Standard Dynamic Contrast-enhanced MRI Research Articles

Abstract A024: DCE-MRI-based biophysical simulation to forecast NAT response in HER2+ breast cancer patients, with glucose characterization and orthogonal validation using FDG-PET and 64Cu studies

Abstract Background Regions of high metabolic activity within a tumor can increase tumor perfusion. Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) and 18Fluorodeoxyglucose (FDG) positron emission tomography (PET) are imaging methods used in cancer diagnosis; the former identifies perfusion of the tumor microenvironment and the latter provides a coarse readout of glucose metabolic activity. Here, we perform a pilot analysis in a unique cohort of patients who underwent DCE-MRI, 18FDG-PET and 64Cu-DOTA-Traztuzumab. We characterize the performance of our perfusion model SimBioSys-Microvasculature (SBS-MV). We use this model in 3D simulations of individual HER2+ breast cancer patients to forecast response to neoadjuvant chemotherapy (NAT). Methods SBS-MV is a modified Tofts model for pharmacokinetic modeling of DCE-MRI data, utilizing a tissue segmentation model developed in-house to inform parameter fits and ensure that the derived parameters fall within acceptable values on a tissue-by-tissue level. We evaluated the stability of the SBS-MV model by analyzing a separate cohort of 10 patients who underwent ultrafast DCE-MRI. By undersampling the original high temporal resolution data, we mimicked possible variation in standard-of-care (SoC) DCE studies and accessed the variability of our fits with respect to different subsets. Next, we analyzed data from a different cohort of 18 patients, each of whom underwent a pre-NAT SoC DCE-MRI study, a mid-treatment FDG PET study, and a mid-treatment 64Cu-DOTA-Trastuzumab study during treatment. Tissue segmentation and SBS-MV fits were performed on the pre-NAT DCE-MRI. Tightly cropped boxes encompassing the tumor in the 3D SBS-MV volumes and the PET volumes were created. Using these identified regions, all summary statistics were calculated and cross-correlated between imaging modalities. Results SBS-MV was stable to SoC-mimicking temporally undersampled ultrafast DCE-MRI series, demonstrating relatively little variability in the output parameters (a voxel wise median deviation of approximately 0.005 min−1). Further, we demonstrate that SBS-MV parameters (Ktrans, ve) are correlated with FDG SUVSA and 64Cu SUVSA. The glucose concentrations used in the biophysical simulations, which are derived from SBS-MV fits, were correlated with FDG SUVSA, indicating that downstream applications of the SBS-MV models still carry this perfusion information. Finally, using SBS-MV, our biophysical simulations of HER2-targeted therapy accurately predict patient response. In this small cohort, our biophysical simulations performed well, yielding predictive accuracy of 0.83 (sensitivity=0.83, specificity=0.83). Conclusion Our MV model extracts biologically meaningful perfusion parameters from standard clinical DCE-MRI time series, providing the same benefits of a comprehensive kinetic analysis without impacting clinical workflow. This approach could be used in research and clinical settings, offering actionable information on what drives individual patient therapeutic response for a more personalized care. Citation Format: John Whitman, Vikram Adhikarla, Russell Rockne, Lusine Tumyan, Joanne Mortimer, Wei Huang, Dorys Lopez Ramos, Joseph R. Peterson, John A. Cole. DCE-MRI-based biophysical simulation to forecast NAT response in HER2+ breast cancer patients, with glucose characterization and orthogonal validation using FDG-PET and 64Cu studies [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr A024.

Loss of aquaporin-4 results in glymphatic system dysfunction via brain-wide interstitial fluid stagnation.

