Quantitative multimodality imaging in cancer research and therapy

A multimodal machine learning model for the stratification of breast cancer risk

Machine learning models for the diagnosis of breast cancer can facilitate the prediction of cancer risk and subsequent patient management among other clinical tasks. For the models to impact clinical practice, they ought to follow standard workflows, help interpret mammography and ultrasound data, evaluate clinical contextual information, handle incomplete data and be validated in prospective settings. Here we report the development and testing of a multimodal model leveraging mammography and ultrasound modules for the stratification of breast cancer risk based on clinical metadata, mammography and trimodal ultrasound (19,360 images of 5,216 breasts) from 5,025 patients with surgically confirmed pathology across medical centres and scanner manufacturers. Compared with the performance of experienced radiologists, the model performed similarly at classifying tumours as benign or malignant and was superior at pathology-level differential diagnosis. With a prospectively collected dataset of 191 breasts from 187 patients, the overall accuracies of the multimodal model and of preliminary pathologist-level assessments of biopsied breast specimens were similar (90.1% vs 92.7%, respectively). Multimodal models may assist diagnosis in oncology.

Ultra-fast light-field microscopy with event detection

The event detection technique has been introduced to light-field microscopy, boosting its imaging speed in orders of magnitude with simultaneous axial resolution enhancement in scattering medium.

Generation of transmitochondrial cybrids in cancer cells

At odds with historical views suggesting that mitochondrial functions are largely dispensable for cancer cells, it is now clear that mitochondria have a major impact on malignant transformation, tumor progression and response to treatment. Mitochondria are indeed critical for neoplastic cells not only as an abundant source of ATP and other metabolic intermediates, but also as gatekeepers of apoptotic cell death and inflammation. Interestingly, while mitochondrial components are mostly encoded by nuclear genes, mitochondria contain a small, circular genome that codes for a few mitochondrial proteins, ribosomal RNAs and transfer RNAs. Here, we describe a straightforward method to generate transmitochondrial cybrids, i.e., cancer cells depleted of their mitochondrial DNA and reconstituted with intact mitochondria from another cellular source. Once established, transmitochondrial cybrids can be stably propagated and are valuable to dissect the specific impact of the mitochondrial genome on cancer cell functions.

Multimodal Image Classification by Multiview Latent Pattern Extraction, Selection, and Correlation

The large amount of data available in the modern big data era opens new opportunities to expand our knowledge by integrating information from heterogeneous sources. Multiview learning has recently achieved tremendous success in deriving complementary information from multiple data modalities. This article proposes a framework called multiview latent space projection (MVLSP) to integrate features extracted from multiple sources in a discriminative way to facilitate binary and multiclass classifications. Our approach is associated with three innovations. First, most existing multiview learning algorithms promote pairwise consistency between two views and do not have a natural extension to applications with more than two views. MVLSP finds optimum mappings from a common latent space to match the feature space in each of the views. As the matching is performed on a view-by-view basis, the framework can be readily extended to multiview applications. Second, feature selection in the common latent space can be readily achieved by adding a class view, which matches the latent space representations of training samples with their corresponding labels. Then, high-order view correlations are extracted by considering feature-label correlations. Third, a technique is proposed to optimize the integration of different latent patterns based on their correlations. The experimental results on the prostate image dataset demonstrate the effectiveness of the proposed method.

Engineering Heterogeneous Tumor Models for Biomedical Applications

Tumor tissue engineering holds great promise for replicating the physiological and behavioral characteristics of tumors in vitro. Advances in this field have led to new opportunities for studying the tumor microenvironment and exploring potential anti-cancer therapeutics. However, the main obstacle to the widespread adoption of tumor models is the poor understanding and insufficient reconstruction of tumor heterogeneity. In this review, the current progress of engineering heterogeneous tumor models is discussed. First, the major components of tumor heterogeneity are summarized, which encompasses various signaling pathways, cell proliferations, and spatial configurations. Then, contemporary approaches are elucidated in tumor engineering that are guided by fundamental principles of tumor biology, and the potential of a bottom-up approach in tumor engineering is highlighted. Additionally, the characterization approaches and biomedical applications of tumor models are discussed, emphasizing the significant role of engineered tumor models in scientific research and clinical trials. Lastly, the challenges of heterogeneous tumor models in promoting oncology research and tumor therapy are described and key directions for future research are provided.

Light-to-Heat Conversion of Optically Trapped Hot Brownian Particles

Anisotropic hybrid nanostructures stand out as promising therapeutic agents in photothermal conversion-based treatments. Accordingly, understanding local heat generation mediated by light-to-heat conversion of absorbing multicomponent nanoparticles at the single-particle level has forthwith become a subject of broad and current interest. Nonetheless, evaluating reliable temperature profiles around a single trapped nanoparticle is challenging from all of the experimental, computational, and fundamental viewpoints. Committed to filling this gap, the heat generation of an anisotropic hybrid nanostructure is explored by means of two different experimental approaches from which the local temperature is measured in a direct or indirect way, all in the context of hot Brownian motion theory. The results were compared with analytical results supported by the numerical computation of the wavelength-dependent absorption efficiencies in the discrete dipole approximation for scattering calculations, which has been extended to inhomogeneous nanostructures. Overall, we provide a consistent and comprehensive view of the heat generation in optical traps of highly absorbing particles from the viewpoint of the hot Brownian motion theory.

AIEgens Cross-linked Iron Oxide Nanoparticles Synchronously Amplify Bimodal Imaging Signals in Situ by Tumor Acidity-Mediated Click Reaction

Activatable dual-modal molecular imaging probes present a promising tool for the diagnosis of malignant tumors. However, synchronously enhancing dual-modal imaging signals under a single stimulus is challenging. Herein, we propose an activatable bimodal probe that integrates aggregation-induced emission luminogens (AIEgens) and iron oxide nanoparticles (IOs) to synergistically enhance near-infrared fluorescence (NIRF) intensity and magnetic resonance (MR) contrast through a tumor acidity-mediated click reaction. Tumor acidity-responsive IOs containing dibenzocyclooctyne groups (termed cDIOs) and AIEgens containing azide groups (termed AATs) can be covalently cross-linked in response to tumor acidity, which leads to a simultaneous enhancement in NIRF intensity (≈12.4-fold) and r2 relaxivity (≈2.8-fold). cDIOs and AATs were effectively activated in mice orthotropic breast tumor, and the cross-linking prolonged their retention in tumor, further augmenting the bimodal signals and expanding imaging time frame. This facile strategy leverages the inherent properties of probes themselves and demonstrates promise in future translational studies.

Multimodal prediction of neoadjuvant treatment outcome by serial FDG PET and MRI in women with locally advanced breast cancer

PURPOSE

To investigate combined MRI and 18F-FDG PET for assessing breast tumor metabolism/perfusion mismatch and predicting pathological response and recurrence-free survival (RFS) in women treated for breast cancer.

