Current status in spatiotemporal analysis of contrast-based perfusion MRI

In perfusion MRI, image voxels form a spatially organized network of systems, all exchanging indicator with their immediate neighbors. Yet the current paradigm for perfusion MRI analysis treats all voxels or regions-of-interest as isolated systems supplied by a single global source. This simplification not only leads to long-recognized systematic errors but also fails to leverage the embedded spatial structure within the data. Since the early 2000s, a variety of models and implementations have been proposed to analyze systems with between-voxel interactions. In general, this leads to large and connected numerical inverse problems that are intractible with conventional computational methods. With recent advances in machine learning, however, these approaches are becoming practically feasible, opening up the way for a paradigm shift in the approach to perfusion MRI. This paper seeks to review the work in spatiotemporal modelling of perfusion MRI using a coherent, harmonized nomenclature and notation, with clear physical definitions and assumptions. The aim is to introduce clarity in the state-of-the-art of this promising new approach to perfusion MRI, and help to identify gaps of knowledge and priorities for future research.

The Role of Imaging Biomarkers to Guide Pharmacological Interventions Targeting Tumor Hypoxia

Hypoxia is a common feature of solid tumors that contributes to angiogenesis, invasiveness, metastasis, altered metabolism and genomic instability. As hypoxia is a major actor in tumor progression and resistance to radiotherapy, chemotherapy and immunotherapy, multiple approaches have emerged to target tumor hypoxia. It includes among others pharmacological interventions designed to alleviate tumor hypoxia at the time of radiation therapy, prodrugs that are selectively activated in hypoxic cells or inhibitors of molecular targets involved in hypoxic cell survival (i.e., hypoxia inducible factors HIFs, PI3K/AKT/mTOR pathway, unfolded protein response). While numerous strategies were successful in pre-clinical models, their translation in the clinical practice has been disappointing so far. This therapeutic failure often results from the absence of appropriate stratification of patients that could benefit from targeted interventions. Companion diagnostics may help at different levels of the research and development, and in matching a patient to a specific intervention targeting hypoxia. In this review, we discuss the relative merits of the existing hypoxia biomarkers, their current status and the challenges for their future validation as companion diagnostics adapted to the nature of the intervention.

Incorporating cross-voxel exchange into the analysis of dynamic contrast-enhanced imaging data: theory, simulations and experimental results

Predictions of tumour perfusion are key determinants of drug delivery and responsiveness to therapy. Pharmacokinetic models allow for the estimation of perfusion properties of tumour tissues but many assume no dispersion associated with tracer transport away from the capillaries and through the tissue. At the level of a voxel, this translates to assuming no cross-voxel tracer exchange, often leading to the misinterpretation of derived perfusion parameters. Tofts model (TM), a compartmental model widely used in oncology, also makes this assumption. A more realistic description is required to quantify kinetic properties of tracers, such as convection and diffusion. We propose a Cross-Voxel Exchange Model (CVXM) for analysing cross-voxel tracer kinetics.In silicodatasets quantifying the roles of convection and diffusion in tracer transport (which TM ignores) were employed to investigate the interpretation of Tofts' perfusion parameters compared to CVXM. TM returned inaccurate values ofKtransandvewhere diffusive and convective mechanisms are pronounced (up to 20% and 300% error respectively). A mathematical equation, developed in this work, predicts and gives the correct physiological interpretation of Tofts've.Finally, transport parameters were derived from dynamic contrast enhanced-magnetic resonance imaging of a TS-415 human cervical carcinoma xenograft by using TM and CVXM. The latter deduced lower values ofKtransandvecompared to TM (lower by up to 63% and 76% respectively). It also allowed the detection of a diffusive flux (mean diffusivity 155μm²s^-1) in the tumour tissue, as well as an increased convective flow at the periphery (mean velocity 2.3μm s^-1detected). The results serve as a proof of concept establishing the feasibility of using CVXM for accurately determining transport metrics that characterize the exchange of tracer between voxels. CVXM needs to be investigated further as its parameters can be linked to the tumour microenvironment properties (permeability, pressure…), potentially leading to enhanced personalized treatment planning.

