Engineering Novel Targeted Boron‐10‐Enriched Theranostic Nanomedicine to Combat against Murine Brain Tumors via MR Imaging‐Guided Boron Neutron Capture Therapy

Glioblastoma multiforme (GBM) is a very common type of “incurable” malignant brain tumor. Although many treatment options are currently available, most of them eventually fail due to its recurrence. Boron neutron capture therapy (BNCT) emerges as an alternative noninvasive therapeutic treatment modality. The major challenge in treating GBMs using BNCT is to achieve selective imaging, targeting, and sufficient accumulation of boron‐containing drug at the tumor site so that effective destruction of tumor cells can be achieved without harming the normal brain cells. To tackle this challenge, this study demonstrates for the first time that an unprecedented ¹⁰B‐enriched (96% ¹⁰B enrichment) boron nanoparticle nanomedicine (¹⁰BSGRF NPs) surface‐modified with a Fluorescein isothiocyanate (FITC)‐labeled RGD‐K peptide can pass through the brain blood barrier, selectively target at GBM brain tumor sites, and deliver high therapeutic dosage (50.5 µg ¹⁰B g^?1 cells) of boron atoms to tumor cells with a good tumor‐to‐blood boron ratio of 2.8. The ¹⁰BSGRF NPs not only can enhance the contrast of magnetic resonance (MR) imaging to help diagnose the location/size/progress of brain tumor, but also effectively suppress murine brain tumors via MR imaging‐guided BNCT, prolonging the half‐life of mice from 22 d (untreated group) to 39 d.     

A reduced-order model based on the coupled 1D-3D finite element simulations for an efficient analysis of hemodynamics problems

A reduced-order model for an efficient analysis of cardiovascular hemodynamics problems using multiscale approach is presented in this work. Starting from a patient-specific computational mesh obtained by medical imaging techniques, an analysis methodology based on a two-step automatic procedure is proposed. First a coupled 1D-3D Finite Element Simulation is performed and the results are used to adjust a reduced-order model of the 3D patient-specific area of interest. Then, this reduced-order model is coupled with the 1D model. In this way, three-dimensional effects are accounted for in the 1D model in a cost effective manner, allowing fast computation under different scenarios. The methodology proposed is validated using a patient-specific aortic coarctation model under rest and non-rest conditions.     

Microdosimetric analysis for high LET radiation

For short range high linear energy transfer (LET) radiation therapy the biological effects are strongly affected by the heterogeneity of the specific energy (z) distribution delivered to tumour cells. Three-dimensional (3-D) dosimetry information at the cellular level is required for this study. An ideal approach would be the reconstruction of the cell and the radiation source microdistribution from sequential autoradiographic sections, which is, however, not a practical solution. In this paper, a novel microdosimetry analysis method, which obtains the specific energy (z) distribution directly from the morphological information in individual autoradiographic sections, is applied to human glioblastoma multifore (GBM) and normal brain tissue specimens in boron neutron capture therapy. The results are consistent with Monte Carlo simulation and demonstrate a uniform radiation source distribution in both GBM and normal brain tissues. We also hypothesise a biophysical model based on specific energy for survival analysis. The specific energy distributions to cell nuclei were calculated with a uniform radiation source distribution. By combining this microdosimetric analysis with measured cell survival data at the low dose region, a cell survival curve at high doses is predicted, which is consistent with the commonly used simple exponential curve model for high LET radiation.     

NeXt for neuro‐radiosurgery: A fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique

