (LGPAs) from lacrimal gland malignant epithelial tumors (LGMETs).

METHODS

Seventy-seven cases with LG epithelial tumors confirmed by histopathology (47 LGPAs and 30 LGMETs) underwent DWI and DCE-MRI. The quantitative parameters including the apparent diffusion coefficient (ADC), the volume transfer constant (K^trans), the efflux rate constant from the extravascular extracellular space (EES) to blood plasma (K_ep), and the extravascular extracellular volume fraction (V_e) were used to differentiate LGPAs from LGMETs. Independent-samples t test was conducted to compare these parameters. The diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

Compared with LGPAs, LGMETs had significantly lower ADC value (1.090 ± 0.169mm²/s) (P < 0.001), higher K^trans value (0.892 ± 0.517/min) (P = 0.001), and K_ep value (1.300 ± 1.131/min) (P = 0.002). ADC as a diagnostic index showed a better diagnostic efficacy in predicting malignant tumors (AUC 0.914, sensitivity 90.0%, specificity 85.1%, and accuracy 87.0%) than K^trans and K_ep alone. The combination of ADC and K^trans presented the optimal diagnostic performance for the differentiation (AUC 0.938, sensitivity 93.3%, specificity 87.2%, accuracy 89.6%).

CONCLUSION

The quantitative parameters including ADC, K^trans, and K_ep derived from DWI and DCE-MRI might be potential imaging biomarkers in differentiating LGPAs from LGMETs. The combination of ADC and K^trans is superior to other quantitative parameters in distinguishing LGPAs from LGMETs.

Clinical MR Biomarkers

Non-invasive magnetic resonance imaging (MRI) techniques are increasingly applied in the clinic with a fast growing body of evidence regarding its value for clinical decision making. In contrast to biochemical or histological markers, the key advantages of imaging biomarkers are the non-invasive nature and the spatial and temporal resolution of these approaches. The following chapter focuses on clinical applications of novel MR biomarkers in humans with a strong focus on oncologic diseases. These include both clinically established biomarkers (part 1-4) and novel MRI techniques that recently demonstrated high potential for clinical utility (part 5-7).

Soft tissue sarcoma: DWI and DCE-MRI parameters correlate with Ki-67 labeling index

OBJECTIVES

To examine the correlation of diffusion-weighted and dynamic contrast-enhanced magnetic resonance imaging (MRI) parameters with Ki-67 labeling index (LI) in soft tissue sarcoma (STS).

METHODS

The institutional review board approved this retrospective study, and the requirement for informed consent was waived. Thirty-six patients with STS who underwent 3.0-T MRI, including diffusion-weighted and dynamic contrast-enhanced MRI, between July 2011 and February 2018, were included in this study. The mean and minimum apparent diffusion coefficients (ADCs) (ADC_mean and ADC_min, respectively), volume transfer constant, reflux rate, and volume fraction of the extravascular extracellular matrix of each lesion were independently analyzed by two readers. Their relationship with the Ki-67 LI was examined using Spearman's correlation analyses. Differences between low- and high-proliferation groups based on Ki-67 LI were evaluated statistically. Optimal cut-off points were determined using the area under the curve analysis for significant parameters. Interobserver agreement was assessed with the intraclass correlation coefficient.

RESULTS

ADC_mean (ρ = - 0.333, p = 0.047) was significantly and inversely correlated with Ki-67 LI. The high-proliferation group showed a significantly lower ADC_mean than did the low-proliferation group (median, 1.08 vs. 1.20; p = 0.048). When a cut-off ADC_mean value of 1.16 × 10^-3 mm²/s was used, the sensitivity, specificity, and area under the curve for differentiating low- and high-proliferation groups were 75.0%, 60.0%, and 0.712, respectively. Interobserver agreements between the two readers were almost perfect for all parameters.

CONCLUSIONS

ADC_mean was correlated with Ki-67 LI and could help differentiate between STS with low and high proliferation potential.

KEY POINTS

• ADC _meanwas significantly and inversely correlated with Ki-67 labeling index in soft tissue sarcoma. • In the high-proliferation group, ADC _meanvalues were significantly lower than those of the low-proliferation group.

Beyond T2 and 3T: New MRI techniques for clinicians

Technological advances in Magnetic Resonance Imaging (MRI) in terms of field strength and hybrid MR systems have led to improvements in tumor imaging in terms of anatomy and functionality. This review paper discusses the applications of such advances in the field of radiation oncology with regards to treatment planning, therapy guidance and monitoring tumor response and predicting outcome.

Comparison between MRI-derived ADC maps and 18FLT-PET in pre-operative glioblastoma

BACKGROUND AND PURPOSE

Among principal MRI sequences used for a better pre-therapeutic characterization of glioblastoma (GBM), DWI-derived ADC is expected to be a good parameter for the evaluation of cellularity, due to restricted water diffusivity. We aimed here to compare ADC maps to ¹⁸FLT-PET, a proliferation tracer, in GBM cases.

MATERIALS AND METHODS

Patients underwent ¹⁸FLT-PET, followed by multiparametric magnetic resonance imaging (MRI) just prior to surgery. We analysed in this study twenty GBM confirmed patients. The 5th percentile (5p) of the ADC values were thresholded to define the ADC_min ROI, while the 95th percentile (95p) of the SUV FLT values were used to define the FLT_max ROI. The statistical and spatial correlations between these two groups of ROIs were analyzed.

RESULTS

We did not observe any significant correlations between ADC_min and FLT_max cut-off values (R²=0.0285), neither between ADC_min and FLT_max ROIs (mean Dice=0.09±0.12). Mean ADC values in the FLT_max defined ROI were significantly higher than the values in the ADC_min ROI (P<0.001). Mean FLT values in the FLT_max ROI were significantly higher than the values in the ADC_min ROI (P<0.001).

CONCLUSIONS

When comparing ADC maps to ¹⁸FLT uptake, we did not observe significant anatomical overlap nor correlation, between the regions of low ADC and high FLT disabling to clearly link ADC values to cellular proliferation. The exact significance of ADC maps in GBM has yet to be elaborated.

Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials

Physiological properties of tumors can be measured both in vivo and noninvasively by diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging. Although these techniques have been used for more than two decades to study tumor diffusion, perfusion, and/or permeability, the methods and studies on how to reduce measurement error and bias in the derived imaging metrics is still lacking in the literature. This is of paramount importance because the objective is to translate these quantitative imaging biomarkers (QIBs) into clinical trials, and ultimately in clinical practice. Standardization of the image acquisition using appropriate phantoms is the first step from a technical performance standpoint. The next step is to assess whether the imaging metrics have clinical value and meet the requirements for being a QIB as defined by the Radiological Society of North America's Quantitative Imaging Biomarkers Alliance (QIBA). The goal and mission of QIBA and the National Cancer Institute Quantitative Imaging Network (QIN) initiatives are to provide technical performance standards (QIBA profiles) and QIN tools for producing reliable QIBs for use in the clinical imaging community. Some of QIBA's development of quantitative diffusion-weighted imaging and dynamic contrast-enhanced QIB profiles has been hampered by the lack of literature for repeatability and reproducibility of the derived QIBs. The available research on this topic is scant and is not in sync with improvements or upgrades in MRI technology over the years. This review focuses on the need for QIBs in oncology applications and emphasizes the importance of the assessment of their reproducibility and repeatability. Level of Evidence: 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;49:e101-e121.

