Heterogeneity in DCE-MRI parametric maps: a biomarker for treatment response?

Multi-modal magnetic resonance imaging and histology of vascular function in xenografts using macromolecular contrast agent hyperbranched polyglycerol (HPG-GdF)

Macromolecular gadolinium (Gd)-based contrast agents are in development as blood pool markers for MRI. HPG-GdF is a 583 kDa hyperbranched polyglycerol doubly tagged with Gd and Alexa 647 nm dye, making it both MR and histologically visible. In this study we examined the location of HPG-GdF in whole-tumor xenograft sections matched to in vivo DCE-MR images of both HPG-GdF and Gadovist. Despite its large size, we have shown that HPG-GdF extravasates from some tumor vessels and accumulates over time, but does not distribute beyond a few cell diameters from vessels. Fractional plasma volume (fPV) and apparent permeability-surface area product (aPS) parameters were derived from the MR concentration-time curves of HPG-GdF. Non-viable necrotic tumor tissue was excluded from the analysis by applying a novel bolus arrival time (BAT) algorithm to all voxels. aPS derived from HPG-GdF was the only MR parameter to identify a difference in vascular function between HCT116 and HT29 colorectal tumors. This study is the first to relate low and high molecular weight contrast agents with matched whole-tumor histological sections. These detailed comparisons identified tumor regions that appear distinct from each other using the HPG-GdF biomarkers related to perfusion and vessel leakiness, while Gadovist-imaged parameter measures in the same regions were unable to detect variation in vascular function. We have established HPG-GdF as a biocompatible multi-modal high molecular weight contrast agent with application for examining vascular function in both MR and histological modalities.

Treatment assessment of radiotherapy using MR functional quantitative imaging

Recent developments in magnetic resonance (MR) functional quantitative imaging have made it a potentially powerful tool to assess treatment response in radiation therapy. With its abilities to capture functional information on underlying tissue characteristics, MR functional quantitative imaging can be valuable in assessing treatment response and as such to optimize therapeutic outcome. Various MR quantitative imaging techniques, including diffusion weighted imaging, diffusion tensor imaging, MR spectroscopy and dynamic contrast enhanced imaging, have been investigated and found useful for assessment of radiotherapy. However, various aspects including data reproducibility, interpretation of biomarkers, image quality and data analysis impose challenges on applications of MR functional quantitative imaging in radiotherapy assessment. All of these challenging issues shall be addressed to help us understand whether MR functional quantitative imaging is truly beneficial and contributes to future development of radiotherapy. It is evident that individualized therapy is the future direction of patient care. MR functional quantitative imaging might serves as an indispensable tool towards this promising direction.

Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome

Tumors exhibit genomic and phenotypic heterogeneity, which has prognostic significance and may influence response to therapy. Imaging can quantify the spatial variation in architecture and function of individual tumors through quantifying basic biophysical parameters such as CT density or MRI signal relaxation rate; through measurements of blood flow, hypoxia, metabolism, cell death, and other phenotypic features; and through mapping the spatial distribution of biochemical pathways and cell signaling networks using PET, MRI, and other emerging molecular imaging techniques. These methods can establish whether one tumor is more or less heterogeneous than another and can identify subregions with differing biology. In this article, we review the image analysis methods currently used to quantify spatial heterogeneity within tumors. We discuss how analysis of intratumor heterogeneity can provide benefit over more simple biomarkers such as tumor size and average function. We consider how imaging methods can be integrated with genomic and pathology data, instead of being developed in isolation. Finally, we identify the challenges that must be overcome before measurements of intratumoral heterogeneity can be used routinely to guide patient care.

[Current reporting in radiology : what will happen tomorrow?]

CLINICAL/METHODICAL ISSUE

Reporting in radiology faces considerable changes in the near future that will be influenced by a broader understanding of the task and increasing technological possibilities.

STANDARD RADIOLOGICAL METHODS

Until now a radiological report could be regarded as a text phrased by a radiologist after viewing imaging data.

METHODICAL INNOVATIONS

New solutions will be accessed by advances in visualization of large datasets, in extracting, analyzing, and communicating metadata as well as by improved integration and interpretation of clinical information.

PERFORMANCE

Virtual reality, texture analysis, growing networks, semantic annotation, data mining and context based presentation have the potential to extensively change the everyday working routine.

ACHIEVEMENTS

Although many of these developments are still in a laboratory phase, the impact on the process of reporting can already be predicted.