Stereotactic neuro‐radiosurgery is a well‐established therapy for intracranial diseases, especially brain metastases and highly invasive cancers that are difficult to treat with conventional surgery or radiotherapy. Nowadays, magnetic resonance imaging (MRI) is the most used modality in radiation therapy for soft‐tissue anatomical districts, allowing for an accurate gross tumor volume (GTV) segmentation. Investigating also necrotic material within the whole tumor has significant clinical value in treatment planning and cancer progression assessment. These pathological necrotic regions are generally characterized by hypoxia, which is implicated in several aspects of tumor development and growth. Therefore, particular attention must be deserved to these hypoxic areas that could lead to recurrent cancers and resistance to therapeutic damage. This article proposes a novel fully automatic method for necrosis extraction (NeXt), using the Fuzzy C‐Means algorithm, after the GTV segmentation. This unsupervised Machine Learning technique detects and delineates the necrotic regions also in heterogeneous cancers. The overall processing pipeline is an integrated two‐stage segmentation approach useful to support neuro‐radiosurgery. NeXt can be exploited for dose escalation, allowing for a more selective strategy to increase radiation dose in hypoxic radioresistant areas. Moreover, NeXt analyzes contrast‐enhanced T1‐weighted MR images alone and does not require multispectral MRI data, representing a clinically feasible solution. This study considers an MRI dataset composed of 32 brain metastatic cancers, wherein 20 tumors present necroses. The segmentation accuracy of NeXt was evaluated using both spatial overlap‐based and distance‐based metrics, achieving these average values: Dice similarity coefficient 95.93% ± 4.23% and mean absolute distance 0.225 ± 0.229 (pixels).     

Customised design and manufacture of protective face masks combining a practitioner-friendly modelling approach and low-cost devices for digitising and additive manufacturing

This project analyses the viability of an efficient modelling approach using a semi-automatic algorithm within a Computer Aided Design (CAD) application in combination with low-cost digitising devices and low-cost Additive Manufacturing (AM) printers when designing and manufacturing patient-specific face masks. The aims of the study were to enable clinical practitioners to utilise the advantages of three-dimensional (3D) scanning, CAD and AM without having to be trained to use design/engineering software. Face features were captured using two 3D devices. The resulting meshes were compared via the Hausdorff Distance method. A semi-automatic modelling procedure was developed with ‘Rhinoceros’ and ‘Grasshopper’ to model the face mask and customise several features. With that procedure, volunteers modelled a face mask in less than 30 minutes in their first attempt. The resulting virtual mask was manufactured with two AM printers. An initial economic study indicated that the presented approach offers a feasible alternative to the current practices.     

A combined within-host and between-hosts modelling framework for the evolution of resistance to antimalarial drugs

The spread of drug resistance represents a significant challenge to many disease control efforts. The evolution of resistance is a complex process influenced by transmission dynamics between hosts as well as infection dynamics within these hosts. This study aims to investigate how these two processes combine to impact the evolution of resistance in malaria parasites. We introduce a stochastic modelling framework combining an epidemiological model of Plasmodium transmission and an explicit within-human infection model for two competing strains. Immunity, treatment and resistance costs are included in the within-host model. We show that the spread of resistance is generally less likely in areas of intense transmission, and therefore of increased competition between strains, an effect exacerbated when costs of resistance are higher. We also illustrate how treatment influences the spread of resistance, with a trade-off between slowing resistance and curbing disease incidence. We show that treatment coverage has a stronger impact on disease prevalence, whereas treatment efficacy primarily affects resistance spread, suggesting that coverage should constitute the primary focus of control efforts. Finally, we illustrate the importance of feedbacks between modelling scales. Overall, our results underline the importance of concomitantly modelling the evolution of resistance within and between hosts.     

An automated approach for three-dimensional quantification of fibrillar structures in optically cleared soft biological tissues

We present a novel approach allowing for a simple, fast and automated morphological analysis of three-dimensional image stacks (z-stacks) featuring fibrillar structures from optically cleared soft biological tissues. Five non-atherosclerotic tissue samples from human abdominal aortas were used to outline the multi-purpose methodology, applicable to various tissue types. It yields a three-dimensional orientational distribution of relative amplitudes, representing the original collagen fibre morphology, identifies regions of isotropy where no preferred fibre orientations are observed and determines structural parameters throughout anisotropic regions for the analysis and numerical modelling of biomechanical quantities such as stress and strain. Our method combines optical tissue clearing with second-harmonic generation imaging, Fourier-based image analysis and maximum-likelihood estimation for distribution fitting. With a new sample preparation method for arteries, we present, for the first time to our knowledge, a continuous three-dimensional distribution of collagen fibres throughout the entire thickness of the aortic wall, revealing novel structural and organizational insights into the three arterial layers.     