Unified platform for multimodal voxel-based analysis to evaluate tumour perfusion and diffusion characteristics before and after radiation treatment evaluated in metastatic brain cancer

OBJECTIVE:

Early changes in tumour behaviour following stereotactic radiosurgery) are potential biomarkers of response. To-date quantitative model-based measures of dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) MRI parameters have shown widely variable findings, which may be attributable to variability in image acquisition, post-processing and analysis. Big data analytic approaches are needed for the automation of computationally intensive modelling calculations for every voxel, independent of observer interpretation.

METHODS:

This unified platform is a voxel-based, multimodality architecture that brings complimentary solute transport processes such as perfusion and diffusion into a common framework. The methodology was tested on synthetic data and digital reference objects and consequently evaluated in patients who underwent volumetric DCE-CT, DCE-MRI and DWI-MRI scans before and after treatment. Three-dimensional pharmacokinetic parameter maps from both modalities were compared as well as the correlation between apparent diffusion coefficient (ADC) values and the extravascular, extracellular volume (Ve). Comparison of histogram parameters was done via Bland-Altman analysis, as well as Student's t-test and Pearson's correlation using two-sided analysis.

RESULTS:

System testing on synthetic Tofts model data and digital reference objects recovered the ground truth parameters with mean relative percent error of 1.07 × 10-7 and 5.60 × 10-4 respectively. Direct voxel-to-voxel Pearson's analysis showed statistically significant correlations between CT and MR which peaked at Day 7 for Ktrans (R = 0.74, p <= 0.0001). Statistically significant correlations were also present between ADC and Ve derived from both DCE-MRI and DCE-CT with highest median correlations found at Day 3 between median ADC and Ve,MRI values (R = 0.6, p < 0.01) The strongest correlation to DCE-CT measurements was found with DCE-MRI analysis using voxelwise T10 maps (R = 0.575, p < 0.001) instead of assigning a fixed T10 value.

CONCLUSION:

The unified implementation of multiparametric transport modelling allowed for more robust and timely observer-independent data analytics. Utility of a common analysis platform has shown higher correlations between pharmacokinetic parameters obtained from different modalities than has previously been reported.

ADVANCES IN KNOWLEDGE:

Utility of a common analysis platform has shown statistically higher correlations between pharmacokinetic parameters obtained from different modalities than has previously been reported.

Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging for differentiating malignant from benign orbital lymphproliferative disorders

BACKGROUND

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been used for assessing orbital lymphoproliferative disorders (OLPDs). However, only the mean values of quantitative parameters were obtained in previous studies and tumor heterogeneity was ignored.

PURPOSE

To assess the value of DCE-MRI derived histogram parameters in differentiating malignant from benign OLPDs.

MATERIAL AND METHODS

Forty-eight OLPDs patients (25 malignant and 23 benign) who had undergone DCE-MRI for pre-treatment evaluation were retrospectively included. Histogram parameters of K^trans, k_ep, and v_e were calculated and compared between two groups using the independent sample's t-test. Receiver operating characteristic (ROC) curve analyses were used to determine the diagnostic value of each significant parameter. Multivariate stepwise logistic regression analysis was used to identify the independent predictors of malignant OLPDs.

RESULTS

Tenth k_ep, mean k_ep, median k_ep, and 90th k_ep were significantly higher in the malignant OLPD group than in the benign OLPD group. Tenth v_e was significantly lower in the malignant OLPD group than in the benign OLPD group. Ninetieth k_ep was the only independent predictor of malignant OLPDs ( P = 0.019), with an area under ROC curve of 0.828, a sensitivity of 92.00%, and a specificity of 78.26% at a cut-off value of 1.057 min^-1.

CONCLUSION

Histogram analysis of DCE-MRI derived parameters may help to differentiate malignant from benign OLPDs. The 90th k_ep hold the potential as an independent predictor for malignant OLPDs.

Dynamic contrast-enhanced magnetic resonance imaging may act as a biomarker for vascular damage in normal appearing brain tissue after radiotherapy in patients with glioblastoma

Background

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising perfusion method and may be useful in evaluating radiation-induced changes in normal-appearing brain tissue.

Purpose

To assess whether radiotherapy induces changes in vascular permeability (K^trans) and the fractional volume of the extravascular extracellular space (V_e) derived from DCE-MRI in normal-appearing brain tissue and possible relationships to radiation dose given.

Material and Methods

Seventeen patients with glioblastoma treated with radiotherapy and chemotherapy were included; five were excluded because of inconsistencies in the radiotherapy protocol or early drop-out. DCE-MRI, contrast-enhanced three-dimensional (3D) T1-weighted (T1W) images and T2-weighted fluid attenuated inversion recovery (T2-FLAIR) images were acquired before and on average 3.3, 30.6, 101.6, and 185.7 days after radiotherapy. Pre-radiotherapy CE T1W and T2-FLAIR images were segmented into white and gray matter, excluding all non-healthy tissue. K^trans and V_e were calculated using the extended Kety model with the Parker population-based arterial input function. Six radiation dose regions were created for each tissue type, based on each patient’s computed tomography-based dose plan. Mean K^trans and V_e were calculated over each dose region and tissue type.

Results

Global K^trans and V_e demonstrated mostly non-significant changes with mean values higher for post-radiotherapy examinations in both gray and white matter compared to pre-radiotherapy. No relationship to radiation dose was found.

Conclusion

Additional studies are needed to validate if K^trans and V_e derived from DCE-MRI may act as potential biomarkers for acute and early-delayed radiation-induced vascular damages. No dose-response relationship was found.

The predictive value of quantitative DCE metrics for immediate therapeutic response of high-intensity focused ultrasound ablation (HIFU) of symptomatic uterine fibroids

PURPOSE

The aim of this study was to investigate the value of quantitative DCE-MRI parameters for predicting the immediate non-perfused volume ratio (NPVR) of HIFU therapy in the treatment of symptomatic uterine fibroids.

