The principal of dynamic contrast enhanced MRI, the method of pharmacokinetic analysis, and its application in the head and neck region

Multiparametric MRI for characterization of the tumour microenvironment

Our understanding of tumour biology has evolved over the past decades and cancer is now viewed as a complex ecosystem with interactions between various cellular and non-cellular components within the tumour microenvironment (TME) at multiple scales. However, morphological imaging remains the mainstay of tumour staging and assessment of response to therapy, and the characterization of the TME with non-invasive imaging has not yet entered routine clinical practice. By combining multiple MRI sequences, each providing different but complementary information about the TME, multiparametric MRI (mpMRI) enables non-invasive assessment of molecular and cellular features within the TME, including their spatial and temporal heterogeneity. With an increasing number of advanced MRI techniques bridging the gap between preclinical and clinical applications, mpMRI could ultimately guide the selection of treatment approaches, precisely tailored to each individual patient, tumour and therapeutic modality. In this Review, we describe the evolving role of mpMRI in the non-invasive characterization of the TME, outline its applications for cancer detection, staging and assessment of response to therapy, and discuss considerations and challenges for its use in future medical applications, including personalized integrated diagnostics.

Diagnostic value of dynamic contrast enhancement combined with conventional MRI in differentiating benign and malignant lacrimal gland epithelial tumours

AIM

To establish the diagnostic value of the quantitative parameters of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) combined with conventional MRI in differentiating of benign and malignant lacrimal gland epithelial tumours.

MATERIALS AND METHODS

A retrospective analysis of primary lacrimal gland epithelial tumours confirmed by histopathology was conducted. Conventional MRI features and DCE-MRI quantitative parameters were collected and subjected to analysis. The diagnostic value was evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

A total of 53 patients were enrolled of which 29 had malignant, whereas 24 had benign tumours. Conventional MRI revealed statistically significant differences between benign and malignant tumours regarding maximum tumour diameter, posterior margin characteristic, bone destruction, and erosion. The Ktrans and Kep values obtained by DCE-MRI were higher in malignant than in benign tumours, with a statistically significant (p<0.001 and p=0.022). A type I time-signal intensity (TIC) curve was more frequent in benign tumours, whereas a type II TIC curve was prevalent in malignant tumours (p=0.001). ROC analysis showed that Ktrans had the best diagnostic value of the DCE-MRI parameters (area under the ROC curve [AUC] of 0.822, 75.9% sensitivity, and 83.3% specificity, p<0.001). The combination of conventional MRI and DCE-MRI factors had the best diagnostic value and balanced sensitivity and specificity (AUC of 0.948, 93.1% sensitivity, and 91.7% specificity, p<0.001).

CONCLUSIONS

The present findings indicate that the combination of quantitative parameters of DCE-MRI and image characteristics of conventional MRI have a high diagnostic value for the diagnosis of benign and malignant lacrimal gland epithelial tumours.

A robust MRI contrast agent for specific display of the interstitial stream

Experimental and clinical studies have reported phenomena of long-range fluid flow in interstitial space. However, its behaviours and functions are yet to be addressed. The imaging of the interstitial stream in vivo can clarify its transportation route and allow further understanding of physiological mechanisms and clinical relevance. Here to illustrate the route of the interstitial stream leading to the kidney, we design and synthesize a magnetic resonance imaging (MRI) contrast agent PAA-g-(DTPA-gadolinium). This MRI agent has a high longitudinal relaxivity for higher MRI contrast and large size to avoid leakage across the interstitial space. Using dynamic contrast enhanced MRI, histochemical staining, and trace element analysis of gadolinium, we track the nano-scale PAA-g-(DTPA-gadolinium) transported in the interstitial stream. The agent can be applied for a wide range of imaging and analysis of tissues and organs, thereby enabling advances in the fields of physiology, pathology, and pharmacology.

Fluid Mechanics Approach to Perfusion Quantification: Vasculature Computational Fluid Dynamics Simulation, Quantitative Transport Mapping (QTM) Analysis of Dynamics Contrast Enhanced MRI, and Application in Nonalcoholic Fatty Liver Disease Classification

OBJECTIVE

We quantify liver perfusion using quantitative transport mapping (QTM) method that is free of arterial input function (AIF). QTM method is validated in a vasculature computational fluid dynamics (CFD) simulation and is applied for processing dynamic contrast enhanced (DCE) MRI images in differentiating liver with nonalcoholic fatty liver disease (NAFLD) from healthy controls using pathology reference in a preclinical rabbit model.

