Comparison of quantitative parameters in cervix cancer measured by dynamic contrast-enhanced MRI and CT

Spatial correlation between in vivo imaging and immunohistochemical biomarkers: A methodological study

In this study, we present a method that enables voxel-by-voxel comparison of in vivo imaging to immunohistochemistry (IHC) biomarkers. As a proof of concept, we investigated the spatial correlation between dynamic contrast enhanced (DCE-)CT parameters and IHC biomarkers Ki-67 (proliferation), HIF-1α (hypoxia), and CD45 (immune cells). 54 whole-mount tumor slices of 15 laryngeal and hypopharyngeal carcinomas were immunohistochemically stained and digitized. Heatmaps of biomarker positivity were created and registered to DCE-CT parameter maps. The adiabatic approximation to the tissue homogeneity model was used to fit the following DCE parameters: Ktrans (transfer constant), Ve (extravascular and extracellular space), and Vi (intravascular space). Both IHC and DCE maps were downsampled to 4 × 4 × 3 mm[3] voxels. The mean values per tumor were used to calculate the between-subject correlations between parameters. For the within-subject (spatial) correlation, values of all voxels within a tumor were compared using the repeated measures correlation (rrm). No between-subject correlations were found between IHC biomarkers and DCE parameters, whereas we found multiple significant within-subject correlations: Ve and Ki-67 (rrm = -0.17, P < .001), Ve and HIF-1α (rrm = -0.12, P < .001), Ktrans and CD45 (rrm = 0.13, P < .001), Vi and CD45 (rrm = 0.16, P < .001), and Vi and Ki-67 (rrm = 0.08, P = .003). The strongest correlation was found between IHC biomarkers Ki-67 and HIF-1α (rrm = 0.35, P < .001). This study shows the technical feasibility of determining the 3 dimensional spatial correlation between histopathological biomarker heatmaps and in vivo imaging. It also shows that between-subject correlations do not reflect within-subject correlations of parameters.

DCE-Qnet: Deep Network Quantification of Dynamic Contrast Enhanced (DCE) MRI

Introduction

Quantification of dynamic contrast-enhanced (DCE)-MRI has the potential to provide valuable clinical information, but robust pharmacokinetic modeling remains a challenge for clinical adoption.

Methods

A 7-layer neural network called DCE-Qnet was trained on simulated DCE-MRI signals derived from the Extended Tofts model with the Parker arterial input function. Network training incorporated B1 inhomogeneities to estimate perfusion (Ktrans, vp, ve), tissue T1 relaxation, proton density and bolus arrival time (BAT). The accuracy was tested in a digital phantom in comparison to a conventional nonlinear least-squares fitting (NLSQ). In vivo testing was conducted in 10 healthy subjects. Regions of interest in the cervix and uterine myometrium were used to calculate the inter-subject variability. The clinical utility was demonstrated on a cervical cancer patient. Test-retest experiments were used to assess reproducibility of the parameter maps in the tumor.

Results

The DCE-Qnet reconstruction outperformed NLSQ in the phantom. The coefficient of variation (CV) in the healthy cervix varied between 5-51% depending on the parameter. Parameter values in the tumor agreed with previous studies despite differences in methodology. The CV in the tumor varied between 1-47%.

Conclusion

The proposed approach provides comprehensive DCE-MRI quantification from a single acquisition. DCE-Qnet eliminates the need for separate T1 scan or BAT processing, leading to a reduction of 10 minutes per scan and more accurate quantification.

Surrogate vascular input function measurements from the superior sagittal sinus are repeatable and provide tissue-validated kinetic parameters in brain DCE-MRI