METHODS

Patients undergoing neoadjuvant chemotherapy (NAC) for locally-advanced breast cancer were imaged at three timepoints (pre, mid, and post-NAC), prior to surgery. Imaging included diffusion-weighted and dynamic contrast-enhanced (DCE-) MRI and quantitative 18F-FDG PET. Tumor imaging measures included apparent diffusion coefficient, peak percent enhancement (PE), peak signal enhancement ratio (SER), functional tumor volume, and washout volume on MRI and standardized uptake value (SUVmax), glucose delivery (K1) and FDG metabolic rate (MRFDG) on PET, with percentage changes from baseline calculated at mid- and post-NAC. Associations of imaging measures with pathological response (residual cancer burden [RCB] 0/I vs. II/III) and RFS were evaluated.

RESULTS

Thirty-five patients with stage II/III invasive breast cancer were enrolled in the prospective study (median age: 43, range: 31-66 years, RCB 0/I: N = 11/35, 31%). Baseline imaging metrics were not significantly associated with pathologic response or RFS (p > 0.05). Greater mid-treatment decreases in peak PE, along with greater post-treatment decreases in several DCE-MRI and 18F-FDG PET measures were associated with RCB 0/I after NAC (p < 0.05). Additionally, greater mid- and post-treatment decreases in DCE-MRI (peak SER, washout volume) and 18F-FDG PET (K1) were predictive of prolonged RFS. Mid-treatment decreases in metabolism/perfusion ratios (MRFDG/peak PE, MRFDG/peak SER) were associated with improved RFS.

CONCLUSION

Mid-treatment changes in both PET and MRI measures were predictive of RCB status and RFS following NAC. Specifically, our results indicate a complementary relationship between DCE-MRI and 18F-FDG PET metrics and potential value of metabolism/perfusion mismatch as a marker of patient outcome.

Recent advances in fluorescence imaging-guided photothermal therapy and photodynamic therapy for cancer: From near-infrared-I to near-infrared-II

Phototherapy (including photothermal therapy, PTT; and photodynamic therapy, PDT) has been widely used for cancer treatment, but conventional PTT/PDT show limited therapeutic effects due to the lack of disease recognition ability. The integration of fluorescence imaging with PTT/PDT can reveal tumor locations in a real-time manner, holding great potential in early diagnosis and precision treatment of cancers. However, the traditional fluorescence imaging in the visible and near-infrared-I regions (VIS/NIR-I, 400-900 nm) might be interfered by the scattering and autofluorescence from tissues, leading to a low imaging resolution and high false positive rate. The deeper near-infrared-II (NIR-II, 1000-1700 nm) fluorescence imaging can address these interferences. Combining NIR-II fluorescence imaging with PTT/PDT can significantly improve the accuracy of tumor theranostics and minimize damages to normal tissues. This review summarized recent advances in tumor PTT/PDT and NIR-II fluorophores, especially discussed achievements, challenges and prospects around NIR-II fluorescence imaging-guided PTT/PDT for cancers.

Tailored magnetic resonance fingerprinting

Neuroimaging of certain pathologies requires both multi-parametric qualitative and quantitative imaging. The role of the quantitative MRI (qMRI) is well accepted but suffers from long acquisition times leading to patient discomfort, especially in geriatric and pediatric patients. Previous studies show that synthetic MRI can be used in order to reduce the scan time and provide qMRI as well as multi-contrast data. However, this approach suffers from artifacts such as partial volume and flow. In order to increase the scan efficiency (the number of contrasts and quantitative maps acquired per unit time), we designed, simulated, and demonstrated rapid, simultaneous, multi-contrast qualitative (T₁ weighted, T₁ fluid attenuated inversion recovery (FLAIR), T₂ weighted, water, and fat), and quantitative imaging (T₁ and T₂ maps) through the approach of tailored MR fingerprinting (TMRF) to cover whole-brain in approximately four minutes. We performed TMRF on in vivo four healthy human brains and in vitro ISMRM/NIST phantom and compared with vendor supplied gold standard (GS) and MRF sequences. All scans were performed on a 3 T GE Premier system and images were reconstructed offline using MATLAB. The reconstructed qualitative images were then subjected to custom DL denoising and gradient anisotropic diffusion denoising. The quantitative tissue parametric maps were reconstructed using a dense neural network to gain computational speed compared to dictionary matching. The grey matter and white matter tissues in qualitative and quantitative data for the in vivo datasets were segmented semi-automatically. The SNR and mean contrasts were plotted and compared across all three methods. The GS images show better SNR in all four subjects compared to MRF and TMRF (GS > TMRF>MRF). The T₁ and T₂ values of MRF are relatively overestimated as compared to GS and TMRF. The scan efficiency for TMRF is 1.72 min^-1 which is higher compared to GS (0.32 min^-1) and MRF (0.90 min^-1).

Omniparticle Contrast Agent for Multimodal Imaging: Synthesis and Characterization in an Animal Model

PURPOSE

Individual imaging modalities have certain advantages, but each suffers from drawbacks that other modalities may overcome. The goal of this study was to create a novel contrast agent suitable for various imaging modalities that after a single administration can bridge and strengthen the collaboration between the research fields as well as enrich the information obtained from any one modality.

PROCEDURES

The contrast agent platform is based on dextran-coated iron oxide nanoparticles (for MRI and MPI) and synthesized using a modified co-precipitation method, followed by a series of conjugation steps with a fluorophore (for fluorescence and photoacoustic imaging), thyroxine (for CT imaging), and chelators for radioisotope labeling (for PET imaging). The fully conjugated agent was then tested in vitro in cell uptake, viability, and phantom studies and in vivo in a model of intraductal injection and in a tumor model.

RESULTS

The agent was synthesized, characterized, and tested in vitro where it showed the ability to produce a signal on MRI/MPI/FL/PA/CT and PET images. Studies in cells showed the expected concentration-dependent uptake of the agent without noticeable toxicity. In vivo studies demonstrated localization of the agent to the ductal tree in mice after intraductal injection with different degrees of resolution, with CT being the best for this particular application. In a model of injected labeled tumor cells, the agent produced a signal with all modalities and showed persistence in tumor cells confirmed by histology.

CONCLUSIONS

A fully functional omniparticle contrast agent was synthesized and tested in vitro and in vivo in two animal models. Results shown here point to the generation of a potent signal in all modalities tested without detrimental toxicity. Future use of this agent includes its exploration in various models of human disease including image-guided diagnostic and therapeutic applications.

Nanoparticles in the diagnosis and treatment of cancer metastases: Current and future perspectives

Metastasis accounts for greater than 90% of cancer-related deaths. Despite recent advancements in conventional chemotherapy, immunotherapy, targeted therapy, and their rational combinations, metastatic cancers remain essentially untreatable. The distinct obstacles to treat metastases include their small size, high multiplicity, redundancy, therapeutic resistance, and dissemination to multiple organs. Recent advancements in nanotechnology provide the numerous applications in the diagnosis and prophylaxis of metastatic diseases, including the small particle size to penetrate cell membrane and blood vessels and their capacity to transport complex molecular 'cargo' particles to various metastatic regions such as bones, brain, liver, lungs, and lymph nodes. Indeed, nanoparticles (NPs) have demonstrated a significant ability to target specific cells within these organs. In this regard, the purpose of this review is to summarize the present state of nanotechnology in terms of its application in the diagnosis and treatment of metastatic cancer. We intensively reviewed applications of NPs in fluorescent imaging, PET scanning, MRI, and photoacoustic imaging to detect metastasis in various cancer models. The use of targeted NPs for cancer ablation in conjunction with chemotherapy, photothermal treatment, immuno therapy, and combination therapy is thoroughly discussed. The current review also highlights the research opportunities and challenges of leveraging engineering technologies with cancer cell biology and pharmacology to fabricate nanoscience-based tools for treating metastases.

