Assessment of repeatability and treatment response in early phase clinical trials using DCE-MRI: comparison of parametric analysis using MR- and CT-derived arterial input functions

Dynamic Contrast-Enhanced (DCE) MRI

The non-invasive dynamic contrast-enhanced MRI (DCE-MRI) method provides valuable insights into tissue perfusion and vascularity. Primarily used in oncology, DCE-MRI is typically utilized to assess morphology and contrast agent (CA) kinetics in the tissue of interest. Interpretation of the temporal signatures of DCE-MRI data includes qualitative, semi-quantitative, and quantitative approaches. Recent advances in MRI technology allow simultaneous high spatial and temporal resolutions in DCE-MRI data acquisition on most vendor platforms, enabling the more desirable approach of quantitative data analysis using pharmacokinetic (PK) modeling. Many technical factors, including signal-to-noise ratio, temporal resolution, quantifications of arterial input function and native tissue T1, and PK model selection, need to be carefully considered when performing quantitative DCE-MRI. Standardization in data acquisition and analysis is especially important in multi-center studies.

MRI Assessment of Changes in Tumor Vascularization during Neoadjuvant Anti-Angiogenic Treatment in Locally Advanced Breast Cancer Patients

Anti-VEGF (vascular endothelial growth factor) treatment improves response rates, but not progression-free or overall survival in advanced breast cancer. It has been suggested that subgroups of patients may benefit from this treatment; however, the effects of adding anti-VEGF treatment to a standard chemotherapy regimen in breast cancer patients are not well studied. Understanding the effects of the anti-vascular treatment on tumor vasculature may provide a selection of patients that can benefit. The aim of this study was to study the vascular effect of bevacizumab using clinical dynamic contrast-enhanced MRI (DCE-MRI). A total of 70 women were randomized to receive either chemotherapy alone or chemotherapy with bevacizumab for 25 weeks. DCE-MRI was performed at baseline and at 12 and 25 weeks, and in addition 25 of 70 patients agreed to participate in an early MRI after one week. Voxel-wise pharmacokinetic analysis was performed using semi-quantitative methods and the extended Tofts model. Vascular architecture was assessed by calculating the fractal dimension of the contrast-enhanced images. Changes during treatment were compared with baseline and between the treatment groups. There was no significant difference in tumor volume at any point; however, DCE-MRI parameters revealed differences in vascular function and vessel architecture. Adding bevacizumab to chemotherapy led to a pronounced reduction in vascular DCE-MRI parameters, indicating decreased vascularity. At 12 and 25 weeks, the difference between the treatment groups is severely reduced.

Multi-parametric MRI for radiotherapy simulation

Magnetic resonance imaging (MRI) has become an important imaging modality in the field of radiotherapy (RT) in the past decade, especially with the development of various novel MRI and image-guidance techniques. In this review article, we will describe recent developments and discuss the applications of multi-parametric MRI (mpMRI) in RT simulation. In this review, mpMRI refers to a general and loose definition which includes various multi-contrast MRI techniques. Specifically, we will focus on the implementation, challenges, and future directions of mpMRI techniques for RT simulation.

Optimal time for early therapeutic response prediction in nasopharyngeal carcinoma with functional magnetic resonance imaging

Background and Purpose

Physiological changes in tumour occur much earlier than morphological changes. They can potentially be used as biomarkers for therapeutic response prediction. This study aimed to investigate the optimal time for early therapeutic response prediction with multi-parametric magnetic resonance imaging (MRI) in patients with nasopharyngeal carcinoma (NPC) receiving concurrent chemo-radiotherapy (CCRT).

Material and Methods

Twenty-seven NPC patients were divided into the responder (N = 23) and the poor-responder (N = 4) groups by their primary tumour post-treatment shrinkages. Single-voxel proton MR spectroscopy (¹H-MRS), diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI were scanned at baseline, weekly during CCRT and post-CCRT. The median choline peak in ¹H-MRS, the median apparent diffusion coefficient (ADC) in DW-MRI, the median influx rate constant (K^trans), reflux rate constant (K_ep), volume of extravascular-extracellular space per unit volume (V_e), and initial area under the time-intensity curve for the first 60 s (iAUC60) in DCE-MRI were compared between the two groups with the Mann-Whitney tests for any significant difference at different time points.

Results

In DW-MRI, the percentage increase in ADC from baseline to week-1 for the responders (median = 11.39%, IQR = 18.13%) was higher than the poor-responders (median = 4.91%, IQR = 7.86%) (p = 0.027). In DCE-MRI, the iAUC60 on week-2 was found significantly higher in the poor-responders (median = 0.398, IQR = 0.051) than the responders (median = 0.192, IQR = 0.111) (p = 0.012). No significant difference was found in median choline peaks in ¹H-MRS at all time points.

Conclusion

Early perfusion and diffusion changes occurred in primary tumours of NPC patients treated with CCRT. The DW-MRI on week-1 and the DCE-MRI on week-2 were the optimal time points for early therapeutic response prediction.

Correlations of tumour permeability parameters with apparent diffusion coefficient in nasopharyngeal carcinoma

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Vascular permeability is associated with diffusability in nasopharyngeal tumour.

