Dynamic contrast-enhanced CT of head and neck tumors: comparison of first-pass and permeability perfusion measurements using two different commercially available tracer kinetics models

Comparative analysis of two mathematical algorithms for the calculation of computed tomography perfusion parameters in the healthy and diseased pancreas

BACKGROUND

The maximum slope (MS) and deconvolution (DC) algorithms are commonly used to post-process computed tomography perfusion (CTP) data. This study aims to analyze the differences between MS and DC algorithms for the calculation of pancreatic CTP parameters.

METHODS

The pancreatic CTP data of 57 patients were analyzed using MS and DC algorithms. Two blinded radiologists calculated pancreatic blood volume (BV) and blood flow (BF). Interobserver correlation coefficients were used to evaluate the consistency between two radiologists. Paired t-tests, Pearson linear correlation analysis, and Bland-Altman analysis were performed to evaluate the correlation and consistency of the CTP parameters between the two algorithms.

RESULTS

Among the 30 subjects with normal pancreas, the BV values in the three pancreatic regions were higher in the case of the MS algorithm than in the case of the DC algorithm (t = 39.35, p < 0.001), and the BF values in the three pancreatic regions were slightly higher for the MS algorithm than for the DC algorithm (t = 2.19, p = 0.031). Similarly, among the 27 patients with acute pancreatitis, the BV values obtained using the MS methods were higher than those obtained using the DC methods (t = 54.14, p < 0.001). Furthermore, the BF values were higher with the MS methods than the DC methods (t = 8.45, p < 0.001). Besides, Pearson linear correlation and Bland-Altman analysis showed that the BF and BV values showed a good correlation and a bad consistency between the two algorithms.

CONCLUSIONS

The BF and BV values measured using MS and DC algorithms had a good correlation but were not consistent.

Computed Tomography Perfusion Analysis of Pancreatic Adenocarcinoma using Deconvolution, Maximum Slope, and Patlak Methods - Evaluation of Diagnostic Accuracy and Interchangeability of Cut-Off Values

PURPOSE

 The goal of this study was to evaluate the diagnostic accuracy of perfusion computed tomography (CT) parameters obtained by different mathematical-kinetic methods for distinguishing pancreatic adenocarcinoma from normal tissue. To determine cut-off values and to assess the interchangeability of cut-off values, which were determined by different methods.

MATERIALS AND METHODS

 Perfusion CT imaging of the pancreas was prospectively performed in 23 patients. 19 patients with histopathologically confirmed pancreatic adenocarcinoma were included in the study. Blood flow (BF), blood volume (BV) and permeability-surface area product (PS) were measured in pancreatic adenocarcinoma and normal tissue with the deconvolution (BF, BV, PS), maximum slope (BF), and Patlak methods (BV, PS). The interchangeability of cut-off values was examined by assessing agreement between BF, BV, and PS measured with different mathematical-kinetic methods.

RESULTS

 Bland-Altman analysis demonstrated poor agreement between perfusion parameters, measured with different mathematical-kinetic methods. According to receiver operating characteristic (ROC) analysis, PS measured with the Patlak method had the significantly lowest diagnostic accuracy (area under ROC curve = 0.748). All other parameters were of high diagnostic accuracy (area under ROC curve = 0.940-0.997), although differences in diagnostic accuracy were not statistically different. Cut-off values for BF of ≤ 91.83 ml/100 ml/min and for BV of ≤ 5.36 ml/100 ml, both measured with the deconvolution method, appear to be the most appropriate cut-off values to distinguish pancreatic adenocarcinoma from normal tissue.

CONCLUSION

 Perfusion parameters obtained by different methods are not interchangeable. Therefore, cut-off values, which were determined using different methods, are not interchangeable either. Perfusion parameters can help to distinguish pancreatic adenocarcinoma from normal tissue with high diagnostic accuracy, except for PS measured with the Patlak method.

KEY POINTS

  · Perfusion CT parameters showed high diagnostic accuracy in differentiating between pancreatic adenocarcinoma and normal tissue.. · Only PS measured with the Patlak method showed a significantly lower diagnostic accuracy.. · Perfusion parameters measured with different mathematical-kinetic methods are not interchangeable.. · A specific cut-off value must be determined for each method and each perfusion parameter..