Accurate vascular input function (VIF) derivation is essential in brain dynamic contrast-enhanced (DCE) MRI. The optimum site for VIF estimation is, however, debated. This study sought to compare VIFs extracted from the internal carotid artery (ICA) and its branches with an arrival-corrected vascular output function (VOF) derived from the superior sagittal sinus (VOF_SSS). DCE-MRI datasets from sixty-six patients with different brain tumours were retrospectively analysed and plasma gadolinium-based contrast agent (GBCA) concentration-time curves used to extract VOF/VIFs from the SSS, the ICA, and the middle cerebral artery. Semi-quantitative parameters across each first-pass VOF/VIF were compared and the relationship between these parameters and GBCA dose was evaluated. Through a test-retest study in 12 patients, the repeatability of each semiquantitative VOF/VIF parameter was evaluated; and through comparison with histopathological data the accuracy of kinetic parameter estimates derived using each VOF/VIF and the extended Tofts model was also assessed. VOF_SSS provided a superior surrogate global input function compared to arteries, with greater contrast-to-noise (p < 0.001), higher peak (p < 0.001, repeated-measures ANOVA), and a greater sensitivity to interindividual plasma GBCA concentration. The repeatability of VOF_SSS derived semi-quantitative parameters was good to excellent (ICC = 0.717-0.888) outperforming arterial based approaches. In contrast to arterial VIFs, kinetic parameters obtained using a SSS derived VOF permitted detection of intertumoural differences in both microvessel surface area and cell density within resected tissue specimens. These results support the usage of an arrival-corrected VOF_SSS as a surrogate vascular input function for kinetic parameter mapping in brain DCE-MRI.

3.0 T Magnetic Resonance Functional Imaging Quantitative Parameters for Differential Diagnosis of Benign and Malignant Lesions of the Breast

Objective: To investigate value of quantitative dynamic contrast-enhanced magnetic resonance imaging (MRI) parameters and apparent diffusion coefficient (ADC) value in differential diagnosis of breast benign and malignant lesions, and their correlation with prognostic factors of breast cancer. Methods: The study collected MRI images and clinical data from 232 female patients suspected of breast cancer. Philips INGENIA 3.0T superconducting magnetic resonance scanner was used for imaging examination. Complete pathological data of patients were collected, and the expression of ER, PR, HER-2, and Ki-67 were further investigated. Results: K_ep was higher in malignant breast lesion group than that in benign breast lesion group, and ADC value was lower in the former group than that in the latter group (both p < 0.05). The areas under the receiver operating characteristic curves for K_ep, ADC, and extravascular volume fraction (V_e) were 0.904 (95% confidence interval [CI]: 0.863-0.945), 0.813 (95% CI: 0.752-0.875), and 0.774 (95% CI: 0.707-0.841), respectively. Furthermore, according to the maximum Youden index, the specificity of K_ep and the sensitivity of ADC were high, which were 97.20% and 96.00%, respectively, with a cutoff value of 0.314 and 0.151, respectively. K_ep value in ER-positive expression group was significantly higher than that in ER-negative expression group (p < 0.05). K_ep value in PR-positive expression group was significantly higher than that in PR-negative expression group (p < 0.05). There was positive correlation between K_ep and expression of Ki-67 (p < 0.05). ADC value was negatively correlated with Ki-67 expression (p < 0.05). Conclusion: Quantitative parameters K_ep and ADC of 3.0 T MR functional imaging can be used as reference indexes for differential diagnosis of benign and malignant breast lesions and for biological behavior evaluation, indicating potential clinical value for noninvasive preoperative evaluation of breast cancer.

Reference region extraction by clustering for the pharmacokinetic analysis of dynamic contrast-enhanced MRI in prostate cancer

PURPOSE

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) measures changes in the concentration of an administered contrast agent to quantitatively evaluate blood circulation in a tumor or normal tissues. This method uses a pharmacokinetic analysis based on the time course of a reference region, such as muscle, rather than arterial input function. However, it is difficult to manually define a homogeneous reference region. In the present study, we developed a method for automatic extraction of the reference region using a clustering algorithm based on a time course pattern for DCE-MRI studies of patients with prostate cancer.

METHODS

Two feature values related to the shape of the time course were extracted from the time course of all voxels in the DCE-MRI images. Each voxel value of T1-weighted images acquired before administration were also added as anatomical data. Using this three-dimensional feature vector, all voxels were segmented into five clusters by the Gaussian mixture model, and one of these clusters that included the gluteus muscle was selected as the reference region.

RESULTS

Each region of arterial vessel, muscle, and fat was segmented as a different cluster from the tumor and normal tissues in the prostate. In the extracted reference region, other tissue elements including scattered fat and blood vessels were removed from the muscle region.