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Both influx and reflux rates have inverse linear correlations with ADC.

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Reflux rate has the strongest inverse linear correlation with ADC.

Background and Purpose

Functional imaging has an established role in therapeutic monitoring of cancer treatments. This study evaluated the correlations of tumour permeability parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and tumour cellularity derived from apparent diffusion coefficient (ADC) in nasopharyngeal carcinoma (NPC).

Material and Methods

Twenty NPC patients were examined with DCE-MRI and RESOLVE diffusion-weighted MRI (DW-MRI). Tumour permeability parameters were quantitatively measured with Tofts compartment model. Volume transfer constant (K^trans), volume of extravascular extracellular space (EES) per unit volume of tissue (V_e), and the flux rate constant between EES and plasma (K_ep) from DCE-MRI scan were measured. The time-intensity curve was plotted from the 60 dynamic phases of DCE-MRI. The initial area under the curve for the first 60 s of the contrast agent arrival (iAUC60) was also calculated. They were compared with the ADC value derived from DW-MRI with Pearson correlation analyses.

Results

Among the DCE-MRI permeability parameters, K_ep had higher linearity in inverse correlation with ADC value (r = −0.69, p = <0.05). K^trans (r = −0.60, p=<0.05) and iAUC60 (r = −0.64, p = <0.05) also had significant inverse correlations with ADC. V_e showed a significant positive correlation with ADC (r = 0.63, p = <0.05).

Conclusions

Nasopharyngeal tumour vascular permeability parameters derived from DCE-MRI scan were correlated linearly with tumour cellularity measured by free water diffusability with ADC. The clinical implementations of these linear correlations in the quantitative assessments of therapeutic response for NPC patients may be worth to further explore.

Maximum Entropy Technique and Regularization Functional for Determining the Pharmacokinetic Parameters in DCE-MRI

This paper aims to solve the arterial input function (AIF) determination in dynamic contrast-enhanced MRI (DCE-MRI), an important linear ill-posed inverse problem, using the maximum entropy technique (MET) and regularization functionals. In addition, estimating the pharmacokinetic parameters from a DCE-MR image investigations is an urgent need to obtain the precise information about the AIF-the concentration of the contrast agent on the left ventricular blood pool measured over time. For this reason, the main idea is to show how to find a unique solution of linear system of equations generally in the form of [Formula: see text] named an ill-conditioned linear system of equations after discretization of the integral equations, which appear in different tomographic image restoration and reconstruction issues. Here, a new algorithm is described to estimate an appropriate probability distribution function for AIF according to the MET and regularization functionals for the contrast agent concentration when applying Bayesian estimation approach to estimate two different pharmacokinetic parameters. Moreover, by using the proposed approach when analyzing simulated and real datasets of the breast tumors according to pharmacokinetic factors, it indicates that using Bayesian inference-that infer the uncertainties of the computed solutions, and specific knowledge of the noise and errors-combined with the regularization functional of the maximum entropy problem, improved the convergence behavior and led to more consistent morphological and functional statistics and results. Finally, in comparison to the proposed exponential distribution based on MET and Newton's method, or Weibull distribution via the MET and teaching-learning-based optimization (MET/TLBO) in the previous studies, the family of Gamma and Erlang distributions estimated by the new algorithm are more appropriate and robust AIFs.

Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis

Background: For the kinetic models used in contrast-based medical imaging, the assignment of the arterial input function named AIF is essential for the estimation of the physiological parameters of the tissue via solving an optimization problem. Objective: In the current study, we estimate the AIF relayed on the modified maximum entropy method. The effectiveness of several numerical methods to determine kinetic parameters and the AIF is evaluated—in situations where enough information about the AIF is not available. The purpose of this study is to identify an appropriate method for estimating this function. Materials and Methods: The modified algorithm is a mixture of the maximum entropy approach with an optimization method, named the teaching-learning method. In here, we applied this algorithm in a Bayesian framework to estimate the kinetic parameters when specifying the unique form of the AIF by the maximum entropy method. We assessed the proficiency of the proposed method for assigning the kinetic parameters in the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), when determining AIF with some other parameter-estimation methods and a standard fixed AIF method. A previously analyzed dataset consisting of contrast agent concentrations in tissue and plasma was used. Results and Conclusions: We compared the accuracy of the results for the estimated parameters obtained from the MMEM with those of the empirical method, maximum likelihood method, moment matching (“method of moments”), the least-square method, the modified maximum likelihood approach, and our previous work. Since the current algorithm does not have the problem of starting point in the parameter estimation phase, it could find the best and nearest model to the empirical model of data, and therefore, the results indicated the Weibull distribution as an appropriate and robust AIF and also illustrated the power and effectiveness of the proposed method to estimate the kinetic parameters.