METHODS

QTM method was validated on a liver perfusion simulation based on fluid dynamics using a rat liver vasculature model and the mass transport equation. In the NAFLD grading task, DCE MRI images of 7 adult rabbits with methionine choline-deficient diet-induced nonalcoholic steatohepatitis (NASH), 8 adult rabbits with simple steatosis (SS) were acquired and processed using QTM method and dual-input two compartment Kety's method respectively. Statistical analysis was performed on six perfusion parameters: velocity magnitude | u | derived from QTM, liver arterial blood flow LBFa, liver venous blood flow LBFv, permeability Ktrans, blood volume Vp and extravascular space volume Ve averaged in liver ROI.

RESULTS

In the simulation, QTM method successfully reconstructed blood flow, reduced error by 48% compared to Kety's method. In the preclinical study, only QTM |u| showed significant difference between high grade NAFLD group and low grade NAFLD group.

CONCLUSION

QTM postprocesses DCE-MRI automatically through deconvolution in space and time to solve the inverse problem of the transport equation. Comparing with Kety's method, QTM method showed higher accuracy and better differentiation in NAFLD classification task.

SIGNIFICANCE

We propose to apply QTM method in liver DCE MRI perfusion quantification.

Sciatic nerve microvascular permeability in type 2 diabetes decreased in patients with neuropathy

Objectives

Clinical and histological studies have found evidence that nerve ischemia is a major contributor to diabetic neuropathy (DN) in type 2 diabetes (T2D). The aim of this study was to investigate peripheral nerve microvascular permeability using dynamic contrast enhanced (DCE) magnetic resonance neurography (MRN) to analyze potential correlations with clinical, electrophysiological, and demographic data.

Methods

Sixty‐five patients (35/30 with/without DN) and 10 controls matched for age and body mass index (BMI) underwent DCE MRN of the distal sciatic nerve with an axial T1‐weighted sequence. Microvascular permeability (K^trans), plasma volume fraction (v_p), and extravascular extracellular volume fraction (v_e) were determined with the extended Tofts model, and subsequently correlated with clinical data.

Results

K^trans and v_e were lower in T2D patients with DN compared to patients without DN (0.037 min⁻¹ ± 0.010 vs. 0.046 min⁻¹ ± 0.014; p = 0.011, and 2.35% ± 3.87 vs. 5.11% ± 5.53; p = 0.003, respectively). In individuals with T2D, K^trans correlated positively with tibial, peroneal, and sural NCVs (r = 0.42; 95%CI = 0.18 to 0.61, 0.50; 95%CI = 0.29 to 0.67, and 0.44; 95%CI = 0.19 to 0.63, respectively), with tibial and peroneal CMAPs (r = 0.27; 95%CI = 0.01 to 0.49 and r = 0.32; 95%CI = 0.07 to 0.53), and with the BMI (r = 0.47; 95%CI = 0.25 to 0.64). Negative correlations were found with the neuropathy deficit score (r = −0.40; 95%CI = −0.60 to −0.16) and age (r = −0.51; 95%CI = −0.67 to −0.31). No such correlations were found for v_p.

Conclusion

This study is the first to find associations of MR nerve perfusion parameters with clinical and electrophysiological parameters related to DN in T2D. The results indicate that a decrease in microvascular permeability but not plasma volume may result in nerve ischemia that subsequently causes demyelination.

Dynamic contrast-enhanced MRI in malignant pleural mesothelioma: prediction of outcome based on DCE-MRI measurements in patients undergoing cytotoxic chemotherapy

Background

The malignant pleural mesothelioma (MPM) response rate to chemotherapy is low. The identification of imaging biomarkers that could help guide the most effective therapy approach for individual patients is highly desirable. Our aim was to investigate the dynamic contrast-enhanced (DCE) MR parameters as predictors for progression-free (PFS) and overall survival (OS) in patients with MPM treated with cisplatin-based chemotherapy.

Methods

Thirty-two consecutive patients with MPM were enrolled in this prospective study. Pretreatment and intratreatment DCE-MRI were scheduled in each patient. The DCE parameters were analyzed using the extended Tofts (ET) and the adiabatic approximation tissue homogeneity (AATH) model. Comparison analysis, logistic regression and ROC analysis were used to identify the predictors for the patient’s outcome.