CITATION FORMAT

· Koell M, Klauss M, Skornitzke S et al. Computed Tomography Perfusion Analysis of Pancreatic Adenocarcinoma with the Deconvolution, Maximum Slope, and Patlak Methods - Evaluation of Diagnostic Accuracy and Interchangeability of Cut-Off Values. Fortschr Röntgenstr 2021; 193: 1062 - 1073.

Dynamic perfusion CT - A promising tool to diagnose pancreatic ductal adenocarcinoma

Background and objective

This study deals with an important issue of setting the role and value of the dynamic computed tomography (CT) perfusion analysis in diagnosing pancreatic ductal adenocarcinoma (PDAC). The study aimed to assess the efficacy of perfusion CT in identifying PDAC, even isodense or hardly depicted in conventional multidetector computed tomography.

Methods

A total of 56 patients with PDAC and 56 control group patients were evaluated in this study. A local perfusion assessment, involving the main perfusion parameters, was evaluated for all the patients. Sensitivity, specificity, positive, and negative predictive values for each perfusion CT parameter were defined using cutoff values calculated using receiver operating characteristic curve analysis. We accomplished logistic regression to identify the probability of PDAC.

Results

Blood flow (BF) and blood volume (BV) values were significant independent diagnostic criteria for the presence of PDAC. If both values exceed the determined cutoff point, the estimated probability for the presence of PDAC was 97.69%.

Conclusions

Basic CT perfusion parameters are valuable in providing the radiological diagnosis of PDAC. The estimated BF and BV parameters may serve as independent diagnostic criteria predicting the probability of PDAC.

Computed Tomography Perfusion Measurements in Renal Lesions Obtained by Bayesian Estimation, Advanced Singular-Value Decomposition Deconvolution, Maximum Slope, and Patlak Models: Intermodel Agreement and Diagnostic Accuracy of Tumor Classification

OBJECTIVES

The aims of this study were to evaluate the agreement of computed tomography (CT)-perfusion parameter values of the normal renal cortex and various renal tumors, which were obtained by different mathematical models, and to evaluate their diagnostic accuracy.

MATERIALS AND METHODS

Perfusion imaging was performed prospectively in 35 patients to analyze 144 regions of interest of the normal renal cortex and 144 regions of interest of renal tumors, including 21 clear-cell renal cell carcinomas (RCC), 6 papillary RCCs, 5 oncocytomas, 1 chromophobe RCC, 1 angiomyolipoma with minimal fat, and 1 tubulocystic RCC. Identical source data were postprocessed and analyzed on 2 commercial software applications with the following implemented mathematical models: maximum slope, Patlak plot, standard singular-value decomposition (SVD), block-circulant SVD, oscillation-limited block-circulant SVD, and Bayesian estimation technique. Results for blood flow (BF), blood volume (BV), and mean transit time (MTT) were recorded. Agreement and correlation between pairs of models and perfusion parameters were assessed. Diagnostic accuracy was evaluated by receiver operating characteristic (ROC) analysis.

RESULTS

Significant differences and poor agreement of BF, BV, and MTT values were noted for most of model comparisons in both the normal renal cortex and different renal tumors. The correlations between most model pairs and perfusion parameters ranged between good and perfect (Spearman ρ = 0.79-1.00), except for BV values obtained by Patlak method (ρ = 0.61-0.72). All mathematical models computed BF and BV values, which differed significantly between clear cell RCCs, papillary RCCs, and oncocytomas, which introduces them as useful diagnostic tests to differentiate between different histologic subgroups (areas under ROC curve, 0.83-0.99). The diagnostic accuracy to discriminate between clear-cell RCCs and the renal cortex was the lowest based on the Patlak plot model (area under ROC curve, 0.76); BF and BV values obtained by other algorithms did not differ significantly in their diagnostic accuracy.

CONCLUSIONS

Quantitative perfusion parameters obtained from different mathematical models cannot be used interchangeably. Based on BF and BV estimates, all models are a useful tool in the differential diagnosis of kidney tumors, with the Patlak plot model yielding a significantly lower diagnostic accuracy.

