Tracer kinetic model selection for dynamic contrast-enhanced magnetic resonance imaging of locally advanced cervical cancer

Dynamic contrast enhanced (DCE) MRI estimation of vascular parameters using knowledge-based adaptive models

We introduce and validate four adaptive models (AMs) to perform a physiologically based Nested-Model-Selection (NMS) estimation of such microvascular parameters as forward volumetric transfer constant, Ktrans, plasma volume fraction, vp, and extravascular, extracellular space, ve, directly from Dynamic Contrast-Enhanced (DCE) MRI raw information without the need for an Arterial-Input Function (AIF). In sixty-six immune-compromised-RNU rats implanted with human U-251 cancer cells, DCE-MRI studies estimated pharmacokinetic (PK) parameters using a group-averaged radiological AIF and an extended Patlak-based NMS paradigm. One-hundred-ninety features extracted from raw DCE-MRI information were used to construct and validate (nested-cross-validation, NCV) four AMs for estimation of model-based regions and their three PK parameters. An NMS-based a priori knowledge was used to fine-tune the AMs to improve their performance. Compared to the conventional analysis, AMs produced stable maps of vascular parameters and nested-model regions less impacted by AIF-dispersion. The performance (Correlation coefficient and Adjusted R-squared for NCV test cohorts) of the AMs were: 0.914/0.834, 0.825/0.720, 0.938/0.880, and 0.890/0.792 for predictions of nested model regions, vp, Ktrans, and ve, respectively. This study demonstrates an application of AMs that quickens and improves DCE-MRI based quantification of microvasculature properties of tumors and normal tissues relative to conventional approaches.

GdDO3NI Enhanced Magnetic Resonance Imaging Allows Imaging of Hypoxia After Brain Injury

BACKGROUND

Brain tissue hypoxia is a common consequence of traumatic brain injury (TBI) due to the rupture of blood vessels during impact and it correlates with poor outcome. The current magnetic resonance imaging (MRI) techniques are unable to provide a direct map of tissue hypoxia.

PURPOSE

To investigate whether GdDO3NI, a nitroimidazole-based T₁ MRI contrast agent allows imaging hypoxia in the injured brain after experimental TBI.

STUDY TYPE

Prospective.

ANIMAL MODEL

TBI-induced mice (controlled cortical impact model) were intravenously injected with either conventional T₁ agent (gadoteridol) or GdDO3NI at 0.3 mmol/kg dose (n = 5 for each cohort) along with pimonidazole (60 mg/kg) at 1 hour postinjury and imaged for 3 hours following which they were euthanized.

FIELD STRENGTH/SEQUENCE

7 T/T₂ -weighted spin echo and T₁ -weighted gradient echo.

ASSESSMENT

Injured animals were imaged with T₂ -weighted spin-echo sequence to estimate the extent of the injury. The mice were then imaged precontrast and postcontrast using a T₁ -weighted gradient-echo sequence for 3 hours postcontrast. Regions of interests were drawn on the brain injury region, the contralateral brain as well as on the cheek muscle region for comparison of contrast kinetics. Brains were harvested immediately post-imaging for immunohistochemical analysis.

STATISTICAL TESTS

One-way analysis of variance and two-sample t-tests were performed with a P < 0.05 was considered statistically significant.

RESULTS

GdDO3NI retention in the injury region at 2.5-3 hours post-injection was significantly higher compared to gadoteridol (mean retention fraction 63.95% ± 27.43% vs. 20.68% ± 7.43% for gadoteridol at 3 hours) while it rapidly cleared out of the muscle region. Pimonidazole staining confirmed the presence of hypoxia in both gadoteridol and GdDO3NI cohorts, and the later cohort showed good agreement with MRI contrast enhancement.

DATA CONCLUSION

GdDO3NI was successfully shown to visualize hypoxia in the brain post-TBI using T₁ -weighted MRI at 2.5-3 hours postcontrast.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 1.