Mathematical modelling of the dynamics of image-informed tumor habitats in a murine model of glioma

Tumors exhibit high molecular, phenotypic, and physiological heterogeneity. In this effort, we employ quantitative magnetic resonance imaging (MRI) data to capture this heterogeneity through imaging-based subregions or "habitats" in a murine model of glioma. We then demonstrate the ability to model and predict the growth of the habitats using coupled ordinary differential equations (ODEs) in the presence and absence of radiotherapy. Female Wistar rats (N = 21) were inoculated intracranially with 10⁶ C6 glioma cells, a subset of which received 20 Gy (N = 5) or 40 Gy (N = 8) of radiation. All rats underwent diffusion-weighted and dynamic contrast-enhanced MRI at up to seven time points. All MRI data at each visit were subsequently clustered using k-means to identify physiological tumor habitats. A family of four models consisting of three coupled ODEs were developed and calibrated to the habitat time series of control and treated rats and evaluated for predictive capability. The Akaike Information Criterion was used for model selection, and the normalized sum-of-square-error (SSE) was used to evaluate goodness-of-fit in model calibration and prediction. Three tumor habitats with significantly different imaging data characteristics (p < 0.05) were identified: high-vascularity high-cellularity, low-vascularity high-cellularity, and low-vascularity low-cellularity. Model selection resulted in a five-parameter model whose predictions of habitat dynamics yielded SSEs that were similar to the SSEs from the calibrated model. It is thus feasible to mathematically describe habitat dynamics in a preclinical model of glioma using biology-based ODEs, showing promise for forecasting heterogeneous tumor behavior.

Towards differentiation of brain tumor from radiation necrosis using multi-parametric MRI: Preliminary results at 4.7 T using rodent models

BACKGROUND

It remains a clinical challenge to differentiate brain tumors from radiation-induced necrosis in the brain. Despite significant improvements, no single MRI method has been validated adequately in the clinical setting.

METHODS

Multi-parametric MRI (mpMRI) was performed to differentiate 9L gliosarcoma from radiation necrosis in animal models. Five types of MRI methods probed complementary information on different scales i.e., T2 (relaxation), CEST based APT (probing mobile proteins/peptides) and rNOE (mobile macromolecules), qMT (macromolecules), diffusion based ADC (cell density) and SSIFT iAUC (cell size), and perfusion based DSC (blood volume and flow).

RESULTS

For single MRI parameters, iAUC and ADC provide the best discrimination of radiation necrosis and brain tumor. For mpMRI, a combination of iAUC, ADC, and APT shows the best classification performance based on a two-step analysis with the Lasso and Ridge regressions.

CONCLUSION

A general mpMRI approach is introduced to choosing candidate multiple MRI methods, identifying the most effective parameters from all the mpMRI parameters, and finding the appropriate combination of chosen parameters to maximize the classification performance to differentiate tumors from radiation necrosis.

MRI techniques for immunotherapy monitoring

MRI is a widely available clinical tool for cancer diagnosis and treatment monitoring. MRI provides excellent soft tissue imaging, using a wide range of contrast mechanisms, and can non-invasively detect tissue metabolites. These approaches can be used to distinguish cancer from normal tissues, to stratify tumor aggressiveness, and to identify changes within both the tumor and its microenvironment in response to therapy. In this review, the role of MRI in immunotherapy monitoring will be discussed and how it could be utilized in the future to address some of the unique clinical questions that arise from immunotherapy. For example, MRI could play a role in identifying pseudoprogression, mixed response, T cell infiltration, cell tracking, and some of the characteristic immune-related adverse events associated with these agents. The factors to be considered when developing MRI imaging biomarkers for immunotherapy will be reviewed. Finally, the advantages and limitations of each approach will be discussed, as well as the challenges for future clinical translation into routine clinical care. Given the increasing use of immunotherapy in a wide range of cancers and the ability of MRI to detect the microstructural and functional changes associated with successful response to immunotherapy, the technique has great potential for more widespread and routine use in the future for these applications.

Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy

Immunotherapy has become a fourth pillar in the treatment of brain tumors and, when combined with radiation therapy, may improve patient outcomes and reduce the neurotoxicity. As with other combination therapies, the identification of a treatment schedule that maximizes the synergistic effect of radiation- and immune-therapy is a fundamental challenge. Mechanism-based mathematical modeling is one promising approach to systematically investigate therapeutic combinations to maximize positive outcomes within a rigorous framework. However, successful clinical translation of model-generated combinations of treatment requires patient-specific data to allow the models to be meaningfully initialized and parameterized. Quantitative imaging techniques have emerged as a promising source of high quality, spatially and temporally resolved data for the development and validation of mathematical models. In this review, we will present approaches to personalize mechanism-based modeling frameworks with patient data, and then discuss how these techniques could be leveraged to improve brain cancer outcomes through patient-specific modeling and optimization of treatment strategies.

Criteria for the design of tissue-mimicking phantoms for the standardization of biophotonic instrumentation

A lack of accepted standards and standardized phantoms suitable for the technical validation of biophotonic instrumentation hinders the reliability and reproducibility of its experimental outputs. In this Perspective, we discuss general criteria for the design of tissue-mimicking biophotonic phantoms, and use these criteria and state-of-the-art developments to critically review the literature on phantom materials and on the fabrication of phantoms. By focusing on representative examples of standardization in diffuse optical imaging and spectroscopy, fluorescence-guided surgery and photoacoustic imaging, we identify unmet needs in the development of phantoms and a set of criteria (leveraging characterization, collaboration, communication and commitment) for the standardization of biophotonic instrumentation.

Quantifying Tumor Heterogeneity via MRI Habitats to Characterize Microenvironmental Alterations in HER2+ Breast Cancer

This study identifies physiological habitats using quantitative magnetic resonance imaging (MRI) to elucidate intertumoral differences and characterize microenvironmental response to targeted and cytotoxic therapy. BT-474 human epidermal growth factor receptor 2 (HER2+) breast tumors were imaged before and during treatment (trastuzumab, paclitaxel) with diffusion-weighted MRI and dynamic contrast-enhanced MRI to measure tumor cellularity and vascularity, respectively. Tumors were stained for anti-CD31, anti-ɑSMA, anti-CD45, anti-F4/80, anti-pimonidazole, and H&E. MRI data was clustered to identify and label each habitat in terms of vascularity and cellularity. Pre-treatment habitat composition was used stratify tumors into two "tumor imaging phenotypes" (Type 1, Type 2). Type 1 tumors showed significantly higher percent tumor volume of the high-vascularity high-cellularity (HV-HC) habitat compared to Type 2 tumors, and significantly lower volume of low-vascularity high-cellularity (LV-HC) and low-vascularity low-cellularity (LV-LC) habitats. Tumor phenotypes showed significant differences in treatment response, in both changes in tumor volume and physiological composition. Significant positive correlations were found between histological stains and tumor habitats. These findings suggest that the differential baseline imaging phenotypes can predict response to therapy. Specifically, the Type 1 phenotype indicates increased sensitivity to targeted or cytotoxic therapy compared to Type 2 tumors.

