A comparison of two methods for estimating DCE-MRI parameters via individual and cohort based AIFs in prostate cancer: a step towards practical implementation

Quantitative dynamic contrast-enhanced magnetic resonance imaging in head and neck cancer: A systematic comparison of different modelling approaches

Background and purpose

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) describes tissue microvasculature and has prognostic and predictive potential in radiotherapy for head and neck cancer (HNC). However, lack in standardization of DCE-MRI hinders comparison of studies and clinical implementation. This study investigated the accuracy and robustness of the population arterial input function (AIF), correlations between pharmacokinetic parameters and their association to T stage and human papillomavirus (HPV) status for HNC.

Materials and methods

DCE-MRI was acquired for 44 HNC patients. Population AIFs were calculated with six different approaches. DCE-MRI was analysed in primary and lymph node tumours using Tofts model (TM) with population AIFs and individual AIFs, extended TM (ETM) with individual AIFs, Brix model (BM), and areas under the curve (AUCs). Intraclass correlation, concordance correlation, Pearson correlation and Whitney Mann U test helped examining the robustness and accuracy of population AIF, correlations between DCE-MRI parameters and their association to T stage and HPV status, respectively.

Results

The population AIF was robust but differed from individual AIFs. There was significant correlation between KtransTM/ETM and ve, TM/ETM, and KtransTM/ETM and Kep, TM/ETM. ABrix and AUCs correlated for lymph nodes. Kep, Brix correlated with ABrix, KtransTM/ETM and Kep, TM/ETM for primary tumours. Kep, TM significantly decreased with increasing T stage. Both the correlations and the parameters' association to T stage were stronger for HPV negative lesions.

Conclusions

Individual AIF was preferred for accurate pharmacokinetic modelling of DCE-MRI. DCE-MRI parameters and their correlations were affected by the lesion type, HPV status and T staging.

Development and validation of a point-based scoring system for predicting axillary lymph node metastasis and disease outcome in breast cancer using clinicopathological and multiparametric MRI features

BACKGROUND

Axillary lymph node (ALN) metastasis is used to select treatment strategies and define the prognosis in breast cancer (BC) patients and is typically assessed using an invasive procedure. Noninvasive, simple, and reliable tools to accurately predict ALN status are desirable. We aimed to develop and validate a point-based scoring system (PSS) for stratifying the ALN metastasis risk of BC based on clinicopathological and quantitative MRI features and to explore its prognostic significance.

METHODS

A total of 219 BC patients were evaluated. The clinicopathological and quantitative MRI features of the tumors were collected. A multivariate logistic regression analysis was used to create the PSS. The performance of the models was evaluated using receiver operating characteristic curves, and the area under the curve (AUC) of the models was calculated. Kaplan-Meier curves were used to analyze the survival outcomes.

RESULTS

Clinical features, including the American Joint Committee on Cancer (AJCC) stage, T stage, human epidermal growth factor receptor-2, estrogen receptor, and quantitative MRI features, including maximum tumor diameter, K_ep, V_e, and TTP, were identified as risk factors for ALN metastasis and were assigned scores for the PSS. The PSS achieved an AUC of 0.799 in the primary cohort and 0.713 in the validation cohort. The recurrence-free survival (RFS) and overall survival (OS) of the high-risk (> 19.5 points) groups were significantly shorter than those of the low-risk (≤ 19.5 points) groups in the PSS.

CONCLUSION

PSS could predict the ALN metastasis risk of BC. A PSS greater than 19.5 was demonstrated to be a predictor of short RFS and OS.

Technical Advancements in Abdominal Diffusion-weighted Imaging

Since its first observation in the 18th century, the diffusion phenomenon has been actively studied by many researchers. Diffusion-weighted imaging (DWI) is a technique to probe the diffusion of water molecules and create a MR image with contrast based on the local diffusion properties. The DWI pixel intensity is modulated by the hindrance the diffusing water molecules experience. This hindrance is caused by structures in the tissue and reflects the state of the tissue. This characteristic makes DWI a unique and effective tool to gain more insight into the tissue's pathophysiological condition. In the past decades, DWI has made dramatic technical progress, leading to greater acceptance in clinical practice. In the abdominal region, however, acquiring DWI with good quality is challenging because of several reasons, such as large imaging volume, respiratory and other types of motion, and difficulty in achieving homogeneous fat suppression. In this review, we discuss technical advancements from the past decades that help mitigate these problems common in abdominal imaging. We describe the use of scan acceleration techniques such as parallel imaging and compressed sensing to reduce image distortion in echo planar imaging. Then we compare techniques developed to mitigate issues due to respiratory motion, such as free-breathing, respiratory-triggering, and navigator-based approaches. Commonly used fat suppression techniques are also introduced, and their effectiveness is discussed. Additionally, the influence of the abovementioned techniques on image quality is demonstrated. Finally, we discuss the current and future clinical applications of abdominal DWI, such as whole-body DWI, simultaneous multiple-slice excitation, intravoxel incoherent motion, and the use of artificial intelligence. Abdominal DWI has the potential to develop further in the future, thanks to scan acceleration and image quality improvement driven by technological advancements. The accumulation of clinical proof will further drive clinical acceptance.

Quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging to predict lymphovascular invasion and survival outcome in breast cancer

BACKGROUND

Lymphovascular invasion (LVI) predicts a poor outcome of breast cancer (BC), but LVI can only be postoperatively diagnosed by histopathology. We aimed to determine whether quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can preoperatively predict LVI and clinical outcome of BC patients.