MATERIALS AND METHODS

A total of 78 symptomatic uterine fibroids in 65 female patients were treated with US-HIFU therapy. All patients underwent conventional MRI and DCE-MRI scans 1 day before and 3 days after HIFU treatment. Permeability parameters K ^trans, K _ep, V _e, and V _p and T1 perfusion parameters BF and BV of pretreatment were measured as a baseline, while NPVR was used to assess immediate ablation efficiency. Data were assigned to NPVR ≧ 70% and NPVR < 70% groups. Then, the predictive performances of different parameters for ablation efficacy were studied to seek the optimal cut-off value, and the length of time to calculate the variable parameters in each case was recorded.

RESULTS

(1) It was observed that the pretreatment K ^trans, K _ep, V _e, and BF values of the NPVR ≧ 70% group were significantly lower compared to the NPVR < 70% group (p < 0.05). (2) The immediate NPVR was negatively correlated with the K ^trans, BF, and BV values before HIFU treatment (r = - 0.561, - 0.712, and - 0.528, respectively, p < 0.05 for all). (3) The AUCs of pretreatment K ^trans, BF, BV values, and K ^trans combined with BF used to predict the immediate NPVR were 0.810, 0.909, 0.795, and 0.922, respectively (p < 0.05 for all). (4) The mean time to calculate the variable parameters in each case was 7.5 min.

CONCLUSION

Higher K ^trans, BF, and BV values at baseline DCE-MRI suggested a poor ablation efficacy of HIFU therapy for symptomatic uterine fibroids, while the pretreatment DCE-MRI parameters could be useful biomarkers for predicting the ablation efficacy in select cases. The software used to calculate DCE-MRI parameters was simpler, quicker, and easier to incorporate into clinical practice.

Discrimination between benign and malignant breast lesions using volumetric quantitative dynamic contrast-enhanced MR imaging

OBJECTIVE

To determine the diagnostic performance of volumetric quantitative dynamic contrast-enhanced MRI (qDCE-MRI) in differentiation between malignant and benign breast lesions.

METHODS

DCE-MRI was performed in 124 patients with 136 breast lesions. Quantitative pharmacokinetic parameters Ktrans, Kep, Ve, Vp and semi-quantitative parameters TTP, MaxCon, MaxSlope, AUC were obtained by using a two-compartment extended Tofts model and three-dimensional volume of interest. Morphologic features (lesion size, margin, internal enhancement pattern) and time-signal intensity curve (TIC) type were also assessed. Logistic regression analysis was used to determine predictors of malignancy, followed by receiver operating characteristics (ROC) analysis to evaluate the diagnostic performance.

RESULTS

qDCE parameters (Ktrans, Kep, Vp, TTP, MaxCon, MaxSlope and AUC), morphological parameters and TIC type were significantly different between malignant and benign lesions (P≤0.001). Multivariate logistic regression analyses showed that Ktrans, Kep, MaxSlope, size, margin and TIC type were independent predictors of malignancy. The diagnostic accuracy of logistic models based on qDCE parameters alone, morphological features plus TIC type, and all parameters combined was 94.9%, 89.0%, and 95.6% respectively.

CONCLUSION

qDCE-MRI can be used to improve diagnostic differentiation between benign and malignant breast lesions in relation to morphology and kinetic analysis.

KEY POINTS

• qDCE-MRI parameters are useful for discriminating between malignant and benign breast lesions. • K trans , K ep and MaxSlope were independent predictors of breast malignancy. • qDCE-MRI has a better diagnostic ability than morphology and kinetic analysis. • qDCE-MRI can be used to improve the diagnostic accuracy of breast malignancy.

Comparison of Two Mathematical Models of Cellularity Calculation

OBJECT: Nowadays, there is increasing evidence that functional magnetic resonance imaging (MRI) modalities, namely, diffusion-weighted imaging (DWI) and dynamic-contrast enhanced MRI (DCE MRI), can characterize tumor architecture like cellularity and vascularity. Previously, two formulas based on a logistic tumor growth model were proposed to predict tumor cellularity with DWI and DCE. The purpose of this study was to proof these formulas. METHODS: 16 patients with head and neck squamous cell carcinomas were included into the study. There were 2 women and 14 men with a mean age of 57.0 ± 7.5 years. In every case, tumor cellularity was calculated using the proposed formulas by Atuegwu et al. In every case, also tumor cell count was estimated on histopathological specimens as an average cell count per 2 to 5 high-power fields. RESULTS: There was no significant correlation between the calculated cellularity and histopathologically estimated cell count by using the formula based on apparent diffusion coefficient (ADC) values. A moderate positive correlation (r=0.515, P=.041) could be identified by using the formula including ADC and V_e values. CONCLUSIONS: The formula including ADC and V_e values is more sensitive to predict tumor cellularity than the formula including ADC values only.

Multiparametric Analysis of Permeability and ADC Histogram Metrics for Classification of Pediatric Brain Tumors by Tumor Grade

BACKGROUND AND PURPOSE

Accurate tumor grading is essential for treatment planning of pediatric brain tumors. We hypothesized that multiparametric analyses of a combination of permeability metrics and ADC histogram metrics would differentiate high- and low-grade tumors with high accuracy.

MATERIALS AND METHODS

DTI and dynamic contrast-enhanced MR imaging using T1-mapping with flip angles of 2°, 5°, 10°, and 15°, followed by a 0.1-mmol/kg body weight gadolinium-based bolus was performed on all patients in addition to standard MR imaging. Permeability data were processed and transfer constant from the blood plasma into the extracellular extravascular space, rate constant from the extracellular extravascular space back into blood plasma, extravascular extracellular volume fraction, and fractional blood plasma volume were calculated from 3D tumor volumes. Apparent diffusion coefficient histogram metrics were calculated for 3 separate tumor volumes derived from T2-FLAIR sequences, T1 contrast-enhanced sequences, and permeability maps, respectively.

RESULTS

Results from 41 patients (0.3-16.76 years of age; mean, 6.22 years) with newly diagnosed contrast-enhancing brain tumors (16 low-grade; 25 high-grade) were included in the institutional review board-approved retrospective analysis. Wilcoxon tests showed a higher transfer constant from blood plasma into extracellular extravascular space and rate constant from extracellular extravascular space back into blood plasma, and lower extracellular extravascular volume fraction (P < .001) in high-grade tumors. The mean ADCs of FLAIR and enhancing tumor volumes were significantly lower in high-grade tumors (P < .001). ROC analysis showed that a combination of extravascular volume fraction and mean ADC of FLAIR volume differentiated high- and low-grade tumors with high accuracy (area under receiver operating characteristic curve = 0.918).

CONCLUSIONS

ADC histogram metrics combined with permeability metrics differentiate low- and high-grade pediatric brain tumors with high accuracy.

Differentiating between benign and malignant sinonasal lesions using dynamic contrast-enhanced MRI and intravoxel incoherent motion

PURPOSE

To explore the value of dynamic contrast-enhanced MRI (DCE-MRI) and intravoxel incoherent motion (IVIM) for distinguishing between benign and malignant sinonasal lesions and investigate the correlations between the two methods.

