Semi-quantitative parameter analysis of DCE-MRI revisited: monte-carlo simulation, clinical comparisons, and clinical validation of measurement errors in patients with type 2 neurofibromatosis

Comprehensive Review of the Utility of Dynamic Contrast-Enhanced MRI for the Diagnosis and Treatment Assessment of Spinal Benign and Malignant Osseous Disease

Conventional MRI is currently the preferred imaging technique for detection and evaluation of malignant spinal lesions. However, this technique is limited in its ability to assess tumor viability. Unlike conventional MRI, dynamic contrast-enhanced (DCE) MRI provides insight into the physiologic and hemodynamic characteristics of malignant spinal tumors and has been utilized in different types of spinal diseases. DCE has been shown to be especially useful in the cancer setting; specifically, DCE can discriminate between malignant and benign vertebral compression fractures as well as between atypical hemangiomas and metastases. DCE has also been shown to differentiate between different types of metastases. Furthermore, DCE can be useful in the assessment of radiation therapy for spinal metastases, including the prediction of tumor recurrence. This review considers data analysis methods utilized in prior studies of DCE-MRI data acquisition and clinical implications.

Multiparametric MRI in Era of Artificial Intelligence for Bladder Cancer Therapies

This review focuses on the principles, applications, and performance of mpMRI for bladder imaging. Quantitative imaging biomarkers (QIBs) derived from mpMRI are increasingly used in oncological applications, including tumor staging, prognosis, and assessment of treatment response. To standardize mpMRI acquisition and interpretation, an expert panel developed the Vesical Imaging-Reporting and Data System (VI-RADS). Many studies confirm the standardization and high degree of inter-reader agreement to discriminate muscle invasiveness in bladder cancer, supporting VI-RADS implementation in routine clinical practice. The standard MRI sequences for VI-RADS scoring are anatomical imaging, including T2w images, and physiological imaging with diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI). Physiological QIBs derived from analysis of DW- and DCE-MRI data and radiomic image features extracted from mpMRI images play an important role in bladder cancer. The current development of AI tools for analyzing mpMRI data and their potential impact on bladder imaging are surveyed. AI architectures are often implemented based on convolutional neural networks (CNNs), focusing on narrow/specific tasks. The application of AI can substantially impact bladder imaging clinical workflows; for example, manual tumor segmentation, which demands high time commitment and has inter-reader variability, can be replaced by an autosegmentation tool. The use of mpMRI and AI is projected to drive the field toward the personalized management of bladder cancer patients.

Multiparametric evaluation of bone tumors utilising diffusion weighted imaging and dynamic contrast enhanced magnetic resonance imaging

AIM

This study aimed to use multiparametric magnetic resonance imaging (MRI) techniques, namely, diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to evaluate bone tumors.

METHODS

Thirty-three patients with primary untreated bone tumors were assessed utilizing DWI and DCE-MRI. Various parameters like ADC values from DWI and percentage peak signal intensity (%PSI), the maximum slope of increase (MSI), and time to peak signal intensity (TTP) values were assessed in different cases, and the final correlation was drawn with histopathological findings.

RESULT

Parameters of semi-quantitative DCE-MRI, i.e., %PSI, MSI and, TTP, correlated significantly with the histopathological characteristics of the tumor (p values < 0.001). Minimum ADC value in the tumor also showed a strong correlation with the tumor characteristic (p values < 0.001). Also, the correlation between parameters of DWI and DCI-MRI is well correlated with each other.

CONCLUSION

The results of this study provide grounds for the integration of multiparametric pre-treatment evaluation of bone tumors. In our study, we not only tried to utilize different parameters of functional MRI in bone tumors as well as re-explored the semi-quantitative analysis of DCE-MRI.

Dynamic Contrast-enhanced and Diffusion-weighted Magnetic Resonance Imaging for Response Evaluation After Single-Dose Ablative Neoadjuvant Partial Breast Irradiation

Purpose

We aimed to evaluate changes in dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) magnetic resonance imaging (MRI) scans acquired before and after single-dose ablative neoadjuvant partial breast irradiation (NA-PBI), and explore the relation between semiquantitative MRI parameters and radiologic and pathologic responses.