Twenty Years On: RECIST as a Biomarker of Response in Solid Tumours an EORTC Imaging Group - ESOI Joint Paper

Response evaluation criteria in solid tumours (RECIST) v1.1 are currently the reference standard for evaluating efficacy of therapies in patients with solid tumours who are included in clinical trials, and they are widely used and accepted by regulatory agencies. This expert statement discusses the principles underlying RECIST, as well as their reproducibility and limitations. While the RECIST framework may not be perfect, the scientific bases for the anticancer drugs that have been approved using a RECIST-based surrogate endpoint remain valid. Importantly, changes in measurement have to meet thresholds defined by RECIST for response classification within thus partly circumventing the problems of measurement variability. The RECIST framework also applies to clinical patients in individual settings even though the relationship between tumour size changes and outcome from cohort studies is not necessarily translatable to individual cases. As reproducibility of RECIST measurements is impacted by reader experience, choice of target lesions and detection/interpretation of new lesions, it can result in patients changing response categories when measurements are near threshold values or if new lesions are missed or incorrectly interpreted. There are several situations where RECIST will fail to evaluate treatment-induced changes correctly; knowledge and understanding of these is crucial for correct interpretation. Also, some patterns of response/progression cannot be correctly documented by RECIST, particularly in relation to organ-site (e.g. bone without associated soft-tissue lesion) and treatment type (e.g. focal therapies). These require specialist reader experience and communication with oncologists to determine the actual impact of the therapy and best evaluation strategy. In such situations, alternative imaging markers for tumour response may be used but the sources of variability of individual imaging techniques need to be known and accounted for. Communication between imaging experts and oncologists regarding the level of confidence in a biomarker is essential for the correct interpretation of a biomarker and its application to clinical decision-making. Though measurement automation is desirable and potentially reduces the variability of results, associated technical difficulties must be overcome, and human adjudications may be required.

Repeatability of tumor perfusion kinetics from dynamic contrast-enhanced MRI in glioblastoma

Background

Dynamic contrast-enhanced MRI (DCE-MRI) parameters have been shown to be biomarkers for treatment response in glioblastoma (GBM). However, variations in analysis and measurement methodology complicate determination of biological changes measured via DCE. The aim of this study is to quantify DCE-MRI variations attributable to analysis methodology and image quality in GBM patients.

Methods

The Extended Tofts model (eTM) and Leaky Tracer Kinetic Model (LTKM), with manually and automatically segmented vascular input functions (VIFs), were used to calculate perfusion kinetic parameters from 29 GBM patients with double-baseline DCE-MRI data. DCE-MRI images were acquired 2-5 days apart with no change in treatment. Repeatability of kinetic parameters was quantified with Bland-Altman and percent repeatability coefficient (%RC) analysis.

Results

The perfusion parameter with the least RC was the plasma volume fraction (v _p ), with a %RC of 53%. The extra-cellular extra-vascular volume fraction (v _e ) %RC was 82% and 81%, for extended Tofts-Kety Model (eTM) and LTKM respectively. The %RC of the volume transfer rate constant (K ^trans ) was 72% for the eTM, and 82% for the LTKM, respectively. Using an automatic VIF resulted in smaller %RCs for all model parameters, as compared to manual VIF.

Conclusions

As much as 72% change in K ^trans (eTM, autoVIF) can be attributable to non-biological changes in the 2-5 days between double-baseline imaging. Poor K ^trans repeatability may result from inferior temporal resolution and short image acquisition time. This variation suggests DCE-MRI repeatability studies should be performed institutionally, using an automatic VIF method and following quantitative imaging biomarkers alliance guidelines.

DCE-MRI is more sensitive than IVIM-DWI for assessing anti-angiogenic treatment-induced changes in colorectal liver metastases

BACKGROUND

Diffusion weighted imaging (DWI) with intravoxel incoherent motion (IVIM) modelling can inform on tissue perfusion without exogenous contrast administration. Dynamic-contrast-enhanced (DCE) MRI can also characterise tissue perfusion, but requires a bolus injection of a Gadolinium-based contrast agent. This study compares the use of DCE-MRI and IVIM-DWI methods in assessing response to anti-angiogenic treatment in patients with colorectal liver metastases in a cohort with confirmed treatment response.

METHODS

This prospective imaging study enrolled 25 participants with colorectal liver metastases to receive Regorafenib treatment. A target metastasis > 2 cm in each patient was imaged before and at 15 days after treatment on a 1.5T MR scanner using slice-matched IVIM-DWI and DCE-MRI protocols. MRI data were motion-corrected and tumour volumes of interest drawn on b=900 s/mm2 diffusion-weighted images were transferred to DCE-MRI data for further analysis. The median value of four IVIM-DWI parameters [diffusion coefficient D (10-3 mm2/s), perfusion fraction f (ml/ml), pseudodiffusion coefficient D* (10-3 mm2/s), and their product fD* (mm2/s)] and three DCE-MRI parameters [volume transfer constant Ktrans (min-1), enhancement fraction EF (%), and their product KEF (min-1)] were recorded at each visit, before and after treatment. Changes in pre- and post-treatment measurements of all MR parameters were assessed using Wilcoxon signed-rank tests (P<0.05 was considered significant). DCE-MRI and IVIM-DWI parameter correlations were evaluated with Spearman rank tests. Functional MR parameters were also compared against Response Evaluation Criteria In Solid Tumours v.1.1 (RECIST) evaluations.

