迈入新世纪的硼中子俘获疗法（BNCT）

扼要叙述进入21世纪之际,硼中子俘获疗法(boron neutorn capture therapy,BNCT)在国际范围内的一些显著进展,包括BNCT的临床定位、肿瘤复发的探索、硼浓度的定量探测、靶向掺硼药物的开发以及我国医院中子照射器的问世.这些BNCT长期开发中的瓶颈趋于缓解,预示了BNCT个性化与例行化的前景更为清晰.     

Mathematical modelling of avascular tumour growth based on diffusion of nutrients and its validation

In this paper, a mathematical model based on the diffusion of nutrients is developed by considering the physiological changes accompanying the growth of avascular tumour. Avascular tumour growth involves the formation of three different zones namely proliferation, quiescent and necrotic zones. The main processes on which avascular tumour growth depends are: (i) diffusion of nutrients through the tumour from the contiguous tissues, (ii) consumption rate of the nutrients by the cells in the tumour, and (iii) cell death by apoptosis and necrosis. In the model, we consider the tumour to be spherical and the principal nutrients responsible for its growth are oxygen and glucose. By solving for the concentration profiles using the model developed, we are able to compute the radii of the quiescent and necrotic zones as well as that of the tumour. The proposed model is also validated using in vitro tumour growth data and Gompertzian empirical relationship parameters available in the literature. Our model is also successful in capturing the saturated volume of the avascular tumour for different nutrient concentrations at the tumour surface.     

Evaluation of two Compton camera models for scintimammography

We study the performance of a Si/LaBr₃:Ce Compton camera model for scintimammography, and compare it with a Si/NaI(Tl) model of similar geometry. The GEANT4 simulation toolkit was used to study the behaviour of the cameras at 511 keV. Certain simulation steps, such as the modelling of radionuclide decay times, scintillation photon transport and interactions with photomultipliers, as well as detector dead time corrections were included to make the modelling of the cameras more realistic than previous studies. The Si/LaBr₃:Ce Compton camera shows superior efficiency of 2.0×10⁻³ and resolution of 5.3 mm over the Si/NaI(Tl) Compton camera model which has the efficiency of 1.6×10⁻³ and resolution of 6.9 mm at a source-to-scatterer distance of interest, 2.5 cm. A similar result sequence is obtained for two breast tumours of 5 mm diameter embedded in the medial region of an average-size breast phantom of thickness 5 cm. Notably, the signal-to-noise ratios (SNR) obtained for the Si/LaBr₃:Ce camera are 9.7 and 3.4 for tumour/background radiation uptakes of 10:1 and 6:1, whereas 6.8 and 2.4 were obtained for the Si/NaI(Tl) camera model for the same tumour/background radiation uptakes respectively. It is therefore envisioned that with lower cost, LaBr₃:Ce could replace NaI(Tl) as the Compton camera absorber.     

Brain Tumor Segmentation in Multimodal MRI Using U-Net Layered Structure

The brain tumour is the mass where some tissues become old or damaged, but they do not die or not leave their space. Mainly brain tumour masses occur due to malignant masses. These tissues must die so that new tissues are allowed to be born and take their place. Tumour segmentation is a complex and time-taking problem due to the tumour’s size, shape, and appearance variation. Manually finding such masses in the brain by analyzing Magnetic Resonance Images (MRI) is a crucial task for experts and radiologists. Radiologists could not work for large volume images simultaneously, and many errors occurred due to overwhelming image analysis. The main objective of this research study is the segmentation of tumors in brain MRI images with the help of digital image processing and deep learning approaches. This research study proposed an automatic model for tumor segmentation in MRI images. The proposed model has a few significant steps, which first apply the pre-processing method for the whole dataset to convert Neuroimaging Informatics Technology Initiative (NIFTI) volumes into the 3D NumPy array. In the second step, the proposed model adopts U-Net deep learning segmentation algorithm with an improved layered structure and sets the updated parameters. In the third step, the proposed model uses state-of-the-art Medical Image Computing and Computer-Assisted Intervention (MICCAI) BRATS 2018 dataset with MRI modalities such as T1, T1Gd, T2, and Fluid-attenuated inversion recovery (FLAIR). Tumour types in MRI images are classified according to the tumour masses. Labelling of these masses carried by state-of-the-art approaches such that the first is enhancing tumour (label 4), edema (label 2), necrotic and non-enhancing tumour core (label 1), and the remaining region is label 0 such that edema (whole tumour), necrosis and active. The proposed model is evaluated and gets the Dice Coefficient (DSC) value for High-grade glioma (HGG) volumes for their test set-a, test set-b, and test set-c 0.9795, 0.9855 and 0.9793, respectively. DSC value for the Low-grade glioma (LGG) volumes for the test set is 0.9950, which shows the proposed model has achieved significant results in segmenting the tumour in MRI using deep learning approaches. The proposed model is fully automatic that can implement in clinics where human experts consume maximum time to identify the tumorous region of the brain MRI. The proposed model can help in a way it can proceed rapidly by treating the tumor segmentation in MRI.     