Dynamic Contrast Enhancement (DCE) MRI-Derived Renal Perfusion and Filtration: Basic Concepts

Dynamic contrast-enhanced (DCE) MRI monitors the transit of contrast agents, typically gadolinium chelates, through the intrarenal regions, the renal cortex, the medulla, and the collecting system. In this way, DCE-MRI reveals the renal uptake and excretion of the contrast agent. An optimal DCE-MRI acquisition protocol involves finding a good compromise between whole-kidney coverage (i.e., 3D imaging), spatial and temporal resolution, and contrast resolution. By analyzing the enhancement of the renal tissues as a function of time, one can determine indirect measures of clinically important single-kidney parameters as the renal blood flow, glomerular filtration rate, and intrarenal blood volumes. Gadolinium-containing contrast agents may be nephrotoxic in patients suffering from severe renal dysfunction, but otherwise DCE-MRI is clearly useful for diagnosis of renal functions and for assessing treatment response and posttransplant rejection.Here we introduce the concept of renal DCE-MRI, describe the existing methods, and provide an overview of preclinical DCE-MRI applications to illustrate the utility of this technique to measure renal perfusion and glomerular filtration rate in animal models.This publication is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This introduction is complemented by two separate publications describing the experimental procedure and data analysis.

Exploring the Inter-voxel Information in Pharmacokinetic Maps for Cervical Carcinoma Prediction

Physiological parameters can be estimated from dynamic contrast enhanced magnetic resonance imaging (DCEMRI) data using pharmacokinetic models. This work evaluates the performance of various pharmacokinetic models through a retrospective study on cervix cancer, including two generalized kinetic models and three 2-compartment exchange models (2CXMs). In the current clinical practice, region of interest (ROI) is treated as a whole and the models are assessed by their top pharmacokinetic parameters. We explore the intervoxel relationship in the pharmacokinetic parameter maps and demonstrate that, for those insignificant parameters, texture descriptors can largely improve their discriminative power. Multi-parametric classifiers are developed to fuse the information carried by physiological parameters and the descriptors. Assessed merely by the top parameter, the DP (distributed parameter) model is the best one with an area under the ROC (receiver operating characteristic) curve (AUC) of 0.80; by combining multiple pharmacokinetic parameters, the ExTofts model is the winner with an AUC of 0.837. Finally, the classifier of the AATH (adiabatic approximation to the tissue homogeneity) model build on combined features achieves an AUC of 0.92.Clinical Relevance - Using data from 36 cervical cancer patients and 17 normal subjects, this work quantitatively compared the various pharmacokinetic models and provided recommendations for model selection in cervical cancer diagnosis.

DCE-MRI pharmacokinetic parameter maps for cervical carcinoma prediction

Pharmacokinetic parameters estimated from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time course data enable the physio-biological interpretation of tissue angiogenesis. This study aims to develop machine learning approaches for cervical carcinoma prediction based on pharmacokinetic parameters. The performance of individual parameters was assessed in terms of their efficacy in differentiating cancerous tissue from normal cervix tissue. The effect of combining parameters was evaluated using the following two approaches: the first approach was based on support vector machines (SVMs) to combine the parameters from one pharmacokinetic model or across several models; the second approach was based on a novel method called APITL (artificial pharmacokinetic images for transfer learning), which was designed to fully utilize the comprehensive pharmacokinetic information acquired from DCE-MRI data. A "winner-takes-all" strategy was employed to consolidate the slice-wise prediction into subject-wise prediction. Experiments were carried out with a dataset comprising 36 patients with cervical cancer and 17 healthy subjects. The results demonstrated that parameter V_e, representing volume fraction of the extracellular extravascular space (EES), attained high discriminative power regardless of the pharmacokinetic model used for estimation. An approximately 10% improvement in the accuracy was achieved with the SVM approach. The APITL method further outperformed SVM and attained a subject-wise prediction accuracy of 94.3%. Our experiment demonstrated that APITL could predict cervical carcinoma with high accuracy and had potential in clinical applications.

Dynamic contrast-enhanced MRI model selection for predicting tumor aggressiveness in papillary thyroid cancers