RESULTS

Significant treatment-induced reductions of DCE-MRI parameters across the cohort were observed for EF (91.2 to 50.8%, P<0.001), KEF (0.095 to 0.045 min-1, P<0.001) and Ktrans (0.109 to 0.078 min-1, P=0.002). For IVIM-DWI, only D (a non-perfusion parameter) increased significantly post treatment (0.83 to 0.97 × 10-3 mm2/s, P<0.001), while perfusion-related parameters showed no change. No strong correlations were found between DCE-MRI and IVIM-DWI parameters. A moderate correlation was found, after treatment, between Ktrans and D* (r=0.60; P=0.002) and fD* (r=0.67; P<0.001). When compared to RECIST v.1.1 evaluations, KEF and D correctly identified most clinical responders, whilst non-responders were incorrectly identified.

CONCLUSION

IVIM-DWI perfusion-related parameters showed limited sensitivity to the anti-angiogenic effects of Regorafenib treatment in colorectal liver metastases and showed low correlation with DCE-MRI parameters, despite profound and significant post-treatment reductions in DCE-MRI measurements.

TRIAL REGISTRATION

NCT03010722 clinicaltrials.gov; registration date 6th January 2015.

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

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

An automatic framework for evaluating the vascular permeability of bone metastases from prostate cancer

Objectives.Vascular permeability can reflect tumorigenesis and metastasis. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can assess microvascular permeability by pharmacokinetic parameter estimation. Most estimation methods require manually selected arterial input function (AIF) or reference regions. However, the result will be unstable due to the annotation, which relies on personal experience. In this study, we propose an automatic framework for evaluating vascular permeability of bone metastases from prostate cancer without selecting AIF.Materials and methods.This retrospective study comprised of 15 prostate cancer patients with bone metastases. Based on clinical consensus for three typical DCE-MRI curve patterns, three characteristic curves as regularization constraints were introduced to the extended Tofts model (ETM) using clustering strategy, and the clustering-based blind identification of multichannel (CBM) framework was then proposed for pharmacokinetic parameter estimation. With automatic segmentation of the whole bone area, we obtained the estimation of the pharmacokinetic parameters in the bone area and quantified for bone metastases. Two experienced radiologists compared the CBM estimations with the diagnostic results and we compared the estimations with those of the ETM in bone metastasis regions to evaluate the feasibility of the CBM framework.Results.The higher signal regions ofK^transandK_epindicated the metastasis of prostate cancer, which is consistent with the cancer area marked by the radiologists. In addition, theK^transandK_epin bone metastasis regions were significantly higher than in normal bone regions (P < 0.001,P < 0.001). The consistency of estimation by using the CBM framework and conventional ETM method was confirmed by Bland-Altman analysis.Conclusion.The proposed CBM framework can provide a fully automatic and reliable quantitative estimation of vascular permeability for bone metastases in prostate cancer patients.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

KEY POINTS

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

Application of T1 Map Information Based on Synthetic MRI for Dynamic Contrast-Enhanced Imaging: A Comparison Study with the Fixed Baseline T1 Value Method

Objective

For an accurate dynamic contrast-enhanced (DCE) MRI analysis, exact baseline T1 mapping is critical. The purpose of this study was to compare the pharmacokinetic parameters of DCE MRI using synthetic MRI with those using fixed baseline T1 values.

Materials and Methods

This retrospective study included 102 patients who underwent both DCE and synthetic brain MRI. Two methods were set for the baseline T1: one using the fixed value and the other using the T1 map from synthetic MRI. The volume transfer constant (K^trans), volume of the vascular plasma space (v_p), and the volume of the extravascular extracellular space (v_e) were compared between the two methods. The interclass correlation coefficients and the Bland-Altman method were used to assess the reliability.

Results

In normal-appearing frontal white matter (WM), the mean values of K^trans, v_e, and v_p were significantly higher in the fixed value method than in the T1 map method. In the normal-appearing occipital WM, the mean values of v_e and v_p were significantly higher in the fixed value method. In the putamen and head of the caudate nucleus, the mean values of K^trans, v_e, and v_p were significantly lower in the fixed value method. In addition, the T1 map method showed comparable interobserver agreements with the fixed baseline T1 value method.

Conclusion

The T1 map method using synthetic MRI may be useful for reflecting individual differences and reliable measurements in clinical applications of DCE MRI.

Quantifying MRI T1 relaxation in flowing blood: implications for arterial input function measurement in DCE-MRI

OBJECTIVES

To investigate the feasibility of accurately quantifying the concentration of MRI contrast agent in flowing blood by measuring its T1 in a large vessel. Such measures are often used to obtain patient-specific arterial input functions for the accurate fitting of pharmacokinetic models to dynamic contrast enhanced MRI data. Flow is known to produce errors with this technique, but these have so far been poorly quantified and characterised in the context of pulsatile flow with a rapidly changing T1 as would be expected in vivo.

METHODS

A phantom was developed which used a mechanical pump to pass fluid at physiologically relevant rates. Measurements of T₁ were made using high temporal resolution gradient recalled sequences suitable for DCE-MRI of both constant and pulsatile flow. These measures were used to validate a virtual phantom that was then used to simulate the expected errors in the measurement of an AIF in vivo.

