Reliability of dynamic contrast-enhanced magnetic resonance imaging data in primary brain tumours: a comparison of Tofts and shutter speed models

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.

Feasibility of [18F]fluoropivalate hybrid PET/MRI for imaging lower and higher grade glioma: a prospective first-in-patient pilot study

PURPOSE

MRI and PET are used in neuro-oncology for the detection and characterisation of lesions for malignancy to target surgical biopsy and to plan surgical resections or stereotactic radiosurgery. The critical role of short-chain fatty acids (SCFAs) in brain tumour biology has come to the forefront. The non-metabolised SCFA radiotracer, [18F]fluoropivalate (FPIA), shows low background signal in most tissues except eliminating organs and has appropriate human dosimetry. Tumour uptake of the radiotracer is, however, unknown. We investigated the uptake characteristics of FPIA in this pilot PET/MRI study.

METHODS

Ten adult glioma subjects were identified based on radiological features using standard-of-care MRI prior to any surgical intervention, with subsequent histopathological confirmation of glioma subtype and grade (lower-grade - LGG - and higher-grade - HGG - patients). FPIA was injected as an intravenous bolus injection (range 342-368 MBq), and dynamic PET and MRI data were acquired simultaneously over 66 min.

RESULTS

All patients tolerated the PET/MRI protocol. Three patients were reclassified following resection and histology. Tumour maximum standardised uptake value (SUVmax,60) increased in the order LGG (WHO grade 2) < HGG (WHO grade 3) < HGG (WHO grade 4). The net irreversible solute transfer, Ki, and influx rate constant, K1, were significantly higher in HGG (p < 0.05). Of the MRI variables studied, DCE-MRI-derived extravascular-and-extracellular volume fraction (ve) was high in tumours of WHO grade 4 compared with other grades (p < 0.05). SLC25A20 protein expression was higher in HGG compared with LGG.

CONCLUSION

Tumoural FPIA PET uptake is higher in HGG compared to LGG. This study supports further investigation of FPIA PET/MRI for brain tumour imaging in a larger patient population.

CLINICAL TRIAL REGISTRATION

Clinicaltrials.gov, NCT04097535.

Quantitative Cerebral Blood Volume Image Synthesis from Standard MRI Using Image-to-Image Translation for Brain Tumors

Background Cerebral blood volume (CBV) maps derived from dynamic susceptibility contrast-enhanced (DSC) MRI are useful but not commonly available in clinical scenarios. Purpose To test image-to-image translation techniques for generating CBV maps from standard MRI sequences of brain tumors using the bookend technique DSC MRI as ground-truth references. Materials and Methods A total of 756 MRI examinations, including quantitative CBV maps produced from bookend DSC MRI, were included in this retrospective study. Two algorithms, the feature-consistency generative adversarial network (GAN) and three-dimensional encoder-decoder network with only mean absolute error loss, were trained to synthesize CBV maps. The performance of the two algorithms was evaluated quantitatively using the structural similarity index (SSIM) and qualitatively by two neuroradiologists using a four-point Likert scale. The clinical value of combining synthetic CBV maps and standard MRI scans of brain tumors was assessed in several clinical scenarios (tumor grading, prognosis prediction, differential diagnosis) using multicenter data sets (four external and one internal). Differences in diagnostic and predictive accuracy were tested using the z test. Results The three-dimensional encoder-decoder network with T1-weighted images, contrast-enhanced T1-weighted images, and apparent diffusion coefficient maps as the input achieved the highest synthetic performance (SSIM, 86.29% ± 4.30). The mean qualitative score of the synthesized CBV maps by neuroradiologists was 2.63. Combining synthetic CBV with standard MRI improved the accuracy of grading gliomas (standard MRI scans area under the receiver operating characteristic curve [AUC], 0.707; standard MRI scans with CBV maps AUC, 0.857; z = 15.17; P < .001), prediction of prognosis in gliomas (standard MRI scans AUC, 0.654; standard MRI scans with CBV maps AUC, 0.793; z = 9.62; P < .001), and differential diagnosis between tumor recurrence and treatment response in gliomas (standard MRI scans AUC, 0.778; standard MRI scans with CBV maps AUC, 0.853; z = 4.86; P < .001) and brain metastases (standard MRI scans AUC, 0.749; standard MRI scans with CBV maps AUC, 0.857; z = 6.13; P < .001). Conclusion GAN image-to-image translation techniques produced accurate synthetic CBV maps from standard MRI scans, which could be used for improving the clinical evaluation of brain tumors. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Branstetter in this issue.