Environmental stress level to model tumor cell growth and survival

Survival of living tumor cells underlies many influences such as nutrient saturation, oxygen level, drug concentrations or mechanical forces. Data-supported mathematical modeling can be a powerful tool to get a better understanding of cell behavior in different settings. However, under consideration of numerous environmental factors mathematical modeling can get challenging. We present an approach to model the separate influences of each environmental quantity on the cells in a collective manner by introducing the "environmental stress level". It is an immeasurable auxiliary variable, which quantifies to what extent viable cells would get in a stressed state, if exposed to certain conditions. A high stress level can inhibit cell growth, promote cell death and influence cell movement. As a proof of concept, we compare two systems of ordinary differential equations, which model tumor cell dynamics under various nutrient saturations respectively with and without considering an environmental stress level. Particle-based Bayesian inversion methods are used to quantify uncertainties and calibrate unknown model parameters with time resolved measurements of in vitro populations of liver cancer cells. The calibration results of both models are compared and the quality of fit is quantified. While predictions of both models show good agreement with the data, there is indication that the model considering the stress level yields a better fitting. The proposed modeling approach offers a flexible and extendable framework for considering systems with additional environmental factors affecting the cell dynamics.

Iron–Gold Nanoflowers: A Promising Tool for Multimodal Imaging and Hyperthermia Therapy

The development of nanoplatforms prepared to perform both multimodal imaging and combined therapies in a single entity is a fast-growing field. These systems are able to improve diagnostic accuracy and therapy success. Multicomponent Nanoparticles (MCNPs), composed of iron oxide and gold, offer new opportunities for Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) diagnosis, as well as combined therapies based on Magnetic Hyperthermia (MH) and Photothermal Therapy (PT). In this work, we describe a new seed-assisted method for the synthesis of Au@Fe Nanoparticles (NPs) with a flower-like structure. For biomedical purposes, Au@Fe NPs were functionalized with a PEGylated ligand, leading to high colloidal stability. Moreover, the as-obtained Au@Fe-PEG NPs exhibited excellent features as both MRI and CT Contrast Agents (CAs), with high r2 relaxivity (60.5 mM−1⋅s−1) and X-ray attenuation properties (8.8 HU mM−1⋅HU). In addition, these nanoflowers presented considerable energy-to-heat conversion under both Alternating Magnetic Fields (AMFs) (∆T ≈ 2.5 °C) and Near-Infrared (NIR) light (∆T ≈ 17 °C). Finally, Au@Fe-PEG NPs exhibited very low cytotoxicity, confirming their potential for theranostics applications.

Study on the RF transparency of electrically floating and ground PET inserts in a 3T clinical MRI system

PURPOSE

The positron emission tomography (PET) insert for a magnetic resonance imaging (MRI) system that implements the radiofrequency (RF) built-in body coil of the MRI system as a transmitter is designed to be RF-transparent, as the coil resides outside the RF-shielded PET ring. This approach reduces the design complexities (e.g., large PET ring diameter) related to implementing a transmit coil inside the PET ring. However, achieving the required field transmission into the imaging region of interest (ROI) becomes challenging because of the RF shield of the PET insert. In this study, a modularly RF-shielded PET insert is used to investigate the RF transparency considering two electrical configurations of the RF shield, namely the electrical floating and ground configurations. The purpose is to find the differences, advantages and disadvantages of these two configurations.

METHODS

Eight copper-shielded PET detector modules (intermodular gap: 3 mm) were oriented cylindrically with an inner-diameter of 234 mm. Each PET module included four-layer LYSO scintillation crystal blocks and front-end readout electronics. RF-shielded twisted-pair cables were used to connect the front-end electronics with the power sources and PET data acquisition systems located outside the MRI room. In the ground configuration, both the detector and cable shields were connected to the RF ground of the MRI system. In the floating configuration, only the RF shields of the PET modules were isolated from the RF ground. Experiments were conducted using two cylindrical homogeneous phantoms in a 3T clinical MRI system, in which the built-in body RF coil (a cylindrical volume coil of diameter 700 mm and length 540 mm) was implemented as a transceiver.

RESULTS

For both PET configurations, the RF and MR imaging performances were lower than those for the MRI-only case, and the MRI-system provided SAR values that were almost double. The RF homogeneity and field strength, and the SNR of the MR images were mostly higher for the floating PET configuration than they were for the ground PET configuration. However, for a shorter axial FOV of 125 mm, both configurations offered almost the same performance with high RF homogeneities (e.g., 76 ± 10%). Moreover, for both PET configurations, 56 ± 6% larger RF pulse amplitudes were required for MR imaging purposes. The increased power is mostly absorbed in the conductive shields in the form of shielding RF eddy currents; as a result, the SAR values only in the phantoms were estimated to be close to the MRI-only values.

CONCLUSIONS

The floating PET configuration showed higher RF transparency under all experimental setups. For a relatively short axial FOV of 125 mm, the ground configuration also performed well which indicated that an RF-penetrable PET insert with the conventional design (e.g., the ground configuration) might also become possible. However, some design modifications (e.g., a wider intermodular gap and using the RF receiver coil inside the PET insert) should improve the RF performance to the level of the MRI-only case. This article is protected by copyright. All rights reserved.

Spatially resolved isotope tracing reveals tissue metabolic activity

Isotope tracing has helped to determine the metabolic activities of organs. Methods to probe metabolic heterogeneity within organs are less developed. We couple stable-isotope-labeled nutrient infusion to matrix-assisted laser desorption ionization imaging mass spectrometry (iso-imaging) to quantitate metabolic activity in mammalian tissues in a spatially resolved manner. In the kidney, we visualize gluconeogenic flux and glycolytic flux in the cortex and medulla, respectively. Tricarboxylic acid cycle substrate usage differs across kidney regions; glutamine and citrate are used preferentially in the cortex and fatty acids are used in the medulla. In the brain, we observe spatial gradations in carbon inputs to the tricarboxylic acid cycle and glutamate under a ketogenic diet. In a carbohydrate-rich diet, glucose predominates throughout but in a ketogenic diet, 3-hydroxybutyrate contributes most strongly in the hippocampus and least in the midbrain. Brain nitrogen sources also vary spatially; branched-chain amino acids contribute most in the midbrain, whereas ammonia contributes in the thalamus. Thus, iso-imaging can reveal the spatial organization of metabolic activity.