PRACTICAL RECOMMENDATIONS

As the leading community in information analysis and technology, radiology as a subject should strive to lead and shape these impending changes.

Classification of dynamic contrast enhanced MR images of cervical cancers using texture analysis and support vector machines

Dynamic contrast enhanced MRI (DCE-MRI) provides insight into the vascular properties of tissue. Pharmacokinetic models may be fitted to DCE-MRI uptake patterns, enabling biologically relevant interpretations. The aim of our study was to determine whether treatment outcome for 81 patients with locally advanced cervical cancer could be predicted from parameters of the Brix pharmacokinetic model derived from pre-chemoradiotherapy DCE-MRI. First-order statistical features of the Brix parameters were used. In addition, texture analysis of Brix parameter maps was done by constructing gray level co-occurrence matrices (GLCM) from the maps. Clinical factors and first- and second-order features were used as explanatory variables for support vector machine (SVM) classification, with treatment outcome as response. Classification models were validated using leave-one-out cross-model validation. A random value permutation test was used to evaluate model significance. Features derived from first-order statistics could not discriminate between cured and relapsed patients (specificity 0%-20%, p-values close to unity). However, second-order GLCM features could significantly predict treatment outcome with accuracies (~70%) similar to the clinical factors tumor volume and stage (69%). The results indicate that the spatial relations within the tumor, quantified by texture features, were more suitable for outcome prediction than first-order features.

Spatial heterogeneity analysis of brain activation in fMRI

In many brain diseases it can be qualitatively observed that spatial patterns in blood oxygenation level dependent (BOLD) activation maps appear more (diffusively) distributed than in healthy controls. However, measures that can quantitatively characterize this spatial distributiveness in individual subjects are lacking. In this study, we propose a number of spatial heterogeneity measures to characterize brain activation maps. The proposed methods focus on different aspects of heterogeneity, including the shape (compactness), complexity in the distribution of activated regions (fractal dimension and co-occurrence matrix), and gappiness between activated regions (lacunarity). To this end, functional MRI derived activation maps of a language and a motor task were obtained in language impaired children with (Rolandic) epilepsy and compared to age-matched healthy controls. Group analysis of the activation maps revealed no significant differences between patients and controls for both tasks. However, for the language task the activation maps in patients appeared more heterogeneous than in controls. Lacunarity was the best measure to discriminate activation patterns of patients from controls (sensitivity 74%, specificity 70%) and illustrates the increased irregularity of gaps between activated regions in patients. The combination of heterogeneity measures and a support vector machine approach yielded further increase in sensitivity and specificity to 78% and 80%, respectively. This illustrates that activation distributions in impaired brains can be complex and more heterogeneous than in normal brains and cannot be captured fully by a single quantity. In conclusion, heterogeneity analysis has potential to robustly characterize the increased distributiveness of brain activation in individual patients.

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A number of spatial heterogeneity measures of activation maps were explored in children with Rolandic epilepsy and language impairment

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Proposed measures capture the complexity, shape and distribution of brain activation patterns

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For a language task all proposed measures revealed that patients exhibit more heterogeneous activation patterns than controls, whereas for a motor task no differences appeared

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Spatial heterogeneity of activation patterns is a complex feature that cannot be captured by one single measure, but shape and lacunarity represented the best descriptive measures

Changes in primary breast cancer heterogeneity may augment midtreatment MR imaging assessment of response to neoadjuvant chemotherapy

PURPOSE

To evaluate whether changes in magnetic resonance (MR) imaging heterogeneity may aid assessment for pathologic complete response (pCR) to neoadjuvant chemotherapy (NACT) in primary breast cancer and to compare pCR with standard Response Evaluation Criteria in Solid Tumors response.

MATERIALS AND METHODS

Institutional review board approval, with waiver of informed consent, was obtained for this retrospective analysis of 36 consecutive female patients, with unilateral unifocal primary breast cancer larger than 2 cm in diameter who were receiving sequential anthracycline-taxane NACT between October 2008 and October 2012. T2- and T1-weighted dynamic contrast material-enhanced MR imaging was performed before, at midtreatment (after three cycles), and after NACT. Changes in tumor entropy (irregularity) and uniformity (gray-level distribution) were determined before and after MR image filtration (for different-sized features). Entropy and uniformity for pathologic complete responders and nonresponders were compared by using the Mann-Whitney U test and receiver operating characteristic analysis.