Assessment of Hyperosmolar Blood-Brain Barrier Opening in Glioblastoma via Histology with Evans Blue and DCE-MRI

BACKGROUND

While the blood-brain barrier (BBB) is often compromised in glioblastoma (GB), the perfusion and consequent delivery of drugs are highly heterogeneous. Moreover, the accessibility of drugs is largely impaired in the margins of the tumor and for infiltrating cells at the origin of tumor recurrence. In this work, we evaluate the value of methods to assess hemodynamic changes induced by a hyperosmolar shock in the core and the margins of a tumor in a GB model.

METHODS

Osmotic shock was induced with an intracarotid infusion of a hypertonic solution of mannitol in mice grafted with U87-MG cells. The distribution of fluorescent dye (Evans blue) within the brain was assessed via histology. Dynamic contrast-enhanced (DCE)-MRI with an injection of Gadolinium-DOTA as the contrast agent was also used to evaluate the effect on hemodynamic parameters and the diffusion of the contrast agent outside of the tumor area.

RESULTS

The histological study revealed that the fluorescent dye diffused much more largely outside of the tumor area after osmotic shock than in control tumors. However, the study of tumor hemodynamic parameters via DCE-MRI did not reveal any change in the permeability of the BBB, whatever the studied MRI parameter.

CONCLUSIONS

The use of hypertonic mannitol infusion seems to be a promising method to increase the delivery of compounds in the margins of GB. Nevertheless, the DCE-MRI analysis method using gadolinium-DOTA as a contrast agent seems of limited value for determining the efficacy of opening the BBB in GB after osmotic shock.

Impact of Arterial Input Function and Pharmacokinetic Models on DCE-MRI Biomarkers for Detection of Vascular Effect Induced by Stroma-Directed Drug in an Orthotopic Mouse Model of Pancreatic Cancer

PURPOSE

We demonstrated earlier in mouse models of pancreatic ductal adenocarcinoma (PDA) that K^trans derived from dynamic contrast-enhanced (DCE) MRI detected microvascular effect induced by PEGPH20, a hyaluronidase which removes stromal hyaluronan, leading to reduced interstitial fluid pressure in the tumor (Clinical Cancer Res (2019) 25: 2314-2322). How the choice of pharmacokinetic (PK) model and arterial input function (AIF) may impact DCE-derived markers for detecting such an effect is not known.

PROCEDURES

Retrospective analyses of the DCE-MRI of the orthotopic PDA model are performed to examine the impact of individual versus group AIF combined with Tofts model (TM), extended-Tofts model (ETM), or shutter-speed model (SSM) on the ability to detect the microvascular changes induced by PEGPH20 treatment.

RESULTS

Individual AIF exhibit a marked difference in peak gadolinium concentration. However, across all three PK models, k_ep values show a significant correlation between individual versus group-AIF (p < 0.01). Regardless individual or group AIF, when k_ep is obtained from fitting the DCE-MRI data using the SSM, k_ep shows a significant increase after PEGPH20 treatment (p < 0.05 compared to the baseline); %change of k_ep from baseline to post-treatment is also significantly different between PEGPH20 versus vehicle group (p < 0.05). In comparison, when k_ep is derived from the TM, only the use of individual AIF leads to a significant increase of k_ep after PEGPH20 treatment, whereas the %change of k_ep is not different between PEGPH20 versus vehicle group. Group AIF but not individual AIF allows detection of a significant increase of V_p (derived from the ETM) in PEGPH20 versus vehicle group (p < 0.05). Increase of V_p is consistent with a large increase of mean capillary lumen area estimated from immunostaining.

