Current and emerging quantitative magnetic resonance imaging methods for assessing and predicting the response of breast cancer to neoadjuvant therapy

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.

Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting

BACKGROUND

The purpose of this study was to determine whether advanced quantitative magnetic resonance imaging (MRI) can be deployed outside of large, research-oriented academic hospitals and into community care settings to predict eventual pathological complete response (pCR) to neoadjuvant therapy (NAT) in patients with locally advanced breast cancer.

METHODS

Patients with stage II/III breast cancer (N = 28) were enrolled in a multicenter study performed in community radiology settings. Dynamic contrast-enhanced (DCE) and diffusion-weighted (DW)-MRI data were acquired at four time points during the course of NAT. Estimates of the vascular perfusion and permeability, as assessed by the volume transfer rate (K^trans) using the Patlak model, were generated from the DCE-MRI data while estimates of cell density, as assessed by the apparent diffusion coefficient (ADC), were calculated from DW-MRI data. Tumor volume was calculated using semi-automatic segmentation and combined with K^trans and ADC to yield bulk tumor blood flow and cellularity, respectively. The percent change in quantitative parameters at each MRI scan was calculated and compared to pathological response at the time of surgery. The predictive accuracy of each MRI parameter at different time points was quantified using receiver operating characteristic curves.

RESULTS

Tumor size and quantitative MRI parameters were similar at baseline between groups that achieved pCR (n = 8) and those that did not (n = 20). Patients achieving a pCR had a larger decline in volume and cellularity than those who did not achieve pCR after one cycle of NAT (p < 0.05). At the third and fourth MRI, changes in tumor volume, K^trans, ADC, cellularity, and bulk tumor flow from baseline (pre-treatment) were all significantly greater (p < 0.05) in the cohort who achieved pCR compared to those patients with non-pCR.

CONCLUSIONS

Quantitative analysis of DCE-MRI and DW-MRI can be implemented in the community care setting to accurately predict the response of breast cancer to NAT. Dissemination of quantitative MRI into the community setting allows for the incorporation of these parameters into the standard of care and increases the number of clinical community sites able to participate in novel drug trials that require quantitative MRI.

Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL

Dynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). A total of 384 patients (median age: 49 y/o) were included. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. AUCs estimated for the combined versus highest AUCs among single features were 0.81 (95% confidence interval [CI]: 0.76, 0.86) versus 0.79 (95% CI: 0.73, 0.85) in the full cohort, 0.83 (95% CI: 0.77, 0.92) versus 0.73 (95% CI: 0.61, 0.84) in HR-positive/HER2-negative, 0.88 (95% CI: 0.79, 0.97) versus 0.78 (95% CI: 0.63, 0.89) in HR-positive/HER2-positive, 0.83 (95% CI not available) versus 0.75 (95% CI: 0.46, 0.81) in HR-negative/HER2-positive, and 0.82 (95% CI: 0.74, 0.91) versus 0.75 (95% CI: 0.64, 0.83) in triple negatives. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype.

Contradiction between amide-CEST signal and pH in breast cancer explained with metabolic MRI

PURPOSE

Metabolic MRI is a noninvasive technique that can give new insights into understanding cancer metabolism and finding biomarkers to evaluate or monitor treatment plans. Using this technique, a previous study has shown an increase in pH during neoadjuvant chemotherapy (NAC) treatment, while recent observation in a different study showed a reduced amide proton transfer (APT) signal during NAC treatment (negative relation). These findings are counterintuitive, given the known intrinsic positive relation of APT signal to pH.

METHODS

In this study we combined APT MRI and 31 P-MRSI measurements to unravel the relation between the APT signal and pH in breast cancer. Twenty-two breast cancer patients were scanned with a 7 T MRI before and after the first cycle of NAC treatment. pH was determined by the chemical shift of inorganic phosphate (Pi).

RESULTS

While APT signals have a positive relation to pH and amide content, we observed a direct negative linear correlation between APT signals and pH in breast tumors in vivo.

CONCLUSIONS

As differentiation of cancer stages was confirmed by observation of a linear correlation between cell proliferation marker PE/Pi (phosphoethanolamine over inorganic phosphate) and pH in the tumor, our data demonstrates that the concentration of mobile proteins likely supersedes the contribution of the exchange rate to the APT signal.