Relation-Aware Shared Representation Learning for Cancer Prognosis Analysis With Auxiliary Clinical Variables and Incomplete Multi-Modality Data

The integrative analysis of complementary phenotype information contained in multi-modality data (e.g., histopathological images and genomic data) has advanced the prognostic evaluation of cancers. However, multi-modality based prognosis analysis confronts two challenges: (1) how to explore underlying relations inherent in different modalities data for learning compact and discriminative multi-modality representations; (2) how to take full consideration of incomplete multi-modality data for constructing accurate and robust prognostic model, since a host of complete multi-modality data are not always available. Additionally, many existing multi-modality based prognostic methods commonly ignore relevant clinical variables (e.g., grade and stage), which, however, may provide supplemental information to promote the performance of model. In this paper, we propose a relation-aware shared representation learning method for prognosis analysis of cancers, which makes full use of clinical information and incomplete multi-modality data. The proposed method learns multi-modal shared space tailored for prognostic model via a dual mapping. Within the shared space, it equips with relational regularizers to explore the potential relations (i.e., feature-label and feature-feature relations) among multi-modality data for inducing discriminatory representations and simultaneously obtaining extra sparsity for alleviating overfitting. Moreover, it regresses and incorporates multiple auxiliary clinical attributes with dynamic coefficients to meliorate performance. Furthermore, in training stage, a partial mapping strategy is employed to extend and train a more reliable model with incomplete multi-modality data. We have evaluated our method on three public datasets derived from The Cancer Genome Atlas (TCGA) project, and the experimental results demonstrate the superior performance of the proposed method.

Quantification of tumor heterogeneity: from data acquisition to metric generation

Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care.

Cross-Modality Imaging of Murine Tumor Vasculature-a Feasibility Study

Tumor vasculature and angiogenesis play a crucial role in tumor progression. Their visualization is therefore of utmost importance to the community. In this proof-of-principle study, we have established a novel cross-modality imaging (CMI) pipeline to characterize exactly the same murine tumors across scales and penetration depths, using orthotopic models of melanoma cancer. This allowed the acquisition of a comprehensive set of vascular parameters for a single tumor. The workflow visualizes capillaries at different length scales, puts them into the context of the overall tumor vessel network and allows quantification and comparison of vessel densities and morphologies by different modalities. The workflow adds information about hypoxia and blood flow rates. The CMI approach includes well-established technologies such as magnetic resonance imaging (MRI), positron emission tomography (PET), computed tomography (CT), and ultrasound (US), and modalities that are recent entrants into preclinical discovery such as optical coherence tomography (OCT) and high-resolution episcopic microscopy (HREM). This novel CMI platform establishes the feasibility of combining these technologies using an extensive image processing pipeline. Despite the challenges pertaining to the integration of microscopic and macroscopic data across spatial resolutions, we also established an open-source pipeline for the semi-automated co-registration of the diverse multiscale datasets, which enables truly correlative vascular imaging. Although focused on tumor vasculature, our CMI platform can be used to tackle a multitude of research questions in cancer biology.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Engineering of magnetic nanoparticles as magnetic particle imaging tracers

Magnetic particle imaging (MPI) has recently emerged as a promising non-invasive imaging technique because of its signal linearly propotional to the tracer mass, ability to generate positive contrast, low tissue background, unlimited tissue penetration depth, and lack of ionizing radiation. The sensitivity and resolution of MPI are highly dependent on the properties of magnetic nanoparticles (MNPs), and extensive research efforts have been focused on the design and synthesis of tracers. This review examines parameters that dictate the performance of MNPs, including size, shape, composition, surface property, crystallinity, the surrounding environment, and aggregation state to provide guidance for engineering MPI tracers with better performance. Finally, we discuss applications of MPI imaging and its challenges and perspectives in clinical translation.

Cross-Modality Imaging of Murine Tumor Vasculature-a Feasibility Study

Tumor vasculature and angiogenesis play a crucial role in tumor progression. Their visualization is therefore of utmost importance to the community. In this proof-of-principle study, we have established a novel cross-modality imaging (CMI) pipeline to characterize exactly the same murine tumors across scales and penetration depths, using orthotopic models of melanoma cancer. This allowed the acquisition of a comprehensive set of vascular parameters for a single tumor. The workflow visualizes capillaries at different length scales, puts them into the context of the overall tumor vessel network and allows quantification and comparison of vessel densities and morphologies by different modalities. The workflow adds information about hypoxia and blood flow rates. The CMI approach includes well-established technologies such as magnetic resonance imaging (MRI), positron emission tomography (PET), computed tomography (CT), and ultrasound (US), and modalities that are recent entrants into preclinical discovery such as optical coherence tomography (OCT) and high-resolution episcopic microscopy (HREM). This novel CMI platform establishes the feasibility of combining these technologies using an extensive image processing pipeline. Despite the challenges pertaining to the integration of microscopic and macroscopic data across spatial resolutions, we also established an open-source pipeline for the semi-automated co-registration of the diverse multiscale datasets, which enables truly correlative vascular imaging. Although focused on tumor vasculature, our CMI platform can be used to tackle a multitude of research questions in cancer biology.

Atlas construction and spatial normalisation to facilitate radiation-induced late effects research in childhood cancer

Reducing radiation-induced side effects is one of the most important challenges in paediatric cancer treatment. Recently, there has been growing interest in using spatial normalisation to enable voxel-based analysis of radiation-induced toxicities in a variety of patient groups. The need to consider three-dimensional distribution of doses, rather than dose-volume histograms, is desirable but not yet explored in paediatric populations. In this paper, we investigate the feasibility of atlas construction and spatial normalisation in paediatric radiotherapy. We used planning computed tomography (CT) scans from twenty paediatric patients historically treated with craniospinal irradiation to generate a template CT that is suitable for spatial normalisation. This childhood cancer population representative template was constructed using groupwise image registration. An independent set of 53 subjects from a variety of childhood malignancies was then used to assess the quality of the propagation of new subjects to this common reference space using deformable image registration (i.e. spatial normalisation). The method was evaluated in terms of overall image similarity metrics, contour similarity and preservation of dose-volume properties. After spatial normalisation, we report a dice similarity coefficient of 0.95 ± 0.05, 0.85 ± 0.04, 0.96 ± 0.01, 0.91 ± 0.03, 0.83 ± 0.06 and 0.65 ± 0.16 for brain and spinal canal, ocular globes, lungs, liver, kidneys and bladder. We then demonstrated the potential advantages of an atlas-based approach to study the risk of second malignant neoplasms after radiotherapy. Our findings indicate satisfactory mapping between a heterogeneous group of patients and the template CT. The poorest performance was for organs in the abdominal and pelvic region, likely due to respiratory and physiological motion and to the highly deformable nature of abdominal organs. More specialised algorithms should be explored in the future to improve mapping in these regions. This study is the first step toward voxel-based analysis in radiation-induced toxicities following paediatric radiotherapy.

Fe3O4-Au Core-Shell Nanoparticles as a Multimodal Platform for In Vivo Imaging and Focused Photothermal Therapy

In this study, we report the synthesis of gold-coated iron oxide nanoparticles capped with polyvinylpyrrolidone (Fe@Au NPs). The as-synthesized nanoparticles (NPs) exhibited good stability in aqueous media and excellent features as contrast agents (CA) for both magnetic resonance imaging (MRI) and X-ray computed tomography (CT). Additionally, due to the presence of the local surface plasmon resonances of gold, the NPs showed exploitable "light-to-heat" conversion ability in the near-infrared (NIR) region, a key attribute for effective photothermal therapies (PTT). In vitro experiments revealed biocompatibility as well as excellent efficiency in killing glioblastoma cells via PTT. The in vivo nontoxicity of the NPs was demonstrated using zebrafish embryos as an intermediate step between cells and rodent models. To warrant that an effective therapeutic dose was achieved inside the tumor, both intratumoral and intravenous routes were screened in rodent models by MRI and CT. The pharmacokinetics and biodistribution confirmed the multimodal imaging CA capabilities of the Fe@AuNPs and revealed constraints of the intravenous route for tumor targeting, dictating intratumoral administration for therapeutic applications. Finally, Fe@Au NPs were successfully used for an in vivo proof of concept of imaging-guided focused PTT against glioblastoma multiforme in a mouse model.