CONCLUSION

Our results suggest that k_ep derived from SSM and V_p from ETM, both using group AIF, are optimal for the detection of microvascular changes induced by stroma-directed drug PEGPH20. These analyses provide insights in the choice of PK model and AIF for optimal DCE protocol design in mouse pancreatic cancer models.

Evaluation of blood-brain barrier integrity by the analysis of dynamic contrast-enhanced MRI - a comparison of quantitative and semi-quantitative methods

Disruption of the blood-brain barrier (BBB) is a key feature of various brain disorders. To assess its integrity a parametrization of dynamic magnetic resonance imaging (DCE MRI) with a contrast agent (CA) is broadly used. Parametrization can be done quantitatively or semi-quantitatively. Quantitative methods directly describe BBB permeability but exhibit several drawbacks such as high computation demands, reproducibility issues, or low robustness. Semi-quantitative methods are fast to compute, simply mathematically described, and robust, however, they do not describe the status of BBB directly but only as a variation of CA concentration in measured tissue. Our goal was to elucidate differences between five semi-quantitative parameters: maximal intensity (Imax), normalized permeability index (NPI), and difference in DCE values between three timepoints: baseline, 5 min, and 15 min (delta5-0, delta15-0, delta15-5) and two quantitative parameters: transfer constant (Ktrans) and an extravascular fraction (Ve). For the purpose of comparison, we analyzed DCE data of four patients 12-15 days after the stroke with visible CA enhancement. Calculated parameters showed abnormalities spatially corresponding with the ischemic lesion, however, findings in individual parameters morphometrically differed. Ktrans and Ve were highly correlated. Delta5-0 and delta15-0 were prominent in regions with rapid CA enhancement and highly correlated with Ktrans. Abnormalities in delta15-5 and NPI were more homogenous with less variable values, smoother borders, and less detail than Ktrans. Moreover, only delta15-5 and NPI were able to distinguish vessels from extravascular space. Our comparison provides important knowledge for understanding and interpreting parameters derived from DCE MRI by both quantitative and semi-quantitative methods.

Assessing Tumour Haemodynamic Heterogeneity and Response to Choline Kinase Inhibition Using Clustered Dynamic Contrast Enhanced MRI Parameters in Rodent Models of Glioblastoma

To investigate the utility of DCE-MRI derived pharmacokinetic parameters in evaluating tumour haemodynamic heterogeneity and treatment response in rodent models of glioblastoma, imaging was performed on intracranial F98 and GL261 glioblastoma bearing rodents. Clustering of the DCE-MRI-based parametric maps (using Tofts, extended Tofts, shutter speed, two-compartment, and the second generation shutter speed models) was performed using a hierarchical clustering algorithm, resulting in areas with poor fit (reflecting necrosis), low, medium, and high valued pixels representing parameters Ktrans, ve, Kep, vp, τi and Fp. There was a significant increase in the number of necrotic pixels with increasing tumour volume and a significant correlation between ve and tumour volume suggesting increased extracellular volume in larger tumours. In terms of therapeutic response in F98 rat GBMs, a sustained decrease in permeability and perfusion and a reduced cell density was observed during treatment with JAS239 based on Ktrans, Fp and ve as compared to control animals. No significant differences in these parameters were found for the GL261 tumour, indicating that this model may be less sensitive to JAS239 treatment regarding changes in vascular parameters. This study demonstrates that region-based clustered pharmacokinetic parameters derived from DCE-MRI may be useful in assessing tumour haemodynamic heterogeneity with the potential for assessing therapeutic response.

A systematic review on the use of quantitative imaging to detect cancer therapy adverse effects in normal-appearing brain tissue