Role of perfusion parameters on DCE-MRI and ADC values on DWMRI for invasive ductal carcinoma at 3.0 Tesla

BACKGROUND

The value of apparent diffusion coefficient (ADC) values and quantitative parameters (Ktrans, Kep, Ve) in detecting prognostic factor at 3.0 Tesla remains unclear, especially in predicting prognosis of breast cancer.

METHODS

A total of 151 patients with IDC underwent breast DCE-MRI and DWI-MRI at 3.0 Tesla following surgery. The ADC values were acquired with b values of 0 and 1000 s/mm². The relationship between ADC values or DCE-MRI quantitative parameters and size, histologic grade (HG), lymph node metastasis (LNM), ER, PR, and Ki67 was evaluated. The predictive values of ADC, Ktrans, Kep, and Ve to prognosis of IDC were assessed.

RESULTS

ADC value was positively related to size (P = 0.04) and HER2 (P = 0.046) expression and negatively related to ER (P = 0.012) and PR (P < 0.001) expression. Ktrans value has positive correlation with size (P < 0.001), HG (P < 0.001), LNM (P < 0.001), HER2 (P = 0.007), and Ki67 (P < 0.001) expression and negative correlation with ER (P < 0.001) and PR (P < 0.001) expression. Kep value was positively related to size (P < 0.001) and negatively related to ER (P < 0.001) and PR (P < 0.001) expression. Ve value was negatively related to HER2 expression (P = 0.004). The Cox hazard ratio (HR) of ADC, Ktrans, Kep, and Ve values on survival was 5.26 (P = 0.093), 1.081 (P = 0.002), 1.006 (P = 0.941), and 0.883 (P = 0.926), respectively.

CONCLUSIONS

Ktrans value was a best predictive indicator of HG, LNM, ER, PR, and Ki67 expression, and ADC value was the best predictive indicator of HER2. Preoperative use of the 3.0 Tesla could provide important information to determine the optimal treatment plan.

Repeatability, reproducibility, and accuracy of quantitative mri of the breast in the community radiology setting

BACKGROUND

Quantitative diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI) have the potential to impact patient care by providing noninvasive biological information in breast cancer.

PURPOSE/HYPOTHESIS

To quantify the repeatability, reproducibility, and accuracy of apparent diffusion coefficient (ADC) and T1 -mapping of the breast in community radiology practices.

STUDY TYPE

Prospective.

SUBJECTS/PHANTOM

Ice-water DW-MRI and T1 gel phantoms were used to assess accuracy. Normal subjects (n = 3) and phantoms across three sites (one academic, two community) were used to assess reproducibility. Test-retest analysis at one site in normal subjects (n = 12) was used to assess repeatability.

FIELD STRENGTH/SEQUENCE

3T Siemens Skyra MRI quantitative DW-MRI and T1 -mapping.

ASSESSMENT

Quantitative DW-MRI and T1 -mapping parametric maps of phantoms and fibroglandular and adipose tissue of the breast.

STATISTICAL TESTS

Average values of breast tissue were quantified and Bland-Altman analysis was performed to assess the repeatability of the MRI techniques, while the Friedman test assessed reproducibility.

RESULTS

ADC measurements were reproducible across sites, with an average difference of 1.6% in an ice-water phantom and 7.0% in breast fibroglandular tissue. T1 measurements in gel phantoms had an average difference of 2.8% across three sites, whereas breast fibroglandular and adipose tissue had 8.4% and 7.5% average differences, respectively. In the repeatability study, we found no bias between first and second scanning sessions (P = 0.1). The difference between repeated measurements was independent of the mean for each MRI metric (P = 0.156, P = 0.862, P = 0.197 for ADC, T1 of fibroglandular tissue, and T1 of adipose tissue, respectively).