Results

Patients with higher pretreatment ET and AATH-calculated K^trans and v_e values had longer OS (P≤.006). Patients with a more prominent reduction in ET-calculated K^trans and k_ep values during the early phase of chemotherapy had longer PFS (P =.008). No parameter was identified to predict PFS. Pre-treatment ET-calculated K^trans was found to be an independent predictive marker for longer OS (P=.02) demonstrating the most favourable discrimination performance compared to other DCE parameters with an estimated sensitivity of 89% and specificity of 78% (AUC 0.9, 95% CI 0.74-0.98, cut off > 0.08 min^-1).

Conclusions

In the present study, higher pre-treatment ET-calculated K^trans values were associated with longer OS. The results suggest that DCE-MRI might provide additional information for identifying MPM patients that may respond to chemotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09277-x.

Troponin T Is Negatively Associated With 3 Tesla Magnetic Resonance Peripheral Nerve Perfusion in Type 2 Diabetes

OBJECTIVE

The pathogenesis of diabetic polyneuropathy (DN) is poorly understood and given the increasing prevalence of DN, there is a need for clinical or imaging biomarkers that quantify structural and functional nerve damage. While clinical studies have found evidence of an association between elevated levels of troponin T (hsTNT) and N-terminal pro brain natriuretic peptide (proBNP) with microvascular compromise in type 2 diabetes (T2D), their implication in mirroring DN nerve perfusion changes remains unclear. The objective of this study was, therefore, to investigate whether hsTNT and proBNP assays are associated with MRI nerve perfusion in T2D.

METHODS

In this prospective cross-sectional single-center case-control study, 56 participants (44 with T2D, 12 healthy control subjects) consented to undergo magnetic resonance neurography (MRN) including dynamic contrast-enhanced (DCE) perfusion imaging of the right leg. Using the extended Tofts model, primary outcome parameters that were quantified are the sciatic nerve's microvascular permeability (K^trans), the extravascular extracellular volume fraction (v_e), and the plasma volume fraction (v_p), as well as hsTNT and proBNP values from serological workup. Further secondary outcomes were clinical, serological, and electrophysiological findings.

RESULTS

In T2D patients, hsTNT was negatively correlated with K^trans (r=-0.38; p=0.012) and v_e (r=-0.30; p=0.048) but not with v_p (r=-0.16; p=0.294). HsTNT, K^trans, and v_e were correlated with peroneal nerve conduction velocities (NCVs; r=-0.44; p=0.006, r=0.42; p=0.008, r=0.39; p=0.014), and tibial NCVs (r=-0.38;p=0.022, r=0.33; p=0.048, r=0.37; p=0.025). No such correlations were found for proBNP.

CONCLUSIONS

This study is the first to find that hsTNT is correlated with a decrease of microvascular permeability and a reduced extravascular extracellular volume fraction of nerves in patients with T2D. The results indicate that hsTNT may serve as a potential marker for the assessment of nerve perfusion in future studies on DN.

Vascularity and Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging

Angiogenesis is a key step in the initiation and progression of an invasive breast cancer. High microvessel density by morphological characterization predicts metastasis and poor survival in women with invasive breast cancers. However, morphologic characterization is subject to variability and only can evaluate a limited portion of an invasive breast cancer. Consequently, breast Magnetic Resonance Imaging (MRI) is currently being evaluated to assess vascularity. Recently, through the new field of radiomics, dynamic contrast enhanced (DCE)-MRI is being used to evaluate vascular density, vascular morphology, and detection of aggressive breast cancer biology. While DCE-MRI is a highly sensitive tool, there are specific features that limit computational evaluation of blood vessels. These include (1) DCE-MRI evaluates gadolinium contrast and does not directly evaluate biology, (2) the resolution of DCE-MRI is insufficient for imaging small blood vessels, and (3) DCE-MRI images are very difficult to co-register. Here we review computational approaches for detection and analysis of blood vessels in DCE-MRI images and present some of the strategies we have developed for co-registry of DCE-MRI images and early detection of vascularization.

Quantitative dynamic contrast-enhanced MRI and readout segmentation of long variable echo-trains diffusion-weighted imaging in differentiating parotid gland tumors

PURPOSE

To evaluate the ability of quantitative dynamic contrast-enhanced (DCE)-MRI and readout segmentation of long variable echo-trains diffusion-weighted imaging (RESOLVE-DWI) in differentiating parotid tumors (PTs) with different histological types.