Cancer therapy for both central nervous system (CNS) and non-CNS tumors has been previously associated with transient and long-term cognitive deterioration, commonly referred to as 'chemo fog'. This therapy-related damage to otherwise normal-appearing brain tissue is reported using post-mortem neuropathological analysis. Although the literature on monitoring therapy effects on structural magnetic resonance imaging (MRI) is well established, such macroscopic structural changes appear relatively late and irreversible. Early quantitative MRI biomarkers of therapy-induced damage would potentially permit taking these treatment side effects into account, paving the way towards a more personalized treatment planning.This systematic review (PROSPERO number 224196) provides an overview of quantitative tomographic imaging methods, potentially identifying the adverse side effects of cancer therapy in normal-appearing brain tissue. Seventy studies were obtained from the MEDLINE and Web of Science databases. Studies reporting changes in normal-appearing brain tissue using MRI, PET, or SPECT quantitative biomarkers, related to radio-, chemo-, immuno-, or hormone therapy for any kind of solid, cystic, or liquid tumor were included. The main findings of the reviewed studies were summarized, providing also the risk of bias of each study assessed using a modified QUADAS-2 tool. For each imaging method, this review provides the methodological background, and the benefits and shortcomings of each method from the imaging perspective. Finally, a set of recommendations is proposed to support future research.

DCE-MRI detected vascular permeability changes in the rat spinal cord do not explain shorter latency times for paresis after carbon ions relative to photons

BACKGROUND AND PURPOSE

Radiation-induced myelopathy, an irreversible complication occurring after a long symptom-free latency time, is preceded by a fixed sequence of magnetic resonance- (MR-) visible morphological alterations. Vascular degradation is assumed the main reason for radiation-induced myelopathy. We used dynamic contrast-enhanced (DCE-) MRI to identify different vascular changes after photon and carbon ion irradiation, which precede or coincide with morphological changes.

MATERIALS AND METHODS

The cervical spinal cord of rats was irradiated with iso-effective photon or carbon (¹²C-)ion doses. Afterwards, animals underwent frequent DCE-MR imaging until they developed symptomatic radiation-induced myelopathy (paresis II). Measurements were performed at certain time points: 1 month, 2 months, 3 months, 4 months, and 6 months after irradiation, and when animals showed morphological (such as edema/syrinx/contrast agent (CA) accumulation) or neurological alterations (such as, paresis I, and paresis II). DCE-MRI data was analyzed using the extended Toft's model.

RESULTS

Fit quality improved with gradual disintegration of the blood spinal cord barrier (BSCB) towards paresis II. Vascular permeability increased three months after photon irradiation, and rapidly escalated after animals showed MR-visible morphological changes until paresis II. After ¹²C-ion irradiation, vascular permeability increased when animals showed morphological alterations and increased further until animals had paresis II. The volume transfer constant and the plasma volume showed no significant changes.

CONCLUSION

Only after photon irradiation, DCE-MRI provides a temporal advantage in detecting early physiological signs in radiation-induced myelopathy compared to morphological MRI. As a generally lower level of vascular permeability after ¹²C-ions led to an earlier development of paresis as compared to photons, we conclude that other mechanisms dominate the development of paresis II.

The LEGATOS technique: A new tissue-validated dynamic contrast-enhanced MRI method for whole-brain, high-spatial resolution parametric mapping

PURPOSE

A DCE-MRI technique that can provide both high spatiotemporal resolution and whole-brain coverage for quantitative microvascular analysis is highly desirable but currently challenging to achieve. In this study, we sought to develop and validate a novel dual-temporal resolution (DTR) DCE-MRI-based methodology for deriving accurate, whole-brain high-spatial resolution microvascular parameters.

METHODS

Dual injection DTR DCE-MRI was performed and composite high-temporal and high-spatial resolution tissue gadolinium-based-contrast agent (GBCA) concentration curves were constructed. The high-temporal but low-spatial resolution first-pass GBCA concentration curves were then reconstructed pixel-by-pixel to higher spatial resolution using a process we call LEGATOS. The accuracy of kinetic parameters (K^trans , v_p , and v_e ) derived using LEGATOS was evaluated through simulations and in vivo studies in 17 patients with vestibular schwannoma (VS) and 13 patients with glioblastoma (GBM). Tissue from 15 tumors (VS) was examined with markers for microvessels (CD31) and cell density (hematoxylin and eosin [H&E]).

RESULTS

LEGATOS derived parameter maps offered superior spatial resolution and improved parameter accuracy compared to the use of high-temporal resolution data alone, provided superior discrimination of plasma volume and vascular leakage effects compared to other high-spatial resolution approaches, and correlated with tissue markers of vascularity (P ≤ 0.003) and cell density (P ≤ 0.006).