CONCLUSIONS

Our proposed method can automatically extract the reference region using the clustering algorithm with three types of features based on the time course pattern and anatomical data. This method may be useful for evaluating tumor circulatory function in DCE-MRI studies.

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.

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

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

Hypoxia in cervical cancer: from biology to imaging

PURPOSE

Hypoxia imaging may improve identification of cervical cancer patients at risk of treatment failure and be utilized in treatment planning and monitoring, but its clinical potential is far from fully realized. Here, we briefly describe the biology of hypoxia in cervix tumors of relevance for imaging, and evaluate positron emission tomography (PET) and magnetic resonance imaging (MRI) techniques that have shown promise for assessing hypoxia in a clinical setting. We further discuss emerging imaging approaches, and how imaging can play a role in future treatment strategies to target hypoxia.

METHODS

We performed a PubMed literature search, using keywords related to imaging and hypoxia in cervical cancer, with a particular emphasis on studies correlating imaging with other hypoxia measures and treatment outcome.

RESULTS

Only a few and rather small studies have utilized PET with tracers specific for hypoxia, and no firm conclusions regarding preferred tracer or clinical potential can be drawn so far. Most studies address indirect hypoxia imaging with dynamic contrast-enhanced techniques. Strong evidences for a role of these techniques in hypoxia imaging have been presented. Pre-treatment images have shown significant association to outcome in several studies, and images acquired during fractionated radiotherapy may further improve risk stratification. Multiparametric MRI and multimodality PET/MRI enable combined imaging of factors of relevance for tumor hypoxia and warrant further investigation.

CONCLUSIONS

Several imaging approaches have shown promise for hypoxia imaging in cervical cancer. Evaluation in large clinical trials is required to decide upon the optimal modality and approach.

Quantitative Perfusion and Permeability Biomarkers in Brain Cancer from Tomographic CT and MR Images

Dynamic contrast-enhanced perfusion and permeability imaging, using computed tomography and magnetic resonance systems, are important techniques for assessing the vascular supply and hemodynamics of healthy brain parenchyma and tumors. These techniques can measure blood flow, blood volume, and blood-brain barrier permeability surface area product and, thus, may provide information complementary to clinical and pathological assessments. These have been used as biomarkers to enhance the treatment planning process, to optimize treatment decision-making, and to enable monitoring of the treatment noninvasively. In this review, the principles of magnetic resonance and computed tomography dynamic contrast-enhanced perfusion and permeability imaging are described (with an emphasis on their commonalities), and the potential values of these techniques for differentiating high-grade gliomas from other brain lesions, distinguishing true progression from posttreatment effects, and predicting survival after radiotherapy, chemotherapy, and antiangiogenic treatments are presented.

Are complex DCE-MRI models supported by clinical data?

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Improved accuracy of quantitative parameter estimates in dynamic contrast-enhanced CT study with low temporal resolution

PURPOSE

A previously proposed method to reduce radiation dose to patient in dynamic contrast-enhanced (DCE) CT is enhanced by principal component analysis (PCA) filtering which improves the signal-to-noise ratio (SNR) of time-concentration curves in the DCE-CT study. The efficacy of the combined method to maintain the accuracy of kinetic parameter estimates at low temporal resolution is investigated with pixel-by-pixel kinetic analysis of DCE-CT data.

METHODS

The method is based on DCE-CT scanning performed with low temporal resolution to reduce the radiation dose to the patient. The arterial input function (AIF) with high temporal resolution can be generated with a coarsely sampled AIF through a previously published method of AIF estimation. To increase the SNR of time-concentration curves (tissue curves), first, a region-of-interest is segmented into squares composed of 3 × 3 pixels in size. Subsequently, the PCA filtering combined with a fraction of residual information criterion is applied to all the segmented squares for further improvement of their SNRs. The proposed method was applied to each DCE-CT data set of a cohort of 14 patients at varying levels of down-sampling. The kinetic analyses using the modified Tofts' model and singular value decomposition method, then, were carried out for each of the down-sampling schemes between the intervals from 2 to 15 s. The results were compared with analyses done with the measured data in high temporal resolution (i.e., original scanning frequency) as the reference.