RESULTS

With NACT, there was an increase in uniformity and a decrease in entropy on T2-weighted and contrast-enhanced subtracted T1-weighted MR images for all filters (uniformity: 23.45% and 22.62%; entropy: -19.15% and -19.26%, respectively). There were eight complete pathologic responders. An area under the curve of 0.84 for T2-weighted MR imaging entropy and uniformity (P = .004 and .003) and 0.66 for size (P = .183) for pCR was found, giving a sensitivity and specificity of 87.5% and 82.1% for entropy and 87.5% and 78.6% for uniformity compared with 50% and 82.1%, respectively, for tumor size change for association with pCR.

CONCLUSION

Tumors become more homogeneous with treatment. An increase in T2-weighted MR imaging uniformity and a decrease in T2-weighted MR imaging entropy following NACT may provide an earlier indication of pCR than tumor size change.

Dynamic contrast-enhanced magnetic resonance imaging: applications in oncology

Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) allows functional characterisation of tissue perfusion characteristics and acts as a biomarker for tumour angiogenesis. It involves serial acquisition of MRI images before and after injection of contrast, as such, tissue perfusion and permeability can be assessed based on the signal enhancement kinetics. The ability to evaluate whole tumour volumes in a non-invasive manner makes DCE MRI especially attractive for potential oncological applications. Here we provide an overview of the current research involving DCE MRI as a biomarker for the diagnosis and characterisation of malignancies, prediction of the therapeutic response and survival outcomes, as well as radiation therapy planning.

DCE-MRI in hepatocellular carcinoma-clinical and therapeutic image biomarker

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) enables tumor vascular physiology to be assessed. Within the tumor tissue, contrast agents (gadolinium chelates) extravasate from intravascular into the extravascular extracellular space (EES), which results in a signal increase on T1-weighted MRI. The rate of contrast agents extravasation to EES in the tumor tissue is determined by vessel leakiness and blood flow. Thus, the signal measured on DCE-MRI represents a combination of permeability and perfusion. The semi-quantitative analysis is based on the calculation of heuristic parameters that can be extracted from signal intensity-time curves. These enhancing curves can also be deconvoluted by mathematical modeling to extract quantitative parameters that may reflect tumor perfusion, vascular volume, vessel permeability and angiogenesis. Because hepatocellular carcinoma (HCC) is a hypervascular tumor, many emerging therapies focused on the inhibition of angiogenesis. DCE-MRI combined with a pharmacokinetic model allows us to produce highly reproducible and reliable parametric maps of quantitative parameters in HCC. Successful therapies change quantitative parameters of DCE-MRI, which may be used as early indicators of tumor response to anti-angiogenesis agents that modulate tumor vasculature. In the setting of clinical trials, DCE-MRI may provide relevant clinical information on the pharmacodynamic and biologic effects of novel drugs, monitor treatment response and predict survival outcome in HCC patients.

Indexed distribution analysis for improved significance testing of spatially heterogeneous parameter maps: application to dynamic contrast-enhanced MRI biomarkers

PURPOSE

To develop significance testing methodology applicable to spatially heterogeneous parametric maps of biophysical and physiological measurements arising from imaging studies.

THEORY

Heterogeneity can confound statistical analyses. Indexed distribution analysis (IDA) transforms a reference distribution, establishing correspondences across parameter maps to which significance tests are applied.

METHODS

Well-controlled simulated and clinical K(trans) data from a dynamic contrast-enhanced magnetic resonance imaging study of bevacizumab were analyzed using conventional significance tests of parameter averages, histogram analysis, and IDA. Repeated pretreatment scans provided negative control; a post treatment scan provided positive control.

RESULTS

Histogram analysis was insensitive to simulated and known effects. Simulation: conventional analysis identified treatment effect (P ≈ 5 × 10(-4)) and direction, but underestimated magnitude (relative error 67-81%); IDA identified treatment effect (P = 0.001), magnitude, direction, and spatial extent (100% accuracy). Bevacizumab: conventional analysis was sensitive to treatment effect (P = 0.01; 95% confidence interval on K(trans) decrease: 23-37%); IDA was sensitive to treatment effect (P < 0.05; K(trans) decrease approximately 25%), inferred its spatial extent to be 94-96%, and inferred that K(trans) decrease is independent of baseline value, an inference that conventional and histogram analyses cannot make.