RESULTS

The relationship between measured T1 values and flow velocity was found to be non-linear. The subsequent error in quantification of contrast agent concentration in a measured AIF was shown.

CONCLUSIONS

The T1 measurement of flowing blood using standard DCE- MRI sequences are subject to large measurement errors which are non-linear in relation to flow velocity.

ADVANCES IN KNOWLEDGE

This work qualitatively and quantitatively demonstrates the difficulties of accurately measuring the T1 of flowing blood using DCE-MRI over a wide range of physiologically realistic flow velocities and pulsatilities. Sources of error are identified and proposals made to reduce these.

Assessment of metastatic lymph nodes in head and neck squamous cell carcinomas using simultaneous 18F-FDG-PET and MRI

In this study, we investigate the feasibility of using dynamic contrast enhanced magnetic resonance imaging (DCE-MRI), diffusion weighted imaging (DWI), and dynamic positron emission tomography (PET) for detection of metastatic lymph nodes in head and neck squamous cell carcinoma (HNSCC) cases. Twenty HNSCC patients scheduled for lymph node dissection underwent DCE-MRI, dynamic PET, and DWI using a PET-MR scanner within one week prior to their planned surgery. During surgery, resected nodes were labeled to identify their nodal levels and sent for routine clinical pathology evaluation. Quantitative parameters of metastatic and normal nodes were calculated from DCE-MRI (v_e, v_p, PS, F_p, K^trans), DWI (ADC) and PET (K_i, K₁, k₂, k₃) to assess if an individual or a combination of parameters can classify normal and metastatic lymph nodes accurately. There were 38 normal and 11 metastatic nodes covered by all three imaging methods and confirmed by pathology. 34% of all normal nodes had volumes greater than or equal to the smallest metastatic node while 4 normal nodes had SUV > 4.5. Among the MRI parameters, the median v_p, F_p, PS, and K^trans values of the metastatic lymph nodes were significantly lower (p = <0.05) than those of normal nodes. v_e and ADC did not show any statistical significance. For the dynamic PET parameters, the metastatic nodes had significantly higher k₃ (p value = 8.8 × 10^-8) and K_i (p value = 5.3 × 10^-8) than normal nodes. K₁ and k₂ did not show any statistically significant difference. K_i had the best separation with accuracy = 0.96 (sensitivity = 1, specificity = 0.95) using a cutoff of K_i = 5.3 × 10^-3 mL/cm³/min, while k₃ and volume had accuracy of 0.94 (sensitivity = 0.82, specificity = 0.97) and 0.90 (sensitivity = 0.64, specificity = 0.97) respectively. 100% accuracy can be achieved using a multivariate logistic regression model of MRI parameters after thresholding the data with K_i < 5.3 × 10^-3 mL/cm³/min. The results of this preliminary study suggest that quantitative MRI may provide additional value in distinguishing metastatic nodes, particularly among small nodes, when used together with FDG-PET.

Improving the Reliability of Pharmacokinetic Parameters at Dynamic Contrast-enhanced MRI in Astrocytomas: A Deep Learning Approach

Background Pharmacokinetic (PK) parameters obtained from dynamic contrast agent-enhanced (DCE) MRI evaluates the microcirculation permeability of astrocytomas, but the unreliability from arterial input function (AIF) remains a challenge. Purpose To develop a deep learning model that improves the reliability of AIF for DCE MRI and to validate the reliability and diagnostic performance of PK parameters by using improved AIF in grading astrocytomas. Materials and Methods This retrospective study included 386 patients (mean age, 52 years ± 16 [standard deviation]; 226 men) with astrocytomas diagnosed with histopathologic analysis who underwent dynamic susceptibility contrast (DSC)-enhanced and DCE MRI preoperatively from April 2010 to January 2018. The AIF was obtained from each sequence: AIF obtained from DSC-enhanced MRI (AIF_DSC) and AIF measured at DCE MRI (AIF_DCE). The model was trained to translate AIF_DCE into AIF_DSC, and after training, outputted neural-network-generated AIF (AIF_{generated DSC}) with input AIF_DCE. By using the three different AIFs, volume transfer constant (K^trans), fractional volume of extravascular extracellular space (V_e), and vascular plasma space (V_p) were averaged from the tumor areas in the DCE MRI. To validate the model, intraclass correlation coefficients and areas under the receiver operating characteristic curve (AUCs) of the PK parameters in grading astrocytomas were compared by using different AIFs. Results The AIF-generated, DSC-derived PK parameters showed higher AUCs in grading astrocytomas than those derived from AIF_DCE (mean K^trans, 0.88 [95% confidence interval {CI}: 0.81, 0.93] vs 0.72 [95% CI: 0.63, 0.79], P = .04; mean V_e, 0.87 [95% CI: 0.79, 0.92] vs 0.70 [95% CI: 0.61, 0.77], P = .049, respectively). K^trans and V_e showed higher intraclass correlation coefficients for AIF_{generated DSC} than for AIF_DCE (0.91 vs 0.38, P < .001; and 0.86 vs 0.60, P < .001, respectively). In AIF analysis, baseline signal intensity (SI), maximal SI, and wash-in slope showed higher intraclass correlation coefficients with AIF_{generated DSC} than AIF_DCE (0.77 vs 0.29, P < .001; 0.68 vs 0.42, P = .003; and 0.66 vs 0.45, P = .01, respectively. Conclusion A deep learning algorithm improved both reliability and diagnostic performance of MRI pharmacokinetic parameters for differentiating astrocytoma grades. © RSNA, 2020 Online supplemental material is available for this article.