METHODS

A total of 189 consecutive BC patients who underwent multiparametric MRI scans were retrospectively evaluated. Quantitative (K^trans, V_e, K_ep) and semiquantitative DCE-MRI parameters (W_- in, W_- out, TTP), and clinicopathological features were compared between LVI-positive and LVI-negative groups. All variables were calculated by using univariate logistic regression analysis to determine the predictors for LVI. Multivariate logistic regression was used to build a combined-predicted model for LVI-positive status. Receiver operating characteristic (ROC) curves evaluated the diagnostic efficiency of the model and Kaplan-Meier curves showed the relationships with the clinical outcomes. Multivariate analyses with a Cox proportional hazard model were used to analyze the hazard ratio (HR) for recurrence-free survival (RFS) and overall survival (OS).

RESULTS

LVI-positive patients had a higher K_ep value than LVI-negative patients (0.92 ± 0.30 vs. 0.81 ± 0.23, P = 0.012). N2 stage [odds ratio (OR) = 3.75, P = 0.018], N3 stage (OR = 4.28, P = 0.044), and K_ep value (OR = 5.52, P = 0.016) were associated with LVI positivity. The combined-predicted LVI model that incorporated the N stage and K_ep yielded an accuracy of 0.735 and a specificity of 0.801. The median RFS was significantly different between the LVI-positive and LVI-negative groups (31.5 vs. 34.0 months, P = 0.010) and between the combined-predicted LVI-positive and LVI-negative groups (31.8 vs. 32.0 months, P = 0.007). The median OS was not significantly different between the LVI-positive and LVI-negative groups (41.5 vs. 44.0 months, P = 0.270) and between the combined-predicted LVI-positive and LVI-negative groups (42.8 vs. 43.5 months, P = 0.970). LVI status (HR = 2.40), N2 (HR = 3.35), and the combined-predicted LVI model (HR = 1.61) were independently associated with disease recurrence.

CONCLUSION

The quantitative parameter of K_ep could predict LVI. LVI status, N stage, and the combined-predicted LVI model were predictors of a poor RFS but not OS.

Eye-specific quantitative dynamic contrast-enhanced MRI analysis for patients with intraocular masses

OBJECTIVE

Dynamic contrast enhanced (DCE)-MRI is currently not generally used for intraocular masses as lesions are small, have an inhomogeneous T₁ and the eye is prone to motion. The aim of this paper is to address these eye-specific challenges, enabling accurate ocular DCE-MRI.

MATERIALS & METHODS

DCE-MRI of 19 uveal melanoma (UM) patients was acquired using a fat-suppressed 3D spoiled gradient echo sequence with TWIST (time-resolved angiography with stochastic trajectories sequence). The analysis consisted of a two-step registration method to correct for both head and eye motion. A T₁ map was calculated to convert signal intensities to concentrations. Subsequently, the Tofts model was fitted voxel wise to obtain K^trans and v_e.

RESULTS

Registration significantly improved the concentration curve quality (p < 0.001). The T₁ of melanotic lesions was significantly lower than amelanotic lesions (888 ms vs 1350 ms, p = 0.03). The average achieved B₁⁺ in the lesions was 91%. The average K^trans was 0.46 min^-1 (range 0.13-1.0) and the average v_e was 0.22 (range 0.10-0.51).

CONCLUSION

Using this eye-specific analysis, DCE of intraocular masses is possible which might aid in the diagnosis, prognosis and follow-up of UM.

Prostate Cancer Growth Inhibition by 1-(3,5-Dimethylphenyl)-6-methyl-1H-pyrazolo[4,3-c]pyridin-4(5H)-one via Down-regulation of Phosphorylation PI3K/AKT and STA3/JAK2

In the present study, 1-(3,5-dimethylphenyl)-6-methyl-1H-pyrazolo[4,3-c]pyridin-4 (5H)-one (DPMPP) was investigated as an antiproliferative agent for prostate cancer cells and the mechanism of its action was studied. Cell lines 22Rv1 and SGC‑7901 were used as in vitro models of prostate cancer. The DPMPP treatment inhibited proliferation of 22Rv1 and SGC‑7901 cells in dose-depended manner. The viability of 22Rv1 and SGC‑7901 cells was reduced to 21 and 19%, respectively after treatment with 32 µM DPMPP. In DPMPP treated (16 µM) 22Rv1 and SGC‑7901 cells apoptosis increased to 62.78 and 68.51%, respectively. Moreover, DPMPP treatment caused cell cycle arrest in S phase and inhibition of PI3K/AKT activation. In the same time ROS production showed elevation and MMP (Matrix MetalloProteinase) decreased in the cells. Apparently DPMPP induces cytotoxicity through induction of oxidative response and apoptosis in prostate cancer cells in vitro. The PI3K/Akt/ERK phosphorylation was inhibited, while p21 and p53, death receptor, expression was promoted by DPMPP treatment. Therefore, DPMPP has a potential to be used as a therapeutic agent for treatment of prostate cancer.

Selection of Fitting Model and Arterial Input Function for Repeatability in Dynamic Contrast-Enhanced Prostate MRI

RATIONALE AND OBJECTIVES

Analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging is notable for the variability of calculated parameters. The purpose of this study was to evaluate the level of measurement variability and error/variability due to modeling in DCE magnetic resonance imaging parameters.