The purpose of this study was to identify the optimal tracer kinetic model from T1 -weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data and evaluate whether parameters estimated from the optimal model predict tumor aggressiveness determined from histopathology in patients with papillary thyroid carcinoma (PTC) prior to surgery. In this prospective study, 18 PTC patients underwent pretreatment DCE-MRI on a 3 T MR scanner prior to thyroidectomy. This study was approved by the institutional review board and informed consent was obtained from all patients. The two-compartment exchange model, compartmental tissue uptake model, extended Tofts model (ETM) and standard Tofts model were compared on a voxel-wise basis to determine the optimal model using the corrected Akaike information criterion (AICc) for PTC. The optimal model is the one with the lowest AICc. Statistical analysis included paired and unpaired t-tests and a one-way analysis of variance. Bonferroni correction was applied for multiple comparisons. Receiver operating characteristic (ROC) curves were generated from the optimal model parameters to differentiate PTC with and without aggressive features, and AUCs were compared. ETM performed best with the lowest AICc and the highest Akaike weight (0.44) among the four models. ETM was preferred in 44% of all 3419 voxels. The ETM estimates of Ktrans in PTCs with the aggressive feature extrathyroidal extension (ETE) were significantly higher than those without ETE (0.78 ± 0.29 vs. 0.34 ± 0.18 min-1 , P = 0.005). From ROC analysis, cut-off values of Ktrans , ve and vp , which discriminated between PTCs with and without ETE, were determined at 0.45 min-1 , 0.28 and 0.014 respectively. The sensitivities and specificities were 86 and 82% (Ktrans ), 71 and 82% (ve ), and 86 and 55% (vp ), respectively. Their respective AUCs were 0.90, 0.71 and 0.71. We conclude that ETM Ktrans has shown potential to classify tumors with and without aggressive ETE in patients with PTC.

A Comparative Study of Two-Compartment Exchange Models for Dynamic Contrast-Enhanced MRI in Characterizing Uterine Cervical Carcinoma

A variety of tracer kinetic methods have been employed to assess tumor angiogenesis. The Standard two-Compartment model (SC) used in cervix carcinoma was less frequent, and Adiabatic Approximation to the Tissue Homogeneity (AATH) and Distributed Parameter (DP) model are lacking. This study compares two-compartment exchange models (2CXM) (AATH, SC, and DP) for determining dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters in cervical cancer, with the aim of investigating the potential of various parameters derived from 2CXM for tumor diagnosis and exploring the possible relationship between these parameters in patients with cervix cancer. Parameters (tissue blood flow, F_p; tissue blood volume, V_p; interstitial volume, V_e; and vascular permeability, PS) for regions of interest (ROI) of cervix lesions and normal cervix tissue were estimated by AATH, SC, and DP models in 36 patients with cervix cancer and 17 healthy subjects. All parameters showed significant differences between lesions and normal tissue with a P value less than 0.05, except for PS from the AATH model, F_p from the SC model, and V_p from the DP model. Parameter V_e from the AATH model had the largest AUC (r = 0.85). Parameters F_p and V_p from SC and DP models and V_e and PS from AATH and DP models were highly correlated, respectively, (r > 0.8) in cervix lesions. Cervix cancer was found to have a very unusual microcirculation pattern, with over-growth of cancer cells but without evident development of angiogenesis. V_e has the best performance in identifying cervix cancer. Most physiological parameters derived from AATH, SC, and DP models are linearly correlated in cervix cancer.

In vitro pharmacokinetic phantom for two-compartment modeling in DCE-MRI

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an established minimally-invasive method for assessment of extravascular leakage, hemodynamics, and tissue viability. However, differences in acquisition protocols, variety of pharmacokinetic models, and uncertainty on physical sources of MR signal hamper the reliability and widespread use of DCE-MRI in clinical practice. Measurements performed in a controlled in vitro setup could be used as a basis for standardization of the acquisition procedure, as well as objective evaluation and comparison of pharmacokinetic models. In this paper, we present a novel flow phantom that mimics a two-compartmental (blood plasma and extravascular extracellular space/EES) vascular bed, enabling systemic validation of acquisition protocols. The phantom consisted of a hemodialysis filter with two compartments, separated by hollow fiber membranes. The aim of this phantom was to vary the extravasation rate by adjusting the flow in the two compartments. Contrast agent transport kinetics within the phantom was interpreted using two-compartmental pharmacokinetic models. Boluses of gadolinium-based contrast-agent were injected in a tube network connected to the hollow fiber phantom; time-intensity curves (TICs) were obtained from image series, acquired using a T1-weighted DCE-MRI sequence. Under the assumption of a linear dilution system, the TICs obtained from the input and output of the system were then analyzed by a system identification approach to estimate the trans-membrane extravasation rates in different flow conditions. To this end, model-based deconvolution was employed to determine (identify) the impulse response of the investigated dilution system. The flow rates in the EES compartment significantly and consistently influenced the estimated extravasation rates, in line with the expected trends based on simulation results. The proposed phantom can therefore be used to model a two-compartmental vascular bed and can be employed to test and optimize DCE-MRI acquisition sequences in order to determine a standardized acquisition procedure leading to consistent quantification results.