DCE-MRI of Tumor Hypoxia and Hypoxia-Associated Aggressiveness

Tumor hypoxia is associated with resistance to treatment, aggressive growth, metastatic dissemination, and poor clinical outcome in many cancer types. The potential of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to assess the extent of hypoxia in tumors has been investigated in several studies in our laboratory. Cervical carcinoma, melanoma, and pancreatic ductal adenocarcinoma (PDAC) xenografts have been used as models of human cancer, and the transfer rate constant (K^trans) and the extravascular extracellular volume fraction (v_e) have been derived from DCE-MRI data by using Tofts standard pharmacokinetic model and a population-based arterial input function. K^trans was found to reflect naturally occurring and treatment-induced hypoxia when hypoxia was caused by low blood perfusion, radiation responsiveness when radiation resistance was due to hypoxia, and metastatic potential when metastasis was hypoxia-induced. K^trans was also associated with outcome for patients with locally-advanced cervical carcinoma treated with cisplatin-based chemoradiotherapy. Together, the studies imply that DCE-MRI can provide valuable information on the hypoxic status of cervical carcinoma, melanoma, and PDAC. In this communication, we review and discuss the studies and provide some recommendations as to how DCE-MRI data can be analyzed and interpreted to assess tumor hypoxia.

Distinguishing metastatic triple-negative breast cancer from nonmetastatic breast cancer using second harmonic generation imaging and resonance Raman spectroscopy

Triple-negative breast cancer (TNBC) is an aggressive subset of breast cancer that is more common in African-American and Hispanic women. Early detection followed by intensive treatment is critical to improving poor survival rates. The current standard to diagnose TNBC from histopathology of biopsy samples is invasive and time-consuming. Imaging methods such as mammography and magnetic resonance (MR) imaging, while covering the entire breast, lack the spatial resolution and specificity to capture the molecular features that identify TNBC. Two nonlinear optical modalities of second harmonic generation (SHG) imaging of collagen, and resonance Raman spectroscopy (RRS) potentially offer novel rapid, label-free detection of molecular and morphological features that characterize cancerous breast tissue at subcellular resolution. In this study, we first applied MR methods to measure the whole-tumor characteristics of metastatic TNBC (4T1) and nonmetastatic estrogen receptor positive breast cancer (67NR) models, including tumor lactate concentration and vascularity. Subsequently, we employed for the first time in vivo SHG imaging of collagen and ex vivo RRS of biomolecules to detect different microenvironmental features of these two tumor models. We achieved high sensitivity and accuracy for discrimination between these two cancer types by quantitative morphometric analysis and nonnegative matrix factorization along with support vector machine. Our study proposes a new method to combine SHG and RRS together as a promising novel photonic and optical method for early detection of TNBC.

Delineation of Tumor Habitats based on Dynamic Contrast Enhanced MRI

Tumor heterogeneity can be elucidated by mapping subregions of the lesion with differential imaging characteristics, called habitats. Dynamic Contrast Enhanced (DCE-)MRI can depict the tumor microenvironments by identifying areas with variable perfusion and vascular permeability, since individual tumor habitats vary in the rate and magnitude of the contrast uptake and washout. Of particular interest is identifying areas of hypoxia, characterized by inadequate perfusion and hyper-permeable vasculature. An automatic procedure for delineation of tumor habitats from DCE-MRI was developed as a two-part process involving: (1) statistical testing in order to determine the number of the underlying habitats; and (2) an unsupervised pattern recognition technique to recover the temporal contrast patterns and locations of the associated habitats. The technique is examined on simulated data and DCE-MRI, obtained from prostate and brain pre-clinical cancer models, as well as clinical data from sarcoma and prostate cancer patients. The procedure successfully identified habitats previously associated with well-perfused, hypoxic and/or necrotic tumor compartments. Given the association of tumor hypoxia with more aggressive tumor phenotypes, the obtained in vivo information could impact management of cancer patients considerably.

Gaussian mixture model-based classification of dynamic contrast enhanced MRI data for identifying diverse tumor microenvironments: preliminary results

Tumor hypoxia develops heterogeneously, affects radiation sensitivity and the development of metastases. Prognostic information derived from the in vivo characterization of the spatial distribution of hypoxic areas in solid tumors can be of value for radiation therapy planning and for monitoring the early treatment response. Tumor hypoxia is caused by an imbalance between the supply and consumption of oxygen. The tumor oxygen supply is inherently linked to its vasculature and perfusion which can be evaluated by dynamic contrast enhanced (DCE-) MRI using the contrast agent Gd-DTPA. Thus, we hypothesize that DCE-MRI data may provide surrogate information regarding tumor hypoxia. In this study, DCE-MRI data from a rat prostate tumor model were analysed with a Gaussian mixture model (GMM)-based classification to identify perfused, hypoxic and necrotic areas for a total of ten tumor slices from six rats, of which one slice was used as training data for GMM classifications. The results of pattern recognition analyzes were validated by comparison to corresponding Akep maps defining the perfused area (0.84 ± 0.09 overlap), hematoxylin and eosin (H&E)-stained tissue sections defining necrosis (0.64 ± 0.15 overlap) and pimonidazole-stained sections defining hypoxia (0.72 ± 0.17 overlap), respectively. Our preliminary data indicate the feasibility of a GMM-based classification to identify tumor hypoxia, necrosis and perfusion/permeability from non-invasively acquired, in vivo DCE-MRI data alone, possibly obviating the need for invasive procedures, such as biopsies, or exposure to radioactivity, such as positron emission tomography (PET) exams.