Deep learning and machine learning-based early survival predictions of glioblastoma patients using pre-operative three-dimensional brain magnetic resonance imaging modalities

To propose and implement an automated machine learning (ML) based methodology to predict the overall survival of glioblastoma multiforme (GBM) patients. In the proposed methodology, we used deep learning (DL) based 3D U-shaped Convolutional Neural Network inspired encoder-decoder architecture to segment the brain tumor. Further, feature extraction was performed on these segmented and raw magnetic resonance imaging (MRI) scans using a pre-trained 2D residual neural network. The dimension-reduced principal components were integrated with clinical data and the handcrafted features of tumor subregions to compare the performance of regression-based automated ML techniques. Through the proposed methodology, we achieved the mean squared error (MSE) of 87 067.328, median squared error of 30 915.66, and a SpearmanR correlation of 0.326 for survival prediction (SP) with the validation set of Multimodal Brain Tumor Segmentation 2020 dataset. These results made the MSE far better than the existing automated techniques for the same patients. Automated SP of GBM patients is a crucial topic with its relevance in clinical use. The results proved that DL-based feature extraction using 2D pre-trained networks is better than many heavily trained 3D and 2D prediction models from scratch. The ensembled approach has produced better results than single models. The most crucial feature affecting GBM patients' survival is the patient's age, as per the feature importance plots presented in this work. The most critical MRI modality for SP of GBM patients is the T2 fluid attenuated inversion recovery, as evident from the feature importance plots.     

Emerging Nanotechnology and Advanced Materials for Cancer Radiation Therapy

Radiation therapy (RT) including external beam radiotherapy (EBRT) and internal radioisotope therapy (RIT) has been widely used for clinical cancer treatment. However, owing to the low radiation absorption of tumors, high doses of ionizing radiations are often needed during RT, leading to severe damages to normal tissues adjacent to tumors. Meanwhile, the RT efficacies are limited by different mechanisms, among which the tumor hypoxia‐associated radiation resistance is a well‐known one, as there exists hypoxia inside most solid tumors while oxygen is essential to enhance radiation‐induced DNA damages. With the development in nanotechnology, there have been great interests in using nanomedicine strategies to enhance radiation responses of tumors. Nanomaterials containing high‐Z elements to absorb radiation rays (e.g. X‐ray) can act as radio‐sensitizers to deposit radiation energy within tumors and promote treatment efficacy. Nanoscale carriers are able to deliver therapeutic radioisotopes into tumors for internal RIT, or chemotherapeutic drugs for synergistically combined chemo‐radiotherapy. As uncovered in recent studies, the tumor microenvironment could be modulated by various nanomedicine approaches to overcome hypoxia‐associated radiation resistance. Herein, the authors will summarize the applications of nanomedicine for RT cancer treatment, and pay particular attention to the latest development of ‘advanced materials' for enhanced cancer RT.     

Predictions of tumour morphological stability and evaluation against experimental observations

The hallmark of malignant tumours is their spread into neighbouring tissue and metastasis to distant organs, which can lead to life threatening consequences. One of the defining characteristics of aggressive tumours is an unstable morphology, including the formation of invasive fingers and protrusions observed both in vitro and in vivo. In spite of extensive biological, clinical and modelling study and research at physical scales ranging from the molecular to the tissue, the driving dynamics of tumour invasiveness are not completely understood, partly because it is challenging to observe and study cancer as a multi-scale system. Mathematical modelling has been applied to provide further insights into these complex invasive and metastatic behaviours. Modelling a solid tumour as an incompressible fluid, we consider three possible constitutive relations to describe tumour growth, namely Darcy''s law, Stokes'' law and the combined Darcy–Stokes law. We study the tumour morphological stability described by each model and evaluate the consistency between theoretical model predictions and experimental data from in vitro three-dimensional multicellular tumour spheroids. The analysis reveals that the Stokes model is the most consistent with the experimental observations, and that it predicts our experimental tumour growth is marginally stable. We further show that it is feasible to extract parameter values from a limited set of data and create a self-consistent modelling framework that can be extended to the multi-scale study of cancer.     