Methods and Materials

We analyzed 3.0T DCE and DW-MRI of 36 patients with low-risk breast cancer who were treated with single-dose NA-PBI, followed by breast-conserving surgery 6 or 8 months later. MRI was acquired before NA-PBI and 1 week, 2, 4, and 6 months after NA-PBI. Breast radiologists assessed the radiologic response and breast pathologists scored the pathologic response after surgery. Patients were grouped as either pathologic responders or nonresponders (<10% vs ≥10% residual tumor cells). The semiquantitative MRI parameters evaluated were time to enhancement (TTE), 1-minute relative enhancement (RE_1min), percentage of enhancing voxels (%EV), distribution of washout curve types, and apparent diffusion coefficient (ADC).

Results

In general, the enhancement increased 1 week after NA-PBI (baseline vs 1 week median – TTE: 15s vs 10s; RE_1min: 161% vs 197%; %EV: 47% vs 67%) and decreased from 2 months onward (6 months median – TTE: 25s; RE_1min: 86%; %EV: 12%). Median ADC increased from 0.83 × 10⁻³ mm²/s at baseline to 1.28 × 10⁻³ mm²/s at 6 months. TTE, RE_1min, and %EV showed the most potential to differentiate between radiologic responses, and TTE, RE_1min, and ADC between pathologic responses.

Conclusions

Semiquantitative analyses of DCE and DW-MRI showed changes in relative enhancement and ADC 1 week after NA-PBI, indicating acute inflammation, followed by changes indicating tumor regression from 2 to 6 months after radiation therapy. A relation between the MRI parameters and radiologic and pathologic responses could not be proven in this exploratory study.

Bridging the macro to micro resolution gap with angiographic optical coherence tomography and dynamic contrast enhanced MRI

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Diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging for malignant ovarian tumors: a systematic review and meta-analysis

BACKGROUND

Accurate preoperative diagnosis of malignant ovarian tumors (MOTs) is particularly important for selecting the optimal treatment strategy and avoiding overtreatment.

PURPOSE

To evaluate the diagnostic efficacy of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for MOTs.

MATERIAL AND METHODS

A systematic search was performed in PubMed, Embase, the Cochrane Library, and Web of Science databases to find relevant original articles up to October 2019. The included studies were assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Studies on the diagnosis of MOTs with quantitative or semi-quantitative DCE-MRI were analyzed separately. The bivariate random-effects model was used to assess the diagnostic authenticity. Meta-regression analyses were performed to analyze the potential heterogeneity.

RESULTS

For semi-quantitative DCE-MRI, the pooled sensitivity, specificity, positive likelihood ratio (LR), negative LR, diagnostic odds ratio (DOR), and the area under the summary receiver operating characteristic curves (AUC) were 85% (95% confidence interval [CI] 0.75-0.92), 85% (95% CI 0.77-0.91), 5.8 (95% CI 3.8-8.8), 0.17 (95% CI 0.10-0.30), 33 (95% CI 18-61), and 0.92 (95% CI 0.89-0.94), respectively. For quantitative DCE-MRI, the pooled sensitivity, specificity, positive LR, negative LR, DOR, and AUC were 88% (95% CI 0.65-0.96), 93% (95% CI 0.78-0.98), 12.3 (95% CI 3.4-43.9), 0.13 (95% CI 0.04-0.45), 91 (95% CI 10-857), and 0.96 (95% CI 0.94-0.98), respectively.

CONCLUSION

DCE-MRI has great diagnostic value for MOTs. Semi-quantitative DCE-MRI may be a relatively mature approach; however, quantitative DCE-MRI appears to be more promising than semi-quantitative DCE-MRI.