RESULTS

The patients' AIFs were estimated to high accuracy based on the 11 orthonormal bases of arterial impulse responses established in the previous paper. In addition, noise in the images was effectively reduced by using five principal components of the tissue curves for filtering. Kinetic analyses using the proposed method showed superior results compared to those with down-sampling alone; they were able to maintain the accuracy in the quantitative histogram parameters of volume transfer constant [standard deviation (SD), 98th percentile, and range], rate constant (SD), blood volume fraction (mean, SD, 98th percentile, and range), and blood flow (mean, SD, median, 98th percentile, and range) for sampling intervals between 10 and 15 s.

CONCLUSIONS

The proposed method of PCA filtering combined with the AIF estimation technique allows low frequency scanning for DCE-CT study to reduce patient radiation dose. The results indicate that the method is useful in pixel-by-pixel kinetic analysis of DCE-CT data for patients with cervical cancer.

Models and methods for analyzing DCE-MRI: a review

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

Multi-parametric Magnetic Resonance Imaging, and specifically Dynamic Contrast Enhanced (DCE) MRI, play increasingly important roles in detection and staging of prostate cancer (PCa). One of the actively investigated approaches to DCE MRI analysis involves pharmacokinetic (PK) modeling to extract quantitative parameters that may be related to microvascular properties of the tissue. It is well-known that the prescribed arterial blood plasma concentration (or Arterial Input Function, AIF) input can have significant effects on the parameters estimated by PK modeling. The purpose of our study was to investigate such effects in DCE MRI data acquired in a typical clinical PCa setting. First, we investigated how the choice of a semi-automated or fully automated image-based individualized AIF (iAIF) estimation method affects the PK parameter values; and second, we examined the use of method-specific averaged AIF (cohort-based, or cAIF) as a means to attenuate the differences between the two AIF estimation methods. Two methods for automated image-based estimation of individualized (patient-specific) AIFs, one of which was previously validated for brain and the other for breast MRI, were compared. cAIFs were constructed by averaging the iAIF curves over the individual patients for each of the two methods. Pharmacokinetic analysis using the Generalized kinetic model and each of the four AIF choices (iAIF and cAIF for each of the two image-based AIF estimation approaches) was applied to derive the volume transfer rate (K(trans)) and extravascular extracellular volume fraction (ve) in the areas of prostate tumor. Differences between the parameters obtained using iAIF and cAIF for a given method (intra-method comparison) as well as inter-method differences were quantified. The study utilized DCE MRI data collected in 17 patients with histologically confirmed PCa. Comparison at the level of the tumor region of interest (ROI) showed that the two automated methods resulted in significantly different (p<0.05) mean estimates of ve, but not of K(trans). Comparing cAIF, different estimates for both ve, and K(trans) were obtained. Intra-method comparison between the iAIF- and cAIF-driven analyses showed the lack of effect on ve, while K(trans) values were significantly different for one of the methods. Our results indicate that the choice of the algorithm used for automated image-based AIF determination can lead to significant differences in the values of the estimated PK parameters. K(trans) estimates are more sensitive to the choice between cAIF/iAIF as compared to ve, leading to potentially significant differences depending on the AIF method. These observations may have practical consequences in evaluating the PK analysis results obtained in a multi-site setting.

Voxelwise comparison of perfusion parameters estimated using dynamic contrast enhanced (DCE) computed tomography and DCE-magnetic resonance imaging in locally advanced cervical cancer

PURPOSE

Dynamic contrast enhanced (DCE) imaging has gained interest as an imaging modality for assessment of tumor characteristics and response to cancer treatment. However, for DCE-magnetic resonance imaging (MRI) tissue contrast enhancement may vary depending on imaging sequence and temporal resolution. The aim of this study is to compare DCE-MRI to DCE-computed tomography (DCE-CT) as the gold standard.