METHODS AND MATERIALS

Patients with sinonasal lesions (42 benign and 31 malignant) who underwent DCE-MRI and IVIM before confirmation by histopathology were enrolled in this prospective study. Parameters derived from DCE-MRI and IVIM were measured, the optimal cut-off values for differential diagnosis were determined, and the correlations between the two methods were evaluated. Statistical analyses were performed using the Wilcoxon rank sum test, receiver operating characteristic (ROC) curve analysis, and Spearman's rank correlation.

RESULTS

Significantly higher K^trans and K_ep values but lower D and f values were found in malignant lesions than in benign lesions (all p<0.001). There were no significant differences in the V_e and D* values between the two groups. The area under the curve (AUC) of K^trans was significantly higher than those of other parameters. There was no significant difference between the AUCs of DCE-MRI and IVIM with parameters combined (p=0.86). Significant inverse but weak correlations were found between D and K^trans (r=-0.46, p<0.001), f and K^trans (r=-0.41, p<0.001), D and K_ep (r=-0.37, p=0.008), and f and K_ep (r=-0.33, p=0.004).

CONCLUSIONS

DCE-MRI and IVIM can effectively differentiate between benign and malignant sinonasal lesions. IVIM findings correlate with DCE-MRI results and may represent an alternative to DCE-MRI.

DCE- and DW-MRI as early imaging biomarkers of treatment response in a preclinical model of triple negative breast cancer

This work evaluates quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) parameters as early biomarkers of response in a preclinical model of triple negative breast cancer (TNBC). The standard Tofts' model of DCE-MRI returns estimates of the volume transfer constant (K^trans ) and the extravascular extracellular volume fraction (v_e ). DW-MRI returns estimates of the apparent diffusion coefficient (ADC). Mice (n = 38) were injected subcutaneously with MDA-MB-231. Tumors were grown to approximately 275 mm³ and sorted into the following groups: saline controls, low-dose Abraxane (15 mg/kg) and high-dose Abraxane (25 mg/kg). Animals were imaged at days zero, one and three. On day three, tumors were extracted for immunohistochemistry. The positive percentage change in ADC on day one was significantly higher in both treatment groups relative to the control group (p < 0.05). In addition, the positive percentage change in K^trans was significantly higher than controls (p < 0.05) on day one for the high-dose group and on days one and three for the low-dose group. The percentage change in tumor volume was significantly different between the high-dose and control groups on day three (p = 0.006). Histology confirmed differences at day three through reduced numbers of proliferating cells (Ki67 staining) in the high-dose group (p = 0.03) and low-dose group (p = 0.052) compared with the control group. Co-immunofluorescent staining of vascular maturity [using von Willebrand Factor (vWF) and α-smooth muscle actin (α-SMA)] indicated significantly higher vascular maturation in the low-dose group compared with the controls on day three (p = 0.03), and trending towards significance in the high-dose group compared with controls on day three (p = 0.052). These results from quantitative imaging with histological validation indicate that ADC and K^trans have the potential to serve as early biomarkers of treatment response in murine studies of TNBC.

Combined diffusion-weighted imaging and dynamic contrast-enhanced MRI for differentiating radiologically indeterminate malignant from benign orbital masses

AIM

To evaluate the performance of the combination of diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for differentiating radiologically indeterminate malignant from benign orbital masses.

MATERIALS AND METHODS

Sixty-five patients with orbital masses (36 benign and 29 malignant) underwent DW and DCE MRI examinations for pre-treatment evaluation. The apparent diffusion coefficient (ADC) was derived from DW imaging data using the mono-exponential model. The volume transfer constant (K^trans), the flux rate constant between the extravascular extracellular space and the plasma (K_ep), and the extravascular extracellular volume fraction (V_e) were calculated using modified Tofts model. Differences in quantitative metrics were tested using independent-samples t test. Receiver operating characteristic (ROC) curve analyses were used to determine and compare the diagnostic ability of each significant metric.

RESULTS

The malignant group demonstrated significantly lower ADC (0.711±0.260 versus 1.187±0.389, p<0.001) and higher K_ep values (1.265±0.637 versus 0.871±0.610, p=0.008) than the benign group. Optimal diagnostic performance (area under the ROC curve [AUC], 0.941; sensitivity, 0.966; specificity, 0.917) could be achieved using combined ADC and K_ep values as the diagnostic index. The diagnostic performance of the combination of ADC and K_ep was significantly better than K_ep alone (p=0.006). Compared with ADC alone, combined ADC and K_ep values also showed higher AUC (0.941 versus 0.898), although the difference did not reach statistical significance (p=0.220).

CONCLUSION

K_ep and ADC could help to differentiate radiologically indeterminate malignant from benign orbital masses. The combination of DW and DCE MRI might improve the differentiating performance.

Differentiation of olfactory neuroblastomas from nasal squamous cell carcinomas using MR diffusion kurtosis imaging and dynamic contrast-enhanced MRI

PURPOSE

To evaluate the use of magnetic resonance (MR) diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced MR imaging (DCE-MRI) in the differentiation of olfactory neuroblastomas (ONBs) from squamous cell carcinomas (SCCs).

MATERIALS AND METHODS

DKI and DCE-MRI were performed in 17 patients with ONBs and 23 patients with SCCs on a 3T MR scanner. Parameters derived from DKI and DCE-MRI were measured and compared between ONBs and SCCs using an independent samples t-test. The sensitivity, specificity, accuracy, positive predictive values (PPV), negative predictive values (NPV), and the area under the receiver operating characteristic (ROC) curve were determined.

RESULTS

The mean kurtosis (K) value of ONBs was significantly higher than that of SCCs (P < 0.001), and the mean fractional volume in the extravascular extracellular space (V_e ) value of ONBs was lower than that of SCCs (P < 0.001). The ROC curve analyses yielded a cutoff K value of 0.953, with a sensitivity of 94.1%, a specificity of 69.6%, and an accuracy of 80.0%; the cutoff V_e value was 0.493, with a sensitivity of 70.6%, a specificity of 95.7%, and an accuracy of 85.0%. A parallel test with K value >0.953 or V_e value ≤0.493 achieved a sensitivity of 94.1%, a specificity of 100.0%, and an accuracy of 97.5% for differentiating ONBs from SCCs.

CONCLUSION

The K value of DKI and V_e value of DCE-MRI have potential use in the differentiation of ONBs and SCCs.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:354-361.

Dynamic contrast-enhanced MR imaging pharmacokinetic parameters as predictors of treatment response of brain metastases in patients with lung cancer

OBJECTIVES

To determine the diagnostic accuracy of pharmacokinetic parameters measured by dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in predicting the response of brain metastases to antineoplastic therapy in patients with lung cancer.