Variations of quantitative perfusion measurement on dynamic contrast enhanced CT for colorectal cancer: implication of standardized image protocol

Tumor angiogenesis is considered an important prognostic factor. With an increasing emphasis on imaging evaluation of the tumor microenvironment, dynamic contrast enhanced-computed tomography (DCE-CT) has evolved as an important functional technique in this setting. Yet many questions remain as to how and when these functional measurements should be performed for each agent and tumor type, and what quantitative models should be used in the fitting process. In this study, we evaluated the variations of perfusion measurement on DCE-CT for rectal cancer patients from (1) different tracer kinetic models, (2) different scan acquisition lengths, and (3) different scan intervals. A total of seven commonly used models were studied: the adiabatic approximation to the tissue homogeneity (AATH) model, adiabatic approximation to the homogeneity tissue with fixed transit time (AATHFT) model, the Tofts model (TM), the extended Tofts model (ETM), Patlak model, Logan model, and the model-free deconvolution method. Akaike's information criterion was used to identify the best fitting model. The interchangeability of different models was further evaluated using Bland-Altman analysis. All models gave comparable blood volume (BV) measurements except the Patlak method. While for the volume transfer constant (K_trans) estimation, AATHFT, AATH, and ETM generated reasonable agreement among each other but not for the other models. Regarding the blood flow (BF) measurement, no two models were interchangeable. In addition, the perfusion parameters were compared with four acquisition times (45, 65, 85, and 105 s) and four temporal intervals (1, 2, 3, and 4 s). No significant difference was observed in the volume transfer constant (K_trans), BV, and BF measurements when comparing data acquired over 65 s with data acquired over 105 s using any of the DCE models in this study. Yet increasing the temporal interval led to a significant overestimation of BF in the deconvolution method. In conclusion, the perfusion measurement is indeed model dependent and the image acquisition/processing technique is dependent. The radiation dose of DCE-CT was an average of 1.5-2 times an abdomen/pelvic CT, which is not insubstantial. To take the DCE-CT forward as a biomarker in oncology, prospective studies should be carefully designed with the optimal image acquisition and analysis technique.

Use of patient outcome endpoints to identify the best functional CT imaging parameters in metastatic renal cell carcinoma patients

OBJECTIVE

To use the patient outcome endpoints overall survival and progression-free survival to evaluate functional parameters derived from dynamic contrast-enhanced CT.

METHODS

69 patients with metastatic renal cell carcinoma had dynamic contrast-enhanced CT scans at baseline and after 5 and 10 weeks of treatment. Blood volume, blood flow and standardized perfusion values were calculated using deconvolution (BV_deconv, BF_deconv and SPV_deconv), blood flow and standardized perfusion values using maximum slope (BF_max and SPV_max) and blood volume and permeability surface area product using the Patlak model (BV_patlak and PS). Histogram data for each were extracted and associated to patient outcomes. Correlations and agreements were also assessed.

RESULTS

The strongest associations were observed between patient outcome and medians and modes for BV_deconv, BV_patlak and BF_deconv at baseline and during the early ontreatment period (p < 0.05 for all). For the relative changes in median and mode between baseline and weeks 5 and 10, PS seemed to have opposite associations dependent on treatment. Interobserver correlations were excellent (r ≥ 0.9, p < 0.001) with good agreement for BF_deconv, BF_max, SPV_deconv and SPV_max and moderate to good (0.5 < r < 0.7, p < 0.001) for BV_deconv and BV_patlak. Medians had a better reproducibility than modes.

CONCLUSION

Patient outcome was used to identify the best functional imaging parameters in patients with metastatic renal cell carcinoma. Taking patient outcome and reproducibility into account, BV_deconv, BV_patlak and BF_deconv provide the most clinically meaningful information, whereas PS seems to be treatment dependent. Standardization of acquisition protocols and post-processing software is necessary for future clinical utilization. Advances in knowledge: Taking patient outcome and reproducibility into account, BV_deconv, BV_patlak and BF_deconv provide the most clinically meaningful information. PS seems to be treatment dependent.