The glymphatic system is a fluid transport network of cerebrospinal fluid (CSF) entering the brain along arterial perivascular spaces, exchanging with interstitial fluid (ISF), ultimately establishing directional clearance of interstitial solutes. CSF transport is facilitated by the expression of aquaporin-4 (AQP4) water channels on the perivascular endfeet of astrocytes. Mice with genetic deletion of AQP4 (AQP4 KO) exhibit abnormalities in the brain structure and molecular water transport. Yet, no studies have systematically examined how these abnormalities in structure and water transport correlate with glymphatic function. Here, we used high-resolution 3D magnetic resonance (MR) non-contrast cisternography, diffusion-weighted MR imaging (MR-DWI) along with intravoxel-incoherent motion (IVIM) DWI, while evaluating glymphatic function using a standard dynamic contrast-enhanced MR imaging to better understand how water transport and glymphatic function is disrupted after genetic deletion of AQP4. AQP4 KO mice had larger interstitial spaces and total brain volumes resulting in higher water content and reduced CSF space volumes, despite similar CSF production rates and vascular density compared to wildtype mice. The larger interstitial fluid volume likely resulted in increased slow but not fast MR diffusion measures and coincided with reduced glymphatic influx. This markedly altered brain fluid transport in AQP4 KO mice may result from a reduction in glymphatic clearance, leading to enlargement and stagnation of fluid in the interstitial space. Overall, diffusion MR is a useful tool to evaluate glymphatic function and may serve as valuable translational biomarker to study glymphatics in human disease.

Screening Breast MRI Primer: Indications, Current Protocols, and Emerging Techniques.

Breast dynamic contrast-enhanced MRI (DCE-MRI) is the most sensitive imaging modality for the detection of breast cancer. Screening MRI is currently performed predominantly in patients at high risk for breast cancer, but it could be of benefit in patients at intermediate risk for breast cancer and patients with dense breasts. Decreasing scan time and image interpretation time could increase cost-effectiveness, making screening MRI accessible to a larger group of patients. Abbreviated breast MRI (Ab-MRI) reduces scan time by decreasing the number of sequences obtained, but as multiple delayed contrast enhanced sequences are not obtained, no kinetic information is available. Ultrafast techniques rapidly acquire multiple sequences during the first minute of gadolinium contrast injection and provide information about both lesion morphology and vascular kinetics. Diffusion-weighted imaging is a noncontrast MRI technique with the potential to detect mammographically occult cancers. This review article aims to discuss the current indications of breast MRI as a screening tool, examine the standard breast DCE-MRI technique, and explore alternate screening MRI protocols, including Ab-MRI, ultrafast MRI, and noncontrast diffusion-weighted MRI, which can decrease scan time and interpretation time.

Abstract PS13-50: Relationship of dedicated breast PET and MRI features in breast cancer patients receiving neoadjuvant chemotherapy