METHODS

In this retrospective study, 123 patients with 145 histologically proven PTs who underwent both RESOLVE-DWI and DCE-MRI were enrolled including 51 pleomorphic adenomas (PAs), 52 Warthin's tumors (WTs), 27 other benign neoplasms (OBNs), and 15 malignant tumors (MTs). Quantitative parameters of DCE-MRI (K^trans, K_ep, and V_e) and the apparent diffusion coefficient (ADC) of lesions were calculated and analyzed. Kruskal-Wallis tests with Dunn-Bonferroni correction, logistic regression analyses, and receiver operating characteristic curve were used for statistical analyses.

RESULTS

PAs exhibited a lowest K^trans among these four PTs. WTs demonstrated the highest K_ep and lowest V_e values. WTs and MTs showed lower ADC_min values than PAs and OBNs. The combination of K_ep and V_e provided 98.1% sensitivity, 85% specificity, and 98.7% accuracy for differentiating WTs from the other three PTs. The ADC_min cutoff value of ≤ 0.826 yielded 80.0% sensitivity, 92.3% specificity, and 90.3% accuracy for the differentiation of MTs from PAs and OBNs. K^trans with a cutoff value of ≤ 0.185 achieved a sensitivity, specificity, and accuracy of 84.3, 70.4, and 79.5%, respectively, for discriminating PAs from OBNs.

CONCLUSION

The combination of quantitative DCE-MRI and RESOLVE-DWI is beneficial for characterizing four histological types of PTs.

Quantifying lumbar vertebral perfusion by a Tofts model on DCE-MRI using segmental versus aortic arterial input function

The purpose of this study was to investigate the influence of arterial input function (AIF) selection on the quantification of vertebral perfusion using axial dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). In this study, axial DCE-MRI was performed on 2 vertebrae in each of eight healthy volunteers (mean age, 36.9 years; 5 men) using a 1.5-T scanner. The pharmacokinetic parameters K^trans, v_e, and v_p, derived using a Tofts model on axial DCE-MRI of the lumbar vertebrae, were evaluated using various AIFs: the population-based aortic AIF (AIF_PA), a patient-specific aortic AIF (AIF_A) and a patient-specific segmental arterial AIF (AIF_SA). Additionally, peaks and delay times were changed to simulate the effects of various AIFs on the calculation of perfusion parameters. Nonparametric analyses including the Wilcoxon signed rank test and the Kruskal–Wallis test with a Dunn–Bonferroni post hoc analysis were performed. In simulation, K^trans and v_e increased as the peak in the AIF decreased, but v_p increased when delay time in the AIF increased. In humans, the estimated K^trans and v_e were significantly smaller using AIF_A compared to AIF_SA no matter the computation style (pixel-wise or region-of-interest based). Both these perfusion parameters were significantly greater using AIF_SA compared to AIF_A.

Imaging-based characterization of convective tissue properties

Convective transport is an important phenomenon for nanomedicine delivery. We present an imaging-based approach to recover tissue properties that are significant in the accumulation of nanoparticles delivered via systemic methods. The classical pharmacokinetic analysis develops governing equations for the particle transport from a first principle mass balance. Fundamentally, the governing equations for compartmental mass balance represent a spatially invariant mass transport between compartments and do not capture spatially variant convection phenomena. Further, the parameters recovered from this approach do not necessarily have direct meaning with respect to the governing equations for convective transport. In our approach, a framework is presented for directly measuring permeability in the sense of Darcy flow through porous tissue. Measurements from our approach are compared to an extended Tofts model as a control. We demonstrate that a pixel-wise iterative clustering algorithm may be applied to reduce the parameter space of the measurements. We show that measurements obtained from our approach are correlated with measurements obtained from the extended Tofts model control. These correlations demonstrate that the proposed approach contains similar information to an established compartmental model and may be useful in providing an alternative theoretical framework for parameterizing mathematical models for treatment planning and diagnostic studies involving nanomedicine where convection dominated effects are important.

Radiomics derived from dynamic contrast-enhanced MRI pharmacokinetic protocol features: the value of precision diagnosis ovarian neoplasms

OBJECTIVES

To evaluate the efficiency of 2- and 3-class classification predictive tasks constructed from radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) pharmacokinetic (PK) protocol in discriminating among benign, borderline, and malignant ovarian tumors.

