Decoding Intratumoral Heterogeneity of Breast Cancer by Multiparametric In Vivo Imaging: A Translational Study

Combined Metabolic and Functional Tumor Volumes on [18F]FDG-PET/MRI in Neuroblastoma Using Voxel-Wise Analysis

PURPOSE

The purpose of our study was to evaluate the association between the [18F]FDG standard uptake value (SUV) and the apparent diffusion coefficient (ADC) in neuroblastoma (NB) by voxel-wise analysis.

METHODS

From our prospective observational PET/MRI study, a subcohort of patients diagnosed with NB with both baseline imaging and post-chemotherapy imaging was further investigated. After registration and tumor segmentation, metabolic and functional tumor volumes were calculated from the ADC and SUV values using dedicated software allowing for voxel-wise analysis. Under the mean of thresholds, each voxel was assigned to one of three virtual tissue groups: highly vital (v) (low ADC and high SUV), possibly low vital (lv) (high ADC and low SUV), and equivocal (e) with high ADC and high SUV or low ADC and low SUV. Moreover, three clusters were generated from the total tumor volumes using the method of multiple Gaussian distributions. The Pearson's correlation coefficient between the ADC and the SUV was calculated for each group.

RESULTS

Out of 43 PET/MRIs in 21 patients with NB, 16 MRIs in 8 patients met the inclusion criteria (PET/MRIs before and after chemotherapy). The proportion of tumor volumes were 26%, 36%, and 38% (v, lv, e) at baseline, 0.03%, 66%, and 34% after treatment in patients with response, and 42%, 25%, and 33% with progressive disease, respectively. In all clusters, the ADC and the SUV correlated negatively. In the cluster that corresponded to highly vital tissue, the ADC and the SUV showed a moderate negative correlation before treatment (R = -0.18; p < 0.0001) and the strongest negative correlation after treatment (R = -0.45; p < 0.0001). Interestingly, only patients with progression (n = 2) under therapy had a relevant part in this cluster post-treatment.

CONCLUSION

Our results indicate that voxel-wise analysis of the ADC and the SUV is feasible and can quantify the different quality of tissue in neuroblastic tumors. Monitoring ADCs as well as SUV levels can quantify tumor dynamics during therapy.

From Chaos to Opportunity: Decoding Cancer Heterogeneity for Enhanced Treatment Strategies

Cancer manifests as a multifaceted disease, characterized by aberrant cellular proliferation, survival, migration, and invasion. Tumors exhibit variances across diverse dimensions, encompassing genetic, epigenetic, and transcriptional realms. This heterogeneity poses significant challenges in prognosis and treatment, affording tumors advantages through an increased propensity to accumulate mutations linked to immune system evasion and drug resistance. In this review, we offer insights into tumor heterogeneity as a crucial characteristic of cancer, exploring the difficulties associated with measuring and quantifying such heterogeneity from clinical and biological perspectives. By emphasizing the critical nature of understanding tumor heterogeneity, this work contributes to raising awareness about the importance of developing effective cancer therapies that target this distinct and elusive trait of cancer.

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.

Advances in PET imaging of cancer

Molecular imaging has experienced enormous advancements in the areas of imaging technology, imaging probe and contrast development, and data quality, as well as machine learning-based data analysis. Positron emission tomography (PET) and its combination with computed tomography (CT) or magnetic resonance imaging (MRI) as a multimodality PET-CT or PET-MRI system offer a wealth of molecular, functional and morphological data with a single patient scan. Despite the recent technical advances and the availability of dozens of disease-specific contrast and imaging probes, only a few parameters, such as tumour size or the mean tracer uptake, are used for the evaluation of images in clinical practice. Multiparametric in vivo imaging data not only are highly quantitative but also can provide invaluable information about pathophysiology, receptor expression, metabolism, or morphological and functional features of tumours, such as pH, oxygenation or tissue density, as well as pharmacodynamic properties of drugs, to measure drug response with a contrast agent. It can further quantitatively map and spatially resolve the intertumoural and intratumoural heterogeneity, providing insights into tumour vulnerabilities for target-specific therapeutic interventions. Failure to exploit and integrate the full potential of such powerful imaging data may lead to a lost opportunity in which patients do not receive the best possible care. With the desire to implement personalized medicine in the cancer clinic, the full comprehensive diagnostic power of multiplexed imaging should be utilized.