Abstract Introduction: Dedicated breast positron emission tomography (dbPET) is an emerging imaging technique with the spatial resolution needed to assess functionality and intra-tumor heterogeneity in primary breast lesions. Breast cancer patients may benefit from dbPET imaging combined with molecularly targeted agents to non-invasively assess and predict response to targeted therapy in the neoadjuvant treatment setting. We have previously observed that [18F]-fluorodeoxyglucose (FDG) PET provides tumor metabolic information complementary to the angiogenic properties reflected by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for characterizing triple-negative breast cancers (TNBC) (1). In this study, we examined the relationship between FDG-dbPET and MRI features in a cohort of breast cancer patients receiving neoadjuvant chemotherapy (NAC). Methods: With institutional review board approval, patients with biopsy-proven locally-advanced breast cancer were imaged with breast MRI and dbPET before (T0) and after three weeks (T1) of NAC. Standard DCE-MRI was obtained using a dedicated breast coil. Patients also underwent dbPET with 5 mCi of FDG at 45 minutes post-injection. Functional tumor volumes (FTV) were calculated from DCE-MRI by summing all voxels with an early percent enhancement (PE) exceeding 70% within a manually defined volume of interest (VOI). Maximum and mean PE (PEMax, PEMean) values within the VOI were also computed for analyses. Tumors were segmented in dbPET images using semi-automated threshold-driven methods. Body weight-corrected maximum and mean standardized uptake values (SUVMax, SUVMean), total lesion glycolysis (TLG), and metabolic tumor volume (MTV) were calculated for FDG-dbPET. Percent change relative to T0 (Δ = 100*(T1 - T0)/T0) was calculated for each feature. Spearman’s correlation coefficient was used to evaluate the relationship between MRI and dbPET features. Results: Of the 16 patients enrolled in this study, 13 patients (N = 15 unique tumors) with MRI and dbPET at T0 and T1 were included in the analysis. 46% (6/13) of the patients had TNBC. Our initial findings indicated that ΔPEMax and ΔSUVMax had the highest correlation (ρ = 0.59, p = 0.022). FTV and TLG at T1 were also correlated (ρ = 0.56, p = 0.032). Among all imaging features, ΔMTV showed the largest post-treatment difference between TNBC (-54.5%) and non-TNBC (-6.06%) groups. Among MRI features, ΔFTV exhibited the largest difference between the groups: -70.4% in TNBC and -43.1% in non-TNBC. ΔSUVMax and ΔTLG were additional dbPET features with large differences between TNBC and non-TNBC patients (Table 1). Conclusion: This exploratory study suggests that post-treatment ΔSUVMax and TLG provide complementary metabolic information to angiogenic properties (ΔPEMax and FTV, respectively) reflected by MRI. Other dbPET features may provide independent information adjunct to MRI for describing primary breast tumors. Patients with TNBC exhibited larger reductions in FDG uptake values and metabolic volume than non-TNBC patients. These observed reductions may improve early treatment response in patients with TNBC, enabling more precise treatment guidance. Further studies in larger cohorts are needed to validate these initial observations. 1. Bolouri MS, et al. Triple-Negative and Non-Triple-Negative Invasive Breast Cancer: Association between MR and Fluorine 18 Fluorodeoxyglucose PET Imaging. Radiology 2013;269:354-61 Comparison of ΔMRI and ΔFDG-dbPET in TNBC vs non-TNBC patientsAll Tumors (N = 15 tumors) Median(IQR)TNBC (N = 6 tumors) Median(IQR)non-TNBC (N = 9 tumors) Median(IQR)DCE-MRIΔFTV (%)-66.1 (-77.6, -19.1)-70.4 (-79.0, -62.1)-43.1 (-72.5, -2.95)ΔPEMax (%)-9.91 (-31.5, 19.5)-10.3 (-26.3, 24.2)-9.91 (-31.8, 8.42)ΔPEMean (%)-10.7 (-22.4, 2.77)-9.57 (-12.8, 0.29)-17.0 (-26.0, 2.33)FDG-dbPETΔSUVMax (%)-31.6 (-53.9, -20.6)-47.3 (-55.7, -41.1)-23.1 (-31.6, 1.13)ΔSUVMean (%)-34.1 (-65.4, -14.2)-48.5 (-74.6, -9.74)-34.1 (-44.5, -14.8)ΔMTV (%)-6.18 (-57.0, 38.5)-54.5 (-75.4, 15.3)-6.06 (-47.2, 38.9)ΔTLG (%)-64.9 (-75.2, 23.3)-75.2 (-84.0, -60.0)-47.9 (-65.2, 31.1) Citation Format: Deep K Hathi, Ella F Jones, Wen Li, David C Newitt, Ruby Guo, Youngho Seo, Robert R Flavell, Bonnie N Joe, Diane Heditsian, Susie Brain, ISPY-2 Imaging Working Group, ISPY-2 Consortium, Laura J Esserman, Nola M Hylton. Relationship of dedicated breast PET and MRI features in breast cancer patients receiving neoadjuvant chemotherapy [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS13-50.

Utilizing Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to Analyze Interstitial Fluid Flow and Transport in Glioblastoma and the Surrounding Parenchyma in Human Patients.