MATERIAL AND METHODS

Thirteen patients with advanced cervical cancer were scanned once prior to chemo-radiation and during chemo-radiation with DCE-CT and -MRI in immediate succession. A total of 22 paired DCE-CT and -MRI scans were acquired for comparison. Kinetic modeling using the extended Tofts model was applied to both image series. Furthermore the similarity of the spatial distribution was evaluated using a Γ analysis. The correlation between the two imaging techniques was evaluated using Pearson's correlation and the parameter means were compared using a Student's t-test (p < 0.05).

RESULTS

A significant positive correlation between DCE-CT and -MRI was found for all kinetic parameters. The results showing the best correlation with the DCE-CT-derived parameters were obtained using a population-based input function for MRI. The median Pearson's correlations were: volume transfer constant K(trans) (r = 0.9), flux rate constant kep (r = 0.77), extracellular volume fraction ve (r = 0.58) and blood plasma volume fraction vp (r = 0.83). All quantitative parameters were found to be significantly different as estimated by DCE-CT and -MRI. The Γ analysis in normalized maps revealed that 45% of the voxels failed to find a voxel with the corresponding value allowing for an uncertainty of 3 mm in position and 3% in value (Γ3,3). By reducing the criteria, the Γ-failure rates were: Γ3,5 (37% failure), Γ3,10 (26% failure) and at Γ3,15 (19% failure).

CONCLUSION

Good to excellent correlations but significant bias was found between DCE-CT and -MRI. Both the Pearson's correlation and the Γ analysis proved that the spatial information was similar when analyzing the two sets of DCE data using the extended Tofts model. Improvement of input function sampling is needed to improve kinetic quantification using DCE-MRI.

Basic T1 Perfusion Magnetic Resonance Imaging Evaluation of the Therapeutic Effect of Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer

Objective

The objective of this study was to evaluate the dynamic changes of blood perfusion coinciding with tumor regression after neoadjuvant chemotherapy (NACT) in locally advanced cervical cancer (LACC).

Methods

Thirty patients with LACC received conventional 3.0-T magnetic resonance imaging and perfusion-weighted imaging scans at 3 different times (before NACT, 2 weeks after the first NACT, and 2 weeks after the second NACT). Characteristics of time-intensity diagrams and patterns of blood perfusion maps according to the parameter of area under the curve (AUC) were observed. Eight perfusion parameters were compared among 3 time points at 2 different chemotherapy-sensitive groups by the software of Basic T1 Perfusion.

Results

The effective chemotherapy rate was 73.3% (22/30). The characteristic of time-intensity diagrams in cervical cancer was a rapid onset with plateau. There were 3 patterns of AUC perfusion maps. The common perfusion map was rich blood supply type in the effective chemotherapy group and peripheral blood supply type in the ineffective chemotherapy group. Four parameter values (relative enhancement, maximum enhancement, wash-in rate, and AUC) were significantly reduced 2 weeks after the second NACT than those before the therapy (P = 0.000; P = 0.009; P = 0.011; and P = 0.000) in the effective chemotherapy group, especially the value of relative enhancement 2 weeks after the first NACT, was obviously decreased compared to that before the therapy (P = 0.042). The value of time to peak 2 weeks after the second NACT was significantly longer than that before the therapy in the effective chemotherapy group (P = 0.001). There were no obvious changes of blood perfusion parameters among the 3 different times in the ineffective chemotherapy group.

Conclusions

Tumor blood perfusion has obviously decreased after effective NACT in the treatment of LACC.

A comparison of dynamic contrast-enhanced CT and MR imaging-derived measurements in patients with cervical cancer