DATA CONCLUSION

Community radiology practices can perform repeatable, reproducible, and accurate quantitative T1 -mapping and DW-MRI. This has the potential to dramatically expand the number of sites that can participate in multisite clinical trials and increase clinical translation of quantitative MRI techniques for cancer response assessment.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted magnetic resonance imaging for predicting the response of locally advanced breast cancer to neoadjuvant therapy: a meta-analysis

This meta-analysis assesses the prognostic value of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) performed during neoadjuvant therapy (NAT) of locally advanced breast cancer. A systematic literature search was conducted to identify studies of quantitative DCE-MRI and DW-MRI performed during breast cancer NAT that report the sensitivity and specificity for predicting pathological complete response (pCR). Details of the study population and imaging parameters were extracted from each study for subsequent meta-analysis. Metaregression analysis, subgroup analysis, study heterogeneity, and publication bias were assessed. Across 10 studies that met the stringent inclusion criteria for this meta-analysis (out of 325 initially identified studies), we find that MRI had a pooled sensitivity of 0.91 [95% confidence interval (CI), 0.80 to 0.96] and specificity of 0.81(95% CI, 0.68 to 0.89) when adjusted for covariates. Quantitative DCE-MRI exhibits greater specificity for predicting pCR than semiquantitative DCE-MRI (p<0.001). Quantitative DCE-MRI and DW-MRI are able to predict, early in the course of NAT, the eventual response of breast tumors, with a high level of specificity and sensitivity. However, there is a high degree of heterogeneity in published studies highlighting the lack of standardization in the field.

Imaging Considerations and Interprofessional Opportunities in the Care of Breast Cancer Patients in the Neoadjuvant Setting

OBJECTIVE

To discuss standard-of-care and emerging imaging techniques employed for screening and detection, diagnosis and staging, monitoring response to therapy, and guiding cancer treatments.

DATA SOURCES

Published journal articles indexed in the National Library of Medicine database and relevant websites.

CONCLUSION

Imaging plays a fundamental role in the care of cancer patients and specifically, breast cancer patients in the neoadjuvant setting, providing an excellent opportunity for interprofessional collaboration between oncologists, researchers, radiologists, and oncology nurses. Quantitative imaging strategies to assess cellular, molecular, and vascular characteristics within the tumor is needed to better evaluate initial diagnosis and treatment response.

IMPLICATIONS FOR NURSING PRACTICE

Nurses caring for patients in all settings must continue to seek education on emerging imaging techniques. Oncology nurses provide education about the test, ensure the patient has appropriate pre-testing instructions, and manage patient expectations about timing of results availability.

Correlation of tumor characteristics derived from DCE-MRI and DW-MRI with histology in murine models of breast cancer

The purpose of this work was to determine the relationship between the apparent diffusion coefficient (ADC, from diffusion-weighted (DW) MRI), the extravascular, extracellular volume fraction (ve , from dynamic contrast-enhanced (DCE) MRI), and histological measurement of the extracellular space fraction. Athymic nude mice were injected with either human epidermal growth factor receptor 2 positive (HER2+) BT474 (n = 15) or triple negative MDA-MB-231 (n = 20) breast cancer cells, treated with either Herceptin (n = 8), Abraxane (low dose n = 7, high dose n = 6), or saline (n = 7 for each cell line), and imaged using DW- and DCE-MRI before, during, and after treatment. After the final imaging acquisition, the tissue was resected and evaluated by histological analysis. H&E-stained central slices were scanned using a digital brightfield microscope and evaluated with thresholding techniques to calculate the extracellular space. For both BT474 and MDA-MB-231, the median ADC of the central slice exhibited a significantly positive correlation with the corresponding central slice extracellular space as measured by H&E (p = 0.03, p < 0.01, respectively). Median ve calculated from the central slice showed differing results between the two cell lines. For BT474, a significant correlation between ve and extracellular space was calculated (p = 0.02), while MDA-MB-231 tumors did not demonstrate a significant correlation (p = 0.64). Additionally, there was no correlation discovered between ADC and ve with either whole tumor analysis or central slice analysis (p > 0.05). While ADC correlates well with the histologically determined fraction of extracellular space, these data add to the growing body of literature that suggests that ve derived from DCE-MRI is not a reliable biomarker of extracellular space for a range of physiological conditions.

