Multispectral quantification of tissue types in a RIF-1 tumor model with histological validation. Part I

Imaging Genomics of Glioma Revisited: Analytic Methods to Understand Spatial and Temporal Heterogeneity

An improved understanding of the cellular and molecular biologic processes responsible for brain tumor development, growth, and resistance to therapy is fundamental to improving clinical outcomes. Imaging genomics is the study of the relationships between microscopic, genetic, and molecular biologic features and macroscopic imaging features. Imaging genomics is beginning to shift clinical paradigms for diagnosing and treating brain tumors. This article provides an overview of imaging genomics in gliomas, in which imaging data including hallmarks such as IDH-mutation, MGMT methylation, and EGFR-mutation status can provide critical insights into the pretreatment and posttreatment stages. This article will accomplish the following: 1) review the methods used in imaging genomics, including visual analysis, quantitative analysis, and radiomics analysis; 2) recommend suitable analytic methods for imaging genomics according to biologic characteristics; 3) discuss the clinical applicability of imaging genomics; and 4) introduce subregional tumor habitat analysis with the goal of guiding future radiogenetics research endeavors toward translation into critically needed clinical applications.

Habitat escalated adaptive therapy (HEAT): a phase 2 trial utilizing radiomic habitat-directed and genomic-adjusted radiation dose (GARD) optimization for high-grade soft tissue sarcoma

BACKGROUND

Soft tissue sarcomas (STS), have significant inter- and intra-tumoral heterogeneity, with poor response to standard neoadjuvant radiotherapy (RT). Achieving a favorable pathologic response (FPR ≥ 95%) from RT is associated with improved patient outcome. Genomic adjusted radiation dose (GARD), a radiation-specific metric that quantifies the expected RT treatment effect as a function of tumor dose and genomics, proposed that STS is significantly underdosed. STS have significant radiomic heterogeneity, where radiomic habitats can delineate regions of intra-tumoral hypoxia and radioresistance. We designed a novel clinical trial, Habitat Escalated Adaptive Therapy (HEAT), utilizing radiomic habitats to identify areas of radioresistance within the tumor and targeting them with GARD-optimized doses, to improve FPR in high-grade STS.

METHODS

Phase 2 non-randomized single-arm clinical trial includes non-metastatic, resectable high-grade STS patients. Pre-treatment multiparametric MRIs (mpMRI) delineate three distinct intra-tumoral habitats based on apparent diffusion coefficient (ADC) and dynamic contrast enhanced (DCE) sequences. GARD estimates that simultaneous integrated boost (SIB) doses of 70 and 60 Gy in 25 fractions to the highest and intermediate radioresistant habitats, while the remaining volume receives standard 50 Gy, would lead to a > 3 fold FPR increase to 24%. Pre-treatment CT guided biopsies of each habitat along with clip placement will be performed for pathologic evaluation, future genomic studies, and response assessment. An mpMRI taken between weeks two and three of treatment will be used for biological plan adaptation to account for tumor response, in addition to an mpMRI after the completion of radiotherapy in addition to pathologic response, toxicity, radiomic response, disease control, and survival will be evaluated as secondary endpoints. Furthermore, liquid biopsy will be performed with mpMRI for future ancillary studies.

DISCUSSION

This is the first clinical trial to test a novel genomic-based RT dose optimization (GARD) and to utilize radiomic habitats to identify and target radioresistance regions, as a strategy to improve the outcome of RT-treated STS patients. Its success could usher in a new phase in radiation oncology, integrating genomic and radiomic insights into clinical practice and trial designs, and may reveal new radiomic and genomic biomarkers, refining personalized treatment strategies for STS.

TRIAL REGISTRATION

NCT05301283.

TRIAL STATUS

The trial started recruitment on March 17, 2022.

Quantification of intratumoural heterogeneity in mice and patients via machine-learning models trained on PET-MRI data

In oncology, intratumoural heterogeneity is closely linked with the efficacy of therapy, and can be partially characterized via tumour biopsies. Here we show that intratumoural heterogeneity can be characterized spatially via phenotype-specific, multi-view learning classifiers trained with data from dynamic positron emission tomography (PET) and multiparametric magnetic resonance imaging (MRI). Classifiers trained with PET-MRI data from mice with subcutaneous colon cancer quantified phenotypic changes resulting from an apoptosis-inducing targeted therapeutic and provided biologically relevant probability maps of tumour-tissue subtypes. When applied to retrospective PET-MRI data of patients with liver metastases from colorectal cancer, the trained classifiers characterized intratumoural tissue subregions in agreement with tumour histology. The spatial characterization of intratumoural heterogeneity in mice and patients via multimodal, multiparametric imaging aided by machine-learning may facilitate applications in precision oncology.

Virtual Biopsy in Soft Tissue Sarcoma. How Close Are We?

A shift in radiology to a data-driven specialty has been unlocked by synergistic developments in imaging biomarkers (IB) and computational science. This is advancing the capability to deliver "virtual biopsies" within oncology. The ability to non-invasively probe tumour biology both spatially and temporally would fulfil the potential of imaging to inform management of complex tumours; improving diagnostic accuracy, providing new insights into inter- and intra-tumoral heterogeneity and individualised treatment planning and monitoring. Soft tissue sarcomas (STS) are rare tumours of mesenchymal origin with over 150 histological subtypes and notorious heterogeneity. The combination of inter- and intra-tumoural heterogeneity and the rarity of the disease remain major barriers to effective treatments. We provide an overview of the process of successful IB development, the key imaging and computational advancements in STS including quantitative magnetic resonance imaging, radiomics and artificial intelligence, and the studies to date that have explored the potential biological surrogates to imaging metrics. We discuss the promising future directions of IBs in STS and illustrate how the routine clinical implementation of a virtual biopsy has the potential to revolutionise the management of this group of complex cancers and improve clinical outcomes.