METHODS

One hundred and four ovarian lesions were evaluated using preoperative DCE-MRI. Radiomics features were extracted from 7 types of DCE-MR images. To explore the differential ability of radiomics between three types of ovarian tumors, two- and three-class classification tasks were established. The 2-class classification task was divided into three subtasks: benign vs. borderline (task A), benign vs. malignant (task B), and borderline vs. malignant (task C). For the 3-class classification task, 104 lesions were randomly divided into training (72 lesions) and validation (32 lesions) cohorts. The discrimination abilities of the radiomics signatures were established with the training cohort and tested with the independent validation cohort. The predictive performance of the task was evaluated by receiver operating characteristic (ROC) curve, calibration curve analysis, and decision curve analysis (DCA).

RESULTS

For the 2-class classification task, the combination of PK radiomics signatures model (PK model) showed a good diagnostic ability with the highest area under the ROC curves (AUCs) of 0.899, 0.865, and 0.893 for tasks A, B, and C, respectively. Additionally, the 3-class classification task demonstrated a good discrimination performance with AUCs of 0.893, 0.944, and 0.891 for the benign, borderline, and malignant groups, respectively.

CONCLUSIONS

Radiomics analysis based on the DCE-MRI PK protocol showed promise for discriminating among benign, borderline, and malignant ovarian tumors.

KEY POINTS

• Two-class classification predictive task of DCE-MRI PK protocol enabled the classification of 3 categories of ovarian tumors through the pairwise comparison strategy with a perfect diagnostic ability. • Three-class classification predictive task maintained good performance to effectively judge each category of ovarian tumors directly.

Combination of diffusion-weighted imaging and arterial spin labeling at 3.0 T for the clinical staging of nasopharyngeal carcinoma

PURPOSE

To probe the utility of diffusion-weighted imaging (DWI) and 3D arterial spin labeling (ASL) in assessing the clinical stage of nasopharyngeal carcinoma (NPC).

MATERIALS AND METHODS

This prospective study included sixty-five newly diagnosed NPC patients who underwent DWI and 3D ASL scans on a 3.0-T magnetic resonance imaging (MRI) system. The apparent diffusion coefficient (ADC) and the tumor blood flow (TBF) of NPC were measured. Tumors were classified as low or high T, N and American Joint Committee on Cancer (AJCC) stages. Student's t-test was used to evaluate the differences between tumors with low and high clinical stages. Pearson correlation analyses were performed to determine the correlation between MRI parameters and clinical stages. Receiver operating characteristic (ROC) curves were then used to evaluate diagnostic capability.

RESULTS

High T stage (T3/4) NPC showed significantly lower ADC_min (P = 0.000) and higher TBF_max (P = 0.003) and TBF_mean (P = 0.008) values than low T stage (T1/2) NPC. High N stage (N2/3) NPC showed significantly lower ADC_min values (P = 0.023) than low N stage (N0/1) NPC. High AJCC stage (III/IV) NPC showed significantly lower ADC_min (P = 0.000) and higher TBF_max (P = 0.005) and TBF_mean (P = 0.011) values than low AJCC stage (I/II) NPC. ADC_min values showed moderate negative correlations with T stage (r = -0.512, P = 0.000), N stage (r = -0.281, P = 0.023), and AJCC stage (r = -0.494, P = 0.000). TBF_max values showed moderate positive correlations with T stage (r = 0.369, P = 0.003) and AJCC stage (r = 0.346, P = 0.005). Compared with ADC_min and TBF_max alone, the combination of ADC_min and TBF_max improved the accuracy from 72.3% and 75.4% to 78.5%, respectively, for T staging, as well as from 72.3% and 69.2% to 83.1% for AJCC staging.

CONCLUSIONS

ADC_min and TBF_max values in patients with NPC could help evaluate clinical stages. ADC_min and TBF_max values combined could clearly improve the accuracy in the assessment of AJCC stage.