Iron oxide nanoparticle-mediated radiation delivery for glioblastoma treatment

Radiotherapy is a mainstay adjunctive therapy for glioblastoma (GBM). Despite the outcome improvement achieved with radiation, GBM prognosis remains dismal. Here we introduce a tumor-targeted iron oxide nanoparticle (NP) that intensifies the energy transfer of conventional photon radiotherapy on a selective cellular basis. Several NPs were formulated with systematic architectural variation to study and optimize reactive oxygen species (ROS) production. Selected from this screening stage, a biocompatible tumor-targeted NP was tested in vitro using two models of GBM, and then in vivo, using an orthotopic human primary GBM xenograft mouse model. Animals that received intravenous NP before irradiation demonstrated a 3-fold reduction in tumor growth and a 2-fold increase in survival. Cellular damage was investigated using in vivo magnetic resonance spectroscopy, which demonstrated increased therapeutic cytotoxicity specific to the tumor mass. Our work presents a viable therapeutic strategy to improve radiation therapy for GBM.     

Combination of DOM with LES in a gas turbine combustor

A three-dimensional numerical study is conducted to investigate the radiative heat transfer in a model gas turbine combustor. The Discrete Ordinates Method (DOM/S_n) has been implemented to solve the filtered Radiative Transfer Equation (RTE) for the radiation modelling and this has been combined with a Large Eddy Simulation (LES) of the flow, temperature and composition fields within the combustion chamber. The radiation considered in the present work is due only to the hot combustion gases notably carbon dioxide (CO₂) and water vapour (H₂O), which is also known as the ‘non-luminous’ radiation. A benchmark problem of the ideal furnace is considered first to examine the accuracy and computational efficiency of the DOM in the three-dimensional general body fitted co-ordinate systems.     

Identify signature regulatory network for glioblastoma prognosis by integrative mRNA and miRNA co‐expression analysis

Glioblastoma multiforme (GBM) is the most common and aggressive type of primary brain tumor in adults. Patients with this disease have a poor prognosis. The objective of this study is to identify survival‐related individual genes (or miRNAs) and miRNA ‐mRNA pairs in GBM using a multi‐step approach. First, the weighted gene co‐expression network analysis and survival analysis are applied to identify survival‐related modules from mRNA and miRNA expression profiles, respectively. Subsequently, the role of individual genes (or miRNAs) within these modules in GBM prognosis are highlighted using survival analysis. Finally, the integration analysis of miRNA and mRNA expression as well as miRNA target prediction is used to identify survival‐related miRNA ‐mRNA regulatory network. In this study, five genes and two miRNA modules that significantly correlated to patient''s survival. In addition, many individual genes (or miRNAs) assigned to these modules were found to be closely linked with survival. For instance, increased expression of neuropilin‐1 gene (a member of module turquoise) indicated poor prognosis for patients and a group of miRNA ‐mRNA regulatory networks that comprised 38 survival‐related miRNA ‐mRNA pairs. These findings provide a new insight into the underlying molecular regulatory mechanisms of GBM.Inspec keywords: RNA, molecular biophysics, genetics, cancerOther keywords: signature regulatory network, glioblastoma prognosis, mRNA coexpression analysis, miRNA coexpression analysis, glioblastoma multiforme, brain tumour, microRNAs, pathogenesis, genome‐wide regulatory networks, miRNA‐mRNA pairs, weighted gene coexpression network analysis, survival analysis, GBM prognosis, integration analysis, neuropilin‐1 gene, module turquoise, molecular regulatory mechanisms     

Imaging in three-dimensional conformal radiation therapy

By and large, radiation therapy is a noninvasive method of the treatment of cancer requiring knowledge of the precise location and extent of the disease to be destroyed and the organs to be protected from radiation damage. Images have always played a central role in providing the requisite information for this mode of cancer treatment. Different types of images, such as computed tomography (CT); magnetic resonance imaging (MRI), positron emission tomographic (PET), simulator, etc., are used to varying degrees depending upon their relevance to radiation oncology as well as their accessibility. It is often necessary to merge data from various types of images. The availability of three-dimensional information from tomographic images has allowed the introduction of three-dimensional conformal radiation therapy (3DCRT) methods. Images are employed for diagnosing and establishing the extent of the disease, planning and delivery treatments, and evaluating the effectiveness of the treatment in controlling the disease and assessing the damage to normal tissues. Each image type has a unique informational content of importance to radiation oncology. To extract the maximum information from images, it is necessary to employ various image processing tools. These tools allow us to perform such functions as (1) image enhancement; (2) image correlation to register information from various images; (3) segmentation of images to extract the surface outlines of the tumor volume and normal anatomic structures; and (4) two- and three dimensional data visualization. One important aspect of planning radiation treatments is the computation of dose distribution in the patient for a proposed configuration of radiation beams. This step requires tracing rays in a three-dimensional CT image data set to compute radiologic path lengths through the patient's body. Although images are employed to a great advantage in radiation oncology, many problems still remain to be solved. Of the various 3DCRT tasks, the outlining of contours of the volume of intended treatment and normal anatomy on images is highly labor-intensive and fraught with uncertainty. In addition, the integration of data from various imaging modalities is difficult and error prone because of distortions inherent in imaging and also because of the motion, deformation, and displacement of patients and their internal anatomy. Investigations are in progress to find solutions to these problems.     