Detection of cartilage invasion in laryngeal carcinoma with dynamic contrast-enhanced CT

Objective

Staging of laryngeal cancer largely depends on cartilage invasion. Presence of cartilage invasion affects treatment choice and prognosis. On MRI and contrast‐enhanced CT (CECT) it may be challenging to differentiate cartilage invasion from inflammation. The purpose of this study is to compare the diagnostic properties of dynamic contrast‐enhanced CT (DCECT) and CECT for visual detection of cartilage invasion in laryngeal cancer.

Study Design

Prospective cohort study.

Methods

Patients with T3 or T4 laryngeal squamous cell carcinoma treated with total laryngectomy were evaluated using 0.625 mm slice CT. DCECT derived permeability and blood volume maps and CECT images were visually evaluated for the presence of invasion of the cartilaginous T‐stage subsites of laryngeal cancer, by detecting continuity with the tumor‐bulk of increased permeability, increased blood volume, and enhancement. Histological evaluation of the surgical total laryngectomy specimen served as the gold standard. Sensitivity, specificity, negative predictive value, and positive predictive value were calculated and compared using the McNemar and Chi‐squared test.

Results

From 14 included patients, a total of 462 subsites were available for T‐stage analysis, of which 84 were cartilage. The median time between CT imaging and total laryngectomy was 1 day (range 1–34 days). There was no significant difference in the detection of cartilage invasion between DCECT and CECT. The sensitivity of CECT was better for all subsites combined (0.85 vs. 0.75; p < 0.01).

Conclusion

DCECT does not improve visual detection of cartilage invasion in T3 and T4 laryngeal cancer compared to CECT.

Level of Evidence

2b, individual cohort study.

CT-perfusion measurements in pancreatic carcinoma with different kinetic models: Is there a chance for tumour grading based on functional parameters?

BACKGROUND

To evaluate the interchangeability of perfusion parameters obtained with help of models used for post-processing of perfusion-CT images in pancreatic adenocarcinoma and to determine the mean values and ranges of perfusion in different tumour gradings.

METHODS

Perfusion-CT imaging was performed prospectively in 48 consecutive patients with pancreatic adenocarcinoma. In 42 patients biopsy-proven tumor grading was available (4 × G1/24 × G2/14 × G3/6× unknown). Images were post-processed using a model based on the maximum-slope (MS) approach (blood flow-BFMS) + Patlak analysis (P) (blood volume [BVP] and permeability [k-transP]), as well as a model with deconvolution-based (D) analysis (BFD, BVD and k-transD). 50 mL contrast agent were applied with a delay time of 7 s. Perfusion parameters were compared using intraclass correlation coefficient (ICC), the Wilcoxon matched-pairs test and Bland-Altman plots.

RESULTS

Forty eight VOIs of tumours were outlined and analysed. Moderate to good ICC values were found for the perfusion parameters (ICC = 0.62-0.75). Wilcoxon matched-pairs revealed significantly lower values (P < .001 and 0.008), for the BF and BV values obtained using the maximum-slope approach + Patlak analysis compared to deconvolution based analysis. For k-trans measurement, deconvolution revealed significantly lower values (P < 0.001). Different histologic subgroups (G1-G3) did not show significantly different functional parameters.

CONCLUSION

There were significant differences in the perfusion parameters obtained using the different calculation methods, and therefore these parameters are not directly interchangeable. However, the magnitude of pairs of parametric values is in constant relation to each other enabling the use of any of these methods. VPCT parameters did not allow for histologic classification.

Quantitative Myocardial Perfusion with Dynamic Contrast-Enhanced Imaging in MRI and CT: Theoretical Models and Current Implementation

Technological advances in magnetic resonance imaging (MRI) and computed tomography (CT), including higher spatial and temporal resolution, have made the prospect of performing absolute myocardial perfusion quantification possible, previously only achievable with positron emission tomography (PET). This could facilitate integration of myocardial perfusion biomarkers into the current workup for coronary artery disease (CAD), as MRI and CT systems are more widely available than PET scanners. Cardiac PET scanning remains expensive and is restricted by the requirement of a nearby cyclotron. Clinical evidence is needed to demonstrate that MRI and CT have similar accuracy for myocardial perfusion quantification as PET. However, lack of standardization of acquisition protocols and tracer kinetic model selection complicates comparison between different studies and modalities. The aim of this overview is to provide insight into the different tracer kinetic models for quantitative myocardial perfusion analysis and to address typical implementation issues in MRI and CT. We compare different models based on their theoretical derivations and present the respective consequences for MRI and CT acquisition parameters, highlighting the interplay between tracer kinetic modeling and acquisition settings.