Using Variable Flip Angle (VFA) and Modified Look-Locker Inversion Recovery (MOLLI) T1 mapping in clinical OE-MRI

BACKGROUND AND PURPOSE

The imaging technique known as Oxygen-Enhanced MRI is under development as a noninvasive technique for imaging hypoxia in tumours and pulmonary diseases. While promising results have been shown in preclinical experiments, clinical studies have mentioned experiencing difficulties with patient motion, image registration, and the limitations of single-slice images compared to 3D volumes. As clinical studies begin to assess feasibility of using OE-MRI in patients, it is important for researchers to communicate about the practical challenges experienced when using OE-MRI on patients to help the technique advance.

MATERIALS AND METHODS

We report on our experience with using two types of T1 mapping (MOLLI and VFA) for a recently completed OE-MRI clinical study on oropharyngeal squamous cell carcinoma.

RESULTS

We report: (1) the artefacts and practical difficulties encountered in this study; (2) the difference in estimated T1 from each method used - the VFA T1 estimation was higher than the MOLLI estimation by 27% on average; (3) the standard deviation within the tumour ROIs - there was no significant difference in the standard deviation seen within the tumour ROIs from the VFA versus MOLLI; and (4) the OE-MRI response collected from either method. Lastly, we collated the MRI acquisition details from over 45 relevant manuscripts as a convenient reference for researchers planning future studies.

CONCLUSION

We have reported our practical experience from an OE-MRI clinical study, with the aim that sharing this is helpful to researchers planning future studies. In this study, VFA was a more useful technique for using OE-MRI in tumours than MOLLI T1 mapping.

Mapping Cell Viability Quantitatively and Independently From Cell Density in 3D Gels Noninvasively

OBJECTIVE

In biomanufacturing there is a need for quantitative methods to map cell viability and density inside 3D bioreactors to assess health and proliferation over time. Recently, noninvasive MRI readouts of cell density have been achieved. However, the ratio of live to dead cells was not varied. Herein we present an approach for measuring the viability of cells embedded in a hydrogel independently from cell density to map cell number and health.

METHODS

Independent quantification of cell viability and density was achieved by calibrating the 1H magnetization transfer- (MT) and diffusion-weighted NMR signals to samples of known cell density and viability using a multivariate approach. Maps of cell viability and density were generated by weighting NMR images by these parameters post-calibration.

RESULTS

Using this method, the limits of detection (LODs) of total cell density and viable cell density were found to be 3.88 ×108 cells · mL -1· Hz -1/2 and 2.36 ×109 viable cells · mL -1· Hz -1/2 respectively.

CONCLUSION

This mapping technique provides a noninvasive means of visualizing cell viability and number density within optically opaque bioreactors.

SIGNIFICANCE

We anticipate that such nondestructive readouts will provide valuable feedback for monitoring and controlling cell populations in bioreactors.

The Biological Meaning of Radiomic Features

An earlier incorrect version appeared online. This article was corrected on February 10, 2021.

Heterogeneity analysis of MRI T2 maps for measurement of early tumor response to radiotherapy

External beam radiotherapy (XRT) is a widely used cancer treatment, yet responses vary dramatically among patients. These differences are not accounted for in clinical practice, partly due to a lack of sensitive early response biomarkers. We hypothesize that quantitative magnetic resonance imaging (MRI) measures reflecting tumor heterogeneity can provide a sensitive and robust biomarker of early XRT response. MRI T2 mapping was performed every 72 hours following 10 Gy dose XRT in two models of pancreatic cancer propagated in the hind limb of mice. Interquartile range (IQR) of tumor T2 was presented as a potential biomarker of radiotherapy response compared with tumor growth kinetics, and biological validation was performed through quantitative histology analysis. Quantification of tumor T2 IQR showed sensitivity for detection of XRT-induced tumor changes 72 hours after treatment, outperforming T2-weighted and diffusion-weighted MRI, with very good robustness. Histological comparison revealed that T2 IQR provides a measure of spatial heterogeneity in tumor cell density, related to radiation-induced necrosis. Early IQR changes were found to correlate to subsequent tumor volume changes, indicating promise for treatment response prediction. Our preclinical findings indicate that spatial heterogeneity analysis of T2 MRI can provide a translatable method for early radiotherapy response assessment. We propose that the method may in future be applied for personalization of radiotherapy through adaptive treatment paradigms.