MATERIALS AND METHODS

Two prostate DCE scans were performed on 11 treatment-naïve patients with suspected or confirmed prostate peripheral zone cancer within an interval of less than two weeks. Tumor-suspicious and normal-appearing regions of interest (ROI) in the prostate peripheral zone were segmented. Different Tofts-Kety based models and different arterial input functions, with and without bolus arrival time (BAT) correction, were used to extract pharmacokinetic parameters. The percent repeatability coefficient (%RC) of fitted model parameters K_trans, v_e, and k_ep was calculated. Paired t-tests comparing parameters in tumor-suspicious ROIs and in normal-appearing tissue evaluated each parameter's sensitivity to pathology.

RESULTS

Although goodness-of-fit criteria favored the four-parameter extended Tofts-Kety model with the BAT correction included, the simplest two-parameter Tofts-Kety model overall yielded the best repeatability scores. The best %RC in the tumor-suspicious ROI was 63% for k_ep, 28% for v_e, and 83% for K_trans . The best p values for discrimination between tissues were p <10^-5 for k_ep and K_trans, and p = 0.11 for v_e. Addition of the BAT correction to the models did not improve repeatability.

CONCLUSION

The parameter k_ep, using an arterial input functions directly measured from blood signals, was more repeatable than K_trans. Both K_trans and k_ep values were highly discriminatory between healthy and diseased tissues in all cases. The parameter v_e had high repeatability but could not distinguish the two tissue types.

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.

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.

An overview of publicly available patient-centered prostate cancer datasets

Prostate cancer (PCa) is the second most common cancer in men, and the second leading cause of death from cancer in men. Many studies on PCa have been carried out, each taking much time before the data is collected and ready to be analyzed. However, on the internet there is already a wide range of PCa datasets available, which could be used for data mining, predictive modelling or other purposes, reducing the need to setup new studies to collect data. In the current scientific climate, moving more and more to the analysis of "big data" and large, international, multi-site projects using a modern IT infrastructure, these datasets could be proven extremely valuable. This review presents an overview of publicly available patient-centered PCa datasets, divided into three categories (clinical, genomics and imaging) and an "overall" section to enable researchers to select a suitable dataset for analysis, without having to go through days of work to find the right data. To acquire a list of human PCa databases, scientific literature databases and academic social network sites were searched. We also used the information from other reviews. All databases in the combined list were then checked for public availability. Only databases that were either directly publicly available or available after signing a research data agreement or retrieving a free login were selected for inclusion in this review. Data should be available to commercial parties as well. This paper focuses on patient-centered data, so the genomics data section does not include gene-centered databases or pathway-centered databases. We identified 42 publicly available, patient-centered PCa datasets. Some of these consist of different smaller datasets. Some of them contain combinations of datasets from the three data domains: clinical data, imaging data and genomics data. Only one dataset contains information from all three domains. This review presents all datasets and their characteristics: number of subjects, clinical fields, imaging modalities, expression data, mutation data, biomarker measurements, etc. Despite all the attention that has been given to making this overview of publicly available databases as extensive as possible, it is very likely not complete, and will also be outdated soon. However, this review might help many PCa researchers to find suitable datasets to answer the research question with, without the need to start a new data collection project. In the coming era of big data analysis, overviews like this are becoming more and more useful.

Thapsigargin induces apoptosis of prostate cancer through cofilin-1 and paxillin

It is widely considered that endoplasmic reticulum stress may rapidly induce apoptosis. The aim of the present study was to investigate the effect of thapsigargin on the induction of apoptosis in prostate cancer cells, and to explore its possible mechanism. A Cell Counting Kit-8 was selected to determine the effect of thapsigargin (0, 1, 10 and 100 nM) on the proliferation of PC3 cells. Cell proliferation of the prostate cancer cells was effectively inhibited by treatment with thapsigargin, and thapsigargin significantly increased the rate of apoptosis and caspase-3/9 activities in prostate cancer cells. The protein expression of phosphorylated (p)-RAC-α serine threonine-protein kinase, p-mechanistic target of rapamycin, F-actin and paxillin were significantly decreased, and cofilin-1 protein expression was significantly increased by treatment with thapsigargin in prostate cancer cells. Overall, the data of the present study revealed that thapsigargin induced apoptosis in prostate cancer cells through cofilin-1 and paxillin.

Accuracy, repeatability, and interplatform reproducibility of T1 quantification methods used for DCE-MRI: Results from a multicenter phantom study

PURPOSE

To determine the in vitro accuracy, test-retest repeatability, and interplatform reproducibility of T₁ quantification protocols used for dynamic contrast-enhanced MRI at 1.5 and 3 T.

METHODS

A T₁ phantom with 14 samples was imaged at eight centers with a common inversion-recovery spin-echo (IR-SE) protocol and a variable flip angle (VFA) protocol using seven flip angles, as well as site-specific protocols (VFA with different flip angles, variable repetition time, proton density, and Look-Locker inversion recovery). Factors influencing the accuracy (deviation from reference NMR T₁ measurements) and repeatability were assessed using general linear mixed models. Interplatform reproducibility was assessed using coefficients of variation.

RESULTS

For the common IR-SE protocol, accuracy (median error across platforms = 1.4-5.5%) was influenced predominantly by T₁ sample (P < 10^-6 ), whereas test-retest repeatability (median error = 0.2-8.3%) was influenced by the scanner (P < 10^-6 ). For the common VFA protocol, accuracy (median error = 5.7-32.2%) was influenced by field strength (P = 0.006), whereas repeatability (median error = 0.7-25.8%) was influenced by the scanner (P < 0.0001). Interplatform reproducibility with the common VFA was lower at 3 T than 1.5 T (P = 0.004), and lower than that of the common IR-SE protocol (coefficient of variation 1.5T: VFA/IR-SE = 11.13%/8.21%, P = 0.028; 3 T: VFA/IR-SE = 22.87%/5.46%, P = 0.001). Among the site-specific protocols, Look-Locker inversion recovery and VFA (2-3 flip angles) protocols showed the best accuracy and repeatability (errors < 15%).