Design and performance simulation studies of a breast PET insert integrable into a clinical whole-body PET/MRI scanner

Objective. Three different breast positron emission tomography (PET) insert geometries are proposed for integration into an existing magnetic resonance imaging (MRI) breast coil (Breast Biopsy Coil, NORAS MRI products) to be used inside a whole-body PET/MRI scanner (Biograph mMR, Siemens Healthineers) to enhance the sensitivity and spatial resolution of imaging inside the breast.Approach. Monte Carlo simulations were performed to predict and compare the performance characteristics of the three geometries in terms of the sensitivity, spatial resolution, scatter fraction, and noise equivalent count rate (NECR). In addition, the background single count rate due to organ uptake in a clinical scan scenario was predicted using a realistic anthropomorphic phantom.Main results. In the center of the field of view (cFOV), absolute sensitivities of 3.1%, 2.7%, and 2.2% were found for Geometry A (detectors arranged in two cylinders), Geometry B (detectors arranged in two partial cylinders), and Geometry C (detectors arranged in two half cylinders combined with two plates), respectively. The full width at half maximum spatial resolution was determined to be 1.7 mm (Geometry A), 1.8 mm (Geometry B) and 2.0 mm (Geometry C) at 5 mm from the cFOV. Designs with multiple scintillation-crystal layers capable of determining the depth of interaction (DOI) strongly improved the spatial resolution at larger distances from the transaxial cFOV. The system scatter fractions were 33.1% (Geometries A and B) and 32.3% (Geometry C). The peak NECRs occurred at source activities of 300 MBq (Geometry A), 310 MBq (Geometry B) and 340 MBq (Geometry C). The background single-event count rates were 17.1 × 10⁶cps (Geometry A), 15.3 × 10⁶cps (Geometry B) and 14.8 × 10⁶cps (Geometry C). Geometry A in the three-layer DOI variant exhibited the best PET performance characteristics but could be challenging to manufacture. Geometry C had the lowest impact on the spatial resolution and the lowest sensitivity among the investigated geometries.Significance. Geometry B in the two-layer DOI variant represented an effective compromise between the PET performance and manufacturing difficulty and was found to be a promising candidate for the future breast PET insert.

Evaluation of functional and metabolic tumor volume using voxel-wise analysis in childhood rhabdomyosarcoma

BACKGROUND

Cross-sectional imaging-based morphological characteristics of pediatric rhabdomyosarcoma have failed to predict outcomes.

OBJECTIVE

To evaluate the feasibility and possible value of generating tumor sub-volumes using voxel-wise analysis of metabolic and functional data from positron emission tomography/magnetic resonance imaging (PET/MR) or PET/computed tomography (CT) and MRI in rhabdomyosarcoma.

MATERIALS AND METHODS

Thirty-four examinations in 17 patients who received PET/MRI or PET/CT plus MRI were analyzed. The volume of interest included total tumor volume before and after therapy. Apparent diffusion coefficients (ADC) and standard uptake values (SUV) were determined voxel-wise. Voxels were assigned to three different groups based on ADC and SUV: "viable tumor tissue," "intermediate tissue" or "possible necrosis." In a second approach, data were grouped into three clusters using the Gaussian mixture model. The ratio of these clusters to total tumor volume and changes due to chemotherapy were correlated with clinical and histopathological data.

RESULTS

After chemotherapy, the proportion of voxels in the different groups changed significantly. A significant reduction of the proportion of voxels assigned to cluster 1 was found, from a mean of 36.4% to 2.5% (P < 0.001). There was a significant increase in the proportion of voxels in cluster 3 following chemotherapy from 24.8% to 81.6% (P = 0.02). The proportion of voxels in cluster 2 differed depending on the presence or absence of tumor recurrence, falling from 48% to 10% post-chemotherapy in the group with no tumor recurrence (P < 0.05) and from 29% to 23% (P > 0.05) in the group with tumor recurrence.

CONCLUSION

Voxel-wise evaluation of multimodal data in rhabdomyosarcoma is feasible. Our initial results suggest that the different distribution of sub-volumes before and after therapy may have prognostic significance.