Dual‐Modality Surface‐Enhanced Resonance Raman Scattering and Multispectral Optoacoustic Tomography Nanoparticle Approach for Brain Tumor Delineation

Difficulty in visualizing glioma margins intraoperatively remains a major issue in the achievement of gross total tumor resection and, thus, better clinical outcome of glioblastoma (GBM) patients. Here, the potential of a new combined optical + optoacoustic imaging method for intraoperative brain tumor delineation is investigated. A strategy using a newly developed gold nanostar synthesis method, Raman reporter chemistry, and silication method to produce dual‐modality contrast agents for combined surface‐enhanced resonance Raman scattering (SERRS) and multispectral optoacoustic tomography (MSOT) imaging is devised. Following intravenous injection of the SERRS‐MSOT‐nanostars in brain tumor bearing mice, sequential MSOT imaging is performed in vivo and followed by Raman imaging. MSOT is able to accurately depict GBMs three‐dimensionally with high specificity. The MSOT signal is found to correlate well with the SERRS images. Because SERRS enables uniquely sensitive high‐resolution surface detection, it could represent an ideal complementary imaging modality to MSOT, which enables real‐time, deep tissue imaging in 3D. This dual‐modality SERRS‐MSOT‐nanostar contrast agent reported here is shown to enable high precision depiction of the extent of infiltrating GBMs by Raman‐ and MSOT imaging in a clinically relevant murine GBM model and could pave new ways for improved image‐guided resection of brain tumors.     

Nanoparticle‐Enhanced Radiotherapy to Trigger Robust Cancer Immunotherapy

External radiotherapy is extensively used in clinic to destruct tumors by locally applied ionizing‐radiation beams. However, the efficacy of radiotherapy is usually limited by tumor hypoxia‐associated radiation resistance. Moreover, as a local treatment technique, radiotherapy can hardly control tumor metastases, the major cause of cancer death. Herein, core–shell nanoparticles based poly(lactic‐co‐glycolic) acid (PLGA) are fabricate, by encapsulating water‐soluble catalase (Cat), an enzyme that can decompose H₂O₂ to generate O₂, inside the inner core, and loading hydrophobic imiquimod (R837), a Toll‐like‐receptor‐7 agonist, within the PLGA shell. The formed PLGA‐R837@Cat nanoparticles can greatly enhance radiotherapy efficacy by relieving the tumor hypoxia and modulating the immune‐suppressive tumor microenvironment. The tumor‐associated antigens generated postradiotherapy‐induced immunogenic cell death in the presence of such R837‐loaded adjuvant nanoparticles will induce strong antitumor immune responses, which together with cytotoxic T‐lymphocyte associated protein 4 (CTLA‐4) checkpoint blockade will be able to effectively inhibit tumor metastases by a strong abscopal effect. Moreover, a long term immunological memory effect to protect mice from tumor rechallenging is observed post such treatment. This work thus presents a unique nanomedicine approach as a next‐generation radiotherapy strategy to enable synergistic whole‐body therapeutic responses after local treatment, greatly promising for clinical translation.     