An Automated Segmentation Pipeline for Intratumoural Regions in Animal Xenografts Using Machine Learning and Saturation Transfer MRI

Saturation transfer MRI can be useful in the characterization of different tumour types. It is sensitive to tumour metabolism, microstructure, and microenvironment. This study aimed to use saturation transfer to differentiate between intratumoural regions, demarcate tumour boundaries, and reduce data acquisition times by identifying the imaging scheme with the most impact on segmentation accuracy. Saturation transfer-weighted images were acquired over a wide range of saturation amplitudes and frequency offsets along with T₁ and T₂ maps for 34 tumour xenografts in mice. Independent component analysis and Gaussian mixture modelling were used to segment the images and identify intratumoural regions. Comparison between the segmented regions and histopathology indicated five distinct clusters: three corresponding to intratumoural regions (active tumour, necrosis/apoptosis, and blood/edema) and two extratumoural (muscle and a mix of muscle and connective tissue). The fraction of tumour voxels segmented as necrosis/apoptosis quantitatively matched those calculated from TUNEL histopathological assays. An optimal protocol was identified providing reasonable qualitative agreement between MRI and histopathology and consisting of T₁ and T₂ maps and 22 magnetization transfer (MT)-weighted images. A three-image subset was identified that resulted in a greater than 90% match in positive and negative predictive value of tumour voxels compared to those found using the entire 24-image dataset. The proposed algorithm can potentially be used to develop a robust intratumoural segmentation method.

Stacked in-plane histology for quantitative validation of non-invasive imaging biomarkers: Application to an infiltrative brain tumour model

BACKGROUND

While it is generally agreed that histopathology is the gold standard for assessing non-invasive imaging biomarkers, most validation has been by qualitative visual comparison. To date, the difficulties involved in accurately co-registering histology sections with imaging slices have prevented a voxel-by-voxel assessment of imaging modalities. By contrast with previous studies, which focus on improving the registration algorithms, we have taken the approach of improving the quality of the histological processing and analysis.

NEW METHOD

To account for imaging slice orientation and thickness, multiple histology sections were cut in the MR imaging plane and averaged to produce stacked in-plane histology (SIH) maps. When combined with intensity sensitive staining this approach gives histopathology maps, which can be used as the gold standard to validate imaging biomarkers.

RESULTS

We applied this pipeline to a patient-derived mouse model of glioblastoma multiforme (GBM). Increasing the number of stacked histology sections significantly increased SIH measured tumour volume. The SIH technique proposed here resulted in reduced variability of volume measurements and this allowed significant improvements in the quantitative volumetric assessment of multiple MRI modalities. Further, high quality registration enabled a voxel-wise comparison between MRI and histopathology maps. Previous approaches to the validation of imaging biomarkers with histology, have been either qualitative or of limited accuracy. Here we propose a pipeline that allows for a more accurate validation via co-registration with SIH maps, potentially allowing validation in a voxel-wise mode.

CONCLUSION

This work demonstrates that methodically produced SIH maps facilitate the quantitative histopathologic assessment of imaging biomarkers.

Data-driven identification of tumor subregions based on intravoxel incoherent motion reveals association with proliferative activity

PURPOSE

Intravoxel incoherent motion (IVIM) analysis gives information on tissue diffusion and perfusion and may thus have a potential for e.g. tumor tissue characterization. This work aims to study if clustering based on IVIM parameter maps can identify tumor subregions, and to assess the relevance of obtained subregions by histological analysis.

METHODS

Fourteen mice with human neuroendocrine tumors were examined with diffusion-weighted imaging to obtain IVIM parameter maps. Gaussian mixture models with IVIM maps from all tumors as input were used to partition voxels into k clusters, where k = 2 was chosen for further analysis based on goodness of fit. Clustering was performed with and without the perfusion-related IVIM parameter D * , and with and without including spatial information. The validity of the clustering was assessed by comparison with corresponding histologically stained tumor sections. A Ki-67-based index quantifying the degree of tumor proliferation was considered appropriate for the comparison based on the obtained cluster characteristics.

RESULTS

The clustering resulted in one class with low diffusion and high perfusion and another with slightly higher diffusion and low perfusion. Strong agreement was found between tumor subregions identified by clustering and subregions identified by histological analysis, both regarding size and spatial agreement. Neither D * nor spatial information had substantial effects on the clustering results.

CONCLUSIONS

The results of this study show that IVIM parameter maps can be used to identify tumor subregions using a data-driven framework based on Gaussian mixture models. In the studied tumor model, the obtained subregions showed agreement with proliferative activity.

Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

Although cancer often is referred to as "a disease of the genes," it is indisputable that the (epi)genetic properties of individual cancer cells are highly variable, even within the same tumor. Hence, preexisting resistant clones will emerge and proliferate after therapeutic selection that targets sensitive clones. Herein, the authors propose that quantitative image analytics, known as "radiomics," can be used to quantify and characterize this heterogeneity. Virtually every patient with cancer is imaged radiologically. Radiomics is predicated on the beliefs that these images reflect underlying pathophysiologies, and that they can be converted into mineable data for improved diagnosis, prognosis, prediction, and therapy monitoring. In the last decade, the radiomics of cancer has grown from a few laboratories to a worldwide enterprise. During this growth, radiomics has established a convention, wherein a large set of annotated image features (1-2000 features) are extracted from segmented regions of interest and used to build classifier models to separate individual patients into their appropriate class (eg, indolent vs aggressive disease). An extension of this conventional radiomics is the application of "deep learning," wherein convolutional neural networks can be used to detect the most informative regions and features without human intervention. A further extension of radiomics involves automatically segmenting informative subregions ("habitats") within tumors, which can be linked to underlying tumor pathophysiology. The goal of the radiomics enterprise is to provide informed decision support for the practice of precision oncology.

