Tracer Kinetic Model Selection for Dynamic Contrast-Enhanced Computed Tomography Imaging of Prostate Cancer:

4D perfusion CT of prostate cancer for image-guided radiotherapy planning: A proof of concept study

Purpose

Advanced forms of prostate cancer (PCa) radiotherapy with either external beam therapy or brachytherapy delivery techniques aim for a focal boost and thus require accurate lesion localization and lesion segmentation for subsequent treatment planning. This study prospectively evaluated dynamic contrast-enhanced computed tomography (DCE-CT) for the detection of prostate cancer lesions in the peripheral zone (PZ) using qualitative and quantitative image analysis compared to multiparametric magnet resonance imaging (mpMRI) of the prostate.

Methods

With local ethics committee approval, 14 patients (mean age, 67 years; range, 57–78 years; PSA, mean 8.1 ng/ml; range, 3.5–26.0) underwent DCE-CT, as well as mpMRI of the prostate, including standard T2, diffusion-weighted imaging (DWI), and DCE-MRI sequences followed by transrectal in-bore MRI-guided prostate biopsy. Maximum intensity projections (MIP) and DCE-CT perfusion parameters (CTP) were compared between healthy and malignant tissue. Two radiologists independently rated image quality and the tumor lesion delineation quality of PCa using a five-point ordinal scale. MIP and CTP were compared using visual grading characteristics (VGC) and receiver operating characteristics (ROC)/area under the curve (AUC) analysis.

Results

The PCa detection rate ranged between 57 to 79% for the two readers for DCE-CT and was 92% for DCE-MRI. DCE-CT perfusion parameters in PCa tissue in the PZ were significantly different compared to regular prostate tissue and benign lesions. Image quality and lesion visibility were comparable between DCE-CT and DCE-MRI (VGC: AUC 0.612 and 0.651, p>0.05).

Conclusion

Our preliminary results suggest that it is feasible to use DCE-CT for identification and visualization, and subsequent segmentation for focal radiotherapy approaches to PCa.

Assessment of prostate cancer with integrated CT-perfusion using a sector-wise approach

OBJECTIVE

The role of computed tomography perfusion (CTP) in characterizing primary prostate cancer (PCa) is not definitely known. The aim of the present study was to investigate the relationship between CTP parameters and histopathological features of PCa tissue, using a sector-wise approach.

MATERIAL AND METHODS

Fifty-one patients with biopsy-proven PCa underwent prospectively a CTP scan prior to radical prostatectomy. Blood flow (BF), mean blood volume (BV) and mean transit time (MTT) were calculated, with the prostate being divided into eight sectors. Corresponding sector-wise histopathological analysis of whole-mount prostatectomy specimens was performed to determine tumoral area (mm²), mean microvessel density (MVD), Gleason patterns (primary, secondary) and total Gleason score. Spearman's rank correlation coefficient was used to analyze the association between CTP and histopathological parameters.

RESULTS

BF correlated weakly with tumoral area [ρs coefficient (p-value): 0.25 (0.00)] and MVD [ρs coefficient (p-value): 0.23 (0.00)]. No valuable correlation was found between CTP parameters and primary and secondary Gleason patterns, whereas total Gleason score was weakly correlated with BV [ρs coefficient (p-value): 0.22 (0.00)] and MTT [ρs coefficient (p-value): 0.25 (0.00)].

CONCLUSION

BF correlates weakly with size and vascularity of PCa. There is a need for further studies to elucidate the association between CTP parameters and other histopathological parameters.

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.

Mathematical Models of Contrast Transport Kinetics for Cancer Diagnostic Imaging: A Review

Angiogenesis plays a fundamental role in cancer growth and the formation of metastasis. Novel cancer therapies aimed at inhibiting angiogenic processes and/or disrupting angiogenic tumor vasculature are currently being developed and clinically tested. The need for earlier and improved cancer diagnosis, and for early evaluation and monitoring of therapeutic response to angiogenic treatment, have led to the development of several imaging methods for in vivo noninvasive assessment of angiogenesis. The combination of dynamic contrast-enhanced imaging with mathematical modeling of the contrast agent kinetics enables quantitative assessment of the structural and functional changes in the microvasculature that are associated with tumor angiogenesis. In this paper, we review quantitative imaging of angiogenesis with dynamic contrast-enhanced magnetic resonance imaging, computed tomography, and ultrasound.

Models and methods for analyzing DCE-MRI: a review

PURPOSE

To present a review of most commonly used techniques to analyze dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches.

METHODS

DCE-MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well-established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA's perfusion into tissue and estimate perfusion-related parameters (indexes) from signal- or concentration-time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases.

RESULTS

Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors.

CONCLUSIONS

Both nonparametric and parametric approaches for DCE-MRI analysis possess the ability to quantify tissue perfusion.