METHODS

Forty-four consecutive patients with lung cancer, harbouring 123 newly diagnosed brain metastases prospectively underwent conventional 3-T MRI at baseline (within 1 month before treatment), during the early (7-10 weeks) and midterm (5-7 months) post-treatment period. An additional DCE MRI sequence was performed during baseline and early post-treatment MRI to evaluate baseline pharmacokinetic parameters (K ^trans, k _ep, v _e, v _p) and their early variation (∆K ^trans, ∆k _ep, ∆v _e, ∆v _p). The objective response was judged by the volume variation of each metastasis from baseline to midterm MRI. ROC curve analysis determined the best DCE MRI parameter to predict the objective response.

RESULTS

Baseline DCE MRI parameters were not associated with the objective response. Early ∆K ^trans, ∆v _e and ∆v _p were significantly associated with the objective response (p = 0.02, p = 0.001 and p = 0.02, respectively). The best predictor of objective response was ∆v _e with an area under the curve of 0.93 [95% CI = 0.87, 0.99].

CONCLUSIONS

DCE MRI and early ∆v _e may be a useful tool to predict the objective response of brain metastases in patients with lung cancer.

KEY POINTS

• DCE MRI could predict the response of brain metastases from lung cancer • ∆v _e was the best predictor of response • DCE MRI could be used to individualize patients' follow-up.

Diagnostic Accuracy of T1-Weighted Dynamic Contrast-Enhanced-MRI and DWI-ADC for Differentiation of Glioblastoma and Primary CNS Lymphoma

BACKGROUND AND PURPOSE

Glioblastoma and primary CNS lymphoma dictate different neurosurgical strategies; it is critical to distinguish them preoperatively. However, current imaging modalities do not effectively differentiate them. We aimed to examine the use of DWI and T1-weighted dynamic contrast-enhanced-MR imaging as potential discriminative tools.

MATERIALS AND METHODS

We retrospectively reviewed 18 patients with primary CNS lymphoma and 36 matched patients with glioblastoma with pretreatment DWI and dynamic contrast-enhanced-MR imaging. VOIs were drawn around the tumor on contrast-enhanced T1WI and FLAIR images; these images were transferred onto coregistered ADC maps to obtain the ADC and onto dynamic contrast-enhanced perfusion maps to obtain the plasma volume and permeability transfer constant. Histogram analysis was performed to determine the mean and relative ADC_mean and relative 90th percentile values for plasma volume and the permeability transfer constant. Nonparametric tests were used to assess differences, and receiver operating characteristic analysis was performed for optimal threshold calculations.

RESULTS

The enhancing component of primary CNS lymphoma was found to have significantly lower ADC_mean (1.1 × 10^-3 versus 1.4 × 10^-3; P < .001) and relative ADC_mean (1.5 versus 1.9; P < .001) and relative 90th percentile values for plasma volume (3.7 versus 5.0; P < .05) than the enhancing component of glioblastoma, but not significantly different relative 90th percentile values for the permeability transfer constant (5.4 versus 4.4; P = .83). The nonenhancing portions of glioblastoma and primary CNS lymphoma did not differ in these parameters. On the basis of receiver operating characteristic analysis, mean ADC provided the best threshold (area under the curve = 0.83) to distinguish primary CNS lymphoma from glioblastoma, which was not improved with normalized ADC or the addition of perfusion parameters.

CONCLUSIONS

ADC was superior to dynamic contrast-enhanced-MR imaging perfusion, alone or in combination, in differentiating primary CNS lymphoma from glioblastoma.

Prognostic value of preoperative dynamic contrast-enhanced MRI perfusion parameters for high-grade glioma patients

Introduction

The prognostic value of the dynamic contrast-enhanced (DCE) MRI perfusion and its histogram analysis-derived metrics is not well established for high-grade glioma (HGG) patients. The aim of this prospective study was to investigate DCE perfusion transfer coefficient (K^trans), vascular plasma volume fraction (v_p), extracellular volume fraction (v_e), reverse transfer constant (k_ep), and initial area under gadolinium concentration time curve (IAUGC) as predictors of progression-free (PFS) and overall survival (OS) in HGG patients.

Methods

Sixty-nine patients with suspected anaplastic astrocytoma or glioblastoma underwent preoperative DCE-MRI scans. DCE perfusion whole tumor region histogram parameters, clinical details, and PFS and OS data were obtained. Univariate, multivariate, and Kaplan–Meier survival analyses were conducted. Receiver operating characteristic (ROC) curve analysis was employed to identify perfusion parameters with the best differentiation performance.

Results

On univariate analysis, v_e and skewness of v_p had significant negative impacts, while k_ep had significant positive impact on OS (P < 0.05). v_e was also a negative predictor of PFS (P < 0.05). Patients with lower v_e and IAUGC had longer median PFS and OS on Kaplan–Meier analysis (P < 0.05). K^trans and v_e could also differentiate grade III from IV gliomas (area under the curve 0.819 and 0.791, respectively).

Conclusions

High v_e is a consistent predictor of worse PFS and OS in HGG glioma patients. v_p skewness and k_ep are also predictive for OS. K^trans and v_e demonstrated the best diagnostic performance for differentiating grade III from IV gliomas.

Automated Processing of Dynamic Contrast-Enhanced MRI: Correlation of Advanced Pharmacokinetic Metrics with Tumor Grade in Pediatric Brain Tumors

BACKGROUND AND PURPOSE

Pharmacokinetic parameters from dynamic contrast-enhanced MR imaging have proved useful for differentiating brain tumor grades in adults. In this study, we retrospectively reviewed dynamic contrast-enhanced perfusion data from children with newly diagnosed brain tumors and analyzed the pharmacokinetic parameters correlating with tumor grade.

MATERIALS AND METHODS

Dynamic contrast-enhanced MR imaging data from 38 patients were analyzed by using commercially available software. Subjects were categorized into 2 groups based on pathologic analyses consisting of low-grade (World Health Organization I and II) and high-grade (World Health Organization III and IV) tumors. Pharmacokinetic parameters were compared between the 2 groups by using linear regression models. For parameters that were statistically distinct between the 2 groups, sensitivity and specificity were also estimated.

RESULTS

Eighteen tumors were classified as low-grade, and 20, as high-grade. Transfer constant from the blood plasma into the extracellular extravascular space (K^trans), rate constant from extracellular extravascular space back into blood plasma (K_ep), and extracellular extravascular volume fraction (V_e) were all significantly correlated with tumor grade; high-grade tumors showed higher K^trans, higher K_ep, and lower V_e. Although all 3 parameters had high specificity (range, 82%-100%), K_ep had the highest specificity for both grades. Optimal sensitivity was achieved for V_e, with a combined sensitivity of 76% (compared with 71% for K^trans and K_ep).