MRI of the Pancreas

MRI has played a critical role in the evaluation of patients with pancreatic pathologies, from screening of patients at high risk for pancreatic cancer to the evaluation of pancreatic cysts and indeterminate pancreatic lesions. The high mortality associated with pancreatic adenocarcinomas has spurred much interest in developing effective screening tools, with MRI using magnetic resonance cholangiopancreatography (MRCP) playing a central role in the hopes of identifying cancers at earlier stages amenable to curative resection. Ongoing efforts to improve the resolution and robustness of imaging of the pancreas using MRI may thus one day reduce the mortality of this deadly disease. However, the increasing use of cross-sectional imaging has also generated a concomitant clinical conundrum: How to manage incidental pancreatic cystic lesions that are found in over a quarter of patients who undergo MRCP. Efforts to improve the specificity of MRCP for patients with pancreatic cysts and with indeterminate pancreatic masses may be achieved with continued technical advances in MRI, including diffusion-weighted and T₁ -weighted dynamic contrast-enhanced MRI. However, developments in quantitative MRI of the pancreas remain challenging, due to the small size of the pancreas and its upper abdominal location, adjacent to bowel and below the diaphragm. Further research is needed to improve MRI of the pancreas as a clinical tool, to positively affect the lives of patients with pancreatic abnormalities. This review focuses on various MR techniques such as MRCP, quantitative imaging, and dynamic contrast-enhanced imaging and their clinical applicability in the imaging of the pancreas, with an emphasis on pancreatic malignant and premalignant lesions. Level of Evidence 5 Technical Efficacy Stage 3 J. MAGN. RESON. IMAGING 2021;53:347-359.

Optimal mass transport kinetic modeling for head and neck DCE-MRI: Initial analysis

PURPOSE

Current state-of-the-art models for estimating the pharmacokinetic parameters do not account for intervoxel movement of the contrast agent (CA). We introduce an optimal mass transport (OMT) formulation that naturally handles intervoxel CA movement and distinguishes between advective and diffusive flows.

METHOD

Ten patients with head and neck squamous cell carcinoma (HNSCC) were enrolled in the study between June 2014 and October 2015 and underwent DCE MRI imaging prior to beginning treatment. The CA tissue concentration information was taken as the input in the data-driven OMT model. The OMT approach was tested on HNSCC DCE data that provides quantitative information for forward flux ( Φ F ) and backward flux ( Φ B ). OMT-derived Φ F was compared with the volume transfer constant for CA, K trans , derived from the Extended Tofts Model (ETM).

RESULTS

The OMT-derived flows showed a consistent jump in the CA diffusive behavior across the images in accordance with the known CA dynamics. The mean forward flux was 0.0082 ± 0.0091 ( min - 1 ) whereas the mean advective component was 0.0052 ± 0.0086 ( min - 1 ) in the HNSCC patients. The diffusive percentages in forward and backward flux ranged from 8.67% to 18.76% and 12.76% to 30.36%, respectively. The OMT model accounts for intervoxel CA movement and results show that the forward flux ( Φ F ) is comparable with the ETM-derived K trans .

CONCLUSIONS

This is a novel data-driven study based on optimal mass transport principles applied to patient DCE imaging to analyze CA flow in HNSCC.

Identification of Potential MR-Derived Biomarkers for Tumor Tissue Response to 177Lu-Octreotate Therapy in an Animal Model of Small Intestine Neuroendocrine Tumor

Magnetic resonance (MR) methods enable noninvasive, regional tumor therapy response assessment, but associations between MR parameters, underlying biology, and therapeutic effects must be investigated. The aim of this study was to investigate response assessment efficacy and biological associations of MR parameters in a neuroendocrine tumor (NET) model subjected to radionuclide treatment. Twenty-one mice with NETs received 177Lu-octreotate at day 0. MR experiments (day -1, 1, 3, 8, and 13) included T2-weighted, dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) and relaxation measurements (T1/T2*). Tumor tissue was analyzed using proteomics. MR-derived parameters were evaluated for each examination day and for different radial distances from the tumor center. Response assessment efficacy and biological associations were evaluated using feature selection and protein expression correlations, respectively. Reduced tumor growth rate or shrinkage was observed until day 8, followed by reestablished growth in most tumors. The most important MR parameter for response prediction was DCE-MRI-derived pretreatment signal enhancement ratio (SER) at 40% to 60% radial distance, where it correlated significantly also with centrally sampled protein CCD89 (association: DNA damage and repair, proliferation, cell cycle arrest). The second most important was changed diffusion (D) between day -1 and day 3, at 60% to 80% radial distance, where it correlated significantly also with peripherally sampled protein CATA (association: oxidative stress, proliferation, cell cycle arrest, apoptotic cell death). Important information regarding tumor biology in response to radionuclide therapy is reflected in several MR parameters, SER and D in particular. The spatial and temporal information provided by MR methods increases the sensitivity for tumor therapy response.