Recent advances in MRI of the head and neck, skull base and cranial nerves: new and evolving sequences, analyses and clinical applications

MRI is an invaluable diagnostic tool in the investigation and management of patients with pathology of the head and neck. However, numerous technical challenges exist, owing to a combination of fine anatomical detail, complex geometry (that is subject to frequent motion) and susceptibility effects from both endogenous structures and exogenous implants. Over recent years, there have been rapid developments in several aspects of head and neck imaging including higher resolution, isotropic 3D sequences, diffusion-weighted and diffusion-tensor imaging as well as permeability and perfusion imaging. These have led to improvements in anatomic, dynamic and functional imaging. Further developments using contrast-enhanced 3D FLAIR for the delineation of endolymphatic structures and black bone imaging for osseous structures are opening new diagnostic avenues. Furthermore, technical advances in compressed sensing and metal artefact reduction have the capacity to improve imaging speed and quality, respectively. This review explores novel and evolving MRI sequences that can be employed to evaluate diseases of the head and neck, including the skull base.

Machine learning for radiomics-based multimodality and multiparametric modeling

Due to the recent developments of both hardware and software technologies, multimodality medical imaging techniques have been increasingly applied in clinical practice and research studies. Previously, the application of multimodality imaging in oncology has been mainly related to combining anatomical and functional imaging to improve diagnostic specificity and/or target definition, such as positron emission tomography/computed tomography (PET/CT) and single-photon emission CT (SPECT)/CT. More recently, the fusion of various images, such as multiparametric magnetic resonance imaging (MRI) sequences, different PET tracer images, PET/MRI, has become more prevalent, which has enabled more comprehensive characterization of the tumor phenotype. In order to take advantage of these valuable multimodal data for clinical decision making using radiomics, we present two ways to implement the multimodal image analysis, namely radiomic (handcrafted feature) based and deep learning (machine learned feature) based methods. Applying advanced machine (deep) learning algorithms across multimodality images have shown better results compared with single modality modeling for prognostic and/or prediction of clinical outcomes. This holds great potentials for providing more personalized treatment for patients and achieve better outcomes.

The predictive value of quantitative DCE metrics for immediate therapeutic response of high-intensity focused ultrasound ablation (HIFU) of symptomatic uterine fibroids

PURPOSE

The aim of this study was to investigate the value of quantitative DCE-MRI parameters for predicting the immediate non-perfused volume ratio (NPVR) of HIFU therapy in the treatment of symptomatic uterine fibroids.

MATERIALS AND METHODS

A total of 78 symptomatic uterine fibroids in 65 female patients were treated with US-HIFU therapy. All patients underwent conventional MRI and DCE-MRI scans 1 day before and 3 days after HIFU treatment. Permeability parameters K ^trans, K _ep, V _e, and V _p and T1 perfusion parameters BF and BV of pretreatment were measured as a baseline, while NPVR was used to assess immediate ablation efficiency. Data were assigned to NPVR ≧ 70% and NPVR < 70% groups. Then, the predictive performances of different parameters for ablation efficacy were studied to seek the optimal cut-off value, and the length of time to calculate the variable parameters in each case was recorded.

RESULTS

(1) It was observed that the pretreatment K ^trans, K _ep, V _e, and BF values of the NPVR ≧ 70% group were significantly lower compared to the NPVR < 70% group (p < 0.05). (2) The immediate NPVR was negatively correlated with the K ^trans, BF, and BV values before HIFU treatment (r = - 0.561, - 0.712, and - 0.528, respectively, p < 0.05 for all). (3) The AUCs of pretreatment K ^trans, BF, BV values, and K ^trans combined with BF used to predict the immediate NPVR were 0.810, 0.909, 0.795, and 0.922, respectively (p < 0.05 for all). (4) The mean time to calculate the variable parameters in each case was 7.5 min.

CONCLUSION

Higher K ^trans, BF, and BV values at baseline DCE-MRI suggested a poor ablation efficacy of HIFU therapy for symptomatic uterine fibroids, while the pretreatment DCE-MRI parameters could be useful biomarkers for predicting the ablation efficacy in select cases. The software used to calculate DCE-MRI parameters was simpler, quicker, and easier to incorporate into clinical practice.

Dynamic contrast-enhanced magnetic resonance imaging for pretreatment prediction of early chemo-radiotherapy response in larynx and hypopharynx carcinoma

Purpose

This study is to investigate the use of dynamic contrast-enhanced magnetic resonance imaging in predicting early response to CRT (chemo-radiotherapy) in patients with larynx and hypopharynx carcinoma from primary tumors.