Influence of water compartmentation and heterogeneous relaxation on quantitative magnetization transfer imaging in rodent brain tumors

PURPOSE

The goal of this study was to investigate the influence of water compartmentation and heterogeneous relaxation properties on quantitative magnetization transfer (qMT) imaging in tissues, and in particular whether a two-pool model is sufficient to describe qMT data in brain tumors.

METHODS

Computer simulations and in vivo experiments with a series of qMT measurements before and after injection of Gd-DTPA were performed. Both off-resonance pulsed saturation (pulsed) and on-resonance selective inversion recovery (SIR) qMT methods were used, and all data were fit with a two-pool model only.

RESULTS

Simulations indicated that a two-pool fitting of four-pool data yielded accurate measures of pool size ratio (PSR) of macromolecular versus free water protons when there were fast transcytolemmal exchange and slow R1 recovery. The fitted in vivo PSR of both pulsed and SIR qMT methods showed no dependence on R1 variations caused by different concentrations of Gd-DTPA during wash-out, whereas the fitted kex (magnetization transfer exchange rate) changed significantly with R1 .

CONCLUSION

A two-pool model provides reproducible estimates of PSR in brain tumors independent of relaxation properties in the presence of relatively fast transcytolemmal exchange, whereas estimates of kex are biased by relaxation variations. In addition, estimates of PSR in brain tumors using the pulsed and SIR qMT methods agree well with one another. Magn Reson Med 76:635-644, 2016. © 2015 Wiley Periodicals, Inc.

Sodium 3D COncentration MApping (COMA 3D) using (23)Na and proton MRI

Functional changes of sodium 3D MRI signals were converted into millimolar concentration changes using an open-source fully automated MATLAB toolbox. These concentration changes are visualized via 3D sodium concentration maps, and they are overlaid over conventional 3D proton images to provide high-resolution co-registration for easy correlation of functional changes to anatomical regions. Nearly 5000/h concentration maps were generated on a personal computer (ca. 2012) using 21.1T 3D sodium MRI brain images of live rats with spatial resolution of 0.8×0.8×0.8 mm(3) and imaging matrices of 60×60×60. The produced concentration maps allowed for non-invasive quantitative measurement of in vivo sodium concentration in the normal rat brain as a functional response to migraine-like conditions. The presented work can also be applied to sodium-associated changes in migraine, cancer, and other metabolic abnormalities that can be sensed by molecular imaging. The MATLAB toolbox allows for automated image analysis of the 3D images acquired on the Bruker platform and can be extended to other imaging platforms. The resulting images are presented in a form of series of 2D slices in all three dimensions in native MATLAB and PDF formats. The following is provided: (a) MATLAB source code for image processing, (b) the detailed processing procedures, (c) description of the code and all sub-routines, (d) example data sets of initial and processed data. The toolbox can be downloaded at: http://www.vuiis.vanderbilt.edu/~truongm/COMA3D/.

Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy

PURPOSE

To evaluate DRAMMS, an attribute-based deformable registration algorithm, compared to other intensity-based algorithms, for longitudinal breast MRI registration, and to show its applicability in quantifying tumor changes over the course of neoadjuvant chemotherapy.

METHODS

Breast magnetic resonance images from 14 women undergoing neoadjuvant chemotherapy were analyzed. The accuracy of DRAMMS versus five intensity-based deformable registration methods was evaluated based on 2,380 landmarks independently annotated by two experts, for the entire image volume, different image subregions, and patient subgroups. The registration method with the smallest landmark error was used to quantify tumor changes, by calculating the Jacobian determinant maps of the registration deformation.

RESULTS

DRAMMS had the smallest landmark errors (6.05 ± 4.86 mm), followed by the intensity-based methods CC-FFD (8.07 ± 3.86 mm), NMI-FFD (8.21 ± 3.81 mm), SSD-FFD (9.46 ± 4.55 mm), Demons (10.76 ± 6.01 mm), and Diffeomorphic Demons (10.82 ± 6.11 mm). Results show that registration accuracy also depends on tumor versus normal tissue regions and different patient subgroups.

CONCLUSIONS

The DRAMMS deformable registration method, driven by attribute-matching and mutual-saliency, can register longitudinal breast magnetic resonance images with a higher accuracy than several intensity-matching methods included in this article. As such, it could be valuable for more accurately quantifying heterogeneous tumor changes as a marker of response to treatment.