CONCLUSIONS

Pharmacokinetic parameters derived from dynamic contrast-enhanced MR imaging can effectively discriminate low- and high-grade pediatric brain tumors.

Multiparametric MR Imaging in the Assessment of Brain Tumors

Functional MR imaging methods make possible the quantification of dynamic physiologic processes that occur in the brain. Moreover, the use of these advanced imaging techniques in the setting of oncologic treatment of the brain is widely accepted and has found worldwide routine clinical use.

Diffusion-weighted and dynamic contrast-enhanced MRI of pancreatic adenocarcinoma xenografts: associations with tumor differentiation and collagen content

PURPOSE

The aggressiveness of pancreatic ductal adenocarcinoma (PDAC) is highly dependent on the level of differentiation and the composition of the stroma. In this preclinical study, we investigated the potential of diffusion-weighted magnetic resonance imaging (DW-MRI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as noninvasive methods for providing information on the differentiation and the stroma of PDACs.

METHODS

Xenografted tumors initiated from four PDAC cell lines (BxPC-3, Capan-2, MIAPaCa-2, and Panc-1) were included in the study. DW-MRI and DCE-MRI were carried out on a 7.05-T MR scanner, and tumor images of ADC (the apparent diffusion coefficient), K (trans) (the volume transfer constant of Gd-DOTA), and v e (the fractional distribution volume of Gd-DOTA) were produced. The level of differentiation and the amount and structure of collagen I and collagen IV were determined by examining histological preparations.

RESULTS

Differentiated tumors showed lower levels of collagen I and collagen IV than non-differentiated tumors. Significant correlations were found between ADC and v e, and both parameters differentiated clearly between collagen-rich non-differentiated tumors and differentiated tumors containing less collagen.

CONCLUSION

Differentiated PDAC xenografts show higher ADC values and higher v e values than their non-differentiated counterparts. This observation supports the application of parametric MR images as tumor biomarkers in PDAC. Patients showing low values of ADC and v e most likely have non-differentiated tumors with extensive stroma and, hence, poor prognosis.

Mitotic Activity in Glioblastoma Correlates with Estimated Extravascular Extracellular Space Derived from Dynamic Contrast-Enhanced MR Imaging

BACKGROUND AND PURPOSE

A number of parameters derived from dynamic contrast-enhanced MR imaging and separate histologic features have been identified as potential prognosticators in high-grade glioma. This study evaluated the relationships between dynamic contrast-enhanced MRI-derived parameters and histologic features in glioblastoma multiforme.

MATERIALS AND METHODS

Twenty-eight patients with newly presenting glioblastoma multiforme underwent preoperative imaging (conventional imaging and T1 dynamic contrast-enhanced MRI). Parametric maps of the initial area under the contrast agent concentration curve, contrast transfer coefficient, estimate of volume of the extravascular extracellular space, and estimate of blood plasma volume were generated, and the enhancing fraction was calculated. Surgical specimens were used to assess subtype and were graded (World Health Organization classification system) and were assessed for necrosis, cell density, cellular atypia, mitotic activity, and overall vascularity scores. Quantitative assessment of endothelial surface area, vascular surface area, and a vascular profile count were made by using CD34 immunostaining. The relationships between MR imaging parameters and histopathologic features were examined.

RESULTS

High values of contrast transfer coefficient were associated with the presence of frank necrosis (P = .005). High values of the estimate of volume of the extravascular extracellular space were associated with a fibrillary histologic pattern (P < .01) and with increased mitotic activity (P < .05). No relationship was found between mitotic activity and histologic pattern, suggesting that the correlation between the estimate of volume of the extravascular extracellular space and mitotic activity was independent of the histologic pattern.

CONCLUSIONS

A correlation between the estimate of volume of the extravascular extracellular space and mitotic activity is reported. Further work is warranted to establish how dynamic contrast-enhanced MRI parameters relate to more quantitative histologic measurements, including markers of proliferation and measures of vascular endothelial growth factor expression.

Correlation of tumor characteristics derived from DCE-MRI and DW-MRI with histology in murine models of breast cancer

The purpose of this work was to determine the relationship between the apparent diffusion coefficient (ADC, from diffusion-weighted (DW) MRI), the extravascular, extracellular volume fraction (ve , from dynamic contrast-enhanced (DCE) MRI), and histological measurement of the extracellular space fraction. Athymic nude mice were injected with either human epidermal growth factor receptor 2 positive (HER2+) BT474 (n = 15) or triple negative MDA-MB-231 (n = 20) breast cancer cells, treated with either Herceptin (n = 8), Abraxane (low dose n = 7, high dose n = 6), or saline (n = 7 for each cell line), and imaged using DW- and DCE-MRI before, during, and after treatment. After the final imaging acquisition, the tissue was resected and evaluated by histological analysis. H&E-stained central slices were scanned using a digital brightfield microscope and evaluated with thresholding techniques to calculate the extracellular space. For both BT474 and MDA-MB-231, the median ADC of the central slice exhibited a significantly positive correlation with the corresponding central slice extracellular space as measured by H&E (p = 0.03, p < 0.01, respectively). Median ve calculated from the central slice showed differing results between the two cell lines. For BT474, a significant correlation between ve and extracellular space was calculated (p = 0.02), while MDA-MB-231 tumors did not demonstrate a significant correlation (p = 0.64). Additionally, there was no correlation discovered between ADC and ve with either whole tumor analysis or central slice analysis (p > 0.05). While ADC correlates well with the histologically determined fraction of extracellular space, these data add to the growing body of literature that suggests that ve derived from DCE-MRI is not a reliable biomarker of extracellular space for a range of physiological conditions.

Quantitative Evaluation of Diffusion and Dynamic Contrast-Enhanced MR in Tumor Parenchyma and Peritumoral Area for Distinction of Brain Tumors

Purpose

To quantitatively evaluate the diagnostic efficiency of parameters from diffusion and dynamic contrast-enhanced MR which based on tumor parenchyma (TP) and peritumoral (PT) area in classification of brain tumors.

Methods

45 patients (male: 23, female: 22; mean age: 46 y) were prospectively recruited and they underwent conventional, DCE-MR and DWI examination. With each tumor, 10–15 regions of interest (ROIs) were manually placed on TP and PT area. ADC and permeability parameters (K^trans, Ve, Kep and iAUC) were calculated and their diagnostic efficiency was assessed.

Results

In TP, all permeability parameters and ADC value could significantly discriminate Low- from High grade gliomas (HGG) (p<0.001); among theses parameters, Ve demonstrated the highest diagnostic power (iAUC: 0.79, cut-off point: 0.15); the most sensitive and specific index for gliomas grading were K^trans (84%) and Kep (89%). While, in PT area, only K^trans could help in gliomas grading (P = 0.009, cut-off point: 0.03 min^-1). Moreover, in TP, mean Ve and iAUC of primary central nervous system lymphoma (PCNSL) and metastases were significantly higher than that in HGG (p<0.003). Further, in PT area, mean K^trans (p≤0.004) could discriminate PCNSL from HGG and ADC (p≤0.003) could differentiate metastases with HGG.