Use of Indicator Dilution Principle to Evaluate Accuracy of Arterial Input Function Measured With Low-Dose Ultrafast Prostate Dynamic Contrast-Enhanced MRI

Accurately measuring arterial input function (AIF) is essential for quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). We used the indicator dilution principle to evaluate the accuracy of AIF measured directly from an artery following a low-dose contrast media ultrafast DCE-MRI. In total, 15 patients with biopsy-confirmed localized prostate cancers were recruited. Cardiac MRI (CMRI) and ultrafast DCE-MRI were acquired on a Philips 3 T Ingenia scanner. The AIF was measured at iliac arties following injection of a low-dose (0.015 mmol/kg) gadolinium (Gd) contrast media. The cardiac output (CO) from CMRI (CO_CMRI) was calculated from the difference in ventricular volume at diastole and systole measured on the short axis of heart. The CO from DCE-MRI (CO_DCE) was also calculated from the AIF and dose of the contrast media used. A correlation test and Bland–Altman plot were used to compare CO_CMRI and CO_DCE. The average (±standard deviation [SD]) area under the curve measured directly from local AIF was 0.219 ± 0.07 mM·min. The average (±SD) CO_CMRI and CO_DCE were 6.52 ± 1.47 L/min and 6.88 ± 1.64 L/min, respectively. There was a strong positive correlation (r = 0.82, P < .01) and good agreement between CO_CMRI and CO_DCE. The CO_DCE is consistent with the reference standard CO_CMRI. This indicates that the AIF can be measured accurately from an artery with ultrafast DCE-MRI following injection of a low-dose contrast media.

Prognostic role of diffusion weighted and dynamic contrast-enhanced MRI in loco-regionally advanced head and neck cancer treated with concomitant chemoradiotherapy

Background In the study, the value of pre-treatment dynamic contrast-enhanced (DCE) and diffusion weighted (DW) MRI-derived parameters as well as their changes early during treatment was evaluated for predicting disease-free survival (DFS) and overall survival (OS) in patients with locoregionally advanced head and neck squamous carcinoma (HNSCC) treated with concomitant chemoradiotherapy (cCRT) with cisplatin. Patients and methods MRI scans were performed in 20 patients with locoregionally advanced HNSCC at baseline and after 10 Grays (Gy) of cCRT. Tumour apparent diffusion coefficient (ADC) and DCE parameters (volume transfer constant [Ktrans], extracellular extravascular volume fraction [ve], and plasma volume fraction [Vp]) were measured. Relative changes in parameters from baseline to 10 Gy were calculated. Univariate and multivariate Cox regression analysis were conducted. Receiver operating characteristic (ROC) curve analysis was employed to identify parameters with the best diagnostic performance. Results None of the parameters was identified to predict for DFS. On univariate analysis of OS, lower pre-treatment ADC (p = 0.012), higher pre-treatment Ktrans (p = 0.026), and higher reduction in Ktrans (p = 0.014) from baseline to 10 Gy were identified as significant predictors. Multivariate analysis identified only higher pre-treatment Ktrans (p = 0.026; 95% CI: 0.000-0.132) as an independent predictor of OS. At ROC curve analysis, pre-treatment Ktrans yielded an excellent diagnostic accuracy (area under curve [AUC] = 0.95, sensitivity 93.3%; specificity 80 %). Conclusions In our group of HNSCC patients treated with cisplatin-based cCRT, pre-treatment Ktrans was found to be a good predictor of OS.

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

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

Improved repeatability of dynamic contrast-enhanced MRI using the complex MRI signal to derive arterial input functions: a test-retest study in prostate cancer patients

Purpose

The arterial input function (AIF) is a major source of uncertainty in tracer kinetic (TK) analysis of dynamic contrast‐enhanced (DCE)‐MRI data. The aim of this study was to investigate the repeatability of AIFs extracted from the complex signal and of the resulting TK parameters in prostate cancer patients.

Methods

Twenty‐two patients with biopsy‐proven prostate cancer underwent a 3T MRI exam twice. DCE‐MRI data were acquired with a 3D spoiled gradient echo sequence. AIFs were extracted from the magnitude of the signal (AIF_MAGN), phase (AIF_PHASE), and complex signal (AIF_COMPLEX). The Tofts model was applied to extract K^trans, k_ep and v_e. Repeatability of AIF curve characteristics and TK parameters was assessed with the within‐subject coefficient of variation (wCV).

Results

The wCV for peak height and full width at half maximum for AIF_COMPLEX (7% and 8%) indicated an improved repeatability compared to AIF_MAGN (12% and 12%) and AIF_PHASE (12% and 7%). This translated in lower wCV values for K^trans (11%) with AIF_COMPLEX in comparison to AIF_MAGN (24%) and AIF_PHASE (15%). For k_ep, the wCV was 16% with AIF_MAGN, 13% with AIF_PHASE, and 13% with AIF_COMPLEX.