A diffusion-compensated model for the analysis of DCE-MRI data: theory, simulations and experimental results

Accurate quantification of pharmacokinetic parameters in dynamic contrast-enhanced (DCE) MRI may be affected by the passive diffusion of contrast agent (CA) within the tissue. By introducing an additional term into the standard Tofts-Kety (STK) model, we correct for the effects of CA diffusion. We first develop the theory describing a CA diffusion corrected STK model (DTK). The model is then tested in simulation with simple models of diffusion. The DTK model is also fit to 18 in vivo DCE-MRI acquisitions from murine models of cancer and results are compared to those from the STK model. The DTK model returned estimates with significantly lower error than the STK model (p ≪ 0.001). In poorly perfused (i.e., K(trans) ≤ 0.05 min(-1)) regions the STK model returned unphysical ve values, while the DTK model estimated ve with less than 7% error in noise-free simulations. Results in vivo data revealed similar trends. For voxels with low K(trans) values and late peak concentration times the STK model returned ve estimates >1.0 in 40% of the voxels as compared to only 16% for the DTK model. The DTK model presented here shows promise in estimating accurate kinetic parameters in the presence of passive contrast agent diffusion.

Temporal/spatial resolution improvement of in vivo DCE-MRI with compressed sensing-optimized FLASH

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides critical information regarding tumor perfusion and permeability by injecting a T(1) contrast agent, such as Gd-DTPA, and making a time-resolved measurement of signal increase. Both temporal and spatial resolutions are required to be high to achieve an accurate and reproducible estimation of tumor perfusion. However, the dynamic nature of the DCE experiment limits simultaneous improvement of temporal and spatial resolution by conventional methods. Compressed sensing (CS) has become an important tool for the acceleration of imaging times in MRI, which is achieved by enabling the reconstruction of subsampled data. Similarly, CS algorithms can be utilized to improve the temporal/spatial resolution of DCE-MRI, and several works describing retrospective simulations have demonstrated the feasibility of such improvements. In this study, the fast low angle shot sequence was modified to implement a Cartesian, CS-optimized, sub-Nyquist phase encoding acquisition/reconstruction with multiple two-dimensional slice selections and was tested on water phantoms and animal tumor models. The mean voxel-level concordance correlation coefficient for Ak(ep) values obtained from ×4 and ×8 accelerated and the fully sampled data was 0.87±0.11 and 0.83±0.11, respectively (n=6), with optimized CS parameters. In this case, the reduction of phase encoding steps made possible by CS reconstruction improved effectively the temporal/spatial resolution of DCE-MRI data using an in vivo animal tumor model (n=6) and may be useful for the investigation of accelerated acquisitions in preclinical and clinical DCE-MRI trials.

Assessment of hypoxia and radiation response in intramuscular experimental tumors by dynamic contrast-enhanced magnetic resonance imaging

BACKGROUND AND PURPOSE

Studies of intradermal melanoma xenografts have suggested that dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may be a useful method for assessing the extent of hypoxia in tumors. Because the microvascular network of tumors is influenced significantly by the site of growth, we challenged this possibility in the present work by studying relationships between DCE-MRI-derived parameters and hypoxia in intramuscular melanoma xenografts.

MATERIALS AND METHODS

Intramuscular R-18, U-25, and V-27 tumors were subjected to DCE-MRI and measurement of the fraction of radiobiologically hypoxic cells (HF(Rad)). Parametric images of K(trans) and v(e) were produced by pharmacokinetic analysis, and K(trans) and v(e) were related to HF(Rad) in individual tumors.

RESULTS

K(trans) decreased with increasing HF(Rad). The correlations between K(trans) and HF(Rad) were similar for the three tumor lines and were highly significant (P<0.00001). There was no correlation between v(e) and HF(Rad). However, v(e) decreased significantly with increasing cell survival after single dose irradiation.

CONCLUSION

Intramuscular melanoma xenografts show similar inverse correlations between K(trans) and HF(Rad) as intradermal tumors, which support the current clinical attempts to establish DCE-MRI as a method for detecting hypoxia and defining therapeutic targets in tumors.

Magnetic resonance imaging of tumor necrosis

BACKGROUND

The prognostic and predictive value of magnetic resonance (MR) investigations in clinical oncology may be improved by implementing strategies for discriminating between viable and necrotic tissue in tumors. The purpose of this preclinical study was to investigate whether the extent of necrosis in tumors can be assessed by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and/or T(2)-weighted MR imaging.