Advanced Hepatocellular Carcinoma: Perfusion Computed Tomography-Based Kinetic Parameter as a Prognostic Biomarker for Prediction of Patient Survival

OBJECTIVE

The aim of this study was to find prognostic biomarkers in perfusion computed tomography (PCT)-based kinetic parameters for advanced hepatocellular carcinoma (HCC) treated with antiangiogenic chemotherapy.

METHODS

Twenty-two patients with advanced HCC underwent PCT imaging and subsequently received bevacizumab in combination with gemcitabine and oxaliplatin. Pretreatment PCT data within advanced HCC were analyzed using the Tofts-Kety, 2-compartment exchange, adiabatic approximation to the tissue homogeneity (AATH), and distributed parameter models. Blood flow, blood volume, extraction fraction (E), and other 3 parameters were calculated. Kinetic parameters in each model were evaluated with 1-year survival discrimination using Kaplan-Meier analysis and with overall survival using univariate Cox regression analysis.

RESULTS

Only the AATH model-derived E was statistically significantly prognostic for 1-year survival. The increased AATH model-derived E was significantly associated with longer overall survival (P = 0.005).

CONCLUSIONS

The AATH model-derived E was an effective prognostic biomarker for advanced HCC.

Comparison of measurement methods with a mixed effects procedure accounting for replicated evaluations (COM3PARE): method comparison algorithm implementation for head and neck IGRT positional verification

Purpose

Comparison of imaging measurement devices in the absence of a gold-standard comparator remains a vexing problem; especially in scenarios where multiple, non-paired, replicated measurements occur, as in image-guided radiotherapy (IGRT). As the number of commercially available IGRT presents a challenge to determine whether different IGRT methods may be used interchangeably, an unmet need conceptually parsimonious and statistically robust method to evaluate the agreement between two methods with replicated observations. Consequently, we sought to determine, using an previously reported head and neck positional verification dataset, the feasibility and utility of a Comparison of Measurement Methods with the Mixed Effects Procedure Accounting for Replicated Evaluations (COM₃PARE), a unified conceptual schema and analytic algorithm based upon Roy’s linear mixed effects (LME) model with Kronecker product covariance structure in a doubly multivariate set-up, for IGRT method comparison.

Methods

An anonymized dataset consisting of 100 paired coordinate (X/ measurements from a sequential series of head and neck cancer patients imaged near-simultaneously with cone beam CT (CBCT) and kilovoltage X-ray (KVX) imaging was used for model implementation. Software-suggested CBCT and KVX shifts for the lateral (X), vertical (Y) and longitudinal (Z) dimensions were evaluated for bias, inter-method (between-subject variation), intra-method (within-subject variation), and overall agreement using with a script implementing COM₃PARE with the MIXED procedure of the statistical software package SAS (SAS Institute, Cary, NC, USA).

Results

COM₃PARE showed statistically significant bias agreement and difference in inter-method between CBCT and KVX was observed in the Z-axis (both p − value<0.01). Intra-method and overall agreement differences were noted as statistically significant for both the X- and Z-axes (all p − value<0.01). Using pre-specified criteria, based on intra-method agreement, CBCT was deemed preferable for X-axis positional verification, with KVX preferred for superoinferior alignment.

Conclusions

The COM₃PARE methodology was validated as feasible and useful in this pilot head and neck cancer positional verification dataset. COM₃PARE represents a flexible and robust standardized analytic methodology for IGRT comparison. The implemented SAS script is included to encourage other groups to implement COM₃PARE in other anatomic sites or IGRT platforms.

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.