Modeling Dynamic Contrast-Enhanced MRI Data with a Constrained Local AIF

PURPOSE

This study aims to develop a constrained local arterial input function (cL-AIF) to improve quantitative analysis of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) data by accounting for the contrast-agent bolus amplitude error in the voxel-specific AIF.

PROCEDURES

Bayesian probability theory-based parameter estimation and model selection were used to compare tracer kinetic modeling employing either the measured remote-AIF (R-AIF, i.e., the traditional approach) or an inferred cL-AIF against both in silico DCE-MRI data and clinical, cervical cancer DCE-MRI data.

RESULTS

When the data model included the cL-AIF, tracer kinetic parameters were correctly estimated from in silico data under contrast-to-noise conditions typical of clinical DCE-MRI experiments. Considering the clinical cervical cancer data, Bayesian model selection was performed for all tumor voxels of the 16 patients (35,602 voxels in total). Among those voxels, a tracer kinetic model that employed the voxel-specific cL-AIF was preferred (i.e., had a higher posterior probability) in 80 % of the voxels compared to the direct use of a single R-AIF. Maps of spatial variation in voxel-specific AIF bolus amplitude and arrival time for heterogeneous tissues, such as cervical cancer, are accessible with the cL-AIF approach.

CONCLUSIONS

The cL-AIF method, which estimates unique local-AIF amplitude and arrival time for each voxel within the tissue of interest, provides better modeling of DCE-MRI data than the use of a single, measured R-AIF. The Bayesian-based data analysis described herein affords estimates of uncertainties for each model parameter, via posterior probability density functions, and voxel-wise comparison across methods/models, via model selection in data modeling.

Cluster analysis of dynamic contrast enhanced MRI reveals tumor subregions related to locoregional relapse for cervical cancer patients

BACKGROUND

Solid tumors are known to be spatially heterogeneous. Detection of treatment-resistant tumor regions can improve clinical outcome, by enabling implementation of strategies targeting such regions. In this study, K-means clustering was used to group voxels in dynamic contrast enhanced magnetic resonance images (DCE-MRI) of cervical cancers. The aim was to identify clusters reflecting treatment resistance that could be used for targeted radiotherapy with a dose-painting approach.

MATERIAL AND METHODS

Eighty-one patients with locally advanced cervical cancer underwent DCE-MRI prior to chemoradiotherapy. The resulting image time series were fitted to two pharmacokinetic models, the Tofts model (yielding parameters K^trans and ν_e) and the Brix model (A_Brix, k_ep and k_el). K-means clustering was used to group similar voxels based on either the pharmacokinetic parameter maps or the relative signal increase (RSI) time series. The associations between voxel clusters and treatment outcome (measured as locoregional control) were evaluated using the volume fraction or the spatial distribution of each cluster.

RESULTS

One voxel cluster based on the RSI time series was significantly related to locoregional control (adjusted p-value 0.048). This cluster consisted of low-enhancing voxels. We found that tumors with poor prognosis had this RSI-based cluster gathered into few patches, making this cluster a potential candidate for targeted radiotherapy. None of the voxels clusters based on Tofts or Brix parameter maps were significantly related to treatment outcome.

CONCLUSION

We identified one group of tumor voxels significantly associated with locoregional relapse that could potentially be used for dose painting. This tumor voxel cluster was identified using the raw MRI time series rather than the pharmacokinetic maps.

Comparison of linear and nonlinear implementation of the compartmental tissue uptake model for dynamic contrast-enhanced MRI

Purpose

Fitting tracer kinetic models using linear methods is much faster than using their nonlinear counterparts, although this comes often at the expense of reduced accuracy and precision. The aim of this study was to derive and compare the performance of the linear compartmental tissue uptake (CTU) model with its nonlinear version with respect to their percentage error and precision.