Intratumoral in vivo staging of breast cancer by multi-tracer PET and advanced analysis

The staging and local management of breast cancer involves the evaluation of the extent and completeness of excision of both the invasive carcinoma component and also the intraductal component or ductal carcinoma in situ. When both invasive ductal carcinoma and coincident ductal carcinoma in situ are present, assessment of the extent and localization of both components is required for optimal therapeutic planning. We have used a mouse model of breast cancer to evaluate the feasibility of applying molecular imaging to assess the local status of cancers in vivo. Multi-tracer positron emission tomography (PET) and magnetic resonance imaging (MRI) characterize the transition from premalignancy to invasive carcinoma. PET tracers for glucose consumption, membrane synthesis, and neoangiogenesis in combination with a Gaussian mixture model-based analysis reveal image-derived thresholds to separate the different stages within the whole-lesion. Autoradiography, histology, and quantitative image analysis of immunohistochemistry further corroborate our in vivo findings. Finally, clinical data further support our conclusions and demonstrate translational potential. In summary, this preclinical model provides a platform for characterizing multistep tumor progression and provides proof of concept that supports the utilization of advanced protocols for PET/MRI in clinical breast cancer imaging.

Clinical advances in PET-MRI for breast cancer

Imaging is paramount for the early detection and clinical staging of breast cancer, as well as to inform management decisions and direct therapy. PET-MRI is a quantitative hybrid imaging technology that combines metabolic and functional PET data with anatomical detail and functional perfusion information from MRI. The clinical applicability of PET-MRI for breast cancer is an active area of research. In this Review, we discuss the rationale and summarise the clinical evidence for the use of PET-MRI in the diagnosis, staging, prognosis, tumour phenotyping, and assessment of treatment response in breast cancer. The continued development and approval of targeted radiopharmaceuticals, together with radiomics and automated analysis tools, will further expand the opportunity for PET-MRI to provide added value for breast cancer imaging and patient care.

Integration of Multitargeted Polymer-Based Contrast Agents with Photoacoustic Computed Tomography: An Imaging Technique to Visualize Breast Cancer Intratumor Heterogeneity

One of the primary challenges in breast cancer diagnosis and treatment is intratumor heterogeneity (ITH), i.e., the coexistence of different genetically and epigenetically distinct malignant cells within the same tumor. Thus, the identification of ITH is critical for designing better treatments and hence to increase patient survival rates. Herein, we report a noninvasive hybrid imaging technology that integrates multitargeted and multiplexed patchy polymeric photoacoustic contrast agents (MTMPPPCAs) with single-impulse panoramic photoacoustic computed tomography (SIP-PACT). The target specificity ability of MTMPPPCAs to distinguish estrogen and progesterone receptor-positive breast tumors was demonstrated through both fluorescence and photoacoustic measurements and validated by tissue pathology analysis. This work provides the proof-of-concept of the MTMPPPCAs/SIP-PACT system to identify ITH in nonmetastatic tumors, with both high molecular specificity and real-time detection capability.

Advancing Biomarker Development Through Convergent Engagement: Summary Report of the 2nd International Danube Symposium on Biomarker Development, Molecular Imaging and Applied Diagnostics; March 14-16, 2018; Vienna, Austria

Here, we report on the outcome of the 2nd International Danube Symposium on advanced biomarker development that was held in Vienna, Austria, in early 2018. During the meeting, cross-speciality participants assessed critical aspects of non-invasive, quantitative biomarker development in view of the need to expand our understanding of disease mechanisms and the definition of appropriate strategies both for molecular diagnostics and personalised therapies. More specifically, panelists addressed the main topics, including the current status of disease characterisation by means of non-invasive imaging, histopathology and liquid biopsies as well as strategies of gaining new understanding of disease formation, modulation and plasticity to large-scale molecular imaging as well as integrative multi-platform approaches. Highlights of the 2018 meeting included dedicated sessions on non-invasive disease characterisation, development of disease and therapeutic tailored biomarkers, standardisation and quality measures in biospecimens, new therapeutic approaches and socio-economic challenges of biomarker developments. The scientific programme was accompanied by a roundtable discussion on identification and implementation of sustainable strategies to address the educational needs in the rapidly evolving field of molecular diagnostics. The central theme that emanated from the 2nd Donau Symposium was the importance of the conceptualisation and implementation of a convergent approach towards a disease characterisation beyond lesion-counting "lumpology" for a cost-effective and patient-centric diagnosis, therapy planning, guidance and monitoring. This involves a judicious choice of diagnostic means, the adoption of clinical decision support systems and, above all, a new way of communication involving all stakeholders across modalities and specialities. Moreover, complex diseases require a comprehensive diagnosis by converging parameters from different disciplines, which will finally yield to a precise therapeutic guidance and outcome prediction. While it is attractive to focus on technical advances alone, it is important to develop a patient-centric approach, thus asking "What can we do with our expertise to help patients?"