Brain Tumor Segmentation through Level Based Learning Model

Brain tumors are potentially fatal presence of cancer cells over a human brain, and they need to be segmented for accurate and reliable planning of diagnosis. Segmentation process must be carried out in different regions based on which the stages of cancer can be accurately derived. Glioma patients exhibit a different level of challenge in terms of cancer or tumors detection as the Magnetic Resonance Imaging (MRI) images possess varying sizes, shapes, positions, and modalities. The scanner used for sensing the location of tumors cells will be subjected to additional protocols and measures for accuracy, in turn, increasing the time and affecting the performance of the entire model. In this view, Convolutional Neural Networks deliver suitable models for efficient segmentation and thus delivered promising results. The previous strategies and models failed to adhere to diversity of sizes and shapes, proving to be a well-established solution for detecting tumors of bigger size. Tumors tend to be smaller in size and shape during their premature stages and they can easily evade the algorithms of Convolutional Neural Network (CNN). This proposal intends to furnish a detailed model for sensing early stages of cancer and hence perform segmentation irrespective of the current size and shape of tumors. The size of networks and layers will lead to a significant weightage when multiple kernel sizes are involved, especially in multi-resolution environments. On the other hand, the proposed model is designed with a novel approach including a dilated convolution and level-based learning strategy. When the convolution process is dilated, the process of feature extraction deals with multiscale objective and level-based learning eliminates the shortcoming of previous models, thereby enhancing the quality of smaller tumors cells and shapes. The level-based learning approach also encapsulates the feature reconstruction processes which highlights the sensing of small-scale tumors growth. Inclusively, segmenting the images is performed with better accuracy and hence detection becomes better when compared to that of hierarchical approaches.     

Optimal control of some simplified models of tumour growth

In this article we study some optimal control problems for a system of PDEs that describes the growth of a spherical tumour influenced by the mechanical action of chemicals. We make two different choices of the cost functional. We prove existence results, deduce the associated optimality systems and present iterative algorithms for the computation of the solution.     

Immunohistochemical Evaluation of Adaptor Protein FAM159B Expression in Normal and Neoplastic Human Tissues

FAM159B is a so-called adaptor protein. These proteins are essential components in numerous cell signalling pathways. However, little is known regarding FAM159B expression in normal and neoplastic human tissues. The commercially available rabbit polyclonal anti-human FAM159B antibody HPA011778 was initially characterised for its specificity using Western blot analyses and immunocytochemistry and then applied to a large series of formalin-fixed, paraffin-embedded normal and neoplastic human tissue samples. Confirmation of FAM159B’s predicted size and antibody specificity was achieved in BON-1 cells, a neuroendocrine tumour cell line endogenously expressing FAM159B, using targeted siRNA. Immunocytochemical experiments additionally revealed cytoplasmic expression of the adaptor protein. Immunohistochemical staining detected FAM159B expression in neuronal and neuroendocrine tissues such as the cortex, the trigeminal ganglia, dorsal root and intestinal ganglia, the pancreatic islets and the neuroendocrine cells of the bronchopulmonary and gastrointestinal tract, but also in the syncytiotrophoblasts of the placenta. FAM159B was also expressed in many of the 28 tumour entities investigated, with high levels in medullary and anaplastic thyroid carcinomas, parathyroid adenomas, lung and ovarian carcinomas, lymphomas and neuroendocrine tumours of different origins. The antibody HPA011778 can act as a useful tool for basic research and identifying FAM159B expression in tissue samples.     

基于介孔二氧化硅纳米颗粒的新型造影剂研究进展

介孔二氧化硅纳米颗粒作为一种新型的纳米材料,已成为多个领域的研究热点。本文综述了以介孔二氧化硅纳米颗粒为载体合成的新型造影剂的研究进展,重点阐述基于介孔二氧化硅纳米颗粒造影剂在肿瘤相关疾病磁共振成像、光学成像及超声成像等模式中的应用,并对其未来的发展趋势做了展望。