CONCLUSIONS

The VFA protocols with 2 to 3 flip angles optimized for different applications achieved acceptable balance of extensive spatial coverage, accuracy, and repeatability in T₁ quantification (errors < 15%). Further optimization in terms of flip-angle choice for each tissue application, and the use of B₁ correction, are needed to improve the robustness of VFA protocols for T₁ mapping. Magn Reson Med 79:2564-2575, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Distinguishing benign and malignant breast tumors: preliminary comparison of kinetic modeling approaches using multi-institutional dynamic contrast-enhanced MRI data from the International Breast MR Consortium 6883 trial

Comparative preliminary analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data collected in the International Breast MR Consortium 6883 multicenter trial was performed to distinguish benign and malignant breast tumors. Prebiopsy DCE-MRI data from 45 patients with suspicious breast lesions were obtained. Semiquantitative mean signal-enhancement ratio ([Formula: see text]) was calculated for all lesions, and quantitative pharmacokinetic, parameters [Formula: see text], [Formula: see text], and [Formula: see text], were calculated for the subset with available [Formula: see text] maps ([Formula: see text]). Diagnostic performance was estimated for DCE-MRI parameters and compared to standard clinical MRI assessment. Quantitative and semiquantitative metrics discriminated benign and malignant lesions, with receiver operating characteristic area under the curve (AUC) values of 0.71, 0.70, and 0.82 for [Formula: see text], [Formula: see text], and [Formula: see text], respectively ([Formula: see text]). At equal 94% sensitivity, the specificity and positive predictive value of [Formula: see text] (53% and 63%, respectively) and K^trans (42% and 58%) were higher than clinical MRI assessment (32% and 54%). A multivariable model combining [Formula: see text] and clinical MRI assessment had an AUC value of 0.87. Quantitative pharmacokinetic and semiquantitative analyses of DCE-MRI improves discrimination of benign and malignant breast tumors, with our findings suggesting higher diagnostic accuracy using [Formula: see text]. [Formula: see text] has potential to help reduce unnecessary biopsies resulting from routine breast imaging.

Dynamic contrast-enhanced magnetic resonance imaging of prostate cancer: A review of current methods and applications

In many areas of oncology, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has proven to be a clinically useful, non-invasive functional imaging technique to quantify tumor vasculature and tumor perfusion characteristics. Tumor angiogenesis is an essential process for tumor growth, proliferation, and metastasis. Malignant lesions demonstrate rapid extravasation of contrast from the intravascular space to the capillary bed due to leaky capillaries associated with tumor neovascularity. DCE-MRI has the potential to provide information regarding blood flow, areas of hypoperfusion, and variations in endothelial permeability and microvessel density to aid treatment selection, enable frequent monitoring during treatment and assess response to targeted therapy following treatment. This review will discuss the current status of DCE-MRI in cancer imaging, with a focus on its use in imaging prostate malignancies as well as weaknesses that limit its widespread clinical use. The latest techniques for quantification of DCE-MRI parameters will be reviewed and compared.

Robust and efficient pharmacokinetic parameter non-linear least squares estimation for dynamic contrast enhanced MRI of the prostate

PURPOSE

To describe an efficient numerical optimization technique using non-linear least squares to estimate perfusion parameters for the Tofts and extended Tofts models from dynamic contrast enhanced (DCE) MRI data and apply the technique to prostate cancer.

METHODS

Parameters were estimated by fitting the two Tofts-based perfusion models to the acquired data via non-linear least squares. We apply Variable Projection (VP) to convert the fitting problem from a multi-dimensional to a one-dimensional line search to improve computational efficiency and robustness. Using simulation and DCE-MRI studies in twenty patients with suspected prostate cancer, the VP-based solver was compared against the traditional Levenberg-Marquardt (LM) strategy for accuracy, noise amplification, robustness to converge, and computation time.

RESULTS

The simulation demonstrated that VP and LM were both accurate in that the medians closely matched assumed values across typical signal to noise ratio (SNR) levels for both Tofts models. VP and LM showed similar noise sensitivity. Studies using the patient data showed that the VP method reliably converged and matched results from LM with approximate 3× and 2× reductions in computation time for the standard (two-parameter) and extended (three-parameter) Tofts models. While LM failed to converge in 14% of the patient data, VP converged in the ideal 100%.

CONCLUSION

The VP-based method for non-linear least squares estimation of perfusion parameters for prostate MRI is equivalent in accuracy and robustness to noise, while being more reliably (100%) convergent and computationally about 3× (TM) and 2× (ETM) faster than the LM-based method.

Modification of population based arterial input function to incorporate individual variation

This technical note describes how to modify a population-based arterial input function to incorporate variation among the individuals. In DCE-MRI, an arterial input function (AIF) is often distorted by pulsated inflow effect and noise. A population-based AIF (pAIF) has high signal-to-noise ratio (SNR), but cannot incorporate the individual variation. AIF variation is mainly induced by variation in cardiac output and blood volume of the individuals, which can be detected by the full width at half maximum (FWHM) during the first passage and the amplitude of AIF, respectively. Thus pAIF scaled in time and amplitude fitting to the individual AIF may serve as a high SNR AIF incorporating the individual variation. The proposed method was validated using DCE-MRI images of 18 prostate cancer patients. Root mean square error (RMSE) of pAIF from individual AIFs was 0.88±0.48mM (mean±SD), but it was reduced to 0.25±0.11mM after pAIF modification using the proposed method (p<0.0001).