Early Prediction of Tumor Response to Neoadjuvant Chemotherapy and Clinical Outcome in Breast Cancer Using a Novel FDG-PET Parameter for Cancer Stem Cell Metabolism

Cancer stem cells (CSCs) contribute to chemoresistance and tumor relapse. By using the distinct metabolic phenotype of CSC, we designed novel PET parameters for CSC metabolism and investigated their clinical values. Patients with breast cancer who underwent ¹⁸F-FDG PET/CT before neoadjuvant chemotherapy (NAC) were retrospectively included. We developed a method to measure CSC metabolism using standardized uptake value histogram data. The predictive value of novel CSC metabolic parameters for pathologic complete response (pCR) was assessed with multivariable logistic regression. The association between the CSC parameter and disease-free survival (DFS) was also determined. We identified 82 patients with HER2-positive/triple-negative subtypes and 38 patients with luminal tumors. After multivariable analysis, only metabolic tumor volume for CSC (MTVcsc) among metabolic parameters remained the independent predictor of pCR (OR, 0.12; p = 0.022). MTVcsc successfully predicted pathologic tumor response to NAC in HER2-positive/triple-negative subtypes (accuracy, 74%) but not in the luminal subtype (accuracy, 29%). MTVcsc was also predictive of DFS, with a 3-year DFS of 90% in the lower MTVcsc group (<1.75 cm³) versus 72% in the higher group (>1.75 cm³). A novel data-driven PET parameter for CSC metabolism provides early prediction of pCR after NAC and DFS in HER2-positive and triple-negative subtypes.

Quantitative Comparison of Prone and Supine PERCIST Measurements in Breast Cancer

Positron emission tomography (PET) is typically performed in the supine position. However, breast magnetic resonance imaging (MRI) is performed in prone, as this improves visibility of deep breast tissues. With the emergence of hybrid scanners that integrate molecular information from PET and functional information from MRI, it is of great interest to determine if the prognostic utility of prone PET is equivalent to supine. We compared PERCIST (PET Response Criteria in Solid Tumors) measurements between prone and supine FDG-PET in patients with breast cancer and the effect of orientation on predicting pathologic complete response (pCR). In total, 47 patients were enrolled and received up to 6 cycles of neoadjuvant therapy. Prone and supine FDG-PET were performed at baseline (t₀; n = 46), after cycle 1 (t₁; n = 1) or 2 (t₂; n = 10), or after all neoadjuvant therapy (t₃; n = 19). FDG uptake was quantified by maximum and peak standardized uptake value (SUV) with and without normalization to lean body mass; that is, SUV_max, SUV_peak, SUL_max, and SUL_peak. PERCIST measurements were performed for each paired baseline and post-treatment scan. Receiver operating characteristic analysis for the prediction of pCR was performed using logistic regression that included age and tumor size as covariates. SUV and SUL metrics were significantly different between orientation (P < .001), but were highly correlated (P > .98). Importantly, no differences were observed with the PERCIST measurements (P > .6). Overlapping 95% confidence intervals for the receiver operating characteristic analysis suggested no difference at predicting pCR. Therefore, prone and supine PERCIST in this data set were not statistically different.