Brain-Targeted HFn-Cu-REGO Nanoplatform for Site-Specific Delivery and Manipulation of Autophagy and Cuproptosis in Glioblastoma

Durable glioblastoma multiforme (GBM) management requires long-term chemotherapy after surgery to eliminate remaining cancerous tissues. Among chemotherapeutics, temozolomide is considered as the first-line drug for GBM therapy, but the treatment outcome is not satisfactory. Notably, regorafenib, an oral multi-kinase inhibitor, has been reported to exert a markedly superior effect on GBM suppression compared with temozolomide. However, poor site-specific delivery and bioavailability significantly restrict the efficient permeability of regorafenib to brain lesions and compromise its treatment efficacy. Therefore, human H-ferritin (HFn), regorafenib, and Cu²⁺ are rationally designed as a brain-targeted nanoplatform (HFn-Cu-REGO NPs), fulfilling the task of site-specific delivery and manipulating autophagy and cuproptosis against GBM. Herein, HFn affords a preferential accumulation capacity to GBM due to transferrin receptor 1 (TfR1)-mediated active targeting and pH-responsive delivery behavior. Moreover, regorafenib can inhibit autophagosome-lysosome fusion, resulting in lethal autophagy arrest in GBM cells. Furthermore, Cu²⁺ not only facilitates the encapsulation of regorafenib to HFn through coordination interaction but also disturbs copper homeostasis for triggering cuproptosis, resulting in a synergistical effect with regorafenib-mediated lethal autophagy arrest against GBM. Therefore, this work may broaden the clinical application scope of Cu²⁺ and regorafenib in GBM treatment via modulating autophagy and cuproptosis.     

Development and use of augmented reality and 3D printing in consulting patient with complex skull base cholesteatoma

Temporal bone and skull base anatomy is complex and can pose difficulties in patient’s perception of disease and anatomy, perceived risks, and complications of surgery. We wish to demonstrate the development and use of augmented reality (AR) technology and three-dimensional (3D) printing to assist in preoperative patient consultation in the case of a complex skull base cholesteatoma. A series of 3D models were constructed from a patient’s petrous temporal bone computed tomography (CT) scans using CAD (computer-aided design) software to display the cholesteatoma affected temporal bone and related structures. Supplementary labels and titles were also created. A two-dimensional (2D) image was created as an AR recognition marker. Both 3D and 2D assets were uploaded, linked in an AR development platform called Hyperspaces which was then published to the Hyperspaces cloud server to build an AR application for free access using a predefined keyword on an iOS mobile device. Patient’s temporal bone was also fabricated through a fused deposition modelling 3D printer using polylactic acid filament for patient consultation. AR and 3D printing allow patient-specific clinical information and complexities to be made readily available to the patient and doctor at low cost, and aid in understanding complex skull base anatomy and progressive disease such as cholesteatoma. The advancement of AR and 3D printing technologies are making complex and patient-specific 3D medical data visualisation feasible and tangible on a mobile device and in hand. Thus, these technologies can be used as an invaluable patient education and counselling tool offering a powerful medium in specialties where difficult anatomical challenges are encountered.Abbreviations: ABS: acrylonitrile–butadiene–styrene; AM: additive manufacturing; AR: augmented reality; CAD: computer-aided design; CT: computed tomography; DICOM: Digital Imaging and Communication in Medicine; FBX: Filmbox; FDM: fused deposition modelling; JPEG: joint photographic experts group; MRI: magnetic resonance imaging; PLA: polylactic acid; ROI: region of interest; STL: Stereolithography     

Evaluation of a new self-developing instant film for imaging and dosimetry

Radiation sensitive films are standard dosimetric tools in radiation therapy. Films are used for machine quality assurance (QA) and treatment planning software evaluation. With the advent of intensity modulated radiation therapy (IMRT), simple and fast imaging technology is needed for patient-specific verification of radiation fields. Conventional radiographic films are often used. Radiochromic films, e.g. Gafchromic films, were recently introduced to the market. But these films have some disadvantages. JP Laboratories have developed a prototype radiochromic film, called SIFID (self-developing, instant film for imaging and dosimetry) with superior performance such that SIFID is unaffected by ambient light for months, stable up to 90 degrees C and can be archived. SIFID is made of polymerisable diacetylene. The film develops blue colour instantly upon absorbing radiation. We evaluated the film for radiation therapy applications. Our preliminary data demonstrate its feasibility as a dosimetric tool for IMRT QA as well as for other applications.