Conclusions

Quantitative ADC and permeability parameters from Diffusion and DCE-MR in TP and PT area, especially DCE-MR, can aid in gliomas grading and brain tumors discrimination. Their combined application is strongly recommended in the differential diagnosis of these tumor entities.

Quantitative pharmacokinetic analysis of prostate cancer DCE-MRI at 3T: comparison of two arterial input functions on cancer detection with digitized whole mount histopathological validation

Accurate pharmacokinetic (PK) modeling of dynamic contrast enhanced MRI (DCE-MRI) in prostate cancer (PCa) requires knowledge of the concentration time course of the contrast agent in the feeding vasculature, the so-called arterial input function (AIF). The purpose of this study was to compare AIF choice in differentiating peripheral zone PCa from non-neoplastic prostatic tissue (NNPT), using PK analysis of high temporal resolution prostate DCE-MRI data and whole-mount pathology (WMP) validation. This prospective study was performed in 30 patients who underwent multiparametric endorectal prostate MRI at 3.0T and WMP validation. PCa foci were annotated on WMP slides and MR images using 3D Slicer. Foci ≥0.5cm(3) were contoured as tumor regions of interest (TROIs) on subtraction DCE (early-arterial - pre-contrast) images. PK analyses of TROI and NNPT data were performed using automatic AIF (aAIF) and model AIF (mAIF) methods. A paired t-test compared mean and 90th percentile (p90) PK parameters obtained with the two AIF approaches. Receiver operating characteristic (ROC) analysis determined diagnostic accuracy (DA) of PK parameters. Logistic regression determined correlation between PK parameters and histopathology. Mean TROI and NNPT PK parameters were higher using aAIF vs. mAIF (p<0.05). There was no significant difference in DA between AIF methods: highest for p90 volume transfer constant (K(trans)) (aAIF differences in the area under the ROC curve (Az) = 0.827; mAIF Az=0.93). Tumor cell density correlated with aAIF K(trans) (p=0.03). Our results indicate that DCE-MRI using both AIF methods is excellent in discriminating PCa from NNPT. If quantitative DCE-MRI is to be used as a biomarker in PCa, the same AIF method should be used consistently throughout the study.

In Vivo Correlation of Glucose Metabolism, Cell Density and Microcirculatory Parameters in Patients with Head and Neck Cancer: Initial Results Using Simultaneous PET/MRI

Objective

To demonstrate the feasibility of simultaneous acquisition of ¹⁸F-FDG-PET, diffusion-weighted imaging (DWI) and T1-weighted dynamic contrast-enhanced MRI (T1w-DCE) in an integrated simultaneous PET/MRI in patients with head and neck squamous cell cancer (HNSCC) and to investigate possible correlations between these parameters.

Methods

17 patients that had given informed consent (15 male, 2 female) with biopsy-proven HNSCC underwent simultaneous ¹⁸F-FDG-PET/MRI including DWI and T1w-DCE. SUV_max, SUV_mean, ADC_mean, ADC_min and K^trans, k_ep and v_e were measured for each tumour and correlated using Spearman’s ρ.

Results

Significant correlations were observed between SUV_mean and K^trans (ρ = 0.43; p ≤ 0.05); SUV_mean and k_ep (ρ = 0.44; p ≤ 0.05); K^trans and k_ep (ρ = 0.53; p ≤ 0.05); and between k_ep and v_e (ρ = -0.74; p ≤ 0.01). There was a trend towards statistical significance when correlating SUV_max and ADC_min (ρ = -0.35; p = 0.08); SUV_max and K^trans (ρ = 0.37; p = 0.07); SUV_max and k_ep (ρ = 0.39; p = 0.06); and ADC_mean and v_e (ρ = 0.4; p = 0.06).

Conclusion

Simultaneous ¹⁸F-FDG-PET/MRI including DWI and T1w-DCE in patients with HNSCC is feasible and allows depiction of complex interactions between glucose metabolism, microcirculatory parameters and cellular density.

Advances in Magnetic Resonance Imaging and Positron Emission Tomography Imaging for Grading and Molecular Characterization of Glioma

In recent years, the management of glioma has evolved significantly, reflecting our better understanding of the underlying mechanisms of tumor development, tumor progression, and treatment response. Glioma grade, along with a number of underlying molecular and genetic biomarkers, has been recognized as an important prognostic and predictive factor that can help guide the management of patients. This article highlights advances in magnetic resonance imaging (MRI), including diffusion-weighted imaging, diffusion tensor imaging, magnetic resonance spectroscopy, dynamic contrast-enhanced imaging, and perfusion MRI, as well as position emission tomography using various tracers including methyl-(11)C-l-methionine and O-(2-(18)F-fluoroethyl)-l-tyrosine. Use of multiparametric imaging data has improved the diagnostic strength of imaging, introduced the potential to noninvasively interrogate underlying molecular features of low-grade glioma and to guide local therapies such as surgery and radiotherapy.

Advanced magnetic resonance imaging of the physical processes in human glioblastoma

The most common malignant primary brain tumor, glioblastoma multiforme (GBM) is a devastating disease with a grim prognosis. Patient survival is typically less than two years and fewer than 10% of patients survive more than five years. Magnetic resonance imaging (MRI) can have great utility in the diagnosis, grading, and management of patients with GBM as many of the physical manifestations of the pathologic processes in GBM can be visualized and quantified using MRI. Newer MRI techniques such as dynamic contrast enhanced and dynamic susceptibility contrast MRI provide functional information about the tumor hemodynamic status. Diffusion MRI can shed light on tumor cellularity and the disruption of white matter tracts in the proximity of tumors. MR spectroscopy can be used to study new tumor tissue markers such as IDH mutations. MRI is helping to noninvasively explore the link between the molecular basis of gliomas and the imaging characteristics of their physical processes. We, here, review several approaches to MR-based imaging and discuss the potential for these techniques to quantify the physical processes in glioblastoma, including tumor cellularity and vascularity, metabolite expression, and patterns of tumor growth and recurrence. We conclude with challenges and opportunities for further research in applying physical principles to better understand the biologic process in this deadly disease. See all articles in this Cancer Research section, "Physics in Cancer Research."

Diagnosis of brain tumors using dynamic contrast-enhanced perfusion imaging with a short acquisition time

This study sought to determine the diagnostic utility of perfusion parameters derived from dynamic contrast-enhanced (DCE) perfusion MRI with a short acquisition time (approximately 3.5 min) in patients with glioma, brain metastasis, and primary CNS lymphoma (PCNSL).

