Predicting the Cancer Tumor Position in Liver Using Finite Element Analysis (FEA) and Artificial Intelligence (AI)

The computational power and advantages of the Finite Element Method (FEM) are noticeable. When dealing with high nonlinearity of the materials and geometrical complexity, FEM is a powerful solution, depending on the correct definition of the problem. The availability of this method has benefited many engineering areas. In the field of biomechanics and, more specifically, in Computer-Assisted Surgery, FEM is even more appreciated. This approach, however, comes at a high computational cost. Thus, a significant delay in the response impedes its implementation for real-time applications in clinical practices, even by using parallelization or utilizing Graphics Processing Unit (GPU). This is where an alternative approach is needed to accelerate FEM-based simulations to provide the desired outputs and minimizing the time lag, preventing using FEM during intra-operative applications. A novel technique that may help to overcome the obstacles mentioned above and improve the response time is the field of Machine Learning (ML). In particular, the Artificial Neural Network (ANN), as a subset of ML, has demonstrated high potentials in computer vision and pattern recognition, whose implementation can be extended to replace a FEM model once it has been trained with sufficient inputs. In this work, a FEM-ML framework is established to drastically increase the response time for predicting tumor and internal structures’ locations in the human liver for surgical applications by using ANN. This technique takes advantage of the FEM results to train a model capable of capturing large deformations of liver tissue during the surgical intervention while reporting back the nodal locations of the components with high accuracy and efficiency. For doing so, a biomechanical model of the liver, accounting for the effect of the stiffness of blood vessels, is developed, and multiple simulations with random nodal loads on the surface of the liver are conducted in the commercial software Abaqus to produce the input required for the ANN. The ANN then predicts the nodes’ coordinates resulting from the applied forces that can be used to reconstruct the deformed model of the organ

A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin.

The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary

A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin

The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary

Construction of Physics-based brain atlas and its application

Ph.DDOCTOR OF PHILOSOPH

A microfluidic platform culturing two cell lines paralleled under in-vivo like fluidic microenvironment for testing the tumor targeting of nanoparticles

Nanoparticles are attractive in medicine because their surfaces can be chemically modified for targeting specific disease cells, especially for cancer. Providing an in-vivo like platform is crucial to evaluate the biological behaviours of nanoparticles. This paper presents a microfluidic device that could culture two cell lines in parallel in in-vivo like fluidic microenvironments and be used for testing the tumor targeting of folic acid - cholesterol - chitosan (FACC) nanoparticles. The uniformity and uniformity of flow fields inside the cell culture units are investigated using the finite element method and particle tracking technology. HeLa and A549 cells are cultured in the microfluidic chip under continuous media supplementation, mimicking the fluid microenvironment in vivo. Cell introducing processes are presented by the flow behaviours of inks with different colours. The two cell lines are identified by detecting folate receptors on the cellular membranes. The growth curves of the two cell lines are measured. The two cell lines cultured paralleled inside the microfluidic device are treated with FITC-FACC to investigate the targeting of FACC. The tumor targeting of FACC are also detected by in vivo imaging of HeLa cells growth in nude mice models. The results indicate that the microfluidic device could provide a dynamic, uniform and stable fluidic microenvironment to test the tumor targeting of FACC nanoparticles

LARGE TARGET TISSUE NECROSIS OF RADIOFREQUENCY ABLATION USING MATHEMATICAL MODELLING

Radiofrequency ablation (RFA) is a clinic tool for the treatment of various target tissues. However, one of the major limitations with RFA is the ‘small’ size of target tissues that can be effectively ablated. By small it is meant the size of the target tissue is less than 3 cm in diameter of the tissue otherwise ‘large’ size of tissue in this thesis. A typical problem with RFA for large target tissue is the incompleteness of tumour ablation, which is an important reason for tumour recurring. It is widely agreed that two reasons are responsible for the tumour recurring: (1) the tissue charring and (2) the ‘heat-sink’ effect of large blood vessels (i.e. ≥3 mm in diameter). This thesis study was motivated to more quantitatively understand tissue charring during the RFA procedure and to develop solutions to increase the size of target tissues to be ablated. The thesis study mainly performed three tasks: (1) evaluation of the existing devices and protocols to give a clear understanding of the state of arts of RFA devices in clinic, (2) development of an accurate mathematical model for the RFA procedure to enable a more quantitative understanding of the small target tissue size problem, and (3) development of a new protocol based on the existing device to increase the size of target tissues to be ablated based on the knowledge acquired from (1) and (2). In (1), a design theory called axiomatic design theory (ADT) was applied in order to make the evaluation more objective. In (2), a two-compartment finite element model was developed and verified with in vitro experiments, where liver tissue was taken and a custom-made RFA system was employed; after that, three most commonly used internally cooled RFA systems (constant, pulsed, and temperature-controlled) were employed to demonstrate the maximum size of tumour that can be ablated. In (3) a novel feedback temperature-controlled RFA protocol was proposed to overcome the small target tissue size problem, which includes (a) the judicious selection of control areas and target control temperatures and (b) the use of the tissue temperature instead of electrode tip temperature as a feedback for control. The conclusions that can be drawn from this thesis are given as follows: (1) the decoupled design in the current RFA systems can be a critical reason for the incomplete target tissue necrosis (TTN), (2) using both the constant RFA and pulsed RFA, the largest TTN can be achieved at the maximum voltage applied (MVA) without the roll-off occurrence. Furthermore, the largest TTN sizes for both constant RFA and pulsed RFA are all less than 3 cm in diameter, (3) for target tissues of different sizes, the MVA without the roll-off occurrence is different and it decreases with increase of the target tissue size, (4) the largest TTN achieved by using temperature-controlled RFA under the current commercial protocol is still smaller 3 cm in diameter, and (5) the TTN with and over 3 cm in diameter can be obtained by using temperature-controlled RFA under a new protocol developed in this thesis study, in which the temperature of target tissue around the middle part of electrode is controlled at 90 ℃ for a standard ablation time (i.e. 720 s). There are a couple of contributions with this thesis. First, the underlying reason of the incomplete TTN of the current commercially available RFA systems was found, which is their inadequate design (i.e. decoupled design). This will help to give a guideline in RFA device design or improvement in the future. Second, the thesis has mathematically proved the empirical conclusion in clinic that the limit size of target tissue using the current RFA systems is 3 cm in diameter. This has advanced our understanding of the limit of the RFA technology in general. Third, the novel protocol proposed by the thesis is promising to increase the size of TTN with RFA technology by about 30%. The new protocol also reveals a very complex thermal control problem in the context of human tissues, and solving this problem effectively gives implication to similar problems in other thermal-based tumour ablation processes

Emerging Techniques in Breast MRI

As indicated throughout this chapter, there is a constant effort to move to more sensitive, specific, and quantitative methods for characterizing breast tissue via magnetic resonance imaging (MRI). In the present chapter, we focus on six emerging techniques that seek to quantitatively interrogate the physiological and biochemical properties of the breast. At the physiological scale, we present an overview of ultrafast dynamic contrast-enhanced MRI and magnetic resonance elastography which provide remarkable insights into the vascular and mechanical properties of tissue, respectively. Moving to the biochemical scale, magnetization transfer, chemical exchange saturation transfer, and spectroscopy (both “conventional” and hyperpolarized) methods all provide unique, noninvasive, insights into tumor metabolism. Given the breadth and depth of information that can be obtained in a single MRI session, methods of data synthesis and interpretation must also be developed. Thus, we conclude the chapter with an introduction to two very different, though complementary, methods of data analysis: (1) radiomics and habitat imaging, and (2) mechanism-based mathematical modeling