Multi-scale optoacoustic molecular imaging of brain diseases

The ability to non-invasively visualize endogenous chromophores and exogenous probes and sensors across the entire rodent brain with the high spatial and temporal resolution has empowered optoacoustic imaging modalities with unprecedented capacities for interrogating the brain under physiological and diseased conditions. This has rapidly transformed optoacoustic microscopy (OAM) and multi-spectral optoacoustic tomography (MSOT) into emerging research tools to study animal models of brain diseases. In this review, we describe the principles of optoacoustic imaging and showcase recent technical advances that enable high-resolution real-time brain observations in preclinical models. In addition, advanced molecular probe designs allow for efficient visualization of pathophysiological processes playing a central role in a variety of neurodegenerative diseases, brain tumors, and stroke. We describe outstanding challenges in optoacoustic imaging methodologies and propose a future outlook.

Multifunctional NaYF4:Nd/NaDyF4 nanocrystals as a multimodal platform for NIR-II fluorescence and magnetic resonance imaging

Recently, multimodal imaging nanoprobes based on the complementary advantages of various imaging methods have attracted considerable attention due to their potential application in the biomedical field. As important bioimaging nanoprobes, lanthanide-doped nanocrystals (NCs) would be expected to improve the related biophotonic technology through integrated multimodal bioimaging. Herein, water-soluble and biocompatible NaYF4:Nd/NaDyF4 NCs were prepared by a solvothermal method combined with hydrophobic interaction with phospholipids as a capping agent. The NaYF4:Nd/NaDyF4 NCs exhibit excellent colloidal stability under physiological conditions. Compared with the bare NaYF4:Nd3+ NCs, the second near-infrared (NIR-II, 1000-1700 nm) fluorescence intensities of Nd3+ ions in the NaYF4:Nd/NaDyF4 core-shell NCs at the emissions of 1058 nm and 1332 nm are enhanced by 3.46- and 1.75-fold, respectively. Moreover, the r 2 value of NaYF4:Nd/NaDyF4 NCs as T 2-weighted contrast agents is calculated to be 44.0 mM-1 s-1. As a novel multimodal imaging nanoprobe, the NaYF4:Nd/NaDyF4 NCs can be employed for both NIR-II fluorescence and magnetic resonance imaging (MRI). The phospholipid-modified NaYF4:Nd/NaDyF4 NCs demonstrate in vitro and in vivo multimodal NIR-II fluorescence imaging and MRI of HeLa cells and tumors, respectively. This study provides an effective strategy for the development of novel multimodal probes for the medical application of nanomaterials.

The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology

With the rapid development of new technologies, including artificial intelligence and genome sequencing, radiogenomics has emerged as a state-of-the-art science in the field of individualized medicine. Radiogenomics combines a large volume of quantitative data extracted from medical images with individual genomic phenotypes and constructs a prediction model through deep learning to stratify patients, guide therapeutic strategies, and evaluate clinical outcomes. Recent studies of various types of tumors demonstrate the predictive value of radiogenomics. And some of the issues in the radiogenomic analysis and the solutions from prior works are presented. Although the workflow criteria and international agreed guidelines for statistical methods need to be confirmed, radiogenomics represents a repeatable and cost-effective approach for the detection of continuous changes and is a promising surrogate for invasive interventions. Therefore, radiogenomics could facilitate computer-aided diagnosis, treatment, and prediction of the prognosis in patients with tumors in the routine clinical setting. Here, we summarize the integrated process of radiogenomics and introduce the crucial strategies and statistical algorithms involved in current studies.

Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology

We provide an overview on the use of biological assays to calibrate and initialize mechanism-based models of cancer phenomena. Although artificial intelligence methods currently dominate the landscape in computational oncology, mathematical models that seek to explicitly incorporate biological mechanisms into their formalism are of increasing interest. These models can guide experimental design and provide insights into the underlying mechanisms of cancer progression. Historically, these models have included a myriad of parameters that have been difficult to quantify in biologically relevant systems, limiting their practical insights. Recently, however, there has been much interest calibrating biologically based models with the quantitative measurements available from (for example) RNA sequencing, time-resolved microscopy, and in vivo imaging. In this contribution, we summarize how a variety of experimental methods quantify tumor characteristics from the molecular to tissue scales and describe how such data can be directly integrated with mechanism-based models to improve predictions of tumor growth and treatment response.

Bimodal microwave and ultrasound phantoms for non-invasive clinical imaging

A precise and thorough methodology is presented for the design and fabrication of bimodal phantoms to be used in medical microwave and ultrasound applications. Dielectric and acoustic properties of human soft tissues were simultaneously mimicked. The phantoms were fabricated using polyvinyl alcohol cryogel (PVA-C) as gelling agent at a 10% concentration. Sucrose was employed to control the dielectric properties in the microwave spectrum, whereas cellulose was used as acoustic scatterer for ultrasound. For the dielectric properties at microwaves, a mathematical model was extracted to calculate the complex permittivity of the desired mimicked tissues in the frequency range from 500 MHz to 20 GHz. This model, dependent on frequency and sucrose concentration, was in good agreement with the reference Cole-Cole model. Regarding the acoustic properties, the speed of sound and attenuation coefficient were employed for validation. In both cases, the experimental data were consistent with the corresponding theoretical values for soft tissues. The characterization of these PVA-C phantoms demonstrated a significant performance for simultaneous microwave and ultrasound operation. In conclusion, PVA-C has been validated as gelling agent for the fabrication of complex multimodal phantoms that mimic soft tissues providing a unique tool to be used in a range of clinical applications.

Inter/intra-tumoral dose response variations assessed using FDG-PET/CT feedback images: Impact on tumor control and treatment dose prescription

PURPOSE

To quantify inter/intra-tumoral variations of baseline metabolic activity and dose response. To evaluate their impact on tumor control and treatment dose prescription strategies.

METHODS AND MATERIALS

Tumor voxel baseline metabolic activity, SUV₀, and dose response matrix, DRM, quantified using the pre-treatment and weekly FDG-PET/CT imaging feedback for each of 34 HNSCC patients (25 HPV+ and 9 HVP-) were evaluated. Inter/intra-tumoral variations of tumor voxel (SUV₀, DRM) for each of the HPV- and HPV+ tumor groups were quantified and used to evaluate the variations of individual tumor control probabilities and the efficiency of uniform vs non-uniform treatment dose prescription strategies.