Method

Sixty-two patients with larynx and hypopharynx carcinoma underwent two DCE-MRI studies: a baseline exam before any treatmentanda post-treatment exam 3 weeks after CRT. At the end of treatment, patients were classified as responders, or non-responders according to the Response Evaluation Criteria in Solid Tumors criteria (RECIST). The time intensity curves (TIC) were extracted and processed to obtain time to peak (TTP), maximum slope of increase (MSI), maximum slope of decrease (MSD) and positive enhancement integral (PEI), and the semi-quantitative MRI parameters were compared and analyzed between the two groups.

Results

Fifty-four and 8 patients were included the responder and non-responder groups. It was observed that the MSI, MSD, and PEI were significantly lower post-treatment than pre-treatment(P < 0.05). The pretreatment MSI, MSD, and PEI parameters of responders were significantly higher than those of non-responders (P< 0.05). The post-treatment MSI, MSD, and PEI parameters of responders were significantly lower than those of non-responders (P< 0.05). Based on ROC curve analysis, at a threshold of 154.81 for pretreatment MSI, the corresponding AUC, sensitivity, and specificity were 0.882, 89.3% and 73.5%, respectively.

Conclusion

The semi-quantitative DCE-MRI may aid in the prediction of early response to CRT in patients with larynx and hypopharynx carcinoma.

Differentiation of Hemangioblastoma from Metastatic Brain Tumor using Dynamic Contrast-enhanced MR Imaging

PURPOSE

The aim of this study was to differentiate hemangioblastomas from metastatic brain tumors using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and compare the diagnostic performances with diffusion-weighted imaging (DWI) and dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI).

METHODS

We retrospectively reviewed 7 patients with hemangioblastoma and 15 patients with metastatic adenocarcinoma with magnetic resonance imaging (MRI) including DWI, DSC-MRI, and DCE-MRI. Apparent diffusion coefficient (ADC), relative cerebral blood volume (rCBV), and DCE-MRI parameters (K ^trans, k _ep, v _e, and v _p) were compared between the two groups. The diagnostic performance of each parameter was evaluated with receiver operating characteristic (ROC) curve analysis.

RESULTS

v _p, k _ep, and rCBV were significantly different between patients with hemangioblastoma and those with metastatic brain tumor (p < 0.001, p = 0.005, and p = 0.017, respectively). A v _p cutoff value of 0.012 and a rCBV cutoff value of 8.0 showed the highest accuracy for differentiating hemangioblastoma from metastasis. The area under the ROC curve for v _p and rCBV was 0.99 and 0.89, respectively. A v _p > 0.012 showed 100 % sensitivity, 93.3 % specificity, and 95.5 % accuracy and a rCBV > 8.0 showed 85.7 % sensitivity, 93.3 % specificity, and 90.9 % accuracy for differentiating hemangioblastoma from metastatic brain tumor.

CONCLUSION

DCE-MRI was useful for differentiating hemangioblastoma from metastatic brain tumor.

The Correlations Between MRI Perfusion, Diffusion Parameters, and 18F-FDG PET Metabolic Parameters in Primary Head-and-Neck Cancer: A Cross-Sectional Analysis in Single Institute

This study aimed to investigate the relationships among parameters from dynamic contrast-enhanced (DCE) MRI, diffusion-weighted MRI (DWI), and F-fluorodeoxyglucose (F-FDG) PET in patients with primary head-and-neck squamous cell carcinoma (HNSCC).A total of 34 patients with primary HNSCC underwent DCE-MRI, DWI, and F-FDG PET before treatment. The perfusion parameters (Ktrans, Ktransmax, Kep, Ve, Vp, and AUC60) from DCE-MRI and ADC (ADCmean, ADCmin) values from DWI were calculated within the manually placed ROI around the main tumor. Standardized uptake value (SUVmax, SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG = SUVmean × MTV) were calculated with thresholds of 3.0 SUV. The associations between parameters were evaluated by Pearson correlation analysis.Significant correlations were identified between Ktrans and Kep (r = 0.631), Ktrans and Ve (r = 0.603), Ktrans and ADCmean (r = 0.438), Ktransmax and Kep (r = 0.667), Ktransmax and Vp (r = 0.351), Ve and AUC60 (r = 0.364), Ve and ADCmean (r = 0.590), and Ve and ADCmin (r = 0.361). ADCmin was reversely correlated with TLG (r = -0.347). Tumor volume was significantly associated with Ktransmax (r = 0.348).The demonstrated relationships among parameters from DCE, DWI, and F-FDG PET suggest complex interactions among tumor biologic characteristics. Each diagnostic technique may provide complementary information for HNSCC.