This work is to compare the kinetic parameters derived from the DCE-CT and -MR data of a group of 37 patients with cervical cancer. The modified Tofts model and the reference tissue method were applied to estimate kinetic parameters. In the MR kinetic analyses using the modified Tofts model for each patient data set, both the arterial input function (AIF) measured from DCE-MR images and a population-averaged AIF from the literature were applied to the analyses, while the measured AIF was used for the CT kinetic analysis. The kinetic parameters obtained from both modalities were compared. Significant moderate correlations were found in modified Tofts parameters [volume transfer constant(K(trans) ) and rate constant (k(ep) )] between CT and MR analysis for MR with the measured AIFs (R = 0·45, P<0·01 and R = 0·40, P<0·01 in high-K(trans) region; R = 0·38, P<0·01 and R = 0·80, P<0·01 in low-K(trans) region) as well as with the population-averaged AIF (R = 0·59, P<0·01 and R = 0·62, P<0·01 in high-K(trans) region; R = 0·50, P<0·01 and R = 0·63, P<0·01 in low-K(trans) region), respectively. In addition, from the Bland-Altman plot analysis, it was found that the systematic biases (the mean difference) between the modalities were drastically reduced in magnitude by adopting the population-averaged AIF for the MR analysis instead of the measured ones (from 51·5% to 18·9% for K(trans) and from 21·7% to 4·1% for k(ep) in high-K(trans) region; from 73·0% to 29·4% for K(trans) and from 63·4% to 24·5% for k(ep) in low-K(trans) region). The preliminary results showed the feasibility in the interchangeable use of the two imaging modalities in assessing cervical cancers.

Radiomics: the process and the challenges

"Radiomics" refers to the extraction and analysis of large amounts of advanced quantitative imaging features with high throughput from medical images obtained with computed tomography, positron emission tomography or magnetic resonance imaging. Importantly, these data are designed to be extracted from standard-of-care images, leading to a very large potential subject pool. Radiomics data are in a mineable form that can be used to build descriptive and predictive models relating image features to phenotypes or gene-protein signatures. The core hypothesis of radiomics is that these models, which can include biological or medical data, can provide valuable diagnostic, prognostic or predictive information. The radiomics enterprise can be divided into distinct processes, each with its own challenges that need to be overcome: (a) image acquisition and reconstruction, (b) image segmentation and rendering, (c) feature extraction and feature qualification and (d) databases and data sharing for eventual (e) ad hoc informatics analyses. Each of these individual processes poses unique challenges. For example, optimum protocols for image acquisition and reconstruction have to be identified and harmonized. Also, segmentations have to be robust and involve minimal operator input. Features have to be generated that robustly reflect the complexity of the individual volumes, but cannot be overly complex or redundant. Furthermore, informatics databases that allow incorporation of image features and image annotations, along with medical and genetic data, have to be generated. Finally, the statistical approaches to analyze these data have to be optimized, as radiomics is not a mature field of study. Each of these processes will be discussed in turn, as well as some of their unique challenges and proposed approaches to solve them. The focus of this article will be on images of non-small-cell lung cancer.

Diffusion-weighted and dynamic contrast-enhanced MRI of prostate cancer: correlation of quantitative MR parameters with Gleason score and tumor angiogenesis

OBJECTIVE

The objective of our study was to investigate whether quantitative parameters derived from diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI) correlate with Gleason score and angiogenesis of prostate cancer.

MATERIALS AND METHODS

Seventy-three patients who underwent preoperative MRI and radical prostatectomy were included in our study. A radiologist and pathologist located the dominant tumor on the MR images based on histopathologic correlation. For each dominant tumor, the apparent diffusion coefficient (ADC) value and quantitative DCE-MRI parameters (i.e., contrast agent transfer rate between blood and tissue [K(trans)], extravascular extracellular fractional volume [v(e)], contrast agent backflux rate constant [k(ep)], and blood plasma fractional volume on a voxel-by-voxel basis [v(p)]) were calculated and the Gleason score was recorded. The mean blood vessel count, mean vessel area fraction, and vascular endothelial growth factor (VEGF) expression of the dominant tumor were determined using CD31, CD34, and VEGF antibody stains. Spearman correlation analysis between MR and histopathologic parameters was conducted.

RESULTS

The mean tumor diameter was 15.2 mm (range, 5-28 mm). Of the 73 prostate cancer tumors, five (6.8%) had a Gleason score of 6, 46 (63%) had a Gleason score of 7, and 22 (30.1%) had a Gleason score of greater than 7. ADC values showed a moderate negative correlation with Gleason score (r = -0.376, p = 0.001) but did not correlate with tumor angiogenesis parameters. Quantitative DCE-MRI parameters did not show a significant correlation with Gleason score or VEGF expression (p > 0.05). Mean blood vessel count and mean vessel area fraction parameters estimated from prostate cancer positively correlated with k(ep) (r = 0.440 and 0.453, respectively; p = 0.001 for both).