Quantitative graphical analysis of simultaneous dynamic PET/MRI for assessment of prostate cancer

PURPOSE

Dynamic FDG imaging for prostate cancer characterization is limited by generally small size and low uptake in prostate tumors. Our aim in this pilot study was to explore feasibility of simultaneous PET/MRI to guide localization of prostate lesions for dynamic FDG analysis using a graphical approach.

METHODS

Three patients with biopsy-proven prostate cancer underwent simultaneous FDG PET/MRI, incorporating dynamic prostate imaging. Histology and multiparametric MRI findings were used to localize tumors, which in turn guided identification of tumors on FDG images. Regions of interest were manually placed on tumor and benign prostate tissue. Blood activity was extracted from a region of interest placed on the femoral artery on PET images. FDG data were analyzed by graphical analysis using the influx constant Ki (Patlak analysis) when FDG binding seemed irreversible and distribution volume VT (reversible graphical analysis) when FDG binding seemed reversible given the presence of washout.

RESULTS

Given inherent coregistration, simultaneous acquisition facilitated use of MRI data to localize small lesions on PET and subsequent graphical analysis in all cases. In 2 cases with irreversible binding, tumor had higher Ki than benign using Patlak analysis (0.023 vs 0.006 and 0.019 vs 0.008 mL/cm3 per minute). In 1 case appearing reversible, tumor had higher VT than benign using reversible graphical analysis (0.68 vs 0.52 mL/cm3).

CONCLUSIONS

Simultaneous PET/MRI allows localization of small prostate tumors for dynamic PET analysis. By taking advantage of inclusion of the femoral arteries in the FOV, we applied advanced PET data analysis methods beyond conventional static measures and without blood sampling.

Dynamic contrast-enhanced MRI in mice: an investigation of model parameter uncertainties

PURPOSE

To establish the experimental factors that dominate the uncertainty of hemodynamic parameters in commonly used pharmacokinetic models.

METHODS

By fitting simulation results from a multiregion tissue exchange model (Multiple path, Multiple tracer, Indicator Dilution, 4 region), the precision and accuracy of hemodynamic parameters in dynamic contrast-enhanced MRI with four tracer kinetic models is investigated. The impact of various injection rates as well as imprecise knowledge of the arterial input functions is examined.

RESULTS

Fast injections are beneficial for K(trans) precision within the extended Tofts model and within the two-compartment exchange model but do not affect the other models under investigation. Biases from errors in the arterial input functions are mostly consistent in size and direction for the simple and the extended Tofts model, while they are hardly predictable for the other models. Errors in the hematocrit introduce the greatest loss in parameter accuracy, amounting to an average K(trans) bias of 40% for a 30% overestimation throughout all models.

CONCLUSION

This simulation study allows the detailed inspection of the isolated impact from various experimental conditions on parameter uncertainty. Because parameter uncertainty comparable to human studies was found, this study represents a validation of preclinical dynamic contrast-enhanced MRI for modeling human tumor physiology.

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

BACKGROUND

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) offers a unique capability to probe tumour microvasculature. Different analysis of the acquired data will possibly lead to different conclusions. Therefore, the objective of this study was to investigate under which conditions the Tofts (TM), extended Tofts (ETM), compartmental tissue uptake model (C-TU) and 2-compartment exchange model (2CXM) were the optimal tracer kinetic models (TKMs) for the analysis of DCE-MRI in patients with cervical cancer.

MATERIAL AND METHODS

Ten patients with locally advanced cervical cancer (FIGO: IIA/IIB/IIIB/IVA - 1/5/3/1) underwent DCE-MRI prior to radiotherapy. From the two-parameter TM it was possible to extract the forward volume transfer constant (K(trans)) and the extracellular-extravascular volume fraction (ve). From the three-parameter ETM, additionally the plasma volume fraction (vp) could be extracted. From the three-parameter C-TU it was possible to extract information about the blood flow (Fp), permeability-surface area product (PS) and vp. Finally, the four-parameter 2CXM extended the C-TU to include ve. For each voxel, corrected Akaike information criterion (AICc) values were calculated, taking into account both the goodness-of-fit and the number of model parameters. The optimal model was defined as the model with the lowest AICc.

RESULTS

All four TKMs were the optimal model in different contiguous regions of the cervical tumours. For the 24 999 analysed voxels, the TM was optimal in 17.0%, the ETM was optimal in 2.2%, the C-TU in 23.4% and the 2CXM was optimal in 57.3%. Throughout the tumour, a high correlation was found between K(trans)(TM) and Fp(2CXM), ρ = 0.91.

CONCLUSION

The 2CXM was most often optimal in describing the contrast agent enhancement of pre-treatment cervical cancers, although this model broke down in a subset of the tumour voxels where overfitting resulted in non-physiological parameter estimates. Due to the possible overfitting of the 2CXM, the C-TU was found more robust and when 2CXM was excluded from comparison the C-TU was the preferred model.