Theory and Methods

The linear and nonlinear CTU models were initially compared using simulations with varying noise and temporal sampling. Subsequently, the clinical applicability of the linear model was demonstrated on 14 patients with locally advanced cervical cancer examined with dynamic contrast‐enhanced magnetic resonance imaging.

Results

Simulations revealed equal percentage error and precision when noise was within clinical achievable ranges (contrast‐to‐noise ratio >10). The linear method was significantly faster than the nonlinear method, with a minimum speedup of around 230 across all tested sampling rates. Clinical analysis revealed that parameters estimated using the linear and nonlinear CTU model were highly correlated (ρ ≥ 0.95).

Conclusion

The linear CTU model is computationally more efficient and more stable against temporal downsampling, whereas the nonlinear method is more robust to variations in noise. The two methods may be used interchangeably within clinical achievable ranges of temporal sampling and noise. Magn Reson Med 77:2414–2423, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Pieces-of-parts for supervoxel segmentation with global context: Application to DCE-MRI tumour delineation

Rectal tumour segmentation in dynamic contrast-enhanced MRI (DCE-MRI) is a challenging task, and an automated and consistent method would be highly desirable to improve the modelling and prediction of patient outcomes from tissue contrast enhancement characteristics - particularly in routine clinical practice. A framework is developed to automate DCE-MRI tumour segmentation, by introducing: perfusion-supervoxels to over-segment and classify DCE-MRI volumes using the dynamic contrast enhancement characteristics; and the pieces-of-parts graphical model, which adds global (anatomic) constraints that further refine the supervoxel components that comprise the tumour. The framework was evaluated on 23 DCE-MRI scans of patients with rectal adenocarcinomas, and achieved a voxelwise area-under the receiver operating characteristic curve (AUC) of 0.97 compared to expert delineations. Creating a binary tumour segmentation, 21 of the 23 cases were segmented correctly with a median Dice similarity coefficient (DSC) of 0.63, which is close to the inter-rater variability of this challenging task. A second study is also included to demonstrate the method's generalisability and achieved a DSC of 0.71. The framework achieves promising results for the underexplored area of rectal tumour segmentation in DCE-MRI, and the methods have potential to be applied to other DCE-MRI and supervoxel segmentation problems.

Are complex DCE-MRI models supported by clinical data?

PURPOSE

To ascertain whether complex dynamic contrast enhanced (DCE) MRI tracer kinetic models are supported by data acquired in the clinic and to determine the consequences of limited contrast-to-noise.

METHODS

Generically representative in silico and clinical (cervical cancer) DCE-MRI data were examined. Bayesian model selection evaluated support for four compartmental DCE-MRI models: the Tofts model (TM), Extended Tofts model, Compartmental Tissue Uptake model (CTUM), and Two-Compartment Exchange model.

RESULTS

Complex DCE-MRI models were more sensitive to noise than simpler models with respect to both model selection and parameter estimation. Indeed, as contrast-to-noise decreased, complex DCE models became less probable and simpler models more probable. The less complex TM and CTUM were the optimal models for the DCE-MRI data acquired in the clinic. [In cervical tumors, Ktrans, Fp, and PS increased after radiotherapy (P = 0.004, 0.002, and 0.014, respectively)].

CONCLUSION

Caution is advised when considering application of complex DCE-MRI kinetic models to data acquired in the clinic. It follows that data-driven model selection is an important prerequisite to DCE-MRI analysis. Model selection is particularly important when high-order, multiparametric models are under consideration. (Parameters obtained from kinetic modeling of cervical cancer clinical DCE-MRI data showed significant changes at an early stage of radiotherapy.) Magn Reson Med 77:1329-1339, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Functional imaging for radiotherapy treatment planning: current status and future directions-a review

In recent years, radiotherapy (RT) has been subject to a number of technological innovations. Today, RT is extremely flexible, allowing irradiation of tumours with high doses, whilst also sparing normal tissues from doses. To make use of these additional degrees of freedom, integration of functional image information may play a key role (i) for better staging and tumour detection, (ii) for more accurate RT target volume delineation, (iii) to assess functional information about biological characteristics and individual radiation resistance and (iv) to apply personalized dose prescriptions. In this article, we discuss the current status and future directions of different clinically available functional imaging modalities; CT, MRI, positron emission tomography (PET) as well as the hybrid imaging techniques PET/CT and PET/MRI and their potential for individualized RT.