CT perfusion of the liver: principles and applications in oncology

With the introduction of molecularly targeted chemotherapeutics, there is an increasing need for defining new response criteria for therapeutic success because use of morphologic imaging alone may not fully assess tumor response. Computed tomographic (CT) perfusion imaging of the liver provides functional information about the microcirculation of normal parenchyma and focal liver lesions and is a promising technique for assessing the efficacy of various anticancer treatments. CT perfusion also shows promising results for diagnosing primary or metastatic tumors, for predicting early response to anticancer treatments, and for monitoring tumor recurrence after therapy. Many of the limitations of early CT perfusion studies performed in the liver, such as limited coverage, motion artifacts, and high radiation dose of CT, are being addressed by recent technical advances. These include a wide area detector with or without volumetric spiral or shuttle modes, motion correction algorithms, and new CT reconstruction technologies such as iterative algorithms. Although several issues related to perfusion imaging-such as paucity of large multicenter trials, limited accessibility of perfusion software, and lack of standardization in methods-remain unsolved, CT perfusion has now reached technical maturity, allowing for its use in assessing tumor vascularity in larger-scale prospective clinical trials. In this review, basic principles, current acquisition protocols, and pharmacokinetic models used for CT perfusion imaging of the liver are described. Various oncologic applications of CT perfusion of the liver are discussed and current challenges, as well as possible solutions, for CT perfusion are presented.

Quantifying cerebral blood flow in an adult pig ischemia model by a depth-resolved dynamic contrast-enhanced optical method

Dynamic contrast-enhanced (DCE) near-infrared (NIR) methods have been proposed for bedside monitoring of cerebral blood flow (CBF). These methods have primarily focused on qualitative approaches since scalp contamination hinders quantification. In this study, we demonstrate that accurate CBF measurements can be obtained by analyzing multi-distance time-resolved DCE data with a combined kinetic deconvolution optical reconstruction (KDOR) method. Multi-distance time-resolved DCE-NIR measurements were made in adult pigs (n=8) during normocapnia, hypocapnia and ischemia. The KDOR method was used to calculate CBF from the DCE-NIR measurements. For validation, CBF was measured independently by CT under each condition. The mean CBF difference between the techniques was -1.7 mL/100 g/min with 95% confidence intervals of -16.3 and 12.9 mL/100 g/min; group regression analysis revealed a strong agreement between the two techniques (slope=1.06±0.08, y-intercept=-4.37±4.33 mL/100 g/min, p<0.001). The results of an error analysis suggest that little a priori information is needed to recover CBF, due to the robustness of the analytical method and the ability of time-resolved NIR to directly characterize the optical properties of the extracerebral tissue (where model mismatch is deleterious). The findings of this study suggest that the DCE-NIR approach presented is a minimally invasive and portable means of determining absolute hemodynamics in neurocritical care patients.

Computational fluid dynamics modelling of perfusion measurements in dynamic contrast-enhanced computed tomography: development, validation and clinical applications

Dynamic contrast-enhanced computed tomography (DCE-CT) is an imaging tool that aids in evaluating functional characteristics of tissue at different stages of disease management: diagnostic, radiation treatment planning, treatment effectiveness, and monitoring. Clinical validation of DCE-derived perfusion parameters remains an outstanding problem to address prior to perfusion imaging becoming a widespread standard as a non-invasive quantitative measurement tool. One approach to this validation process has been the development of quality assurance phantoms in order to facilitate controlled perfusion ex vivo. However, most of these systems fail to establish and accurately replicate physiologically relevant capillary permeability and exchange performance. The current work presents the first step in the development of a prospective suite of physics-based perfusion simulations based on coupled fluid flow and particle transport phenomena with the goal of enhancing the understanding of clinical contrast agent kinetics. Existing knowledge about a controllable, two-compartmental fluid exchange phantom was used to validate the computational fluid dynamics (CFD) simulation model presented herein. The sensitivity of CFD-derived contrast uptake curves to contrast injection parameters, including injection duration and flow rate, were quantified and found to be within 10% accuracy. The CFD model was employed to evaluate two commonly used clinical kinetic algorithms used to derive perfusion parameters: Fick's principle and the modified Tofts model. Neither kinetic model was able to capture the true transport phenomena it aimed to represent but if the overall contrast concentration after injection remained identical, then successive DCE-CT evaluations could be compared and could indeed reflect differences in regional tissue flow. This study sets the groundwork for future explorations in phantom development and pharmaco-kinetic modelling, as well as the development of novel contrast agents for DCE 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.