Characterization of tumor heterogeneity using dynamic contrast enhanced CT and FDG-PET in non-small cell lung cancer

PURPOSE

Dynamic contrast-enhanced CT (DCE-CT) quantifies vasculature properties of tumors, whereas static FDG-PET/CT defines metabolic activity. Both imaging modalities are capable of showing intra-tumor heterogeneity. We investigated differences in vasculature properties within primary non-small cell lung cancer (NSCLC) tumors measured by DCE-CT and metabolic activity from FDG-PET/CT.

METHODS

Thirty three NSCLC patients were analyzed prior to treatment. FDG-PET/CT and DCE-CT were co-registered. The tumor was delineated and metabolic activity was segmented on the FDG-PET/CT in two regions: low (<50% maximum SUV) and high (≥50% maximum SUV) metabolic uptake. Blood flow, blood volume and permeability were calculated using a maximum slope, deconvolution algorithm and a Patlak model. Correlations were assessed between perfusion parameters for the regions of interest.

RESULTS

DCE-CT provided additional information on vasculature and tumor heterogeneity that was not correlated to metabolic tumor activity. There was no significant difference between low and high metabolic active regions for any of the DCE-CT parameters. Furthermore, only moderate correlations between maximum SUV and DCE-CT parameters were observed.

CONCLUSIONS

No direct correlation was observed between FDG-uptake and parameters extracted from DCE-CT. DCE-CT may provide complementary information to the characterization of primary NSCLC tumors over FDG-PET/CT imaging.

Classic models for dynamic contrast-enhanced MRI

Dynamic contrast-enhanced MRI (DCE-MRI) is a functional MRI method where T1 -weighted MR images are acquired dynamically after bolus injection of a contrast agent. The data can be interpreted in terms of physiological tissue characteristics by applying the principles of tracer-kinetic modelling. In the brain, DCE-MRI enables measurement of cerebral blood flow (CBF), cerebral blood volume (CBV), blood-brain barrier (BBB) permeability-surface area product (PS) and the volume of the interstitium (ve ). These parameters can be combined to form others such as the volume-transfer constant K(trans) , the extraction fraction E and the contrast-agent mean transit times through the intra- and extravascular spaces. A first generation of tracer-kinetic models for DCE-MRI was developed in the early 1990s and has become a standard in many applications. Subsequent improvements in DCE-MRI data quality have driven the development of a second generation of more complex models. They are increasingly used, but it is not always clear how they relate to the models of the first generation or to the model-free deconvolution methods for tissues with intact BBB. This lack of understanding is leading to increasing confusion on when to use which model and how to interpret the parameters. The purpose of this review is to clarify the relation between models of the first and second generations and between model-based and model-free methods. All quantities are defined using a generic terminology to ensure the widest possible scope and to reveal the link between applications in the brain and in other organs.

Interscanner comparison of dynamic contrast-enhanced MRI in prostate cancer: 1.5 versus 3 T MRI

PURPOSE

The aim of the study was the comparison of the diagnostic potential of dynamic contrast-enhanced magnetic resonance imaging to differentiate between prostate carcinoma and normal prostate tissue as well as prostatitis at 2 different field strengths: 1.5 versus 3 T.

METHODS

Sixty-six patients with biopsy and/or prostatectomy of the prostate were included in the study. Magnetic resonance imaging was performed at 1.5 T in 20 patients with biopsy-proven prostate cancer (PC) and in 8 patients with prostatitis; at 3 T, we analyzed 27 patients with prostatectomy-proven PC and 11 patients with prostatitis. All examinations were performed using a combined body and endorectal coil protocol and a 2-dimensional TurboFLASH T1-weighted gradient echo sequence to calculate plasma flow (PF) and mean transit time (MTT) values. A total of 28 of 38 areas of normal prostate tissue, 20 of 27 areas of PC, and 8 of 11 prostatitis were analyzed at 1.5 or 3T. For the normalization, we calculated PC/normal and prostatitis/normal tissue ratios of PF and MTT for each patient.

RESULTS

Prostate cancer showed higher PF (P < 0.0001) and shorter MTT (P < 0.0001) at 3 T and at 1.5 T (P < 0.0001 for PF and P = 0.0016 for MTT) compared with the normal tissue. In comparison with the normal tissue, prostatitis had a statistically significant higher PF at 1.5 T (P = 0.0156) but not at 3 T (P = 0.17) and no significantly shorter MTT values both at 3 (P = 0.15) and 1.5 T (P = 0.25). Sensitivity and specificity for differentiating PC from prostatitis with PF were 46% and 88% at 1.5 T (cutoff ratio, 2.3) and 89% and 73% at 3 T (cutoff ratio, 1.2), respectively. Sensitivity and specificity for MTT were 77% and 100% at 1.5 T (cutoff ratio, 0.7) and 70% and 100% at 3 T (cutoff ratio, 0.6), respectively. We found no significant relationship between the Gleason score and PF/MTT (P = 0.17/0.11 for 1.5 T and P = 0.23/0.18 for 3 T).