Estrogen Receptor, Progesterone Receptor, and Human Epidermal Growth Factor Receptor-2 Testing in Breast Cancer: Assessing the Value of Repeated Centralized Testing in Excision Specimens

At some tertiary breast care centers, where many patients are referred from other institutions, it is routine to repeat testing for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2/neu) in excision specimens if these tests were performed on the preceding biopsy at the referring facility. The goal of this study is to assess the value of this practice. We documented results from ER, PR, and HER2 testing in 541 consecutive invasive breast cancers excised over a 2.5-year period and analyzed the subset (n=153) for which testing was performed on the excision specimen solely due to the fact that testing on the preceding biopsy was performed at an outside institution. The rates and directions of biopsy-to-excision change were as follows: ER [1.3% (2/153), 100% from (+) to (-)]; PR [4% (6/153), 83% from (+) to (-)]; HER2/neu assessed by immunohistochemistry [21% (29/137)]; HER2/neu assessed by fluorescence in situ hybridization [3.3% (2/61); 50% from amplified to nonamplified and 50% vice versa]. There were no ER(-) and PR(-) biopsy cases that became ER and/or PR(+) in the excision. By coordinate analysis for the hormone receptors [ie, ER and/or PR(+) being indicative of "hormone receptor" (HR) positivity], there were no cases that changed from HR(+) in the biopsy to HR(-) in the excision (or vice versa), which suggests that repeat testing for ER and PR in this setting is of limited value. In an analysis that incorporated both immunohistochemistry and in situ fluorescence hybridization results, there were 2 cases with a clinically significant biopsy-to-excision change in HER2/neu status in which that change was detected primarily because the excision was retested. These findings provide baseline data for formulating policies on whether repeat testing should routinely be performed in the described scenario.

Standardization of Small Animal Imaging-Current Status and Future Prospects

The benefit of small animal imaging is directly linked to the validity and reliability of the collected data. If the data (regardless of the modality used) are not reproducible and/or reliable, then the outcome of the data is rather questionable. Therefore, standardization of the use of small animal imaging equipment, as well as of animal handling in general, is of paramount importance. In a recent paper, guidance for efficient small animal imaging quality control was offered and discussed, among others, the use of phantoms in setting up a quality control program (Osborne et al. 2016). The same phantoms can be used to standardize image quality parameters for multi-center studies or multi-scanners within center studies. In animal experiments, the additional complexity due to animal handling needs to be addressed to ensure standardized imaging procedures. In this review, we will address the current status of standardization in preclinical imaging, as well as potential benefits from increased levels of standardization.

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.

Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients

Background

Radiomics is an emerging field in oncological research. In this study, we aimed at developing a radiomics score (rad-score) to estimate postoperative recurrence and survival in patients with solitary hepatocellular carcinoma (HCC).

Methods

A total of 319 solitary HCC patients (training cohort: n = 212; validation cohort: n = 107) were enrolled. Radiomics features were extracted from the artery phase of preoperatively acquired computed tomography (CT) in all patients. A rad-score was generated by using the least absolute shrinkage and selection operator (lasso) logistic model. Kaplan-Meier and Cox’s hazard regression analyses were used to evaluate the prognostic significance of the rad-score. Final nomograms predicting recurrence and survival of solitary HCC patients were established based on the rad-score and clinicopathological factors. C-index and calibration statistics were used to assess the performance of nomograms.

Results

Six potential radiomics features were selected out of 110 texture features to formulate the rad-score. Low rad-score positively correlated with aggressive tumor phenotypes, like larger tumor size and vascular invasion. Meanwhile, low rad-score was significantly associated with increased recurrence and reduced survival. In addition, multivariate analysis identified the rad-score as an independent prognostic factor (recurrence: Hazard ratio (HR): 2.472, 95% confident interval (CI): 1.339–4.564, p = 0.004;survival: HR: 1.558, 95%CI: 1.022–2.375, p = 0.039). Notably, the nomogram integrating rad-score had a better prognostic performance as compared with traditional staging systems. These results were further confirmed in the validation cohort.

Conclusions

The preoperative CT image based rad-score was an independent prognostic factor for the postoperative outcome of solitary HCC patients. This score may be complementary to the current staging system and help to stratify individualized treatments for solitary HCC patients.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-5024-z) contains supplementary material, which is available to authorized users.

Clustering approach to identify intratumour heterogeneity combining FDG PET and diffusion-weighted MRI in lung adenocarcinoma

OBJECTIVES

Malignant tumours consist of biologically heterogeneous components; identifying and stratifying those various subregions is an important research topic. We aimed to show the effectiveness of an intratumour partitioning method using clustering to identify highly aggressive tumour subregions, determining prognosis based on pre-treatment PET and DWI in stage IV lung adenocarcinoma.