Probabilistic classification of tumour habitats in soft tissue sarcoma

The purpose of this work is to propose a method to characterize tumour heterogeneity on MRI, using probabilistic classification based on a reference tissue. The method uses maps of the apparent diffusion coefficient (ADC), T₂ relaxation, and a calculated map representing high-b-value diffusion-weighted MRI (denoted simDWI) to identify up to five habitats (i.e. sub-regions) of tumours. In this classification method, the parameter values (ADC, T₂ , and simDWI) from each tumour voxel are compared against the corresponding parameter probability distributions in a reference tissue. The probability that a tumour voxel belongs to a specific habitat is the joint probability for all parameters. The classification can be visualized using a custom colour scheme. The proposed method was applied to data from seven patients with biopsy-confirmed soft tissue sarcoma, at three time-points over the course of pre-operative radiotherapy. Fast-spin-echo images with two different echo times and diffusion MRI with three b-values were obtained and used as inputs to the method. Imaging findings were compared with pathology reports from pre-radiotherapy biopsy and post-surgical resection. Regions of hypercellularity, high-T₂ proteinaceous fluid, necrosis, collagenous stroma, and fibrosis were identified within soft tissue sarcoma. The classifications were qualitatively consistent with pathological observations. The percentage of necrosis on imaging correlated strongly with necrosis estimated from FDG-PET before radiotherapy (R²  = 0.97) and after radiotherapy (R²  = 0.96). The probabilistic classification method identifies realistic habitats and reflects the complex microenvironment of tumours, as demonstrated in soft tissue sarcoma.

Data-driven mapping of hypoxia-related tumor heterogeneity using DCE-MRI and OE-MRI

PURPOSE

Previous work has shown that combining dynamic contrast-enhanced (DCE)-MRI and oxygen-enhanced (OE)-MRI binary enhancement maps can identify tumor hypoxia. The current work proposes a novel, data-driven method for mapping tissue oxygenation and perfusion heterogeneity, based on clustering DCE/OE-MRI data.

METHODS

DCE-MRI and OE-MRI were performed on nine U87 (glioblastoma) and seven Calu6 (non-small cell lung cancer) murine xenograft tumors. Area under the curve and principal component analysis features were calculated and clustered separately using Gaussian mixture modelling. Evaluation metrics were calculated to determine the optimum feature set and cluster number. Outputs were quantitatively compared with a previous non data-driven approach.

RESULTS

The optimum method located six robustly identifiable clusters in the data, yielding tumor region maps with spatially contiguous regions in a rim-core structure, suggesting a biological basis. Mean within-cluster enhancement curves showed physiologically distinct, intuitive kinetics of enhancement. Regions of DCE/OE-MRI enhancement mismatch were located, and voxel categorization agreed well with the previous non data-driven approach (Cohen's kappa = 0.61, proportional agreement = 0.75).

CONCLUSION

The proposed method locates similar regions to the previous published method of binarization of DCE/OE-MRI enhancement, but renders a finer segmentation of intra-tumoral oxygenation and perfusion. This could aid in understanding the tumor microenvironment and its heterogeneity. Magn Reson Med 79:2236-2245, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation

Purpose

We aimed to precisely estimate intra-tumoral heterogeneity using spatially regularized spectral clustering (SRSC) on multiparametric MRI data and compare the efficacy of SRSC with the previously reported segmentation techniques in MRI studies.

Procedures

Six NMRI nu/nu mice bearing subcutaneous human glioblastoma U87 MG tumors were scanned using a dedicated small animal 7T magnetic resonance imaging (MRI) scanner. The data consisted of T2 weighted images, apparent diffusion coefficient maps, and pre- and post-contrast T2 and T2* maps. Following each scan, the tumors were excised into 2–3-mm thin slices parallel to the axial field of view and processed for histological staining. The MRI data were segmented using SRSC, K-means, fuzzy C-means, and Gaussian mixture modeling to estimate the fractional population of necrotic, peri-necrotic, and viable regions and validated with the fractional population obtained from histology.

Results

While the aforementioned methods overestimated peri-necrotic and underestimated viable fractions, SRSC accurately predicted the fractional population of all three tumor tissue types and exhibited strong correlations (r_necrotic = 0.92, r_{peri-necrotic} = 0.82 and r_viable = 0.98) with the histology.

Conclusions

The precise identification of necrotic, peri-necrotic and viable areas using SRSC may greatly assist in cancer treatment planning and add a new dimension to MRI-guided tumor biopsy procedures.

Electronic supplementary material

The online version of this article (doi:10.1007/s11307-016-1009-y) contains supplementary material, which is available to authorized users.

Cancer heterogeneity and imaging

There is interest in identifying and quantifying tumor heterogeneity at the genomic, tissue pathology and clinical imaging scales, as this may help better understand tumor biology and may yield useful biomarkers for guiding therapy-based decision making. This review focuses on the role and value of using x-ray, CT, MRI and PET based imaging methods that identify, measure and map tumor heterogeneity. In particular we highlight the potential value of these techniques and the key challenges required to validate and qualify these biomarkers for clinical use.

A Population-Based Gaussian Mixture Model Incorporating 18F-FDG PET and Diffusion-Weighted MRI Quantifies Tumor Tissue Classes

The aim of our study was to create a novel Gaussian mixture modeling (GMM) pipeline to model the complementary information derived from(18)F-FDG PET and diffusion-weighted MRI (DW-MRI) to separate the tumor microenvironment into relevant tissue compartments and follow the development of these compartments longitudinally.