Correlation Between Tumor Metabolism and Semiquantitative Perfusion Magnetic Resonance Imaging Metrics in Non-Small Cell Lung Cancer

PURPOSE

To correlate semiquantitative parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG-PET) for non-small cell lung cancer (NSCLC).

METHODS AND MATERIALS

Twenty-four NSCLC patients who underwent pretreatment ¹⁸F-FDG-PET and DCE-MRI were analyzed. The maximum standardized uptake value (SUVmax) was measured from 18F-FDG-PET. Dynamic contrast-enhanced MRI was obtained on a 3T MRI scanner using 4-dimensional T1-weighted high-resolution imaging with a volume excitation sequence. The DCE-MRI parameters, consisting of mean, median, standard deviation (SD), and median absolute deviation (MAD) of peak enhancement, time to peak (TTP), time to half peak (TTHP), wash-in slope (WIS), wash-out slope (WOS), initial gradient, wash-out gradient, signal enhancement ratio, and initial area under the relative signal enhancement curve taken up to 30, 60, 90, 120, 150, and 180 seconds, TTP, and TTHP (IAUCtthp), were calculated for each lesion. Univariate analysis (UVA) was performed using Spearman correlation. A linear regression model to predict SUVmax from DCE-MRI parameters was developed by multivariate analysis (MVA) using least absolute shrinkage selection operator in combination with leave-one-out cross-validation (LOOCV).

RESULTS

In UVA, mean(WOS) (ρ = -0.456, P = .025), mean(IAUCtthp) (ρ = -0.439, P = .032), median(IAUCtthp) (ρ = -0.543, P = .006), and MAD(IAUCtthp) (ρ = -0.557, P = .005) were statistically significant; all these parameters were negatively correlated with SUVmax. In MVA, a linear combination of SD(WIS), SD(TTP), MAD(TTHP), and MAD(IAUCtthp) was statistically significant for predicting SUVmax (LOOCV-based adjusted R² = 0.298, P = .0006). A decrease in SD(WIS), MAD(TTHP), and MAD(IAUCtthp) and an increase in SD(TTP) were associated with a significant increase in SUVmax.

CONCLUSION

An association was found between SUVmax, the SD, and MAD of DCE-MRI metrics derived during contrast uptake in NSCLC, reflecting that intratumoral heterogeneity in wash-in contrast kinetics is associated with tumor metabolism. Although MAD(IAUCtthp) was a significant feature in both UVA and MVA, the LASSO-based multivariate regression model yielded better predictability of SUVmax than a univariate regression model using MAD(IAUCtthp). This study will facilitate understanding of the complex relationship between tumor vascularization and metabolism and eventually help in guiding targeted therapy.

Difference in the intratumoral distributions of extracellular-fluid and intravascular MR contrast agents in glioblastoma growth