METHODS

Eighteen patients who underwent both baseline PET and DWI were recruited. Pre-treatment imaging of SUV and ADC values were used to form intensity vectors within manually specified ROIs. We applied k-means clustering to intensity vectors to yield distinct subregions, then chose the subregion that best matched the criteria for high SUV and low ADC to identify tumour subregions with high aggressiveness. We stratified patients into high- and low-risk groups based on subregion volume with high aggressiveness and conducted survival analyses. This approach is referred to as the partitioning approach. For comparison, we computed tumour subregions with high aggressiveness without clustering and repeated the described procedure; this is referred to as the voxel-wise approach.

RESULTS

The partitioning approach led to high-risk (median SUVmax = 14.25 and median ADC = 1.26x10^-3 mm²/s) and low-risk (median SUVmax = 14.64 and median ADC = 1.09x10^-3 mm²/s) subgroups. Our partitioning approach identified significant differences in survival between high- and low-risk subgroups (hazard ratio, 4.062, 95% confidence interval, 1.21 - 13.58, p-value: 0.035). The voxel-wise approach did not identify significant differences in survival between high- and low-risk subgroups (p-value: 0.325).

CONCLUSION

Our partitioning approach identified intratumour subregions that were predictors of survival.

KEY POINTS

• Multimodal imaging of PET and DWI is useful for assessing intratumour heterogeneity. • Data-driven clustering identified subregions which might be highly aggressive for lung adenocarcinoma. • The data-driven partitioning results might be predictors of survival.

PET/MRI Hybrid Systems

Over the last decade, the combination of PET and MRI in one system has proven to be highly successful in basic preclinical research, as well as in clinical research. Nowadays, PET/MRI systems are well established in preclinical imaging and are progressing into clinical applications to provide further insights into specific diseases, therapeutic assessments, and biological pathways. Certain challenges in terms of hardware had to be resolved concurrently with the development of new techniques to be able to reach the full potential of both combined techniques. This review provides an overview of these challenges and describes the opportunities that simultaneous PET/MRI systems can exploit in comparison with stand-alone or other combined hybrid systems. New approaches were developed for simultaneous PET/MRI systems to correct for attenuation of 511 keV photons because MRI does not provide direct information on gamma photon attenuation properties. Furthermore, new algorithms to correct for motion were developed, because MRI can accurately detect motion with high temporal resolution. The additional information gained by the MRI can be employed to correct for partial volume effects as well. The development of new detector designs in combination with fast-decaying scintillator crystal materials enabled time-of-flight detection and incorporation in the reconstruction algorithms. Furthermore, this review lists the currently commercially available systems both for preclinical and clinical imaging and provides an overview of applications in both fields. In this regard, special emphasis has been placed on data analysis and the potential for both modalities to evolve with advanced image analysis tools, such as cluster analysis and machine learning.

Optimization, evaluation and calibration of a cross-strip DOI detector

This study depicts the evaluation of a SiPM detector with depth of interaction (DOI) capability via a dual-sided readout that is suitable for high-resolution positron emission tomography and magnetic resonance (PET/MR) imaging. Two different 12  ×  12 pixelated LSO scintillator arrays with a crystal pitch of 1.60 mm are examined. One array is 20 mm-long with a crystal separation by the specular reflector Vikuiti enhanced specular reflector (ESR), and the other one is 18 mm-long and separated by the diffuse reflector Lumirror E60 (E60). An improvement in energy resolution from 22.6% to 15.5% for the scintillator array with the E60 reflector is achieved by taking a nonlinear light collection correction into account. The results are FWHM energy resolutions of 14.0% and 15.5%, average FWHM DOI resolutions of 2.96 mm and 1.83 mm, and FWHM coincidence resolving times of 1.09 ns and 1.48 ns for the scintillator array with the ESR and that with the E60 reflector, respectively. The measured DOI signal ratios need to be assigned to an interaction depth inside the scintillator crystal. A linear and a nonlinear method, using the intrinsic scintillator radiation from lutetium, are implemented for an easy to apply calibration and are compared to the conventional method, which exploits a setup with an externally collimated radiation beam. The deviation between the DOI functions of the linear or nonlinear method and the conventional method is determined. The resulting average of differences in DOI positions is 0.67 mm and 0.45 mm for the nonlinear calibration method for the scintillator array with the ESR and with the E60 reflector, respectively; Whereas the linear calibration method results in 0.51 mm and 0.32 mm for the scintillator array with the ESR and the E60 reflector, respectively; and is, due to its simplicity, also applicable in assembled detector systems.