METHODS

Serial (18)F-FDG PET and apparent diffusion coefficient (ADC) maps derived from DW-MR images of NCI-H460 xenograft tumors were coregistered, and a population-based GMM was implemented on the complementary imaging data. The tumor microenvironment was segmented into 3 distinct regions and correlated with histology. ANCOVA was applied to gauge how well the total tumor volume was a predictor for the ADC and (18)F-FDG, or if ADC was a good predictor of (18)F-FDG for average values in the whole tumor or average necrotic and viable tissues.

RESULTS

The coregistered PET/MR images were in excellent agreement with histology, both visually and quantitatively, and allowed for validation of the last-time-point measurements. Strong correlations were found for the necrotic (r = 0.88) and viable fractions (r = 0.87) between histology and clustering. The GMM provided probabilities for each compartment with uncertainties expressed as a mixture of tissues in which the resolution of scans was inadequate to accurately separate tissues. The ANCOVA suggested that both ADC and (18)F-FDG in the whole tumor (P = 0.0009, P = 0.02) as well as necrotic (P = 0.008, P = 0.02) and viable (P = 0.003, P = 0.01) tissues were a positive, linear function of total tumor volume. ADC proved to be a positive predictor of (18)F-FDG in the whole tumor (P = 0.001) and necrotic (P = 0.02) and viable (P = 0.0001) tissues.

CONCLUSION

The complementary information of (18)F-FDG and ADC longitudinal measurements in xenograft tumors allows for segmentation into distinct tissues when using the novel GMM pipeline. Leveraging the power of multiparametric PET/MRI in this manner has the potential to take the assessment of disease outcome beyond RECIST and could provide an important impact to the field of precision medicine.

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.

Multiparametric MRI analysis for the identification of high intensity focused ultrasound-treated tumor tissue

Purpose

In this study endogenous magnetic resonance imaging (MRI) biomarkers for accurate segmentation of High Intensity Focused Ultrasound (HIFU)-treated tumor tissue and residual or recurring non-treated tumor tissue were identified.

Methods

Multiparametric MRI, consisting of quantitative T₁, T₂, Apparent Diffusion Coefficient (ADC) and Magnetization Transfer Ratio (MTR) mapping, was performed in tumor-bearing mice before (n = 14), 1 h after (n = 14) and 72 h (n = 7) after HIFU treatment. A non-treated control group was included (n = 7). Cluster analysis using the Iterative Self Organizing Data Analysis (ISODATA) technique was performed on subsets of MRI parameters (feature vectors). The clusters resulting from the ISODATA segmentation were divided into a viable and non-viable class based on the fraction of pixels assigned to the clusters at the different experimental time points. ISODATA-derived non-viable tumor fractions were quantitatively compared to histology-derived non-viable tumor volume fractions.

Results

The highest agreement between the ISODATA-derived and histology-derived non-viable tumor fractions was observed for feature vector {T₁, T₂, ADC}. R₁ (1/T₁), R₂ (1/T₂), ADC and MTR each were significantly increased in the ISODATA-defined non-viable tumor tissue at 1 h after HIFU treatment compared to viable, non-treated tumor tissue. R₁, ADC and MTR were also significantly increased at 72 h after HIFU.

Conclusions

This study demonstrates that non-viable, HIFU-treated tumor tissue can be distinguished from viable, non-treated tumor tissue using multiparametric MRI analysis. Clinical application of the presented methodology may allow for automated, accurate and objective evaluation of HIFU treatment.

Simultaneous assessment of vessel size index, relative blood volume, and vessel permeability in a mouse brain tumor model using a combined spin echo gradient echo echo-planar imaging sequence and viable tumor analysis

PURPOSE

Combining multiple imaging biomarkers in one magnetic resonance imaging (MRI) session would be beneficial to gain more data pertaining to tumor vasculature under therapy. Therefore, simultaneous measurement of perfusion, permeability, and vessel size imaging (VSI) using a gradient echo spin echo (GE-SE) sequence with injection of a clinically approved gadolinium (Gd)-based contrast agent was assessed in an orthotopic glioma model.

MATERIALS AND METHODS

A combined spin echo gradient echo echo-planar imaging sequence was implemented using a single contrast agent Gd diethylenetriaminepentaacetic acid (Gd-DTPA). This sequence was tested in a mouse brain tumor model (U87_MG), also under treatment with an antiangiogenic agent (bevacizumab). T2 maps and the apparent diffusion coefficient (ADC) were used to differentiate regions of cell death and viable tumor tissue.

RESULTS

In viable tumor tissue regional blood volume was 5.7 ± 0.6% in controls and 5.2 ± 0.3% in treated mice. Vessel size was 18.1 ± 2.4 μm in controls and 12.8 ± 2.0 μm in treated mice, which correlated with results from immunohistochemistry. Permeability (K(trans) ) was close to zero in treated viable tumor tissue and 0.062 ± 0.024 min(-1) in controls.

CONCLUSION

Our MRI method allows simultaneous assessment of several physiological and morphological parameters and extraction of MRI biomarkers for vasculature. These could be used for treatment monitoring of novel therapeutic agents such as antiangiogenic drugs.