RESULTS

Tumor voxel dose response variation of all tumor voxels assessed using FDG-PET/CT imaging feedback had the mean(CV) = 0.47(47%), which was consistent with those of previously published in vitro tumor clonogenic assay. The HPV- tumors had the mean(CV) dose response, 0.53(49%), significantly larger than those of the HPV+ tumors, 0.45(43%). However, their baseline SUVs were opposite, 6.5(56%) vs 7.7(65%). Comparing to the inter-tumoral variations, both HPV-/+ tumor groups showed larger intra-tumoral variations, (53%, 58%) vs (20%, 31%) for the baseline SUV and (38%, 37%) vs (31%, 21%) for the dose response. Due to the large dose response variations, treatment dose to control the tumor voxels has very broad range with CV of TCD₅₀ = 97% for the HPV- and 67% for the HPV+ tumor group respectively. As a consequence, heterogeneous prescription dose could potentially reduce the treatment integral dose for 92% of the HPV+ tumors and 78% of the HPV- tumors.

CONCLUSIONS

The study demonstrates that tumor dose response assessed using FDG-PET/CT feedback images had a similar distribution to those assessed conventionally using in vitro tumor clonogenic assay. Inter-tumoral dose response variation seems larger for HPV- tumors, but intra-tumoral dose response variations are similar for both HPV groups. These variations cause very large variation on the individual tumor control probability and limit the efficacy of dose escalation and de-escalation in conventional clinical practice. On the other hand, heterogeneous dose prescription guided by metabolic imaging feedback has a potential advantage in radiotherapy.

Medical physics challenges in clinical MR-guided radiotherapy

The integration of magnetic resonance imaging (MRI) for guidance in external beam radiotherapy has faced significant research and development efforts in recent years. The current availability of linear accelerators with an embedded MRI unit, providing volumetric imaging at excellent soft tissue contrast, is expected to provide novel possibilities in the implementation of image-guided adaptive radiotherapy (IGART) protocols. This study reviews open medical physics issues in MR-guided radiotherapy (MRgRT) implementation, with a focus on current approaches and on the potential for innovation in IGART.Daily imaging in MRgRT provides the ability to visualize the static anatomy, to capture internal tumor motion and to extract quantitative image features for treatment verification and monitoring. Those capabilities enable the use of treatment adaptation, with potential benefits in terms of personalized medicine. The use of online MRI requires dedicated efforts to perform accurate dose measurements and calculations, due to the presence of magnetic fields. Likewise, MRgRT requires dedicated quality assurance (QA) protocols for safe clinical implementation.Reaction to anatomical changes in MRgRT, as visualized on daily images, demands for treatment adaptation concepts, with stringent requirements in terms of fast and accurate validation before the treatment fraction can be delivered. This entails specific challenges in terms of treatment workflow optimization, QA, and verification of the expected delivered dose while the patient is in treatment position. Those challenges require specialized medical physics developments towards the aim of fully exploiting MRI capabilities. Conversely, the use of MRgRT allows for higher confidence in tumor targeting and organs-at-risk (OAR) sparing.The systematic use of MRgRT brings the possibility of leveraging IGART methods for the optimization of tumor targeting and quantitative treatment verification. Although several challenges exist, the intrinsic benefits of MRgRT will provide a deeper understanding of dose delivery effects on an individual basis, with the potential for further treatment personalization.

Optimal Control Theory for Personalized Therapeutic Regimens in Oncology: Background, History, Challenges, and Opportunities

Optimal control theory is branch of mathematics that aims to optimize a solution to a dynamical system. While the concept of using optimal control theory to improve treatment regimens in oncology is not novel, many of the early applications of this mathematical technique were not designed to work with routinely available data or produce results that can eventually be translated to the clinical setting. The purpose of this review is to discuss clinically relevant considerations for formulating and solving optimal control problems for treating cancer patients. Our review focuses on two of the most widely used cancer treatments, radiation therapy and systemic therapy, as they naturally lend themselves to optimal control theory as a means to personalize therapeutic plans in a rigorous fashion. To provide context for optimal control theory to address either of these two modalities, we first discuss the major limitations and difficulties oncologists face when considering alternate regimens for their patients. We then provide a brief introduction to optimal control theory before formulating the optimal control problem in the context of radiation and systemic therapy. We also summarize examples from the literature that illustrate these concepts. Finally, we present both challenges and opportunities for dramatically improving patient outcomes via the integration of clinically relevant, patient-specific, mathematical models and optimal control theory.

Spatial Characterization of Tumor Perfusion Properties from 3D DCE-US Perfusion Maps are Early Predictors of Cancer Treatment Response

There is a need for noninvasive repeatable biomarkers to detect early cancer treatment response and spare non-responders unnecessary morbidities and costs. Here, we introduce three-dimensional (3D) dynamic contrast enhanced ultrasound (DCE-US) perfusion map characterization as inexpensive, bedside and longitudinal indicator of tumor perfusion for prediction of vascular changes and therapy response. More specifically, we developed computational tools to generate perfusion maps in 3D of tumor blood flow, and identified repeatable quantitative features to use in machine-learning models to capture subtle multi-parametric perfusion properties, including heterogeneity. Models were developed and trained in mice data and tested in a separate mouse cohort, as well as early validation clinical data consisting of patients receiving therapy for liver metastases. Models had excellent (ROC-AUC > 0.9) prediction of response in pre-clinical data, as well as proof-of-concept clinical data. Significant correlations with histological assessments of tumor vasculature were noted (Spearman R > 0.70) in pre-clinical data. Our approach can identify responders based on early perfusion changes, using perfusion properties correlated to gold-standard vascular properties.

A Microfluidic Platform to design Multimodal PEG - crosslinked Hyaluronic Acid Nanoparticles (PEG-cHANPs) for diagnostic applications

The combination of different imaging modalities can allow obtaining simultaneously morphological and functional information providing a more accurate diagnosis. This advancement can be reached through the use of multimodal tracers, and nanotechnology-based solutions allow the simultaneous delivery of different diagnostic compounds moving a step towards their safe administration for multimodal imaging acquisition. Among different processes, nanoprecipitation is a consolidate method for the production of nanoparticles and its implementation in microfluidics can further improve the control over final product features accelerating its potential clinical translation. A Hydrodynamic Flow Focusing (HFF) approach is proposed to produce through a ONE-STEP process Multimodal Pegylated crosslinked Hyaluronic Acid NanoParticles (PEG-cHANPs). A monodisperse population of NPs with an average size of 140 nm is produced and Gd-DTPA and ATTO488 compounds are co-encapsulated, simultaneously. The results showed that the obtained multimodal nanoparticle could work as MRI/Optical imaging probe. Furthermore, under the Hydrodenticity effect, a boosting of the T1 values with respect to free Gd-DTPA is preserved.