Comparison between the deconvolution and maximum slope 64-MDCT perfusion analysis of the esophageal cancer: is conversion possible?

PURPOSE

To estimate if CT perfusion parameter values of the esophageal cancer, which were obtained with the deconvolution-based software and maximum slope algorithm are in agreement, or at least interchangeable.

METHODS

278 esophageal tumor ROIs, derived from 35 CT perfusion studies that were performed with a 64-MDCT, were analyzed. "Slice-by-slice" and average "whole-covered-tumor-volume" analysis was performed. Tumor blood flow and blood volume were manually calculated from the arterial tumor-time-density graphs, according to the maximum slope methodology (BF(ms) and BV(ms)), and compared with the corresponding perfusion values, which were automatically computed by commercial deconvolution-based software (BF(deconvolution) and BV(deconvolution)), for the same tumor ROIs. Statistical analysis was performed using Wilcoxon matched-pairs test, paired-samples t-test, Spearman and Pearson correlation coefficients, and Bland-Altman agreement plots.

RESULTS

BF(deconvolution) (median: 74.75 ml/min/100g, range, 18.00-230.5) significantly exceeded the BF(ms) (25.39 ml/min/100g, range, 7.13-96.41) (Z=-14.390, p<0.001), while BV(deconvolution) (median: 5.70 ml/100g, range: 2.10-15.90) descended the BV(ms) (9.37 ml/100g, range: 3.44-19.40) (Z=-13.868, p<0.001). Both pairs of perfusion measurements significantly correlated with each other: BF(deconvolution), versus BF(ms) (rS=0.585, p<0.001), and BV(deconvolution), versus BV(ms) (rS=0.602, p<0.001). Geometric mean BF(deconvolution)/BF(ms) ratio was 2.8 (range, 1.1-6.8), while geometric mean BV(deconvolution)/BV(ms) ratio was 0.6 (range, 0.3-1.1), within 95% limits of agreement.

CONCLUSIONS

Significantly different CT perfusion values of the esophageal cancer blood flow and blood volume were obtained by deconvolution-based and maximum slope-based algorithms, although they correlated significantly with each other. Two perfusion-measuring algorithms are not interchangeable because too wide ranges of the conversion factors were found.

Primary colorectal cancer: use of kinetic modeling of dynamic contrast-enhanced CT data to predict clinical outcome

PURPOSE

To compare four different tracer kinetic models for the analysis of dynamic contrast material-enhanced computed tomographic (CT) data with respect to the prediction of 5-year overall survival in primary colorectal cancer.

MATERIALS AND METHODS

This study was approved by the ethical review board. Archival dynamic contrast-enhanced CT data from 46 patients with colorectal cancer, obtained as part of a research study, were analyzed retrospectively by using the distributed parameter, conventional compartmental, adiabatic tissue homogeneity, and generalized kinetic models. Blood flow, blood volume, mean transit time (MTT), permeability-surface area product, extraction fraction, extravascular extracellular volume (v(e)), and volume transfer constant (K(trans)) were compared by using the Friedman test, with statistical significance at 5%. Following receiver operating characteristic analysis, parameters of the different kinetic models and tumor stage were compared with respect to overall survival discrimination, with use of Kaplan Meier analysis and a univariate Cox proportional hazard model, with additional cross-validation and permutation testing.

RESULTS

Blood flow was lower with the distributed parameter model than with the conventional compartmental and adiabatic tissue homogeneity models (P < .0001), and blood flow values determined with the conventional compartmental and adiabatic tissue homogeneity models were similar. Conversely, MTT was longer with the distributed parameter model than with the conventional compartmental and adiabatic tissue homogeneity models (P < .0001). Blood volume, permeability-surface area product, and v(e) were higher with the conventional compartmental model than with the adiabatic tissue homogeneity, distributed parameter, or generalized kinetic models (P < .0001). The extraction fraction was higher with the distributed parameter model than with the adiabatic tissue homogeneity model. With respect to 5-year overall survival, only the distributed parameter model-derived v(e) was predictive of 5-year overall survival with a threshold value of 15.48 mL/100 mL after cross-validation and permutation testing.