CONCLUSIONS

In the presence of heterogeneity, IDA can accurately infer the magnitude, direction, and spatial extent of between samples of parametric maps, which can be visualized spatially with respect to the original parameter maps.

Quantitative CT variables enabling response prediction in neoadjuvant therapy with EGFR-TKIs: are they different from those in neoadjuvant concurrent chemoradiotherapy?

Background and Purpose

To correlate changes of various CT parameters after the neoadjuvant treatment in patients with lung adenocarcinoma with pathologic responses, focused on their relationship with different therapeutic options, particularly of EGFR-TKI and concurrent chemoradiation therapy (CCRT) settings.

Materials and Methods

We reviewed pre-operative CT images of primary tumors and surgical specimens obtained after neoadjuvant therapy (TKI, n = 23; CCRT, n = 28) from 51 patients with lung adenocarcinoma. Serial changes in tumor volume, density, mass, skewness/kurtosis, and size-zone variability/intensity variability) were assessed from CT datasets. The changes in CT parameters were correlated with histopathologic responses, and the relationship between CT variables and histopathologic responses was compared between TKI and CCRT groups.

Results

Tumor volume, mass, kurtosis, and skewness were significant predictors of pathologic response in CCRT group in univariate analysis. Using multivariate analysis, kurtosis was found to be independent predictor. In TKI group, intensity variability and size-zone variability were significantly decreased in pathologic responder group. Intensity variability was found to be an independent predictor for pathologic response on multivariate analysis.

Conclusions

Quantitative CT variables including histogram or texture analysis have potential as a predictive tool for response evaluation, and it may better reflect treatment response than standard response criteria based on size changes.

Could magnetic resonance provide in vivo histology?

The diagnosis of a suspected tumor lesion faces two basic problems: detection and identification of the specific type of tumor. Radiological techniques are commonly used for the detection and localization of solid tumors. Prerequisite is a high intrinsic or enhanced contrast between normal and neoplastic tissue. Identification of the tumor type is still based on histological analysis. The result depends critically on the sampling sites, which given the inherent heterogeneity of tumors, constitutes a major limitation. Non-invasive in vivo imaging might overcome this limitation providing comprehensive three-dimensional morphological, physiological, and metabolic information as well as the possibility for longitudinal studies. In this context, magnetic resonance based techniques are quite attractive since offer at the same time high spatial resolution, unique soft tissue contrast, good temporal resolution to study dynamic processes and high chemical specificity. The goal of this paper is to review the role of magnetic resonance techniques in characterizing tumor tissue in vivo both at morphological and physiological levels. The first part of this review covers methods, which provide information on specific aspects of tumor phenotypes, considered as indicators of malignancy. These comprise measurements of the inflammatory status, neo-vascular physiology, acidosis, tumor oxygenation, and metabolism together with tissue morphology. Even if the spatial resolution is not sufficient to characterize the tumor phenotype at a cellular level, this multiparametric information might potentially be used for classification of tumors. The second part discusses mathematical tools, which allow characterizing tissue based on the acquired three-dimensional data set. In particular, methods addressing tumor heterogeneity will be highlighted. Finally, we address the potential and limitation of using MRI as a tool to provide in vivo tissue characterization.

Regional heterogeneity changes in DCE-MRI as response to isolated limb perfusion in experimental soft-tissue sarcomas

Experimental evidence supports an association between heterogeneity in tumor perfusion and response to chemotherapy/radiotherapy, disease progression and malignancy. Therefore, changes in tumor perfusion may be used to assess early effects of tumor treatment. However, evaluating changes in tumor perfusion during treatment is complicated by extensive changes in tumor type, size, shape and appearance. Therefore, this study assesses the regional heterogeneity of tumors by dynamic contrast-enhanced MRI (DCE-MRI) and evaluates changes in response to isolated limb perfusion (ILP) with tumor necrosis factor alpha and melphalan. Data were acquired in an experimental cancer model, using a macromolecular contrast medium, albumin-(Gd-DTPA)45. Small fragments of BN 175 (a soft-tissue sarcoma) were implanted in eight brown Norway rats. MRI of five drug-treated and three sham-treated rats was performed at baseline and 1 h after ILP intervention. Properly co-registered baseline and follow-up DCE-MRI were used to estimate the volume transfer constant (K(trans) ) pharmacokinetic maps. The regional heterogeneity was estimated in 16 tumor sectors and presented in cumulative map-volume histograms. On average, ILP-treated tumors showed a decrease in regional heterogeneity on the histograms. This study shows that heterogenic changes in regional tumor perfusion, estimated using DCE-MRI pharmacokinetic maps, can be measured and used to assess the short-term effects of a potentially curative treatment on the tumor microvasculature in an experimental soft-tissue sarcoma model.