Conclusion

Repeatability of AIF_PHASE and AIF_COMPLEX is higher than for AIF_MAGN, resulting in a better repeatability of TK parameters. Thus, use of either AIF_PHASE or AIF_COMPLEX improves the robustness of quantitative analysis of DCE‐MRI in prostate cancer.

Accurate Therapeutic Response Assessment of Pancreatic Ductal Adenocarcinoma Using Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging With a Point-of-Care Perfusion Phantom: A Pilot Study

OBJECTIVES

The aim of this study was to test the feasibility of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with concurrent perfusion phantom for monitoring therapeutic response in patients with pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

A prospective pilot study was conducted with 8 patients (7 men and 1 woman) aged 46 to 78 years (mean age, 66 years). Participants had either locally advanced (n = 7) or metastatic (n = 1) PDAC, and had 2 DCE-MRI examinations: one before and one 8 ± 1 weeks after starting first-line chemotherapy. A small triplicate perfusion phantom was imaged with each patient, serving as an internal reference for accurate quantitative image analysis. Tumor perfusion was measured with K using extended Tofts model before and after phantom-based data correction. Results are presented as mean ± SD and 95% confidence intervals (CIs). Statistical difference was evaluated with 1-way analysis of variance.

RESULTS

Tumor-size change of responding group (n = 4) was -12% ± 4% at 8 weeks of therapy, while that of nonresponding group (n = 4) was 18% ± 15% (P = 0.0100). Before phantom-based data correction, the K change of responding tumors was 69% ± 23% (95% CI, 32% to 106%) at 8 weeks, whereas that of nonresponding tumors was -1% ± 41% (95% CI, -65% to 64%) (P = 0.0247). After correction, the data variation in each group was significantly reduced; the K change of responding tumors was 73% ± 6% (95% CI, 64% to 82%) compared with nonresponding tumors of -0% ± 5% (95% CI, -7% to 8%) (P < 0.0001).

CONCLUSIONS

Quantitative DCE-MRI measured the significant perfusion increase of PDAC tumors responding favorably to chemotherapy, with decreased variability after correction using a perfusion phantom.

Functional imaging and circulating biomarkers of response to regorafenib in treatment-refractory metastatic colorectal cancer patients in a prospective phase II study

OBJECTIVE

Regorafenib demonstrated efficacy in patients with metastatic colorectal cancer (mCRC). Lack of predictive biomarkers, potential toxicities and cost-effectiveness concerns highlight the unmet need for better patient selection.

DESIGN

Patients with RAS mutant mCRC with biopsiable metastases were enrolled in this phase II trial. Dynamic contrast-enhanced (DCE) MRI was acquired pretreatment and at day 15 post-treatment. Median values of volume transfer constant (Ktrans), enhancing fraction (EF) and their product KEF (summarised median values of Ktrans× EF) were generated. Circulating tumour (ct) DNA was collected monthly until progressive disease and tested for clonal RAS mutations by digital-droplet PCR. Tumour vasculature (CD-31) was scored by immunohistochemistry on 70 sequential tissue biopsies.

RESULTS

Twenty-seven patients with paired DCE-MRI scans were analysed. Median KEF decrease was 58.2%. Of the 23 patients with outcome data, >70% drop in KEF (6/23) was associated with higher disease control rate (p=0.048) measured by RECIST V. 1.1 at 2 months, improved progression-free survival (PFS) (HR 0.16 (95% CI 0.04 to 0.72), p=0.02), 4-month PFS (66.7% vs 23.5%) and overall survival (OS) (HR 0.08 (95% CI 0.01 to 0.63), p=0.02). KEF drop correlated with CD-31 reduction in sequential tissue biopsies (p=0.04). RAS mutant clones decay in ctDNA after 8 weeks of treatment was associated with better PFS (HR 0.21 (95% CI 0.06 to 0.71), p=0.01) and OS (HR 0.28 (95% CI 0.07-1.04), p=0.06).

CONCLUSIONS

Combining DCE-MRI and ctDNA predicts duration of anti-angiogenic response to regorafenib and may improve patient management with potential health/economic implications.

Comparison of arterial input functions measured from ultra-fast dynamic contrast enhanced MRI and dynamic contrast enhanced computed tomography in prostate cancer patients

The purpose of this study was to evaluate the accuracy of arterial input functions (AIFs) measured from dynamic contrast enhanced (DCE) MRI following a low dose of contrast media injection. The AIFs measured from DCE computed tomography (CT) were used as 'gold standard'. A total of twenty patients received CT and MRI scans on the same day. Patients received 120 ml Iohexol in DCE-CT and a low dose of (0.015 mM kg^-1) of gadobenate dimeglumine in DCE-MRI. The AIFs were measured in the iliac artery and normalized to the CT and MRI contrast agent doses. To correct for different temporal resolution and sampling periods of CT and MRI, an empirical mathematical model (EMM) was used to fit the AIFs first. Then numerical AIFs (AIF_CT and AIF_MRI) were calculated based on fitting parameters. The AIF_MRI was convolved with a 'contrast agent injection' function ([Formula: see text]) to correct for the difference between MRI and CT contrast agent injection times (~1.5 s versus 30 s). The results show that the EMMs accurately fitted AIFs measured from CT and MRI. There was no significant difference (p  >  0.05) between the maximum peak amplitude of AIFs from CT (22.1  ±  4.1 mM/dose) and MRI after convolution (22.3  ±  5.2 mM/dose). The shapes of the AIF_CT and [Formula: see text] were very similar. Our results demonstrated that AIFs can be accurately measured by MRI following low dose contrast agent injection.