Quantitative magnetization transfer imaging of rodent glioma using selective inversion recovery

Magnetization transfer (MT) provides an indirect means to detect noninvasively variations in macromolecular contents in biological tissues, but, so far, there have been only a few quantitative MT (qMT) studies reported in cancer, all of which used off-resonance pulsed saturation methods. This article describes the first implementation of a different qMT approach, selective inversion recovery (SIR), for the characterization of tumor in vivo using a rodent glioma model. The SIR method is an on-resonance method capable of fitting qMT parameters and T1 relaxation time simultaneously without mapping B0 and B1 , which is very suitable for high-field qMT measurements because of the lower saturation absorption rate. The results show that the average pool size ratio (PSR, the macromolecular pool versus the free water pool) in rat 9 L glioma (5.7%) is significantly lower than that in normal rat gray matter (9.2%) and white matter (17.4%), which suggests that PSR is potentially a sensitive imaging biomarker for the assessment of brain tumor. Despite being less robust, the estimated MT exchange rates also show clear differences from normal tissues (19.7 Hz for tumors versus 14.8 and 10.2 Hz for gray and white mater, respectively). In addition, the influence of confounding effects, e.g. B1 inhomogeneity, on qMT parameter estimates is investigated with numerical simulations. These findings not only help to better understand the changes in the macromolecular contents of tumors, but are also important for the interpretation of other imaging contrasts, such as chemical exchange saturation transfer of tumors.

Dynamic contrast-enhanced magnetic resonance imaging in prostate cancer clinical trials: potential roles and possible pitfalls

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) evaluates the tissue microvasculature and may have a role in assessing and predicting therapeutic response in prostate cancer (PCa). In this review, we review principles of DCE-MRI and present the potential quantitative information that can be obtained. We discuss how it may be used as a biomarker for treatment with antiangiogenic and antivascular agents and potentially identify patients with PCa who may benefit from this form of therapy. Likewise, DCE-MRI may play a role in assessing response to combined androgen deprivation therapy and radiation therapy and theoretically could be a prognostic biomarker in evaluating second-generation hormone therapies. We also address the challenges of using DCE-MRI in PCa clinical trials and discuss the difficulties with standardization of this methodology to allow for biomarker validation, with particular reference to PCa.

Early assessment of breast cancer response to neoadjuvant chemotherapy by semi-quantitative analysis of high-temporal resolution DCE-MRI: preliminary results

PURPOSE

To evaluate whether semi-quantitative analysis of high temporal resolution dynamic contrast-enhanced MRI (DCE-MRI) acquired early in treatment can predict the response of locally advanced breast cancer (LABC) to neoadjuvant chemotherapy (NAC).

MATERIALS AND METHODS

As part of an IRB-approved prospective study, 21 patients with LABC provided informed consent and underwent high temporal resolution 3T DCE-MRI before and after 1cycle of NAC. Using measurements performed by two radiologists, the following parameters were extracted for lesions at both examinations: lesion size (short and long axes, in both early and late phases of enhancement), radiologist's subjective assessment of lesion enhancement, and percentages of voxels within the lesion demonstrating progressive, plateau, or washout kinetics. The latter data were calculated using two filters, one selecting for voxels enhancing ≥50% over baseline and one for voxels enhancing ≥100% over baseline. Pretreatment imaging parameters and parameter changes following cycle 1 of NAC were evaluated for their ability to discriminate patients with an eventual pathological complete response (pCR).

RESULTS

All 21 patients completed NAC followed by surgery, with 9 patients achieving a pCR. No pretreatment imaging parameters were predictive of pCR. However, change after cycle 1 of NAC in percentage of voxels demonstrating washout kinetics with a 100% enhancement filter discriminated patients with an eventual pCR with an area under the receiver operating characteristic curve (AUC) of 0.77. Changes in other parameters, including lesion size, did not predict pCR.

CONCLUSION

Semi-quantitative analysis of high temporal resolution DCE-MRI in patients with LABC can discriminate patients with an eventual pCR after one cycle of NAC.