Can DCE-MRI explain the heterogeneity in radiopeptide uptake imaged by SPECT in a pancreatic neuroendocrine tumor model?

Although efficient delivery and distribution of treatment agents over the whole tumor is essential for successful tumor treatment, the distribution of most of these agents cannot be visualized. However, with single-photon emission computed tomography (SPECT), both delivery and uptake of radiolabeled peptides can be visualized in a neuroendocrine tumor model overexpressing somatostatin receptors. A heterogeneous peptide uptake is often observed in these tumors. We hypothesized that peptide distribution in the tumor is spatially related to tumor perfusion, vessel density and permeability, as imaged and quantified by DCE-MRI in a neuroendocrine tumor model. Four subcutaneous CA20948 tumor-bearing Lewis rats were injected with the somatostatin-analog (111)In-DTPA-Octreotide (50 MBq). SPECT-CT and MRI scans were acquired and MRI was spatially registered to SPECT-CT. DCE-MRI was analyzed using semi-quantitative and quantitative methods. Correlation between SPECT and DCE-MRI was investigated with 1) Spearman's rank correlation coefficient; 2) SPECT uptake values grouped into deciles with corresponding median DCE-MRI parametric values and vice versa; and 3) linear regression analysis for median parameter values in combined datasets. In all tumors, areas with low peptide uptake correlated with low perfusion/density/ /permeability for all DCE-MRI-derived parameters. Combining all datasets, highest linear regression was found between peptide uptake and semi-quantitative parameters (R(2)>0.7). The average correlation coefficient between SPECT and DCE-MRI-derived parameters ranged from 0.52-0.56 (p<0.05) for parameters primarily associated with exchange between blood and extracellular extravascular space. For these parameters a linear relation with peptide uptake was observed. In conclusion, the 'exchange-related' DCE-MRI-derived parameters seemed to predict peptide uptake better than the 'contrast amount- related' parameters. Consequently, fast and efficient diffusion through the vessel wall into tissue is an important factor for peptide delivery. DCE-MRI helps to elucidate the relation between vascular characteristics, peptide delivery and treatment efficacy, and may form a basis to predict targeting efficiency.

Fractal analysis in radiological and nuclear medicine perfusion imaging: a systematic review

OBJECTIVES

To provide an overview of recent research in fractal analysis of tissue perfusion imaging, using standard radiological and nuclear medicine imaging techniques including computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET) and single-photon emission computed tomography (SPECT) and to discuss implications for different fields of application.

METHODS

A systematic review of fractal analysis for tissue perfusion imaging was performed by searching the databases MEDLINE (via PubMed), EMBASE (via Ovid) and ISI Web of Science.

RESULTS

Thirty-seven eligible studies were identified. Fractal analysis was performed on perfusion imaging of tumours, lung, myocardium, kidney, skeletal muscle and cerebral diseases. Clinically, different aspects of tumour perfusion and cerebral diseases were successfully evaluated including detection and classification. In physiological settings, it was shown that perfusion under different conditions and in various organs can be properly described using fractal analysis.

CONCLUSIONS

Fractal analysis is a suitable method for quantifying heterogeneity from radiological and nuclear medicine perfusion images under a variety of conditions and in different organs. Further research is required to exploit physiologically proven fractal behaviour in the clinical setting.

KEY POINTS

• Fractal analysis of perfusion images can be successfully performed. • Tumour, pulmonary, myocardial, renal, skeletal muscle and cerebral perfusion have already been examined. • Clinical applications of fractal analysis include tumour and brain perfusion assessment. • Fractal analysis is a suitable method for quantifying perfusion heterogeneity. • Fractal analysis requires further research concerning the development of clinical applications.

Quantitative dynamic ¹⁸FDG-PET and tracer kinetic analysis of soft tissue sarcomas

PURPOSE

To study soft tissue sarcomas using dynamic positron emission tomography (PET) with the glucose analog tracer [(18)F]fluoro-2-deoxy-D-glucose ((18)FDG), to investigate correlations between derived PET image parameters and clinical characteristics, and to discuss implications of dynamic PET acquisition (D-PET).