Twenty-six patients with 29 lesions (4 low-grade glioma, 13 high-grade glioma, 7 metastasis, and 5 PCNSL) underwent DCE-MRI in a 3 T scanner. A ROI was placed on the hotspot of each tumor in maps for volume transfer contrast K^trans, extravascular extracellular volume V_e, and fractional plasma volume V_p. We analyzed differences in parameters between tumors using the Mann–Whitney U test. We calculated sensitivity and specificity using receiver operating characteristics analysis.

Mean K^trans values of LGG, HGG, metastasis and PCNSL were 0.034, 0.31, 0.38, 0.44, respectively. Mean Ve values of each tumors was 0.036, 0.57, 0.47, 0.96, and mean Vp value of each tumors was 0.070, 0.086, 0.26, 0.17, respectively. Compared with other tumor types, low-grade glioma showed lower K^trans (P < 0.01, sensitivity = 88%, specificity = 100%) and lower V_e (P < 0.01, sensitivity = 96%, specificity = 100%). PCNSL showed higher V_e (P < 0.01, sensitivity = 100%, specificity = 88%), but the other perfusion parameters overlapped with those of different histology.

Kinetic parameters derived from DCE-MRI with short acquisition time provide useful information for the differential diagnosis of brain tumors.

Dynamic contrast enhanced MRI detects early response to adoptive NK cellular immunotherapy targeting the NG2 proteoglycan in a rat model of glioblastoma

There are currently no established radiological parameters that predict response to immunotherapy. We hypothesised that multiparametric, longitudinal magnetic resonance imaging (MRI) of physiological parameters and pharmacokinetic models might detect early biological responses to immunotherapy for glioblastoma targeting NG2/CSPG4 with mAb9.2.27 combined with natural killer (NK) cells. Contrast enhanced conventional T1-weighted MRI at 7±1 and 17±2 days post-treatment failed to detect differences in tumour size between the treatment groups, whereas, follow-up scans at 3 months demonstrated diminished signal intensity and tumour volume in the surviving NK+mAb9.2.27 treated animals. Notably, interstitial volume fraction (v_e), was significantly increased in the NK+mAb9.2.27 combination therapy group compared mAb9.2.27 and NK cell monotherapy groups (p = 0.002 and p = 0.017 respectively) in cohort 1 animals treated with 1 million NK cells. v_e was reproducibly increased in the combination NK+mAb9.2.27 compared to NK cell monotherapy in cohort 2 treated with increased dose of 2 million NK cells (p<0.0001), indicating greater cell death induced by NK+mAb9.2.27 treatment. The interstitial volume fraction in the NK monotherapy group was significantly reduced compared to mAb9.2.27 monotherapy (p<0.0001) and untreated controls (p = 0.014) in the cohort 2 animals. NK cells in monotherapy were unable to kill the U87MG cells that highly expressed class I human leucocyte antigens, and diminished stress ligands for activating receptors. A significant association between apparent diffusion coefficient (ADC) of water and v_e in combination NK+mAb9.2.27 and NK monotherapy treated tumours was evident, where increased ADC corresponded to reduced v_e in both cases. Collectively, these data support histological measures at end-stage demonstrating diminished tumour cell proliferation and pronounced apoptosis in the NK+mAb9.2.27 treated tumours compared to the other groups. In conclusion, v_e was the most reliable radiological parameter for detecting response to intralesional NK cellular therapy.

Practical dynamic contrast enhanced MRI in small animal models of cancer: data acquisition, data analysis, and interpretation

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) consists of the continuous acquisition of images before, during, and after the injection of a contrast agent. DCE-MRI allows for noninvasive evaluation of tumor parameters related to vascular perfusion and permeability and tissue volume fractions, and is frequently employed in both preclinical and clinical investigations. However, the experimental and analytical subtleties of the technique are not frequently discussed in the literature, nor are its relationships to other commonly used quantitative imaging techniques. This review aims to provide practical information on the development, implementation, and validation of a DCE-MRI study in the context of a preclinical study (though we do frequently refer to clinical studies that are related to these topics).

Inhibition of SUR1 decreases the vascular permeability of cerebral metastases

Inhibition of sulfonylurea receptor 1 (SUR1) by glyburide has been shown to decrease edema after subarachnoid hemorrhage. We investigated if inhibiting SUR1 reduces cerebral edema due to metastases, the most common brain tumor, and explored the putative association of SUR1 and the endothelial tight junction protein, zona occludens-1 (ZO-1). Nude rats were intracerebrally implanted with small cell lung carcinoma (SCLC) LX1 or A2058 melanoma cells (n = 36). Rats were administered vehicle, glyburide (4.8 µg twice, orally), or dexamethasone (0.35 mg, intravenous). Blood-tumor barrier (BTB) permeability (K (trans)) was evaluated before and after treatment using dynamic contrast-enhanced magnetic resonance imaging. SUR1 and ZO-1 expression was evaluated using immunofluorescence and Western blots. In both models, SUR1 expression was significantly increased (P < .05) in tumors. In animals with SCLC, control mean K (trans) (percent change ± standard error) was 101.8 ± 36.6%, and both glyburide (-21.4 ± 14.2%, P < .01) and dexamethasone (-14.2 ± 13.1%, P < .01) decreased BTB permeability. In animals with melanoma, compared to controls (117.1 ± 43.4%), glyburide lowered BTB permeability increase (3.2 ± 15.4%, P < .05), while dexamethasone modestly lowered BTB permeability increase (63.1 ± 22.1%, P > .05). Both glyburide (P < .001) and dexamethasone (P < .01) decreased ZO-1 gap formation. By decreasing ZO-1 gaps, glyburide was at least as effective as dexamethasone at halting increased BTB permeability caused by SCLC and melanoma. Glyburide is a safe, inexpensive, and efficacious alternative to dexamethasone for the treatment of cerebral metastasis-related vasogenic edema.

Comparison of dynamic contrast-enhanced MRI and quantitative SPECT in a rat glioma model

Pharmacokinetic modeling of dynamic contrast-enhanced (DCE) MRI data provides measures of the extracellular-extravascular volume fraction (v(e) ) and the volume transfer constant (K(trans) ) in a given tissue. These parameter estimates may be biased, however, by confounding issues such as contrast agent and tissue water dynamics, or assumptions of vascularization and perfusion made by the commonly used model. In contrast to MRI, radiotracer imaging with SPECT is insensitive to water dynamics. A quantitative dual-isotope SPECT technique was developed to obtain an estimate of v(e) in a rat glioma model for comparison with the corresponding estimates obtained using DCE-MRI with a vascular input function and reference region model. Both DCE-MRI methods produced consistently larger estimates of v(e) in comparison to the SPECT estimates, and several experimental sources were postulated to contribute to these differences.