CONCLUSION

Parameter values differ significantly between models. Of the models investigated, the distributed parameter model was the best predictor of 5-year overall survival.

SUPPLEMENTAL MATERIAL

http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12120186/-/DC1.

Current status and guidelines for the assessment of tumour vascular support with dynamic contrast-enhanced computed tomography

Dynamic contrast-enhanced computed tomography (DCE-CT) assesses the vascular support of tumours through analysis of temporal changes in attenuation in blood vessels and tissues during a rapid series of images acquired with intravenous administration of iodinated contrast material. Commercial software for DCE-CT analysis allows pixel-by-pixel calculation of a range of validated physiological parameters and depiction as parametric maps. Clinical studies support the use of DCE-CT parameters as surrogates for physiological and molecular processes underlying tumour angiogenesis. DCE-CT has been used to provide biomarkers of drug action in early phase trials for the treatment of a range of cancers. DCE-CT can be appended to current imaging assessments of tumour response with the benefits of wide availability and low cost. This paper sets out guidelines for the use of DCE-CT in assessing tumour vascular support that were developed using a Delphi process. Recommendations encompass CT system requirements and quality assurance, radiation dosimetry, patient preparation, administration of contrast material, CT acquisition parameters, terminology and units, data processing and reporting. DCE-CT has reached technical maturity for use in therapeutic trials in oncology. The development of these consensus guidelines may promote broader application of DCE-CT for the evaluation of tumour vascularity. Key Points • DCE-CT can robustly assess tumour vascular support • DCE-CT has reached technical maturity for use in therapeutic trials in oncology • This paper presents consensus guidelines for using DCE-CT in assessing tumour vascularity.

Opportunities and pitfalls of cancer imaging in clinical trials

Cancer treatment strategies have changed considerably over the past two decades, with increasing emphasis on cancer-specific biological therapies. This situation has led to the incorporation of biomarkers, including those obtained by medical imaging, into trial designs to better understand mechanisms of action and, hopefully, to provide early evidence of treatment efficacy at a molecular or physiological level. Unlike blood tests and tissue samples, an imaging biomarker allows assessment of treatment in the whole tumor, in all tumors in the body, and at multiple time points. This situation has increased the complexity of clinical trials, as each imaging modality has issues related to cost, ease of use, patient compatibility, data analysis, and interpretation. This article reviews strengths and limitations of the current imaging methods available in clinical cancer trials, including MRI, CT, PET, and ultrasonography. The information gained by each test, and the difficulties in acquiring the data and interpreting it are also discussed in order to help researchers plan imaging in clinical trials and interpret data from such studies.

Whole brain perfused blood volume CT: visualization of infarcted tissue compared to quantitative perfusion CT

RATIONALE AND OBJECTIVES

This study determines the value of whole brain color-coded three-dimensional perfused blood volume (PBV) computed tomography (CT) for the visualization of the infarcted tissue in acute stroke patients.

MATERIALS AND METHODS

Nonenhanced CT (NECT), perfusion CT (PCT), and CT angiography (CTA) in 48 patients with acute ischemic stroke were performed. Whole brain PBV was calculated from NECT and CTA data sets using commercial software. PBV slices in identical orientation to the PCT slices were reconstructed and the area of visual perfusion abnormality on PBV maps was measured. The infarct core in the corresponding PCT slices (CBV <2.0 mL/100 g) was measured automatically with commercial software. The ischemic area on PBV and the infarct core on quantitative PCT were compared using the Pearsons-R correlation coefficient. Significance was considered for P < .05.

RESULTS

The quantitative PCT demonstrated a mean infarct core volume of 35.48 +/- 32.17 cm(3), whereas the volume of visual perfusion abnormality of the corresponding PBV slices was 37.16 +/- 37.59 cm(3). The perfusion abnormality in PBV was highly correlated with the infarct core of quantitative PCT for area per slice (r = 0.933, P < .01) as well as volume (r = 0.922, P < .01).

CONCLUSIONS

PBV can serve as surrogate marker corresponding to the infarct core in acute stroke with whole brain coverage.