Contrast enhancement by an extracellular-fluid contrast agent (CA) (Gd-DOTA) depends primarily on the blood-brain-barrier permeability (bp), and transverse-relaxation change caused by intravascular T₂ CA (superparamagnetic iron oxide nanoparticles, SPIONs) is closely associated with the blood volume (BV). Pharmacokinetic (PK) vascular characterization based on single-CA-using dynamic contrast-enhanced MRI (DCE-MRI) has shown significant measurement variation according to the molecular size of the CA. Based on this recognition, this study used a dual injection of Gd-DOTA and SPIONs for tracing the changes of bp and BV in C6 glioma growth (Days 1 and 7 after the tumor volume reached 2 mL). bp was quantified according to the non-PK parameters of Gd-DOTA-using DCE-MRI (wash-in rate, maximum enhancement ratio and initial area under the enhancement curve (IAUC)). BV was estimated by SPION-induced ΔR₂ * and ΔR₂ . With validated measurement reliability of all the parameters (coefficients of variation ≤10%), dual-contrast MRI demonstrated a different region-oriented distribution between Gd-DOTA and SPIONs within a tumor as follows: (a) the BV increased stepwise from the tumor center to the periphery; (b) the tumor periphery maintained the augmented BV to support continuous tumor expansion from Day 1 to Day 7; (c) the internal tumor area underwent significant vascular shrinkage (i.e. decreased ΔR₂ and ΔR₂ ) as the tumor increased in size; (d) the tumor center showed greater bp-indicating parameters, i.e. wash-in rate, maximum enhancement ratio and IAUC, than the periphery on both Days 1 and 7 and (e) the tumor center showed a greater increase of bp than the tumor periphery in tumor growth, as suggested to support tumor viability when there is insufficient blood supply. In the MRI-histologic correlation, a prominent BV increase in the tumor periphery seen in MRI was verified with increased fluorescein isothiocyanate-dextran signals and up-regulated immunoreactivity of CD31-VEGFR. In conclusion, the spatiotemporal alterations of BV and bp in glioblastoma growth, i.e. augmented BV in the tumor periphery and increased bp in the center, can be sufficiently evaluated by MRI with dual injection of extracellular-fluid Gd chelates and intravascular SPION.

Neuroimaging

Imaging is integral to the management of patients with brain tumors. Conventional structural imaging provides exquisite anatomic detail but remains limited in the evaluation of molecular characteristics of intracranial neoplasms. Quantitative and physiologic biomarkers derived from advanced imaging techniques have been increasingly utilized as problem-solving tools to identify glioma grade and assess response to therapy. This chapter provides a comprehensive overview of the imaging strategies used in the clinical assessment of patients with gliomas and describes how novel imaging biomarkers have the potential to improve patient management.

ROCKETSHIP: a flexible and modular software tool for the planning, processing and analysis of dynamic MRI studies

BACKGROUND

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising technique to characterize pathology and evaluate treatment response. However, analysis of DCE-MRI data is complex and benefits from concurrent analysis of multiple kinetic models and parameters. Few software tools are currently available that specifically focuses on DCE-MRI analysis with multiple kinetic models. Here, we developed ROCKETSHIP, an open-source, flexible and modular software for DCE-MRI analysis. ROCKETSHIP incorporates analyses with multiple kinetic models, including data-driven nested model analysis.

RESULTS

ROCKETSHIP was implemented using the MATLAB programming language. Robustness of the software to provide reliable fits using multiple kinetic models is demonstrated using simulated data. Simulations also demonstrate the utility of the data-driven nested model analysis. Applicability of ROCKETSHIP for both preclinical and clinical studies is shown using DCE-MRI studies of the human brain and a murine tumor model.

CONCLUSION

A DCE-MRI software suite was implemented and tested using simulations. Its applicability to both preclinical and clinical datasets is shown. ROCKETSHIP was designed to be easily accessible for the beginner, but flexible enough for changes or additions to be made by the advanced user as well. The availability of a flexible analysis tool will aid future studies using DCE-MRI. A public release of ROCKETSHIP is available at https://github.com/petmri/ROCKETSHIP .

Perfusion magnetic resonance imaging: a comprehensive update on principles and techniques

Perfusion is a fundamental biological function that refers to the delivery of oxygen and nutrients to tissue by means of blood flow. Perfusion MRI is sensitive to microvasculature and has been applied in a wide variety of clinical applications, including the classification of tumors, identification of stroke regions, and characterization of other diseases. Perfusion MRI techniques are classified with or without using an exogenous contrast agent. Bolus methods, with injections of a contrast agent, provide better sensitivity with higher spatial resolution, and are therefore more widely used in clinical applications. However, arterial spin-labeling methods provide a unique opportunity to measure cerebral blood flow without requiring an exogenous contrast agent and have better accuracy for quantification. Importantly, MRI-based perfusion measurements are minimally invasive overall, and do not use any radiation and radioisotopes. In this review, we describe the principles and techniques of perfusion MRI. This review summarizes comprehensive updated knowledge on the physical principles and techniques of perfusion MRI.