Fluorine-19 Magnetic Resonance Imaging and Positron Emission Tomography of Tumor-Associated Macrophages and Tumor Metabolism

The presence of tumor-associated macrophages (TAMs) is significantly associated with poor prognosis of tumors. Currently, magnetic resonance imaging- (MRI-) based TAM imaging methods that use nanoparticles such as superparamagnetic iron oxide and perfluorocarbon nanoemulsions are available for quantitative monitoring of TAM burden in tumors. However, whether MRI-based measurements of TAMs can be used as prognostic markers has not been evaluated yet. In this study, we used positron emission tomography (PET) with ¹⁸F-2-fluoro-2-deoxy-D-glucose (¹⁸F-FDG) as a radioactive tracer and fluorine-19- (¹⁹F-) MRI for imaging mouse breast cancer models to determine any association between TAM infiltration and tumor metabolism. Perfluorocarbon nanoemulsions were intravenously administered to track and quantify TAM infiltration using a 7T MR scanner. To analyze glucose uptake in tumors, ¹⁸F-FDG-PET images were acquired immediately after ¹⁹F-MRI. Coregistered ¹⁸F-FDG-PET and ¹⁹F-MR images enabled comparison of spatial patterns of glucose uptake and TAM distribution in tumors. ¹⁹F-MR signal intensities from tumors exhibited a strong inverse correlation with ¹⁸F-FDG uptake while having a significant positive correlation with tumor growth from days 2 to 7. These results show that combination of ¹⁹F-MRI and ¹⁸F-FDG-PET can improve our understanding of the relationship between TAM and tumor microenvironment.

Integration of PET/MR Hybrid Imaging into Radiation Therapy Treatment

Hybrid PET/MR imaging is in early development for treatment planning. This article briefly reviews research and clinical applications of PET/MR imaging in radiation oncology. With improvements in workflow, more specific tracers, and fast and robust acquisition protocols, PET/MR imaging will play an increasingly important role in better target delineation for treatment planning and have clear advantages in the evaluation of tumor response and in a better understanding of tumor heterogeneity. With advances in treatment delivery and the potential of integrating PET/MR imaging with research on radiomics for radiation oncology, quantitative and physiologic information could lead to more precise and personalized RT.

Automated Generation of Reliable Blood Velocity Parameter Maps from Contrast-Enhanced Ultrasound Data

Objectives

The purpose of this study was the automated generation and validation of parametric blood flow velocity maps, based on contrast-enhanced ultrasound (CEUS) scans.

Materials and Methods

Ethical approval for animal experiments was obtained. CEUS destruction-replenishment sequences were recorded in phantoms and three different tumor xenograft mouse models. Systematic pixel binning and intensity averaging was performed to generate parameter maps of blood flow velocities with different pixel resolution. The 95% confidence interval of the mean velocity, calculated on the basis of the whole tumor segmentation, served as ground truth for the different parameter maps.

Results

In flow phantoms the measured mean velocity values were only weakly influenced by the pixel resolution and correlated with real velocities (r² ≥ 0.94, p < 0.01). In tumor xenografts, however, calculated mean velocities varied significantly (p < 0.0001), depending on the parameter maps' resolution. Pixel binning was required for all in vivo measurements to obtain reliable parameter maps and its degree depended on the tumor model.

Conclusion

Systematic pixel binning allows the automated identification of optimal pixel resolutions for parametric maps, supporting textural analysis of CEUS data. This approach is independent from the ultrasound setup and can be implemented in the software of other (clinical) ultrasound devices.

PET/MRI of the breast

The future clinical use of the combination of positron emission tomography (PET) with 2-Fluoro[F-18]-2-Deoxy-d-Glucose (FDG)and MRI is still unclear. If a patient requires a PET and breast DCE-MRI for staging purposes, both scans can be done in the same visit. In the breast, DCE-MRI is better at lesion detection (sensitivity), margin evaluation, and has a higher specificity than CT. The potential for multiparametric qualitative and quantitative imaging is also an advantage of PET/MRI which provides opportunity to improve tumor characterization and may ultimately lead to outcome prediction. This review discusses technical and clinical aspects of this emerging technology in breast cancer patients.