MATERIAL AND METHODS

D-PET images of 11 patients with soft tissue sarcomas were analyzed voxel-by-voxel using a compartment tracer kinetic model providing estimates of transfer rates between the vascular, non-metabolized, and metabolized compartments. Furthermore, standard uptake values (SUVs) in the early (2 min p.i.; SUVE) and late (45 min p.i.; SUVL) phases of the PET acquisition were obtained. The derived transfer rates K1, k2 and k3, along with the metabolic rate of (18)FDG (MRFDG) and the vascular fraction νp, was fused with the computed tomography (CT) images for visual interpretation. Correlations between D-PET imaging parameters and clinical parameters, i.e. tumor size, grade and clinical status, were calculated with a significance level of 0.05.

RESULTS

The temporal uptake pattern of (18)FDG in the tumor varied considerably from patient to patient. SUVE peak was higher than SUVL peak for four patients. The images of the rate constants showed a systematic pattern, often with elevated intensity in the tumors compared to surrounding tissue. Significant correlations were found between SUVE/L and some of the rate parameters.

CONCLUSIONS

Dynamic (18)FDG-PET may provide additional valuable information on soft tissue sarcomas not obtainable from conventional (18)FDG-PET. The prognostic role of dynamic imaging should be investigated.

Dynamic contrast-enhanced MRI-based biomarkers of therapeutic response in triple-negative breast cancer

OBJECTIVE

To predict the response of breast cancer patients to neoadjuvant chemotherapy (NAC) using features derived from dynamic contrast-enhanced (DCE) MRI.

MATERIALS AND METHODS

60 patients with triple-negative early-stage breast cancer receiving NAC were evaluated. Features assessed included clinical data, patterns of tumor response to treatment determined by DCE-MRI, MRI breast imaging-reporting and data system descriptors, and quantitative lesion kinetic texture derived from the gray-level co-occurrence matrix (GLCM). All features except for patterns of response were derived before chemotherapy; GLCM features were determined before and after chemotherapy. Treatment response was defined by the presence of residual invasive tumor and/or positive lymph nodes after chemotherapy. Statistical modeling was performed using Lasso logistic regression.

RESULTS

Pre-chemotherapy imaging features predicted all measures of response except for residual tumor. Feature sets varied in effectiveness at predicting different definitions of treatment response, but in general, pre-chemotherapy imaging features were able to predict pathological complete response with area under the curve (AUC)=0.68, residual lymph node metastases with AUC=0.84 and residual tumor with lymph node metastases with AUC=0.83. Imaging features assessed after chemotherapy yielded significantly improved model performance over those assessed before chemotherapy for predicting residual tumor, but no other outcomes.

CONCLUSIONS

DCE-MRI features can be used to predict whether triple-negative breast cancer patients will respond to NAC. Models such as the ones presented could help to identify patients not likely to respond to treatment and to direct them towards alternative therapies.

Musculoskeletal tumors: how to use anatomic, functional, and metabolic MR techniques

Although the function of magnetic resonance (MR) imaging in the evaluation of musculoskeletal tumors has traditionally been to help identify the extent of disease prior to treatment, its role continues to evolve as new techniques emerge. Conventional pulse sequences remain heavily used and useful, but with the advent of chemical shift imaging, diffusion-weighted imaging, perfusion imaging and MR spectroscopy, additional quantitative metrics have become available that may help expand the role of MR imaging to include detection, characterization, and reliable assessment of treatment response. This review discusses a multiparametric approach to the evaluation of musculoskeletal tumors, with a focus on the utility and potential added value of various pulse sequences in helping establish a diagnosis, assess pretreatment extent, and evaluate a tumor in the posttreatment setting for recurrence and treatment response.

Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?

Background

Tumor spatial heterogeneity is an important prognostic factor, which may be reflected in medical images

Methods

Image texture analysis is an approach of quantifying heterogeneity that may not be appreciated by the naked eye. Different methods can be applied including statistical-, model-, and transform-based methods.

Results

Early evidence suggests that texture analysis has the potential to augment diagnosis and characterization as well as improve tumor staging and therapy response assessment in oncological practice.

Conclusion

This review provides an overview of the application of texture analysis with different imaging modalities, CT, MRI, and PET, to date and describes the technical challenges that have limited its widespread clinical implementation so far. With further efforts to refine its application, image texture analysis has the potential to develop into a valuable clinical tool for oncologic imaging.

Teaching Points

• Tumor spatial heterogeneity is an important prognostic factor.