Multimodal approach to assess tumour vasculature and potential treatment effect with DCE-US and DCE-MRI quantification in CWR22 prostate tumour xenografts

The aim of this study was to compare intratumoural heterogeneity and longitudinal changes assessed by dynamic contrast-enhanced ultrasound (DCE-US) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in prostate tumour xenografts. In vivo DCE-US and DCE-MRI were obtained 24 h pre- (day 0) and post- (day 2) radiation treatment with a single dose of 7.5 Gy. Characterization of the tumour vasculature was determined by Brix pharmacokinetic analysis of the time-intensity curves. Histogram analysis of voxels showed significant changes (p < 0.001) from day 0 to day 2 in both modalities for kep , the exchange rate constant from the extracellular extravascular space to the plasma, and kel , the elimination rate constant of the contrast. In addition, kep and kel values from DCE-US were significantly higher than those derived from DCE-MRI at day 0 (p < 0.0001) for both groups. At day 2, kel followed the same tendency for both groups, whereas kep showed this tendency only for the treated group in intermediate-enhancement regions. Regarding kep median values, longitudinal changes were not found for any modality. However, at day 2, kep linked to DCE-US was correlated to MVD in high-enhancement areas for the treated group (p = 0.05). In contrast, correlation to necrosis was detected for the control group in intermediate-enhancement areas (p < 0.1). Intratumoural heterogeneity and longitudinal changes in tumour vasculature were assessed for both modalities. Microvascular parameters derived from DCE-US seem to provide reliable biomarkers during radiotherapy as validated by histology. Furthermore, DCE-US could be a stand-alone or a complementary technique.

The application of dynamic contrast-enhanced MRI and diffusion-weighted MRI in patients with maxillofacial tumors

RATIONALE AND OBJECTIVES

To elucidate the characteristics of four types of tumors, including squamous cell carcinoma (SCC), malignant lymphoma (ML), malignant salivary gland tumors (MSGTs), and pleomorphic adenoma (Pleo), in the maxillofacial region using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI）data.

MATERIALS AND METHODS

A total of 59 tumors were included in this research. DCE-MRI and DW-MRI were performed. We applied the Tofts and Kermode model (TK model) for the DCE-MRI data and obtained three dependent parameters: the influx forward volume transfer constant into the extravascular extracellular space from the plasma (K(trans)), the fractional volume of extravascular extracellular space per unit volume of tissue (ve), and the fractional volume of plasma (vp).

RESULTS

Among the K(trans) values, there were no significant differences between the three types of malignant tumors; however, there was a significant difference between the SCC and Pleo (P = .0099). The ve values of the Pleo were highest, with significant differences compared to the other categories (SCC, P = .0012; ML, P = .0017; and MSGT, P = .041). The ML had the lowest ve values, and there were significant differences between ML and the other two types of malignant tumors (SCC, P = .0278 and MSGT, P = .0062). In 14 (24%) cases, apparent diffusion coefficient (ADC) could not be measured because of poor image quality. The ADC values of the ML were lowest, whereas those of Pleo were highest, similar to that observed for ve.

CONCLUSIONS

The Pleo tumors had lower K(trans) values and higher ve values, which are useful for differentiating them from the malignant tumors. Moreover, the ve was also useful for establishing a diagnosis of ML.

A review of technical aspects of T1-weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in human brain tumors

In the last few years, several imaging methods, such as magnetic resonance imaging (MRI) and computed tomography, have been used to investigate the degree of blood-brain barrier (BBB) permeability in patients with neurological diseases including multiple sclerosis, ischemic stroke, and brain tumors. One promising MRI method for assessing the BBB permeability of patients with neurological diseases in vivo is T1-weighted dynamic contrast-enhanced (DCE)-MRI. Here we review the technical issues involved in DCE-MRI in the study of human brain tumors. In the first part of this paper, theoretical models for the DCE-MRI analysis will be described, including the Toft-Kety models, the adiabatic approximation to the tissue homogeneity model and the two-compartment exchange model. These models can be used to estimate important kinetic parameters related to BBB permeability. In the second part of this paper, details of the data acquisition, issues related to the arterial input function, and procedures for DCE-MRI image analysis are illustrated.