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

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

Diffusion-weighted (DW) MRI in lung cancers: ADC test-retest repeatability

PURPOSE

To determine the test-retest repeatability of Apparent Diffusion Coefficient (ADC) measurements across institutions and MRI vendors, plus investigate the effect of post-processing methodology on measurement precision.

METHODS

Thirty malignant lung lesions >2 cm in size (23 patients) were scanned on two occasions, using echo-planar-Diffusion-Weighted (DW)-MRI to derive whole-tumour ADC (b = 100, 500 and 800smm-2). Scanning was performed at 4 institutions (3 MRI vendors). Whole-tumour volumes-of-interest were copied from first visit onto second visit images and from one post-processing platform to an open-source platform, to assess ADC repeatability and cross-platform reproducibility.

RESULTS

Whole-tumour ADC values ranged from 0.66-1.94x10-3mm2s-1 (mean = 1.14). Within-patient coefficient-of-variation (wCV) was 7.1% (95% CI 5.7-9.6%), limits-of-agreement (LoA) -18.0 to 21.9%. Lesions >3 cm had improved repeatability: wCV 3.9% (95% CI 2.9-5.9%); and LoA -10.2 to 11.4%. Variability for lesions <3 cm was 2.46 times higher. ADC reproducibility across different post-processing platforms was excellent: Pearson's R2 = 0.99; CoV 2.8% (95% CI 2.3-3.4%); and LoA -7.4 to 8.0%.

CONCLUSION

A free-breathing DW-MRI protocol for imaging malignant lung tumours achieved satisfactory within-patient repeatability and was robust to changes in post-processing software, justifying its use in multi-centre trials. For response evaluation in individual patients, a change in ADC >21.9% will reflect treatment-related change.

KEY POINTS

• In lung cancer, free-breathing DWI-MRI produces acceptable images with evaluable ADC measurement. • ADC repeatability coefficient-of-variation is 7.1% for lung tumours >2 cm. • ADC repeatability coefficient-of-variation is 3.9% for lung tumours >3 cm. • ADC measurement precision is unaffected by the post-processing software used. • In multicentre trials, 22% increase in ADC indicates positive treatment response.

DCE-MRI, DW-MRI, and MRS in Cancer: Challenges and Advantages of Implementing Qualitative and Quantitative Multi-parametric Imaging in the Clinic

Multi-parametric magnetic resonance imaging (mpMRI) offers a unique insight into tumor biology by combining functional MRI techniques that inform on cellularity (diffusion-weighted MRI), vascular properties (dynamic contrast-enhanced MRI), and metabolites (magnetic resonance spectroscopy) and has scope to provide valuable information for prognostication and response assessment. Challenges in the application of mpMRI in the clinic include the technical considerations in acquiring good quality functional MRI data, development of robust techniques for analysis, and clinical interpretation of the results. This article summarizes the technical challenges in acquisition and analysis of multi-parametric MRI data before reviewing the key applications of multi-parametric MRI in clinical research and practice.

Time-resolved angiography with stochastic trajectories for dynamic contrast-enhanced MRI in head and neck cancer: Are pharmacokinetic parameters affected?

PURPOSE

To investigate the effects of different time-resolved angiography with stochastic trajectories (TWIST) k-space undersampling schemes on calculated pharmacokinetic dynamic contrast-enhanced (DCE) vascular parameters.

METHODS

A digital perfusion phantom was employed to simulate effects of TWIST on characteristics of signal changes in DCE. Furthermore, DCE-MRI was acquired without undersampling in a group of patients with head and neck squamous cell carcinoma and used to simulate a range of TWIST schemes. Errors were calculated as differences between reference and TWIST-simulated DCE parameters. Parametrical error maps were used to display the averaged results from all tumors.

RESULTS

For a relatively wide range of undersampling schemes, errors in pharmacokinetic parameters due to TWIST were under 10% for the volume transfer constant, Ktrans, and total extracellular extravascular space volume, Ve. TWIST induced errors in the total blood plasma volume, Vp, were the largest observed, and these were inversely dependent on the area of the fully sampled k-space. The magnitudes of errors were not correlated with Ktrans, Vp and weakly correlated with Ve.

CONCLUSIONS

The authors demonstrated methods to validate and optimize k-space view-sharing techniques for pharmacokinetic DCE studies using a range of clinically relevant spatial and temporal patient derived data. The authors found a range of undersampling patterns for which the TWIST sequence can be reliably used in pharmacokinetic DCE-MRI. The parameter maps created in the study can help to make a decision between temporal and spatial resolution demands and the quality of enhancement curve characterization.