CONCLUSIONS

The differentiation between PC and the normal tissue is possible with both field strengths. Prostate cancer can be better distinguished from prostatitis at 3 T compared with 1.5 T. The differentiation between prostatitis and the normal tissue is limited at both field strengths.

Tracer-kinetic modeling of dynamic contrast-enhanced MRI and CT: a primer

Dynamic contrast-enhanced computed tomography (DCE-CT) and magnetic resonance imaging (DCE-MRI) are functional imaging techniques. They aim to characterise the microcirculation by applying the principles of tracer-kinetic analysis to concentration-time curves measured in individual image pixels. In this paper, we review the basic principles of DCE-MRI and DCE-CT, with a specific emphasis on the use of tracer-kinetic modeling. The aim is to provide an introduction to the field for a broader audience of pharmacokinetic modelers. In a first part, we first review the key aspects of data acquisition in DCE-CT and DCE-MRI, including a review of basic measurement strategies, a discussion on the relation between signal and concentration, and the problem of measuring reference data in arterial blood. In a second part, we define the four main parameters that can be measured with these techniques and review the most common tracer-kinetic models that are used in this field. We first discuss the models for the capillary bed and then define the most general four-parameter models used today: the two-compartment exchange model, the tissue-homogeneity model, the "adiabatic approximation to the tissue-homogeneity model" and the distributed-parameter model. In simpler tissue types or when the data quality is inadequate to resolve all the features of the more complex models, it is often necessary to resort to simpler models, which are special cases of the general models and hence have less parameters. We discuss the most common of these special cases, i.e. the uptake models, the extended Tofts model, and the one-compartment model. Models for two specific tissue types, liver and kidney, are discussed separately. We conclude with a review of practical aspects of DCE-CT and DCE-MRI data analysis, including the problem of identifying a suitable model for any given data set, and a brief discussion of the application of tracer-kinetic modeling in the context of drug development. Here, an important application of DCE techniques is the derivation of quantitative imaging biomarkers for the assessment of effects of targeted therapeutics on tumors.

Calculation of intravascular signal in dynamic contrast enhanced-MRI using adaptive complex independent component analysis

Assessing tumor response to therapy is a crucial step in personalized treatments. Pharmacokinetic (PK) modeling provides quantitative information about tumor perfusion and vascular permeability that are associated with prognostic factors. A fundamental step in most PK analyses is calculating the signal that is generated in the tumor vasculature. This signal is usually inseparable from the extravascular extracellular signal. It was shown previously using in vivo and phantom experiments that independent component analysis (ICA) is capable of calculating the intravascular time-intensity curve in dynamic contrast enhanced (DCE)-MRI. A novel adaptive complex independent component analysis (AC-ICA) technique is developed in this study to calculate the intravascular time-intensity curve and separate this signal from the DCE-MR images of tumors. The use of the complex-valued DCE-MRI images rather than the commonly used magnitude images satisfied the fundamental assumption of ICA, i.e., linear mixing of the sources. Using an adaptive cost function in ICA through estimating the probability distribution of the tumor vasculature at each iteration resulted in a more robust and accurate separation algorithm. The AC-ICA algorithm provided a better estimate for the intravascular time-intensity curve than the previous ICA-based method. A simulation study was also developed in this study to realistically simulate DCE-MRI data of a leaky tissue mimicking phantom. The passage of the MR contrast agent through the leaky phantom was modeled with finite element analysis using a diffusion model. Once the distribution of the contrast agent in the imaging field of view was calculated, DCE-MRI data was generated by solving the Bloch equation for each voxel at each time point. The intravascular time-intensity curve calculation results were compared to the previously proposed ICA-based intravascular time-intensity curve calculation method that applied ICA to the magnitude of the DCE-MRI data (Mag-ICA) using both simulated and experimental tissue mimicking phantoms. The AC-ICA demonstrated superior performance compared to the Mag-ICA method. AC-ICA provided more accurate estimate of intravascular time-intensity curve, having smaller error between the calculated and actual intravascular time-intensity curves compared to the Mag-ICA. Furthermore, it showed higher robustness in dealing with datasets with different resolution by providing smaller variation between the results of each datasets and having smaller difference between the intravascular time-intensity curves of various resolutions. Thus, AC-ICA has the potential to be used as the intravascular time-intensity curve calculation method in PK analysis and could lead to more accurate PK analysis for tumors.