Background: Glioblastoma (GBM) is the deadliest and most common brain tumor in adults, with poor survival and response to aggressive therapy. Limited access of drugs to tumor cells is one reason for such grim clinical outcomes. A driving force for therapeutic delivery is interstitial fluid flow (IFF), both within the tumor and in the surrounding brain parenchyma. However, convective and diffusive transport mechanisms are understudied. In this study, we examined the application of a novel image analysis method to measure fluid flow and diffusion in GBM patients. Methods: Here, we applied an imaging methodology that had been previously tested and validated in vitro, in silico, and in preclinical models of disease to archival patient data from the Ivy Glioblastoma Atlas Project (GAP) dataset. The analysis required the use of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which is readily available in the database. The analysis results, which consisted of IFF flow velocity and diffusion coefficients, were then compared to patient outcomes such as survival. Results: We characterized IFF and diffusion patterns in patients. We found strong correlations between flow rates measured within tumors and in the surrounding parenchymal space, where we hypothesized that velocities would be higher. Analyzing overall magnitudes indicated a significant correlation with both age and survival in this patient cohort. Additionally, we found that neither tumor size nor resection significantly altered the velocity magnitude. Lastly, we mapped the flow pathways in patient tumors and found a variability in the degree of directionality that we hypothesize may lead to information concerning treatment, invasive spread, and progression in future studies. Conclusions: An analysis of standard DCE-MRI in patients with GBM offers more information regarding IFF and transport within and around the tumor, shows that IFF is still detected post-resection, and indicates that velocity magnitudes correlate with patient prognosis.

Ultrafast dynamic contrast-enhanced breast MRI may generate prognostic imaging markers of breast cancer

BackgroundUltrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-derived kinetic parameters have demonstrated at least equivalent accuracy to standard DCE-MRI in differentiating malignant from benign breast lesions. However, it is unclear if they have any efficacy as prognostic imaging markers. The aim of this study was to investigate the relationship between ultrafast DCE-MRI-derived kinetic parameters and breast cancer characteristics.MethodsConsecutive breast MRI examinations between February 2017 and January 2018 were retrospectively reviewed to determine those examinations that meet the following inclusion criteria: (1) BI-RADS 4–6 MRI performed on a 3T scanner with a 16-channel breast coil and (2) a hybrid clinical protocol with 15 phases of ultrafast DCE-MRI (temporal resolution of 2.7–4.6 s) followed by early and delayed phases of standard DCE-MRI. The study included 125 examinations with 142 biopsy-proven breast cancer lesions. Ultrafast DCE-MRI-derived kinetic parameters (maximum slope [MS] and bolus arrival time [BAT]) were calculated for the entire volume of each lesion. Comparisons of these parameters between different cancer characteristics were made using generalized estimating equations, accounting for the presence of multiple lesions per patient. All comparisons were exploratory and adjustment for multiple comparisons was not performed; P values < 0.05 were considered statistically significant.ResultsSignificantly larger MS and shorter BAT were observed for invasive carcinoma than ductal carcinoma in situ (DCIS) (P < 0.001 and P = 0.008, respectively). Significantly shorter BAT was observed for invasive carcinomas with more aggressive characteristics than those with less aggressive characteristics: grade 3 vs. grades 1–2 (P = 0.025), invasive ductal carcinoma vs. invasive lobular carcinoma (P = 0.002), and triple negative or HER2 type vs. luminal type (P < 0.001).ConclusionsUltrafast DCE-MRI-derived parameters showed a strong relationship with some breast cancer characteristics, especially histopathology and molecular subtype.