Dynamic contrast-enhanced MRI of the patellar bone: How to quantify perfusion

Purpose

To identify the optimal combination of pharmacokinetic model and arterial input function (AIF) for quantitative analysis of blood perfusion in the patellar bone using dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI).

Materials and Methods

This method design study used a random subset of five control subjects from an Institutional Review Board (IRB)‐approved case–control study into patellofemoral pain, scanned on a 3T MR system with a contrast‐enhanced time‐resolved imaging of contrast kinetics (TRICKS) sequence. We systematically investigated the reproducibility of pharmacokinetic parameters for all combinations of Orton and Parker AIF models with Tofts, Extended Tofts (ETofts), and Brix pharmacokinetic models. Furthermore, we evaluated if the AIF should use literature parameters, be subject‐specific, or group‐specific. Model selection was based on the goodness‐of‐fit and the coefficient of variation of the pharmacokinetic parameters inside the patella. This extends previous studies that were not focused on the patella and did not evaluate as many combinations of arterial and pharmacokinetic models.

Results

The vascular component in the ETofts model could not reliably be recovered (coefficient of variation [CV] of v_p >50%) and the Brix model parameters showed high variability of up to 20% for k_el across good AIF models. Compared to group‐specific AIF, the subject‐specific AIF's mostly had higher residual. The best reproducibility and goodness‐of‐fit were obtained by combining Tofts' pharmacokinetic model with the group‐specific Parker AIF.

Conclusion

We identified several good combinations of pharmacokinetic models and AIF for quantitative analysis of perfusion in the patellar bone. The recommended combination is Tofts pharmacokinetic model combined with a group‐specific Parker AIF model.

Level of Evidence: 2

Technical Efficacy: Stage 1

J. Magn. Reson. Imaging 2018;47:848–858.

Effects of arterial input function selection on kinetic parameters in brain dynamic contrast-enhanced MRI

PURPOSE

Kinetic parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) were suggested as a possible instrument for multi-parametric lesion characterization, but have not found their way into clinical practice yet due to inconsistent results. The quantification is heavily influenced by the definition of an appropriate arterial input functions (AIF). Regarding brain tumor DCE-MRI, there are currently several co-existing methods to determine the AIF frequently including different brain vessels as sources. This study quantitatively and qualitatively analyzes the impact of AIF source selection on kinetic parameters derived from commonly selected AIF source vessels compared to a population-based AIF model.

MATERIAL AND METHODS

74 patients with brain lesions underwent 3D DCE-MRI. Kinetic parameters [transfer constants of contrast agent efflux and reflux K^trans and k_ep and, their ratio, v_e, that is used to measure extravascular-extracellular volume fraction and plasma volume fraction v_p] were determined using extended Tofts model in 821 ROI from 4 AIF sources [the internal carotid artery (ICA), the closest artery to the lesion, the superior sagittal sinus (SSS), the population-based Parker model]. The effect of AIF source alteration on kinetic parameters was evaluated by tissue type selective intra-class correlation (ICC) and capacity to differentiate gliomas by WHO grade [area under the curve analysis (AUC)].

RESULTS

Arterial AIF more often led to implausible v_e >100% values (p<0.0001). AIF source alteration rendered different absolute kinetic parameters (p<0.0001), except for k_ep. ICC between kinetic parameters of different AIF sources and tissues were variable (0.08-0.87) and only consistent >0.5 between arterial AIF derived kinetic parameters. Differentiation between WHO III and II glioma was exclusively possible with v_p derived from an AIF in the SSS (p=0.03; AUC 0.74).

CONCLUSION

The AIF source has a significant impact on absolute kinetic parameters in DCE-MRI, which limits the comparability of kinetic parameters derived from different AIF sources. The effect is also tissue-dependent. The SSS appears to be the best choice for AIF source vessel selection in brain tumor DCE-MRI as it exclusively allowed for WHO grades II/III and III/IV glioma distinction (by v_p) and showed the least number of implausible v_e values.

DCE-MRI of the prostate using shutter-speed vs. Tofts model for tumor characterization and assessment of aggressiveness

PURPOSE

To quantify Tofts model (TM) and shutter-speed model (SSM) perfusion parameters in prostate cancer (PCa) and noncancerous peripheral zone (PZ) and to compare the diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to Prostate Imaging and Reporting and Data System (PI-RADS) classification for the assessment of PCa aggressiveness.

MATERIALS AND METHODS

Fifty PCa patients (mean age 60 years old) who underwent MRI at 3.0T followed by prostatectomy were included in this Institutional Review Board-approved retrospective study. DCE-MRI parameters (K^trans , v_e , k_ep [TM&SSM] and intracellular water molecule lifetime τ_i [SSM]) were determined in PCa and PZ. Differences in DCE-MRI parameters between PCa and PZ, and between models were assessed using Wilcoxon signed-rank tests. Receiver operating characteristic (ROC) analysis for differentiation between PCa and PZ was performed for individual and combined DCE-MRI parameters. Diagnostic performance of DCE-MRI parameters for identification of aggressive PCa (Gleason ≥8, grade group [GG] ≥3 or pathology stage pT3) was assessed using ROC analysis and compared with PI-RADSv2 scores.