CONCLUSION

The LEGATOS method can be used to generate accurate, high-spatial resolution microvascular parameter estimates from DCE-MRI.

Differentiating low from high-grade soft tissue sarcomas using post-processed imaging parameters derived from multiple DWI models

PURPOSE

To investigate and histopathologically validate the role of model selection in the design of novel parametric meta-maps towards the discrimination of low from high-grade soft tissue sarcomas (STSs) using multiple Diffusion Weighted Imaging (DWI) models.

METHODS

DWI data of 28 patients were quantified using the mono-exponential, bi-exponential, stretched-exponential and the diffusion kurtosis model. Akaike Weights (AW) were calculated from the corrected Akaike Information Criteria (AICc) to select the most suitable model for every pixel within the tumor volume. Pseudo-colorized classification maps were then generated to depict model suitability, hypothesizing that every single model underpins different tissue properties and cannot solely characterize the whole tumor. Single model parametric maps were turned into meta-maps using the classification map and a histological validation of the model suitability results was conducted on several subregions of different tumors. Several histogram metrics were calculated from all derived maps before and after model selection, statistical analysis was conducted using the Mann-Whitney U test, p-values were adjusted for multiple comparisons and performance of all statistically significant metrics was evaluated using the Receiver Operator Characteristic (ROC) analysis.

RESULTS

The histologic analysis on several tumor subregions confirmed model suitability results on these areas. Only 3 histogram metrics, all derived from the meta-maps, were found to be statistically significant in differentiating low from high-grade STSs with an AUC higher than 89 %.

CONCLUSION

Embedding model selection in the design of the diffusion parametric maps yields to histogram metrics of high discriminatory power in grading STSs.

Application of Distributed Parameter Model to Assessment of Glioma IDH Mutation Status by Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Previous studies using contrast-enhanced imaging for glioma isocitrate dehydrogenase (IDH) mutation assessment showed promising yet inconsistent results, and this study attempts to explore this problem by using an advanced tracer kinetic model, the distributed parameter model (DP). Fifty-five patients with glioma examined using dynamic contrast-enhanced imaging sequence at a 3.0 T scanner were retrospectively reviewed. The imaging data were processed using DP, yielding the following parameters: blood flow F, permeability-surface area product PS, fractional volume of interstitial space Ve, fractional volume of intravascular space Vp, and extraction ratio E. The results were compared with the Tofts model. The Wilcoxon test and boxplot were utilized for assessment of differences of model parameters between IDH-mutant and IDH-wildtype gliomas. Spearman correlation r was employed to investigate the relationship between DP and Tofts parameters. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis and quantified using the area under the ROC curve (AUC). Results showed that IDH-mutant gliomas were significantly lower in F (P = 0.018), PS (P < 0.001), Vp (P < 0.001), E (P < 0.001), and Ve (P = 0.002) than IDH-wildtype gliomas. In differentiating IDH-mutant and IDH-wildtype gliomas, Vp had the best performance (AUC = 0.92), and the AUCs of PS and E were 0.82 and 0.80, respectively. In comparison, Tofts parameters were lower in K^trans (P = 0.013) and Ve (P < 0.001) for IDH-mutant gliomas. No significant difference was observed in Kep (P = 0.525). The AUCs of K^trans, Ve, and Kep were 0.69, 0.79, and 0.55, respectively. Tofts-derived Ve showed a strong correlation with DP-derived Ve (r > 0.9, P < 0.001). K^trans showed a weak correlation with F (r < 0.3, P > 0.16) and a very weak correlation with PS (r < 0.06, P > 0.8), both of which were not statistically significant. The findings by DP revealed a tissue environment with lower vascularity, lower vessel permeability, and lower blood flow in IDH-mutant than in IDH-wildtype gliomas, being hostile to cellular differentiation of oncogenic effects in IDH-mutated gliomas, which might help to explain the better outcomes in IDH-mutated glioma patients than in glioma patients of IDH-wildtype. The advantage of DP over Tofts in glioma DCE data analysis was demonstrated in terms of clearer elucidation of tissue microenvironment and better performance in IDH mutation assessment.