CONCLUSION

There is a moderate correlation between ADC values and Gleason score and between k(ep) and microvessel density of prostate cancer. Although the strength of the correlations is insufficient for immediate diagnostic utility, these results warrant further investigation on the potential of multiparametric MRI to facilitate noninvasive assessment of prostate cancer aggressiveness and angiogenesis.

Fundamentals of tracer kinetics for dynamic contrast-enhanced MRI

Tracer kinetic methods employed for quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) share common roots with earlier tracer studies involving arterial-venous sampling and other dynamic imaging modalities. This article reviews the essential foundation concepts and principles in tracer kinetics that are relevant to DCE MRI, including the notions of impulse response and convolution, which are central to the analysis of DCE MRI data. We further examine the formulation and solutions of various compartmental models frequently used in the literature. Topics of recent interest in the processing of DCE MRI data, such as the account of water exchange and the use of reference tissue methods to obviate the measurement of an arterial input, are also discussed. Although the primary focus of this review is on the tracer models and methods for T(1) -weighted DCE MRI, some of these concepts and methods are also applicable for analysis of dynamic susceptibility contrast-enhanced MRI data.

Pharmacokinetic analysis and k-means clustering of DCEMR images for radiotherapy outcome prediction of advanced cervical cancers

INTRODUCTION

Pharmacokinetic analysis of dynamic contrast enhanced magnetic resonance images (DCEMRI) allows for quantitative characterization of vascular properties of tumors. The aim of this study is twofold, first to determine if tumor regions with similar vascularization could be labeled by clustering methods, second to determine if the identified regions can be associated with local cancer relapse.

MATERIALS AND METHODS

Eighty-one patients with locally advanced cervical cancer treated with chemoradiotherapy underwent DCEMRI with Gd-DTPA prior to external beam radiotherapy. The median follow-up time after treatment was four years, in which nine patients had primary tumor relapse. By fitting a pharmacokinetic two-compartment model function to the temporal contrast enhancement in the tumor, two pharmacokinetic parameters, K(trans) and ύ(e), were estimated voxel by voxel from the DCEMR-images. Intratumoral regions with similar vascularization were identified by k-means clustering of the two pharmacokinetic parameter estimates over all patients. The volume fraction of each cluster was used to evaluate the prognostic value of the clusters.

RESULTS

Three clusters provided a sufficient reduction of the cluster variance to label different vascular properties within the tumors. The corresponding median volume fraction of each cluster was 38%, 46% and 10%. The second cluster was significantly associated with primary tumor control in a log-rank survival test (p-value: 0.042), showing a decreased risk of treatment failure for patients with high volume fraction of voxels.

CONCLUSIONS

Intratumoral regions showing similar vascular properties could successfully be labeled in three distinct clusters and the volume fraction of one cluster region was associated with primary tumor control.

First order correction for T2*-relaxation in determining contrast agent concentration from spoiled gradient echo pulse sequence signal intensity

PURPOSE

To investigate the accuracy of a method neglecting T(2)*-relaxation, for the conversion of spoiled gradient echo pulse sequence signal intensity to contrast agent (CA) concentration, in dynamic contrast enhanced MRI studies. In addition a new closed form conversion expression is proposed that accounts for a first order approximation of T(2)*-relaxation.

MATERIALS AND METHODS

The accuracy of both conversion methods is compared theoretically by means of simulations for four pulse sequences from literature. Both methods are tested in vivo against the numerical conversion method for measuring the arterial input function in mice.

RESULTS

Simulations show that the T(2)*-neglecting method underestimates typical tissue CA concentrations (0 mM to 2 mM) up to 6%, while the errors for arterial concentrations (0 mM to 10 mM) range up to 43%. The results from our first order method are numerically indistinguishable from the simulation input values in tumor tissue, while for arterial concentrations the error is reduced up to a factor 10. In vivo, peak Gd-DOTA concentration is underestimated up to 14% with the T(2)*-neglecting method and up to 0.9% with our first order method.

CONCLUSION

Our conversion method reduces the underestimation of CA concentration severely in a broad physiological concentration range and is easy to perform in any clinical setting.