RESULTS

DCE-MRI parameters were significantly different between TM and SSM and between PZ and PCa (P < 0.037). Diagnostic performances of TM and SSM for differentiation of PCa from PZ were similar (highest AUC TM: K^trans +k_ep 0.76, SSM: τ_i +k_ep 0.80). PI-RADS outperformed TM and SSM DCE-MRI for identification of Gleason ≥8 lesions (AUC PI-RADS: 0.91, highest AUC DCE-MRI: K^trans +τ_i SSM 0.61, P = 0.002). The diagnostic performance of PI-RADS and DCE-MRI for identification of GG ≥3 and pT3 PCa was not significantly different (P > 0.213).

CONCLUSION

SSM DCE-MRI did not increase the diagnostic performance of DCE-MRI for PCa characterization. PI-RADS outperformed both TM and SSM DCE-MRI for identification of aggressive cancer.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:837-849.

Dynamic contrast-enhanced case-control analysis in 3T MRI of prostate cancer can help to characterize tumor aggressiveness

PURPOSE

The aim of this work is to establish normality and tumor tissue ranges for perfusion parameters from dynamic contrast-enhanced (DCE) MR of the peripheral prostate at 3T and to compare the diagnostic performance of quantitative and semi-quantitative parameters.

MATERIALS AND METHODS

Thirty-six patients with prostate carcinomas (18 Gleason-6, 15 Gleason-7, and 3 Gleason-8) and 33 healthy subjects were included. Image analysis workflow comprised four steps: manual segmentation of whole prostate and lesions, series registration, voxelwise T1 mapping and calculation of pharmacokinetic and semi-quantitative parameters.

RESULTS

K^trans, v_e, upslope and AUC60 showed statistically significant differences between healthy peripheral areas and tumors. Curve type showed no association with healthy/tumor peripheral areas (chi-square=0.702). Areas under the ROC curves were 0.64 (95% CI: 0.54-0.75), 0.70 (0.60-0.80), 0.62 (0.51-0.72) and 0.63 (0.52-0.74) for K^trans, v_e, upslope and AUC60, respectively. The optimal cutoff values were: K^trans=0.21min^-1 (sensitivity=0.61, specificity=0.64), v_e=0.36 (0.63, 0.71), upslope=0.59 (0.59, 0.59) and AUC60=2.4 (0.63, 0.64). Significant differences were found between Gleason scores 6 and 7 for normalized K^trans, upslope and AUC60, with good diagnostic accuracy (area under ROC curve 0.80, 95% CI: 0.60-1.00).

CONCLUSION

Quantitative (K^trans and v_e) and semi-quantitative (upslope and AUC60) perfusion parameters showed significant differences between tumors and control areas in the peripheral prostate. Normalized K^trans, upslope and AUC60 values might characterize tumor aggressiveness.

Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications

One in six men will develop prostate cancer in his lifetime. Early detection and accurate diagnosis of the disease can improve cancer survival and reduce treatment costs. Recently, imaging of prostate cancer has greatly advanced since the introduction of multiparametric magnetic resonance imaging (mp-MRI). Mp-MRI consists of T2-weighted sequences combined with functional sequences including dynamic contrast-enhanced MRI, diffusion-weighted MRI, and magnetic resonance spectroscopy imaging. Because of the big data and variations in imaging sequences, detection can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. To improve quantitative assessment of the disease, various computer-aided detection systems have been designed to help radiologists in their clinical practice. This review paper presents an overview of literatures on computer-aided detection of prostate cancer with mp-MRI, which include the technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.

Patient-specific pharmacokinetic parameter estimation on dynamic contrast-enhanced MRI of prostate: Preliminary evaluation of a novel AIF-free estimation method

PURPOSE

To develop and evaluate a prostate-based method (PBM) for estimating pharmacokinetic parameters on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) by leveraging inherent differences in pharmacokinetic characteristics between the peripheral zone (PZ) and transition zone (TZ).

MATERIALS AND METHODS

This retrospective study, approved by the Institutional Review Board, included 40 patients who underwent a multiparametric 3T MRI examination and subsequent radical prostatectomy. A two-step PBM for estimating pharmacokinetic parameters exploited the inherent differences in pharmacokinetic characteristics associated with the TZ and PZ. First, the reference region model was implemented to estimate ratios of K^trans between normal TZ and PZ. Subsequently, the reference region model was leveraged again to estimate values for K^trans and v_e for every prostate voxel. The parameters of PBM were compared with those estimated using an arterial input function (AIF) derived from the femoral arteries. The ability of the parameters to differentiate prostate cancer (PCa) from benign tissue was evaluated on a voxel and lesion level. Additionally, the effect of temporal downsampling of the DCE MRI data was assessed.

RESULTS

Significant differences (P < 0.05) in PBM K^trans between PCa lesions and benign tissue were found in 26/27 patients with TZ lesions and in 33/38 patients with PZ lesions; significant differences in AIF-based K^trans occurred in 26/27 and 30/38 patients, respectively. The 75^th and 100^th percentiles of K^trans and v_e estimated using PBM positively correlated with lesion size (P < 0.05).

CONCLUSION

Pharmacokinetic parameters estimated via PBM outperformed AIF-based parameters in PCa detection. J. Magn. Reson. Imaging 2016;44:1405-1414.

Practical aspects of prostate MRI: hardware and software considerations, protocols, and patient preparation

The use of multiparametric MRI scans for the evaluation of men with prostate cancer has increased dramatically and is likely to continue expanding as new developments come to practice. However, it has not yet gained the same level of acceptance of other imaging tests. Partly, this is because of the use of suboptimal protocols, lack of standardization, and inadequate patient preparation. In this manuscript, we describe several practical aspects of prostate MRI that may facilitate the implementation of new prostate imaging programs or the expansion of existing ones.