Technical and instrumentational foundations of PET/MRI

This paper highlights the origins of combined positron emission tomography (PET) and magnetic resonance imaging (MRI) whole-body systems that were first introduced for applications in humans in 2010. This text first covers basic aspects of each imaging modality before describing the technical and methodological challenges of combining PET and MRI within a single system. After several years of development, combined and even fully-integrated PET/MRI systems have become available and made their way into the clinic. This multi-modality imaging system lends itself to the advanced exploration of diseases to support personalized medicine in a long run. To that extent, this paper provides an introduction to PET/MRI methodology and important technical solutions.

Spectral Clustering Predicts Tumor Tissue Heterogeneity Using Dynamic 18F-FDG PET: A Complement to the Standard Compartmental Modeling Approach

In this study, we described and validated an unsupervised segmentation algorithm for the assessment of tumor heterogeneity using dynamic ¹⁸F-FDG PET. The aim of our study was to objectively evaluate the proposed method and make comparisons with compartmental modeling parametric maps and SUV segmentations using simulations of clinically relevant tumor tissue types. Methods: An irreversible 2-tissue-compartmental model was implemented to simulate clinical and preclinical ¹⁸F-FDG PET time-activity curves using population-based arterial input functions (80 clinical and 12 preclinical) and the kinetic parameter values of 3 tumor tissue types. The simulated time-activity curves were corrupted with different levels of noise and used to calculate the tissue-type misclassification errors of spectral clustering (SC), parametric maps, and SUV segmentation. The utility of the inverse noise variance- and Laplacian score-derived frame weighting schemes before SC was also investigated. Finally, the SC scheme with the best results was tested on a dynamic ¹⁸F-FDG measurement of a mouse bearing subcutaneous colon cancer and validated using histology. Results: In the preclinical setup, the inverse noise variance-weighted SC exhibited the lowest misclassification errors (8.09%-28.53%) at all noise levels in contrast to the Laplacian score-weighted SC (16.12%-31.23%), unweighted SC (25.73%-40.03%), parametric maps (28.02%-61.45%), and SUV (45.49%-45.63%) segmentation. The classification efficacy of both weighted SC schemes in the clinical case was comparable to the unweighted SC. When applied to the dynamic ¹⁸F-FDG measurement of colon cancer, the proposed algorithm accurately identified densely vascularized regions from the rest of the tumor. In addition, the segmented regions and clusterwise average time-activity curves showed excellent correlation with the tumor histology. Conclusion: The promising results of SC mark its position as a robust tool for quantification of tumor heterogeneity using dynamic PET studies. Because SC tumor segmentation is based on the intrinsic structure of the underlying data, it can be easily applied to other cancer types as well.

PET and MRI: Is the Whole Greater than the Sum of Its Parts?

Over the past decades, imaging in oncology has been undergoing a "quiet" revolution to treat images as data, not as pictures. This revolution has been sparked by technological advances that enable capture of images that reflect not only anatomy, but also of tissue metabolism and physiology in situ Important advances along this path have been the increasing power of MRI, which can be used to measure spatially dependent differences in cell density, tissue organization, perfusion, and metabolism. In parallel, PET imaging allows quantitative assessment of the spatial localization of positron-emitting compounds, and it has also been constantly improving in the number of imageable tracers to measure metabolism and expression of macromolecules. Recent years have witnessed another technological advance, wherein these two powerful modalities have been physically merged into combined PET/MRI systems, appropriate for both preclinical or clinical imaging. As with all new enabling technologies driven by engineering physics, the full extent of potential applications is rarely known at the outset. In the work of Schmitz and colleagues, the authors have combined multiparametric MRI and PET imaging to address the important issue of intratumoral heterogeneity in breast cancer using both preclinical and clinical data. With combined PET and MRI and sophisticated machine-learning tools, they have been able identify multiple coexisting regions ("habitats") within living tumors and, in some cases, have been able to assign these habitats to known histologies. This work addresses an issue of fundamental importance to both cancer biology and cancer care. As with most new paradigm-shifting applications, it is not the last word on the subject and introduces a number of new avenues of investigation to pursue. Cancer Res; 76(21); 6163-6. ©2016 AACR.