Nonmass Enhancement Breast Lesions: Diagnostic Performance of Kinetic Assessment on Ultrafast and Standard Dynamic Contrast-Enhanced MRI in Comparison With Morphologic Evaluation

OBJECTIVE. The purpose of this article was to evaluate the diagnostic performance of the kinetic parameters of ultrafast and standard dynamic contrast-enhanced MRI (DCE-MRI) compared with morphologic evaluation in differentiating benign from malignant nonmass enhancement (NME) breast lesions. MATERIALS AND METHODS. A total of 77 consecutive patients with 77 NMEs (23 benign and 54 malignant) underwent 3-T MRI, including one unenhanced and eight contrast-enhanced ultrafast DCE-MRI scans (7-second scans) and standard DCE-MRI scans. The two readers evaluated the lesions' likelihood of malignancy on a continuous scale from 0 to 100% as the morphologic score using standard DCE-MRI. The kinetic curves of ultrafast DCE-MRI were fitted using an empirical mathematical model, ΔS(t) = A × (1 - e-αt), where A is the upper limit of signal intensity, e is the Euler number, and alpha (s-1) is the rate of signal increase. The initial slope of the kinetic curve (A × α) and the initial AUC (AUC30, which is the integration of the kinetic curve from 0 to 30 seconds) were calculated. From standard DCE-MRI, initial enhancement rate and signal enhancement ratio (SER) were calculated. These parameters were compared between benign and malignant NMEs. RESULTS. The morphologic score of malignant NME was statistically significantly higher than that of benign NME (p < 0.0001). The upper limit of signal intensity, rate of signal increase, initial slope of the kinetic curve, and AUC30 of ultrafast DCE-MRI, initial enhancement rate, SER of standard DCE-MRI of malignant NMEs were statistically significantly higher than those of benign NMEs (p = 0.0011, 0.0045, < 0.0001, < 0.0001, 0.0017, and < 0.0001, respectively). AUC ROC analysis found no statistically significant difference between morphologic score, AUC30 of ultrafast DCE-MRI, or SER of standard DCE-MRI. CONCLUSION. The kinetic parameters of ultrafast and standard DCE-MRI were as effective as morphologic evaluation for differentiation between benign and malignant NMEs.

R2* Relaxation Affects Pharmacokinetic Analysis of Dynamic Contrast-Enhanced MRI in Cancer and Underestimates Treatment Response at 7 T.

Effective transverse relaxivity of gadolinium-based contrast agents is often neglected in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Here, we assess time and tissue dependence of R2* enhancement and its impact on pharmacokinetic parameter quantification and treatment monitoring. Multiecho DCE-MRI was performed at 7 T on mice bearing subcutaneous TOV-21G human ovarian cancer xenografts (n = 8) and on the transgenic adenocarcinoma of the mouse prostate (TRAMP) model (n = 7). Subsequently, the TOV-21G tumor-bearing mice were treated with bevacizumab and rescanned 2 days later. Pharmacokinetic analysis (extended Tofts model) was performed using either the first echo signal only (standard single-echo DCE-MRI) or the estimated signal at TE = 0 derived from exponential fitting of R2* relaxation (R2*-corrected). Neglecting R2* enhancement causes underestimation of Gd-DOTA concentration (peak enhancement underestimated by 9.4%–16% in TOV-21G tumors and 13%–20% in TRAMP prostates). Median Ktrans and ve were underestimated in every mouse (TOV-21G Ktrans: 11%–19%, TOV-21G ve: 5.3%–8.9%; TRAMP Ktrans: 8.6%–19%, TRAMP ve: 12%–21%). Bevacizumab treatment reduced Ktrans in all TOV-21G tumors after 48 hours. Treatment effect was significantly greater in all tumors after R2* correction (median change of −0.050 min−1 in R2*-corrected Ktrans vs. −0.037 min−1 in uncorrected Ktrans). R2* enhancement in DCE-MRI is both time- and tissue-dependent and may not be negligible at 7 T in tissue with high Ktrans. This has consequences for the use of Ktrans and other DCE-MRI parameters as biomarkers, because treatment effect size can be underestimated when R2* enhancement is neglected.

Contribution of diffusion-weighted imaging to dynamic contrast-enhanced MRI in the characterization of papillary breast lesions.