• Image texture analysis is an approach of quantifying heterogeneity.

• Different methods can be applied, including statistical-, model-, and transform-based methods.

• Texture analysis could improve the diagnosis, tumor staging, and therapy response assessment.

Evaluation of hepatic tumor response to yttrium-90 radioembolization therapy using texture signatures generated from contrast-enhanced CT images

RATIONALE AND OBJECTIVES

The aim of this study was to explore the use of texture features generated from liver computed tomographic (CT) datasets as potential image-based indicators of patient response to radioembolization (RE) with yttrium-90 ((90)Y) resin microspheres, an emerging locoregional therapy for advanced-stage liver cancer.

MATERIALS AND METHODS

Overall posttherapy survival and percent change in serologic tumor marker at 3 months posttherapy represent the primary clinical outcomes in this study. Thirty advanced-stage liver cancer cases (primary and metastatic) treated with RE over a 3-year period were included. Texture signatures for tumor regions, which were delineated to reveal boundaries with normal regions, were computed from pretreatment contrast-enhanced liver CT studies and evaluated for their ability to classify patient serologic response and survival.

RESULTS

A series of systematic leave-one-out cross-validation studies using soft-margin support vector machine (SVM) classifiers showed hepatic tumor texton and local binary pattern (LBP) signatures both achieve high accuracy (96%) in discriminating subjects in terms of their serologic response. The image-based indicators were also accurate in classifying subjects by survival status (80% and 93% accuracy for texton and LBP signatures, respectively).

CONCLUSIONS

Hepatic texture signatures generated from tumor regions on pretreatment triphasic CT studies were highly accurate in differentiating among subjects in terms of serologic response and survival. These image-based computational markers show promise as potential predictive tools in candidate evaluation for locoregional therapy such as RE.

A dedicated automated injection system for dynamic contrast-enhanced MRI experiments in mice

PURPOSE

To develop a reproducible small-animal dynamic contrast-enhanced (DCE) MRI set-up for mice through which volumes <100 μL can be accurately and safely injected and to test this set-up by means of DCE measurements in resting muscle and tumor tissue.

MATERIALS AND METHODS

The contrast agent (CA) injection system comprised 2 MR-compatible syringe pumps placed 50 cm from the 7T magnet bore where the fringe field is approximately 40 mT. Microbore tubing and T-connector, close to the injection site, minimized dead volume (<10 μL). For DCE-MRI measurements in 8 CB-17 SCID mice with 1500-2500 mm(3) large orthotopic neuroblastoma, a bolus of 10-fold-diluted Gd-DTPA CA solution (0.1 mmol/kg) was delivered (5 μL/s), followed by a 50-μL saline flush. Retro-orbital injections were given instead of tail vein injections, because the peripheral vasculature was reduced because of large tumor burden.

RESULTS

The CA injection was successful in 19 of 24 experiments. Optical assessment showed minimal dispersion of ink-colored CA bolus. Mean (± SD) pharmacokinetic parameters retrieved from DCE-MRI examinations in resting muscle (K(trans) = 0.038 ± 0.025 min(-1), k(ep) = 0.66 ± 0.48 min(-1), v(e) = 0.060 ± 0.014, v(p) = 0.033 ± 0.021) and tumor (K(trans) = 0.082 ± 0.071 min(-1), k(ep) = 0.82 ± 0.80 min(-1), v(e) = 0.121 ± 0.075, v(p) = 0.093 ± 0.051) agreed with those reported previously.

CONCLUSION

We successfully designed and implemented a DCE-MRI set-up system with short injection lines and low dead volume. The system can be used at any field strength with the syringe pumps placed at a sufficiently low fringe field (<40 mT).

Monitoring anti-angiogenic therapy in colorectal cancer murine model using dynamic contrast-enhanced MRI: comparing pixel-by-pixel with region of interest analysis