Quantifying tumor vascular heterogeneity with dynamic contrast-enhanced magnetic resonance imaging: a review

Tumor microvasculature possesses a high degree of heterogeneity in its structure and function. These features have been demonstrated to be important for disease diagnosis, response assessment, and treatment planning. The exploratory efforts of quantifying tumor vascular heterogeneity with DCE-MRI have led to promising results in a number of studies. However, the methodological implementation in those studies has been highly variable, leading to multiple challenges in data quality and comparability. This paper reviews several heterogeneity quantification methods, with an emphasis on their applications on DCE-MRI pharmacokinetic parametric maps. Important methodological and technological issues in experimental design, data acquisition, and analysis are also discussed, with the current opportunities and efforts for standardization highlighted.

On the scope and interpretation of the Tofts models for DCE-MRI

The Tofts model (TM) and extended Tofts model (ETM) have become a standard for the analysis of dynamic contrast-enhanced MRI. In this study, a mathematical analysis is used to identify exactly in which tissue types the Tofts models may be applied. The results show that the TM is accurate if and only if the tissue is weakly vascularised (small blood volume). The ETM is additionally accurate in highly perfused tissues (high blood flow). In tissues that are highly vascularised, or where tracer exchange is very fast or very slow, TM and ETM accurately fit the data but lead to a misinterpretation of the parameters. In tissue types with intermediate vascularity, perfusion and tracer exchange, neither model offers a good fit to the tissue concentrations. A good fit can be obtained with a measured input function by reducing the temporal resolution, but this does not improve the accuracy of the parameters. In conclusion, the Tofts models only produce reliable parameter values if the tissue is weakly vascularized (TM or ETM) or highly perfused (ETM). Without prior knowledge that at least one of these constraints is fulfilled, the physiological interpretation of the values produced by the Tofts models is unclear.

Prostate cancer: differentiation of central gland cancer from benign prostatic hyperplasia by using diffusion-weighted and dynamic contrast-enhanced MR imaging

PURPOSE

To analyze the diffusion and perfusion parameters of central gland (CG) prostate cancer, stromal hyperplasia (SH), and glandular hyperplasia (GH) and to determine the role of these parameters in the differentiation of CG cancer from benign CG hyperplasia.

MATERIALS AND METHODS

In this institutional review board-approved (with waiver of informed consent), HIPAA-compliant study, 38 foci of carcinoma, 38 SH nodules, and 38 GH nodules in the CG were analyzed in 49 patients (26 with CG carcinoma) who underwent preoperative endorectal magnetic resonance (MR) imaging and radical prostatectomy. All carcinomas and hyperplastic foci on MR images were localized on the basis of histopathologic correlation. The apparent diffusion coefficient (ADC), the contrast agent transfer rate between blood and tissue (K(trans)), and extravascular extracellular fractional volume values for all carcinoma, SH, and GH foci were calculated. The mean, standard deviation, 95% confidence interval (CI), and range of each parameter were calculated. Receiver operating characteristic (ROC) and multivariate logistic regression analyses were performed for differentiation of CG cancer from SH and GH foci.

RESULTS

The average ADCs (× 10(-3) mm(2)/sec) were 1.05 (95% CI: 0.97, 1.11), 1.27 (95% CI: 1.20, 1.33), and 1.73 (95% CI: 1.64, 1.83), respectively, in CG carcinoma, SH foci, and GH foci and differed significantly, yielding areas under the ROC curve (AUCs) of 0.99 and 0.78, respectively, for differentiation of carcinoma from GH and SH. Perfusion parameters were similar in CG carcinomas and SH foci, with K(trans) yielding the greatest AUCs (0.75 and 0.58, respectively). Adding K(trans) to ADC in ROC analysis to differentiate CG carcinoma from SH increased sensitivity from 38% to 57% at 90% specificity without noticeably increasing the AUC (0.79).

CONCLUSION

ADCs differ significantly between CG carcinoma, SH, and GH, and the use of them can improve the differentiation of CG cancer from SH and GH. Combining K(trans) with ADC can potentially improve the detection of CG cancer.

SUPPLEMENTAL MATERIAL

http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.10100021/-/DC1.