Objective response rate assessment in oncology: Current situation and future expectations

The tumor objective response rate (ORR) is an important parameter to demonstrate the efficacy of a treatment in oncology. The ORR is valuable for clinical decision making in routine practice and a significant end-point for reporting the results of clinical trials. World Health Organization and Response Evaluation Criteria in Solid Tumors (RECIST) are anatomic response criteria developed mainly for cytotoxic chemotherapy. These criteria are based on the visual assessment of tumor size in morphological images provided by computed tomography (CT) or magnetic resonance imaging. Anatomic response criteria may not be optimal for biologic agents, some disease sites, and some regional therapies. Consequently, modifications of RECIST, Choi criteria and Morphologic response criteria were developed based on the concept of the evaluation of viable tumors. Despite its limitations, RECIST v1.1 is validated in prospective studies, is widely accepted by regulatory agencies and has recently shown good performance for targeted cancer agents. Finally, some alternatives of RECIST were developed as immune-specific response criteria for checkpoint inhibitors. Immune RECIST criteria are based essentially on defining true progressive disease after a confirmatory imaging. Some graphical methods may be useful to show longitudinal change in the tumor burden over time. Tumor tissue is a tridimensional heterogenous mass, and tumor shrinkage is not always symmetrical; thus, metabolic response assessments using positron emission tomography (PET) or PET/CT may reflect the viability of cancer cells or functional changes evolving after anticancer treatments. The metabolic response can show the benefit of a treatment earlier than anatomic shrinkage, possibly preventing delays in drug approval. Computer-assisted automated volumetric assessments, quantitative multimodality imaging in radiology, new tracers in nuclear medicine and finally artificial intelligence have great potential in future evaluations.

Multimodal Molecular Imaging of the Tumour Microenvironment

The tumour microenvironment (TME) surrounding tumour cells is a highly dynamic and heterogeneous composition of immune cells, fibroblasts, precursor cells, endothelial cells, signalling molecules and extracellular matrix (ECM) components. Due to the heterogeneity and the constant crosstalk between the TME and the tumour cells, the components of the TME are important prognostic parameters in cancer and determine the response to novel immunotherapies. To improve the characterization of the TME, novel non-invasive imaging paradigms targeting the complexity of the TME are urgently needed.The characterization of the TME by molecular imaging will (1) support early diagnosis and disease follow-up, (2) guide (stereotactic) biopsy sampling, (3) highlight the dynamic changes during disease pathogenesis in a non-invasive manner, (4) help monitor existing therapies, (5) support the development of novel TME-targeting therapies and (6) aid stratification of patients, according to the cellular composition of their tumours in correlation to their therapy response.This chapter will summarize the most recent developments and applications of molecular imaging paradigms beyond FDG for the characterization of the dynamic molecular and cellular changes in the TME.

Role of Complex Networks for Integrating Medical Images and Radiomic Features of Intracranial Ependymoma Patients in Response to Proton Radiotherapy

Human cancers exhibit phenotypic diversity that medical imaging can precisely and non-invasively detect. Multiple factors underlying innovations and progresses in the medical imaging field exert diagnostic and therapeutic impacts. The emerging field of radiomics has shown unprecedented ability to use imaging information in guiding clinical decisions. To achieve clinical assessment that exploits radiomic knowledge sources, integration between diverse data types is required. A current gap is the accuracy with which radiomics aligns with clinical endpoints. We propose a novel methodological approach that synergizes data volumes (images), tissue-contextualized information breadth, and network-driven resolution depth. Following the Precision Medicine paradigm, disease monitoring and prognostic assessment are tackled at the individual level by examining medical images acquired from two patients affected by intracranial ependymoma (with and without relapse). The challenge of spatially characterizing intratumor heterogeneity is tackled by a network approach that presents two main advantages: (a) Increased detection in the image domain power from high spatial resolution, (b) Superior accuracy in generating hypotheses underlying clinical decisions.

A multimodal imaging-guided nanoreactor for cooperative combination of tumor starvation and multiple mechanism-enhanced mild temperature phototherapy

A unique nanoreactor Fe-PDAP/GOx/ICG is engineered to realize starvation therapy and enhanced phototherapy via multilevel mechanisms for simultaneous glucose consumption, oxygen supply, glutathione (GSH) depletion, and heat-resistance relief.

Microstrip Transmission Line RF Coil for a PET/MRI Insert

Purpose:

We proposed and developed a new microstrip transmission line radiofrequency (RF) coil for a positron emission tomography (PET) insert for MRI, which has low electrical interactions with PET shield boxes. We performed imaging experiments using a single-channel and a four-channel proposed RF coils for proof-of-concept.

Methods:

A conventional microstrip coil consists of a microstrip conductor, a ground conductor, and a dielectric between the two conductors. We proposed a microstrip coil for the PET insert that replaced the conventional single-layer ground conductor with the RF shield of the PET insert. A dielectric material, which could otherwise attenuate gamma photons radiated from the PET imaging tracer, was not used. As proof-of-concept, we compared conventional and the proposed single-channel coils. To study multichannel performance, we further developed a four-channel proposed RF coil. Since the MRI system had a single-channel transmission port, an interfacing four-way RF power division circuit was designed. The coils were implemented as both RF transmitters and receivers in a cylindrical frame of diameter 150 mm. Coil bench performances were tested with a network analyzer (Rohde & Schwarz, Germany), and a homogeneous phantom study was conducted for gradient echo imaging and RF field (B₁) mapping in a 3T clinical MRI system (Verio, Siemens, Erlangen, Germany).

Results:

For all coils, the power reflection coefficient was below −30 dB, and the transmission coefficients in the four-channel configuration were near or below −20 dB. The comparative single-channel coil study showed good similarity between the conventional and proposed coils. The gradient echo image of the four-channel coil showed expected flashing image intensity near the coils and no phase distortion was visible. Transmit B₁ field map resembled the image performance.

Conclusion:

The proposed PET-microstrip coil performed similarly to the conventional microstrip transmission line coil and is promising for the development of a compact coil-PET system capable of simultaneous PET/MRI analysis with an existing MRI system.

Recent advances in the development of nanoparticles for multimodality imaging and therapy of cancer

This review explores recent work directed toward the development of nanoparticles (NPs) for multimodality cancer imaging and targeted cancer therapy. In the growing era of precision medicine, theranostics, or the combined use of targeted molecular probes in diagnosing and treating diseases is playing a particularly powerful role. There is a growing interest, particularly over the past few decades, in the use of NPs as theranostic tools due to their excellent performance in receptor target specificity and reduction in off-target effects when used as therapeutic agents. This review discusses recent advances, as well as the advantages and challenges of the application of NPs in cancer imaging and therapy.

Machine learning for radiomics-based multimodality and multiparametric modeling

Due to the recent developments of both hardware and software technologies, multimodality medical imaging techniques have been increasingly applied in clinical practice and research studies. Previously, the application of multimodality imaging in oncology has been mainly related to combining anatomical and functional imaging to improve diagnostic specificity and/or target definition, such as positron emission tomography/computed tomography (PET/CT) and single-photon emission CT (SPECT)/CT. More recently, the fusion of various images, such as multiparametric magnetic resonance imaging (MRI) sequences, different PET tracer images, PET/MRI, has become more prevalent, which has enabled more comprehensive characterization of the tumor phenotype. In order to take advantage of these valuable multimodal data for clinical decision making using radiomics, we present two ways to implement the multimodal image analysis, namely radiomic (handcrafted feature) based and deep learning (machine learned feature) based methods. Applying advanced machine (deep) learning algorithms across multimodality images have shown better results compared with single modality modeling for prognostic and/or prediction of clinical outcomes. This holds great potentials for providing more personalized treatment for patients and achieve better outcomes.