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

Dynamic contrast-enhanced MRI (DCE-MRI) has been widely used in tumor detection and therapy response evaluation. Pharmacokinetic analysis of DCE-MRI time-course data allows estimation of quantitative imaging biomarkers such as K^trans(rate constant for plasma/interstitium contrast reagent (CR) transfer) and v_e (extravascular and extracellular volume fraction). However, the use of quantitative DCE-MRI in clinical prostate imaging islimited, with uncertainty in arterial input function (AIF, i.e., the time rate of change of the concentration of CR in the blood plasma) determination being one of the primary reasons. In this multicenter data analysis challenge to assess the effects of variations in AIF quantification on estimation of DCE-MRI parameters, prostate DCE-MRI data acquired at one center from 11 prostate cancer patients were shared among nine centers. Each center used its site-specific method to determine the individual AIF from each data set and submitted the results to the managing center. Along with a literature population averaged AIF, these AIFs and their reference-tissue-adjusted variants were used by the managing center to perform pharmacokinetic analysis of the DCE-MRI data sets using the Tofts model (TM). All other variables including tumor region of interest (ROI) definition and pre-contrast T₁ were kept the same to evaluate parameter variations caused by AIF variations only. Considerable pharmacokinetic parameter variations were observed with the within-subject coefficient of variation (wCV) of K^trans obtained with unadjusted AIFs as high as 0.74. AIF-caused variations were larger in K^trans than v_e and both were reduced when reference-tissue-adjusted AIFs were used. The parameter variations were largely systematic, resulting in nearly unchanged parametric map patterns. The CR intravasation rate constant, k_ep (= K^trans/v_e), was less sensitive to AIF variation than K^trans (wCV for unadjusted AIFs: 0.45 for k_epvs. 0.74 for K^trans), suggesting that it might be a more robust imaging biomarker of prostate microvasculature than K^trans.

Bolus arrival time and its effect on tissue characterization with dynamic contrast-enhanced magnetic resonance imaging

Matching the bolus arrival time (BAT) of the arterial input function (AIF) and tissue residue function (TRF) is necessary for accurate pharmacokinetic (PK) modeling of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). We investigated the sensitivity of volume transfer constant ([Formula: see text]) and extravascular extracellular volume fraction ([Formula: see text]) to BAT and compared the results of four automatic BAT measurement methods in characterization of prostate and breast cancers. Variation in delay between AIF and TRF resulted in a monotonous change trend of [Formula: see text] and [Formula: see text] values. The results of automatic BAT estimators for clinical data were all comparable except for one BAT estimation method. Our results indicate that inaccuracies in BAT measurement can lead to variability among DCE-MRI PK model parameters, diminish the quality of model fit, and produce fewer valid voxels in a region of interest. Although the selection of the BAT method did not affect the direction of change in the treatment assessment cohort, we suggest that BAT measurement methods must be used consistently in the course of longitudinal studies to control measurement variability.

Comparison of different population-averaged arterial-input-functions in dynamic contrast-enhanced MRI of the prostate: Effects on pharmacokinetic parameters and their diagnostic performance

PURPOSE

To assess the effect of different population-averaged arterial-input-functions (pAIF) on pharmacokinetic parameters from dynamic contrast-enhanced MRI (DCE-MRI) and their diagnostic accuracy regarding the detection of potentially malignant prostate lesions.

MATERIALS AND METHODS

66 male patients (age 65.4±10.8y) with suspected prostate cancer underwent multiparametric MRI of the prostate including T2-w, DWI-w and DCE-MRI sequences at a 3T MRI scanner. All detected lesions were categorized based on ACR PI-RADS version 2 and divided into 2 groups (A: PI-RADS ≤3, n=32; B: PI-RADS >3, n=34). In each DCE-MRI dataset, pharmacokinetic parameters (Ktrans, Kep and ve) and goodness of fit (chi(2)) were generated using the Tofts model with 3 different pAIFs (fast, intermediate, slow) as provided by a commercially available postprocessing software. Pharmacokinetic parameters, their diagnostic accuracies and model fits were compared for the 3 pAIFs.

RESULTS

Ktrans, Kep and ve differed significantly among the 3 pAIFs (all p<.001). Ktrans and Kep were significantly higher in group B compared to group A (all p<.001). For chi(2), lowest results (representing highest goodness of fit) were found for intermediate pAIF (chi(2) 0.073). ROC analyses revealed comparable diagnostic accuracies for the different pAIFs, which were high for Ktrans and Kep and low for ve.

CONCLUSION

Choosing various pAIF types causes a high variability in pharmacokinetic parameter estimates. Therefore, it is of great importance to consider this as potential artifact and thus keep AIF type selection constant in DCE-MRI studies.