MATERIAL AND METHODS

Three amelanotic human melanoma xenograft lines differing substantially in tumor necrotic fraction, necrotic pattern, extracellular volume fraction, and blood perfusion were used as experimental models of human cancer. MRI was performed at 1.5 T and a spatial resolution of 0.23 × 0.47 × 2.0 mm(3). Gadolinium diethylene-triamine penta-acetic acid (Gd-DTPA) was used as contrast agent. Plots of Gd-DTPA concentration versus time were generated for each voxel, and three parameters were calculated for each curve: the extracellular volume fraction (ν(e)), the final slope (a), and the Gd-DTPA concentration at one minute after the contrast administration (C(1min)). Parametric images of ν(e), a, C(1min), and the signal intensity in T(2)-weighted images (SI(T2W)) were compared with the histology of the imaged tissue.

RESULTS

The ν(e), a, and C(1min) frequency distributions were significantly different for necrotic and viable tissue in all three tumor lines. By using adequate values of ν(e), a, and C(1min) to discriminate between necrotic and viable tissue, significant correlations were found between the fraction of necrotic tissue assessed by MRI and the fraction of necrotic tissue assessed by image analysis of histological preparations. On the other hand, the SI(T2W) frequency distributions did not differ significantly between necrotic and viable tissue in two of the three tumor lines.

CONCLUSION

Necrotic regions in tumor tissue can be identified in parametric images derived from DCE-MRI series, whereas T(2)-weighted images are unsuitable for detection of tumor necrosis.

Dynamic contrast-enhanced magnetic resonance imaging of human cervical carcinoma xenografts: pharmacokinetic analysis and correlation to tumor histomorphology

BACKGROUND AND PURPOSE

Biomarkers that can predict the outcome of treatment accurately are needed for treatment individualization in advanced carcinoma of the uterine cervix. The potential of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was investigated in the present preclinical study.

MATERIALS AND METHODS

CK-160 and TS-415 human cervical carcinoma xenografts were subjected to DCE-MRI at 1.5T using a spatial resolution of 0.23×0.47×2.0mm(3). Parametric images of K(trans) (the volume transfer constant of Gd-DTPA) and v(e) (the extravascular extracellular volume fraction) were produced by pharmacokinetic analysis of the DCE-MRI data and compared with the histomorphology of the imaged tissue.

RESULTS

Analysis of small homogeneous tumor regions showed that K(trans), but not v(e), differed significantly between parenchymal tissue, connective tissue, and necrotic tissue, consistent with the vascularity of these compartments. However, strong correlations between K(trans) and the fractional volume of the compartments could not be detected for larger tumor regions, primarily because the majority of the voxels represented a chaotic mixture of parenchymal, connective, and necrotic tissue.

CONCLUSION

The potential of DCE-MRI in providing detailed information on the histomorphology of cervical carcinoma is limited, mainly because the tumor tissue shows significant morphological heterogeneity at the subvoxel level.

Noninvasive multimodality imaging of the tumor microenvironment: registered dynamic magnetic resonance imaging and positron emission tomography studies of a preclinical tumor model of tumor hypoxia

In vivo knowledge of the spatial distribution of viable, necrotic, and hypoxic areas can provide prognostic information about the risk of developing metastases and regional radiation sensitivity and may be used potentially for localized dose escalation in radiation treatment. In this study, multimodality in vivo magnetic resonance imaging (MRI) and positron emission tomography (PET) imaging using stereotactic fiduciary markers in the Dunning R3327-AT prostate tumor were performed, focusing on the relationship between dynamic contrast-enhanced (DCE) MRI using Magnevist (Gd-DTPA) and dynamic (18)F-fluoromisonidazole ((18)F-Fmiso) PET. The noninvasive measurements were verified using tumor tissue sections stained for hematoxylin/eosin and pimonidazole. To further validate the relationship between (18)F-Fmiso and pimonidazole uptake, (18)F digital autoradiography was performed on a selected tumor and compared with the corresponding pimonidazole-stained slices. The comparison of Akep values (kep = rate constant of movement of Gd-DTPA between the interstitial space and plasma and A = amplitude in the two-compartment model (Hoffmann U, Brix G, Knopp MV, Hess T and Lorenz WJ (1995). Magn Reson Med 33, 506-514) derived from DCE-MRI studies and from early (18)F-Fmiso uptake PET studies showed that tumor vasculature is a major determinant of early (18)F-Fmiso uptake. A negative correlation between the spatial map of Akep and the slope map of late (last 1 hour of the dynamic PET scan) (18)F-Fmiso uptake was observed. The relationships between DCE-MRI and hematoxylin/eosin slices and between (18)F-Fmiso PET and pimonidazole slices confirm the validity of MRI/PET measurements to image the tumor microenvironment and to identify regions of tumor necrosis, hypoxia, and well-perfused tissue.