Tumor-stroma interaction in orthotopic primary pancreatic cancer xenografts during hedgehog pathway inhibition

To test the effects of hedgehog (Hh) pathway inhibition on the stroma of orthotopically grown primary pancreatic cancer xenografts, and investigate the potential to monitor these effects non-invasively using magnetic resonance imaging (MRI), mice bearing orthotopically grown primary pancreatic cancer xenografts were treated with the Hh neutralizing antibody 5E1. Pathway inhibition was determined by RT-PCR using primer sets for human and mouse Hh pathway genes, and effects on stroma assessed by automated image analysis of tissue sections stained for collagen and α-smooth muscle actin (αSMA). MRI provided quantitative biomarkers of stromal density based on magnetization transfer (MT-MRI) and dynamic contrast enhancement (DCE-MRI). Modest growth inhibition was seen in both models tested using 5E1, but was greater in OCIP19, which showed high expression of mouse Hh pathway genes and an extensive fibrous stroma. However, despite profound inhibition of both mouse and human Hh pathway genes, in neither model did we observe depletion of the stroma. Alignment of MT-MRI ratio images to histological sections showed co-registration with areas of fibrosis, although this was confounded by the presence of tumor necrosis. Due to the lack of stromal depletion by 5E1 it was not possible to determine the utility of MT-MRI for monitoring this effect. Cancer- and stromal cell-derived Hh signaling elements are expressed in orthotopic primary pancreatic cancer xenografts, and selective targeting is growth-inhibitory. In contrast to some recent reports, growth inhibition does not involve attenuation of the tumor stroma, pointing to additional effects of Hh signaling in pancreatic cancer.

Erratum to: Ungersma SE, Pacheco G, Ho C, Yee SF, Ross J, van Bruggen N, Peale FV Jr, Ross S, Carano RA. Vessel imaging with viable tumor analysis for quantification of tumor angiogenesis. Magn Reson Med 2010;63:1637–1647

Imaging of tumor microvasculature has become an important tool for studying angiogenesis and monitoring antiangiogenic therapies. Ultrasmall paramagnetic iron oxide contrast agents for indirect imaging of vasculature offer a method for quantitative measurements of vascular biomarkers such as vessel size index, blood volume, and vessel density (Q). Here, this technique is validated with direct comparisons to ex vivo micro-computed tomography angiography and histologic vessel measurements, showing significant correlations between in vivo vascular MRI measurements and ex vivo structural vessel measurements. The sensitivity of the MRI vascular parameters is also demonstrated, in combination with a multispectral analysis technique for segmenting tumor tissue to restrict the analysis to viable tumor tissue and exclude regions of necrosis. It is shown that this viable tumor segmentation increases sensitivity for detection of significant effects on blood volume and Q by two antiangiogenic therapeutics [anti-vascular endothelial growth factor (anti-VEGF) and anti-neuropilin-1] on an HM7 colorectal tumor model. Anti-vascular endothelial growth factor reduced blood volume by 36±3% (p<0.0001) and Q by 52±3% (p<0.0001) at 48 h post-treatment; the effects of anti-neuropilin-1 were roughly half as strong with a reduction in blood volume of 18±6% (p<0.05) and a reduction in Q of 33±5% (p<0.05) at 48 h post-treatment.

ADC response to radiation therapy correlates with induced changes in radiosensitivity

PURPOSE

Magnetic resonance imaging was used to compare the responses of human glioma tumor xenografts to a single fraction of radiation, where a change in radiosensitivity was induced by use of a suture-based ligature.

METHODS

Ischemia was induced by use of a suture-based ligature. Six mice were treated with 800 cGy of 200 kVp x rays while the ligature was applied. An additional six mice had the ligature applied for the same length of time but were not irradiated. Quantitative maps of each tumor were produced of water apparent diffusion coefficient (ADC) and transverse relaxation time (T2). Mice were imaged before and at multiple points after treatment. Volumetric, ADC, and T2 responses of the ligated groups were compared to previously measured responses of the same tumor model to the same radiation treatment, as well as those from an untreated control group.

RESULTS

Application of the ligature without irradiation did not affect tumor ADC values, but did produce a temporary decrease in tumor T2 values. Average tumor T2 was reduced by 6.2% 24 h after the ligature was applied. Average tumor ADC increased by 9.6% 7 days after irradiation with a ligature applied. This response was significantly less than that observed in the same tumor model when no ligature is present (21.8% at 7 days after irradiation).

CONCLUSIONS

These observations indicate that the response of ADC to radiation therapy is not determined entirely by physical dose deposition, but at least in part by radiosensitivity and resultant biological response.

Quantitative multiparametric PROPELLER MRI of diethylnitrosamine-induced hepatocarcinogenesis in wister rat model

PURPOSE

To develop a quantitative multiparametric PROPELLER (periodically rotated overlapping parallel lines with enhanced reconstruction) magnetic resonance imaging (MRI) approach and its application in a diethylnitrosamine (DEN) chemically induced rodent model of hepatocarcinogenesis for lesion characterization.

MATERIALS AND METHODS

In nine rats with 33 cirrhosis-associated hepatic nodules including regenerative nodule (RN), dysplastic nodule (DN), hepatocellular carcinoma (HCC), and cyst, multiparametric PROPELLER MRI (diffusion-weighted, T2/M0 (proton density) mapping and T1-weighted) were performed. Apparent diffusion coefficient (ADC) maps, T2 and M0 maps of each tumor were generated. We compared ADC, T2, and M0 measurements for each type of hepatic nodule, confirmed at histopathology.