Papillary lesions have a broad spectrum of appearances on magnetic resonance imaging (MRI). The purpose of this study was to evaluate whether apparent diffusion coefficient (ADC) values of papillary lesions can be used to characterize lesion as benign or malignant. This retrospective study included 29 papillary lesions. Diagnostic values of dynamic contrast-enhanced MRI (DCE-MRI), DWI-ADC, and DCE-MRI plus DWI-ADC were separately calculated. The malignant papillary lesions (0.744×10-3 mm2 /s) exhibited significantly lower mean ADC values than the benign lesions (1.339×10-3 mm2 /s). Addition of DWI to standard DCE-MRI provided 100% sensitivity. We hypothesized that this combination may prevent unnecessary excisional biopsies.

Automated colorectal tumour segmentation in DCE-MRI using supervoxel neighbourhood contrast characteristics.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a powerful protocol for assessing tumour progression from changes in tissue contrast enhancement. Manual colorectal tumour delineation is a challenging and time consuming task due to the complex enhancement patterns in the 4D sequence. There is a need for a consistent approach to colorectal tumour segmentation in DCE-MRI and we propose a novel method based on detection of the tumour from signal enhancement characteristics of homogeneous tumour subregions and their neighbourhoods. Our method successfully detected 20 of 23 cases with a mean Dice score of 0.68 +/- 0.15 compared to expert annotations, which is not significantly different from expert inter-rater variability of 0.73 +/- 0.13 and 0.77 +/- 0.10. In comparison, a standard DCE-MRI tumour segmentation technique, fuzzy c-means, obtained a Dice score of 0.28 +/- 0.17.

Untersuchung von selbstnavigierenden MR-Sequenzen für die Perfusionsbildgebung der Nieren

Due to the worldwide increasing number of cases of chronic kidney diseases renal imaging – as a non-invasive technique in magnetic resonance imaging – has become a very important tool for an early diagnosis of probable insufficiencies and malfunction. Especially, dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) provides a technique to derive physiological parameters like renal blood flow or glomerular filtration rate. Similar to the entire field of abdominal imaging, the major problems are motion artifacts that primarily arise from the patient's respiration. The self-navigating BLADE-sequence with a post processing motion correction is an approach that does not require breath holding and is therefore also easily applicable to patients who are not able to undergo multiple breath hold examinations. In this work, a T 1-weighted BLADE-sequence was optimized to demonstrate the feasibility of this technique to perfusion imaging. The number of phase-encoding lines of one BLADE has a direct impact on the reduction of motion artifacts. In comparison to standard DCE-MRI sequences, the developed BLADE-sequence with optimized number of phase encoding lines could significantly reduce motion artifacts. A quantitative analysis revealed that up to a 50% displacement of the kidneys could be corrected. Therefore, it was demonstrated that dynamic motion corrected measurements without the need of a breath hold-technique are feasible.

Pixel-by-pixel analysis of DCE MRI curve patterns and an illustration of its application to the imaging of the musculoskeletal system

Dynamic contrast enhanced (DCE) MRI is a widespread method that has found broad application in the imaging of the musculoskeletal (MSK) system. A common way of analyzing DCE MRI images is to look at the shape of the time-intensity curve (TIC) in pixels selected after drawing an ROI in a highly enhanced area. Although often applied to a number of MSK affections, shape analysis has so far not led to a unanimous correlation between these TIC patterns and pathology. We hypothesize that this might be a result of the subjective ROI approach. To overcome the shortcomings of the ROI approach (sampling error and interuser variability, among others), we created a method for a fast and simple classification of DCE MRI where time-curve enhancement shapes are classified pixel by pixel according to their shape. The result of the analysis is rendered in multislice, 2D color-coded images. With this approach, we show not only that differences on a short distance range of the TIC patterns are significant and cannot be appreciated with a conventional ROI analysis but also that the information that shape maps and conventional standard DCE MRI parameter maps convey are substantially different.