Sorafenib is a multi-kinase inhibitor that blocks cell proliferation and angiogenesis. It is currently approved for advanced hepatocellular and renal cell carcinomas in humans, where its major mechanism of action is thought to be through inhibition of vascular endothelial growth factor and platelet-derived growth factor receptors. The purpose of this study was to determine whether pixel-by-pixel analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is better able to capture the heterogeneous response of Sorafenib in a murine model of colorectal tumor xenografts (as compared with region of interest analysis). MRI was performed on a 9.4 T pre-clinical scanner on the initial treatment day. Then either vehicle or drug were gavaged daily (3 days) up to the final image. Four days later, the mice were again imaged. The two-compartment model and reference tissue method of DCE-MRI were used to analyze the data. The results demonstrated that the contrast agent distribution rate constant (K(trans)) were significantly reduced (p < 0.005) at day-4 of Sorafenib treatment. In addition, the K(trans) of nearby muscle was also reduced after Sorafenib treatment. The pixel-by-pixel analysis (compared to region of interest analysis) was better able to capture the heterogeneity of the tumor and the decrease in K(trans) four days after treatment. For both methods, the volume of the extravascular extracellular space did not change significantly after treatment. These results confirm that parameters such as K(trans), could provide a non-invasive biomarker to assess the response to anti-angiogenic therapies such as Sorafenib, but that the heterogeneity of response across a tumor requires a more detailed analysis than has typically been undertaken.

Reproducibility of 2D and 3D fractal analysis techniques for the assessment of spatial heterogeneity of regional blood flow in rectal cancer

PURPOSE

To characterize the two-dimensional (2D) and three-dimensional (3D) fractal properties of rectal cancer regional blood flow assessed by using volumetric helical perfusion computed tomography (CT) and to determine its reproducibility.

MATERIALS AND METHODS

Institutional review board approval and informed consent were obtained. Ten prospective patients (eight men, two women; mean age, 72.3 years) with rectal adenocarcinoma underwent two repeated volumetric helical perfusion CT studies (four-dimensional adaptive spiral mode, 11.4-cm z-axis coverage) without intervening treatment within 24 hours, with regional blood flow derived by using deconvolution analysis. Two-dimensional and 3D fractal analyses of the rectal tumor were performed, after segmentation from surrounding structures by using thresholding, to derive fractal dimension and fractal abundance. Reproducibility was quantitatively assessed by using Bland-Altman statistics. Two-dimensional and 3D lacunarity plots were also generated, allowing qualitative assessment of reproducibility. Statistical significance was at 5%.

RESULTS

Mean blood flow was 63.50 mL/min/100 mL ± 8.95 (standard deviation). Good agreement was noted between the repeated studies for fractal dimension; mean difference was -0.024 (95% limits of agreement: -0.212, 0.372) for 2D fractal analysis and -0.024 (95% limits of agreement: -0.307, 0.355) for 3D fractal analysis. Mean difference for fractal abundance was -0.355 (95% limits of agreement: -0.869, 1.579) for 2D fractal analysis and -0.043 (95% limits of agreement: -1.154, 1.239) for 3D fractal analysis. The 95% limits of agreement were narrower for 3D than 2D analysis. Lacunarity plots also visually confirmed close agreement between repeat studies.

CONCLUSION

Regional blood flow in rectal cancer exhibits fractal properties. Good reproducibility was achieved between repeated studies with 2D and 3D fractal analysis.

DCE-MRI biomarkers of tumour heterogeneity predict CRC liver metastasis shrinkage following bevacizumab and FOLFOX-6

BACKGROUND

There is limited evidence that imaging biomarkers can predict subsequent response to therapy. Such prognostic and/or predictive biomarkers would facilitate development of personalised medicine. We hypothesised that pre-treatment measurement of the heterogeneity of tumour vascular enhancement could predict clinical outcome following combination anti-angiogenic and cytotoxic chemotherapy in colorectal cancer (CRC) liver metastases.

METHODS

Ten patients with 26 CRC liver metastases had two dynamic contrast-enhanced MRI (DCE-MRI) examinations before starting first-line bevacizumab and FOLFOX-6. Pre-treatment biomarkers of tumour microvasculature were computed and a regression analysis was performed against the post-treatment change in tumour volume after five cycles of therapy. The ability of the resulting linear model to predict tumour shrinkage was evaluated using leave-one-out validation. Robustness to inter-visit variation was investigated using data from a second baseline scan.

RESULTS

In all, 86% of the variance in post-treatment tumour shrinkage was explained by the median extravascular extracellular volume (v(e)), tumour enhancing fraction (E(F)), and microvascular uniformity (assessed with the fractal measure box dimension, d(0)) (R(2)=0.86, P<0.00005). Other variables, including baseline volume were not statistically significant. Median prediction error was 12%. Equivalent results were obtained from the second scan.

CONCLUSION

Traditional image analyses may over-simplify tumour biology. Measuring microvascular heterogeneity may yield important prognostic and/or predictive biomarkers.