RESULTS

PROPELLER images and resultant parametric maps were inherently coregistered without image distortion or motion artifacts. All types of hepatic nodules demonstrated complex imaging characteristics within conventional T1- and T2-weighted images. Quantitatively, cysts were distinguished from RN, DN, and HCC with significantly higher ADC and T2; however, there was no significant difference of ADC and T2 between HCC, DN, and RN. Mean tumor M0 values of HCC were significantly higher than those of DN, RN, and cysts.

CONCLUSION

This study exploited quantitative PROPELLER MRI and multidimensional analysis approaches in an attempt to differentiate hepatic nodules in the DEN rodent model of hepatocarcinogenesis. This method offers great potential for parallel parameterization during noninvasive interrogation of hepatic tissue properties.

Robust estimation of the apparent diffusion coefficient (ADC) in heterogeneous solid tumors

The least-squares algorithm is known to bias apparent diffusion coefficient (ADC) values estimated from magnitude MR data, although this effect is commonly assumed to be negligible. In this study the effect of this bias on tumor ADC estimates was evaluated in vivo and was shown to introduce a consistent and significant underestimation of ADC, relative to those given by a robust maximum likelihood approach (on average, a 23.4 +/- 12% underestimation). Monte Carlo simulations revealed that the magnitude of the bias increased with ADC and decreasing signal-to-noise ratio (SNR). In vivo, this resulted in a much-reduced ability to resolve necrotic regions from surrounding viable tumor tissue compared with a maximum likelihood approach. This has significant implications for the evaluation of diffusion MR data in vivo, in particular in heterogeneous tumor tissue, when evaluating bi- and multiexponential tumor diffusion models for the modeling of data acquired with larger b-values (b > 1000 s/mm(2)) and for data with modest SNR. Use of a robust approach to modeling magnitude MR data from tumors is therefore recommended over the least-squares approach when evaluating data from heterogeneous tumors.

Temporal and dose dependence of T2 and ADC at 9.4 T in a mouse model following single fraction radiation therapy

The purpose of this study is to use magnetic resonance imaging to monitor the response of human glioma tumor xenografts to single fraction radiation therapy. Mice were divided into four treatment groups (n = 6 per group) that received 50, 200, 400, or 800 cGy of 200 kVp x rays. A fifth group (n = 6) received no radiation dose and served as the control. Quantitative maps of the treated tumor tissue were produced of water apparent diffusion coefficient (ADC) and transverse relaxation time (T2). Mice were imaged before and at multiple time points after treatment. There was a statistically significant difference in tumor growth relative to that of the control for all treatment groups. Only the highest dose group showed T2 values that were significantly different at all measured time points after treatment. In this group, there was an 8.3% increase in T2 relative to controls 2 days after treatment, but when measured 14 days after treatment, mean tumor T2 had dropped to 10.1% below the initial value. ADC showed statistically significant differences from the control at all dose points. A radiation dose dependence was observed. In the highest dose group, the fractional increases in ADC were higher than those observed for T2. ADC was sensitive to radiation-induced changes in lower dose groups that did not have significant T2 change. At all doses, elevation of mean tumor ADC preceded deviations in tumor growth from the control. These observations support the potential application of ADC as a time and dose sensitive marker of tumor response to radiation therapy.

Multispectral analysis of multimodal images

INTRODUCTION

An increasing number of multimodal images represent a valuable increase in available image information, but at the same time it complicates the extraction of diagnostic information across the images. Multispectral analysis (MSA) has the potential to simplify this problem substantially as unlimited number of images can be combined, and tissue properties across the images can be extracted automatically.

MATERIALS AND METHODS

We have developed a software solution for MSA containing two algorithms for unsupervised classification, an EM-algorithm finding multinormal class descriptions and the k-means clustering algorithm, and two for supervised classification, a Bayesian classifier using multinormal class descriptions and a kNN-algorithm. The software has an efficient user interface for the creation and manipulation of class descriptions, and it has proper tools for displaying the results.

RESULTS

The software has been tested on different sets of images. One application is to segment cross-sectional images of brain tissue (T1- and T2-weighted MR images) into its main normal tissues and brain tumors. Another interesting set of images are the perfusion maps and diffusion maps, derived images from raw MR images. The software returns segmentations that seem to be sensible.

DISCUSSION

The MSA software appears to be a valuable tool for image analysis with multimodal images at hand. It readily gives a segmentation of image volumes that visually seems to be sensible. However, to really learn how to use MSA, it will be necessary to gain more insight into what tissues the different segments contain, and the upcoming work will therefore be focused on examining the tissues through for example histological sections.

Quantification of viable tumor microvascular characteristics by multispectral analysis

Tumor heterogeneity complicates the quantification of tumor microvascular characteristics assessed by dynamic contrast-enhanced MRI (DCE-MRI). To address this issue a novel approach was developed that combines DCE-MRI with diffusion-based multispectral (MS) analysis to quantify the microvascular characteristics of specific tumor tissue populations. Diffusion-based MS segmentation (feature space: apparent diffusion coefficient, T(2) and proton density) was performed to identify tumor tissue populations and the DCE-MRI characteristics were determined for each tissue class. The ability of this MS DCE-MRI technique to detect microvascular changes due to treatment with an antibody (G6-31) to vascular endothelial growth factor-A (VEGF) was evaluated in a tumor xenograft mouse model. Anti-VEGF treatment resulted in a significant reduction in K(trans) for the MS viable tumor tissue class (-0.0034 +/- 0.0022 min(-1), P < 0.01) at 24 hr posttreatment that differ significantly from the change observed in the control group (0.0002 +/- 0.0025 min(-1)). Viable tumor K(trans) for the anti-VEGF group was also reduced 62% relative to the pretreatment values (P < 0.01). Necrotic tissue classes were found to add only noise to DCE-MRI estimates. This approach provides a means to measure physiological parameters within the viable tumor and address the issue of tumor heterogeneity that complicates DCE-MRI analysis.