Quantitative pharmacokinetic analysis of prostate cancer DCE-MRI at 3T: comparison of two arterial input functions on cancer detection with digitized whole mount histopathological validation

Accurate pharmacokinetic (PK) modeling of dynamic contrast enhanced MRI (DCE-MRI) in prostate cancer (PCa) requires knowledge of the concentration time course of the contrast agent in the feeding vasculature, the so-called arterial input function (AIF). The purpose of this study was to compare AIF choice in differentiating peripheral zone PCa from non-neoplastic prostatic tissue (NNPT), using PK analysis of high temporal resolution prostate DCE-MRI data and whole-mount pathology (WMP) validation. This prospective study was performed in 30 patients who underwent multiparametric endorectal prostate MRI at 3.0T and WMP validation. PCa foci were annotated on WMP slides and MR images using 3D Slicer. Foci ≥0.5cm(3) were contoured as tumor regions of interest (TROIs) on subtraction DCE (early-arterial - pre-contrast) images. PK analyses of TROI and NNPT data were performed using automatic AIF (aAIF) and model AIF (mAIF) methods. A paired t-test compared mean and 90th percentile (p90) PK parameters obtained with the two AIF approaches. Receiver operating characteristic (ROC) analysis determined diagnostic accuracy (DA) of PK parameters. Logistic regression determined correlation between PK parameters and histopathology. Mean TROI and NNPT PK parameters were higher using aAIF vs. mAIF (p<0.05). There was no significant difference in DA between AIF methods: highest for p90 volume transfer constant (K(trans)) (aAIF differences in the area under the ROC curve (Az) = 0.827; mAIF Az=0.93). Tumor cell density correlated with aAIF K(trans) (p=0.03). Our results indicate that DCE-MRI using both AIF methods is excellent in discriminating PCa from NNPT. If quantitative DCE-MRI is to be used as a biomarker in PCa, the same AIF method should be used consistently throughout the study.

Analyzing the blood-brain barrier: the benefits of medical imaging in research and clinical practice

A dysfunctional BBB is a common feature in a variety of brain disorders, a fact stressing the need for diagnostic tools designed to assess brain vessels' permeability in space and time. Biological research has benefited over the years various means to analyze BBB integrity. The use of biomarkers for improper BBB functionality is abundant. Systemic administration of BBB impermeable tracers can both visualize brain regions characterized by BBB impairment, as well as lead to its quantification. Additionally, locating molecular, physiological content in regions from which it is restricted under normal BBB functionality undoubtedly indicates brain pathology-related BBB disruption. However, in-depth research into the BBB's phenotype demands higher analytical complexity than functional vs. pathological BBB; criteria which biomarker based BBB permeability analyses do not meet. The involvement of accurate and engineering sciences in recent brain research, has led to improvements in the field, in the form of more accurate, sensitive imaging-based methods. Improvements in the spatiotemporal resolution of many imaging modalities and in image processing techniques, make up for the inadequacies of biomarker based analyses. In pre-clinical research, imaging approaches involving invasive procedures, enable microscopic evaluation of BBB integrity, and benefit high levels of sensitivity and accuracy. However, invasive techniques may alter normal physiological function, thus generating a modality-based impact on vessel's permeability, which needs to be corrected for. Non-invasive approaches do not affect proper functionality of the inspected system, but lack in spatiotemporal resolution. Nevertheless, the benefit of medical imaging, even in pre-clinical phases, outweighs its disadvantages. The innovations in pre-clinical imaging and the development of novel processing techniques, have led to their implementation in clinical use as well. Specialized analyses of vessels' permeability add valuable information to standard anatomical inspections which do not take the latter into consideration.

Automatic individual arterial input functions calculated from PCA outperform manual and population-averaged approaches for the pharmacokinetic modeling of DCE-MR images

BACKGROUND

To introduce a segmentation method to calculate an automatic arterial input function (AIF) based on principal component analysis (PCA) of dynamic contrast enhanced MR (DCE-MR) imaging and compare it with individual manually selected and population-averaged AIFs using calculated pharmacokinetic parameters.

METHODS

The study included 65 individuals with prostate examinations (27 tumors and 38 controls). Manual AIFs were individually extracted and also averaged to obtain a population AIF. Automatic AIFs were individually obtained by applying PCA to volumetric DCE-MR imaging data and finding the highest correlation of the PCs with a reference AIF. Variability was assessed using coefficients of variation and repeated measures tests. The different AIFs were used as inputs to the pharmacokinetic model and correlation coefficients, Bland-Altman plots and analysis of variance tests were obtained to compare the results.

RESULTS

Automatic PCA-based AIFs were successfully extracted in all cases. The manual and PCA-based AIFs showed good correlation (r between pharmacokinetic parameters ranging from 0.74 to 0.95), with differences below the manual individual variability (RMSCV up to 27.3%). The population-averaged AIF showed larger differences (r from 0.30 to 0.61).

CONCLUSION

The automatic PCA-based approach minimizes the variability associated to obtaining individual volume-based AIFs in DCE-MR studies of the prostate.

Quantitative Imaging Network: Data Sharing and Competitive AlgorithmValidation Leveraging The Cancer Imaging Archive

The Quantitative Imaging Network (QIN), supported by the National Cancer Institute, is designed to promote research and development of quantitative imaging methods and candidate biomarkers for the measurement of tumor response in clinical trial settings. An integral aspect of the QIN mission is to facilitate collaborative activities that seek to develop best practices for the analysis of cancer imaging data. The QIN working groups and teams are developing new algorithms for image analysis and novel biomarkers for the assessment of response to therapy. To validate these algorithms and biomarkers and translate them into clinical practice, algorithms need to be compared and evaluated on large and diverse data sets. Analysis competitions, or "challenges," are being conducted within the QIN as a means to accomplish this goal. The QIN has demonstrated, through its leveraging of The Cancer Imaging Archive (TCIA), that data sharing of clinical images across multiple sites is feasible and that it can enable and support these challenges. In addition to Digital Imaging and Communications in Medicine (DICOM) imaging data, many TCIA collections provide linked clinical, pathology, and "ground truth" data generated by readers that could be used for further challenges. The TCIA-QIN partnership is a successful model that provides resources for multisite sharing of clinical imaging data and the implementation of challenges to support algorithm and biomarker validation.