Diffusion-weighted PROPELLER MRI for quantitative assessment of liver tumor necrotic fraction and viable tumor volume in VX2 rabbits

PURPOSE

To test the hypothesis that diffusion-weighted (DW)-PROPELLER (periodically rotated overlapping parallel lines with enhanced reconstruction) MRI provides more accurate liver tumor necrotic fraction (NF) and viable tumor volume (VTV) measurements than conventional DW-SE-EPI (spin echo echo-planar imaging) methods.

MATERIALS AND METHODS

Our institutional Animal Care and Use Committee approved all experiments. In six rabbits implanted with 10 VX2 liver tumors, DW-PROPELLER and DW-SE-EPI scans were performed at contiguous axial slice positions covering each tumor volume. Apparent diffusion coefficient maps of each tumor were used to generate spatially resolved tumor viability maps for NF and VTV measurements. We compared NF, whole tumor volume (WTV), and VTV measurements to corresponding reference standard histological measurements based on correlation and concordance coefficients and the Bland-Altman analysis.

RESULTS

DW-PROPELLER generally improved image quality with less distortion compared to DW-SE-EPI. DW-PROPELLER NF, WTV, and VTV measurements were strongly correlated and satisfactorily concordant with histological measurements. DW-SE-EPI NF measurements were weakly correlated and poorly concordant with histological measurements. Bland-Altman analysis demonstrated that DW-PROPELLER WTV and VTV measurements were less biased from histological measurements than the corresponding DW-SE-EPI measurements.

CONCLUSION

DW-PROPELLER MRI can provide spatially resolved liver tumor viability maps for accurate NF and VTV measurements, superior to DW-SE-EPI approaches. DW-PROPELLER measurements may serve as a noninvasive surrogate for pathology, offering the potential for more accurate assessments of therapy response than conventional anatomic size measurements.

Multispectral tissue characterization in a RIF-1 tumor model: monitoring the ADC and T2 responses to single-dose radiotherapy. Part II

A multispectral (MS) approach that combines apparent diffusion coefficient (ADC) and T(2) parameter maps with k-means (KM) clustering was employed to distinguish multiple compartments within viable tumor tissue (V1 and V2) and necrosis (N1 and N2) following single-dose (1000 cGy) radiotherapy in a radiation-induced fibrosarcoma (RIF-1) tumor model. The contributions of cell kill and tumor growth kinetics to the radiotherapy-induced response were investigated. A larger pretreatment V1 volume was correlated with decreased tumor growth delay (TGD) (r = 0.68) and cell kill (r = 0.71). There was no correlation for the pretreatment V2 volume. These results suggest that V1 tissue is well oxygenated and radiosensitive, whereas V2 tissue is hypoxic and therefore radioresistant. The relationship between an early ADC response and vasogenic edema and formation of necrosis was investigated. A trend for increased ADC was observed prior to an increase in the necrotic fraction (NF). Because there were no changes in T(2), these observations suggest that the early increase in ADC is more likely based on a slight reduction in cell density, rather than radiation-induced vasogenic edema. Quantitative assessments of individual tissue regions, tumor growth kinetics, and cell kill should provide a more accurate means of monitoring therapy in preclinical animal models because such assessments can minimize the issue of intertumor variability.

Correlation of MRI biomarkers with tumor necrosis in Hras5 tumor xenograft in athymic rats

Magnetic resonance imaging (MRI) can measure the effects of therapies targeting the tumor vasculature and has demonstrated that vascular-damaging agents (VDA) induce acute vascular shutdown in tumors in human and animal models. However, at subtherapeutic doses, blood flow may recover before the induction of significant levels of necrosis. We present the relationship between changes in MRI biomarkers and tumor necrosis. Multiple MRI measurements were taken at 4.7 T in athymic rats (n = 24) bearing 1.94 +/- 0.2-cm3 subcutaneous Hras5 tumors (ATCC 41000) before and 24 hours after clinically relevant doses of the VDA, ZD6126 (0-10 mg/kg, i.v.). We measured effective transverse relaxation rate (R2*), initial area under the gadolinium concentration-time curve (IAUGC(60/150)), equivalent enhancing fractions (EHF(60/150)), time constant (K(trans)), proportion of hypoperfused voxels as estimated from fit failures in K(trans) analysis, and signal intensity (SI) in T2-weighted MRI (T(2)W). ZD6126 treatment induced > 90% dose-dependent tumor necrosis at 10 mg/kg; correspondingly, SI changes were evident from T2W MRI. Although R2* did not correlate, other MRI biomarkers significantly correlated with necrosis at doses of > or = 5 mg/kg ZD6126. These data on Hras5 tumors suggest that the quantification of hypoperfused voxels might provide a useful biomarker of tumor necrosis.