4D-MRI in Radiotherapy

Four-dimensional (4D) imaging provides a useful estimation of tissue motion pattern and range for radiation therapy of moving targets. 4D-CT imaging has been a standard care of practice for stereotactic body radiation therapy of moving targets. Recently, 4D-MRI has become an emerging developmental area in radiotherapy. In comparison with 4D-CT imaging, 4D-MRI provides better spatial rendering of radiotherapy targets in abdominal and pelvis regions with improved visualization of soft tissue motion. Successful implementation of 4D-MRI requires an integration of optimized acquisition protocols, advanced image reconstruction techniques, and sufficient hardware capabilities. The proposed chapter intends to introduce basic theories, current research, development, and applications of 4D-MRI in radiotherapy

Forward Electrophysiological Modeling and Inverse Problem for Uterine Contractions during Pregnancy

Uterine contractile dysfunction during pregnancy is a significant healthcare challenge that imposes heavy medical and financial burdens on both human beings and society. In the U.S., about 12% of babies are born prematurely each year, which is a leading cause of neonatal mortality and increases the possibility of having subsequent health problems. Post-term birth, in which a baby is born after 42 weeks of gestation, can cause risks for both the newborn and the mother. Currently, there is a limited understanding of how the uterus transitions from quiescence to excitation, which hampers our ability to detect labor and treat major obstetric syndromes associated with contractile dysfunction. Therefore, it is critical to develop objective methods to investigate the underlying contractile mechanism using a non-invasive sensing technique. This dissertation focuses on the multiscale forward electromagnetic modeling of uterine contractile activities and the inverse estimation of underlying source currents from abdominal magnetic field measurements. We develop a realistic multiscale forward electromagnetic model of uterine contractions in the pregnant uterus, taking into account current electrophysiological and anatomical knowledge of the uterus. Previous models focused on generating contractile forces at the organ level or on ionic concentration changes at the cellular level. Our approach is to characterize the electromagnetic fields of uterine contractions jointly at the cellular, tissue, and organ levels. At the cellular level, focusing on both plateau-type and bursting-type action potentials, we introduce a generalized version of the FitzHugh-Nagumo equations and analyze its response behavior based on bifurcation theory. To represent the anisotropy of the myometrium, we introduce a random conductivity tensor model for the fiber orientations at the tissue level. Specifically, we divide the uterus into contiguous regions, each of which is assigned a random fiber angle. We also derive analytical expressions for the spiking frequency and propagation velocity of the bursting potential. At the organ level, we propose a realistic four-compartment volume conductor, in which the uterus is modeled based on the magnetic resonance imaging scans of a near-term woman and the abdomen is curved to match the device used to take the magnetomyography measurements. To mimic the effect of the sensing direction, we incorporate a sensor array model on the surface of abdomen. We illustrate our approach using numerical examples and compute the magnetic field using the finite element method. Our results show that fiber orientation and initiation location are the key factors affecting the magnetic field pattern, and that our multiscale forward model flexibly characterizes the limited-propagation local contractions at term. These results are potentially important as a tool for interpreting the non-invasive measurements of uterine contractions. We also consider the inverse problem of uterine contractions during pregnancy. Our aim is to estimate the myometrial source currents that generate the external magnetomyography measurements. Existing works approach this problem using synthetic electromyography data. Our approach instead proceeds in two stages: develop a linear approximation model and conduct the estimation. In the first stage, we derive a linear approximation model of the sensor-oriented magnetic field measurements with respect to source current dipoles in the myometrium, based on a lead-field matrix. In particular, this lead-field matrix is analytically computed from distributed current dipoles in the myometrium according to quasi-static Maxwell\u27s equations, using the finite element method. In the second stage, we solve a constrained least-squares problem to estimate the source currents, from which we predict the intrauterine pressure. We demonstrate our approach through numerical examples with synthetic data that are generated using our multiscale forward model. In the simulations, we assume that the excitation is located at the fundus of the uterus. We also illustrate our approach using real data sets, one of which has simultaneous contractile pressure measurements. The results show that our method well captures the short-distance propagation of uterine contractile activities during pregnancy, the change of excitation area in subsequent contractions or even in a single contraction, and the timing of uterine contractions. These findings are helpful in understanding the physiological and functional properties of the uterus, potentially enabling the diagnosis of labor and the treatment of obstetric syndromes associated with contractile dysfunction such as preterm birth and post-term birth

Genomic hallmarks and therapeutic implications of G0 cell cycle arrest in cancer

BACKGROUND: Therapy resistance in cancer is often driven by a subpopulation of cells that are temporarily arrested in a non-proliferative G0 state, which is difficult to capture and whose mutational drivers remain largely unknown. RESULTS: We develop methodology to robustly identify this state from transcriptomic signals and characterise its prevalence and genomic constraints in solid primary tumours. We show that G0 arrest preferentially emerges in the context of more stable, less mutated genomes which maintain TP53 integrity and lack the hallmarks of DNA damage repair deficiency, while presenting increased APOBEC mutagenesis. We employ machine learning to uncover novel genomic dependencies of this process and validate the role of the centrosomal gene CEP89 as a modulator of proliferation and G0 arrest capacity. Lastly, we demonstrate that G0 arrest underlies unfavourable responses to various therapies exploiting cell cycle, kinase signalling and epigenetic mechanisms in single-cell data. CONCLUSIONS: We propose a G0 arrest transcriptional signature that is linked with therapeutic resistance and can be used to further study and clinically track this state

Genomic hallmarks and therapeutic implications of G0 cell cycle arrest in cancer

BackgroundTherapy resistance in cancer is often driven by a subpopulation of cells that are temporarily arrested in a non-proliferative G0 state, which is difficult to capture and whose mutational drivers remain largely unknown.ResultsWe develop methodology to robustly identify this state from transcriptomic signals and characterise its prevalence and genomic constraints in solid primary tumours. We show that G0 arrest preferentially emerges in the context of more stable, less mutated genomes which maintain TP53 integrity and lack the hallmarks of DNA damage repair deficiency, while presenting increased APOBEC mutagenesis. We employ machine learning to uncover novel genomic dependencies of this process and validate the role of the centrosomal gene CEP89 as a modulator of proliferation and G0 arrest capacity. Lastly, we demonstrate that G0 arrest underlies unfavourable responses to various therapies exploiting cell cycle, kinase signalling and epigenetic mechanisms in single-cell data.ConclusionsWe propose a G0 arrest transcriptional signature that is linked with therapeutic resistance and can be used to further study and clinically track this state

Genomic hallmarks and therapeutic implications of G0 cell cycle arrest in cancer

BACKGROUND: Therapy resistance in cancer is often driven by a subpopulation of cells that are temporarily arrested in a non-proliferative G0 state, which is difficult to capture and whose mutational drivers remain largely unknown. RESULTS: We develop methodology to robustly identify this state from transcriptomic signals and characterise its prevalence and genomic constraints in solid primary tumours. We show that G0 arrest preferentially emerges in the context of more stable, less mutated genomes which maintain TP53 integrity and lack the hallmarks of DNA damage repair deficiency, while presenting increased APOBEC mutagenesis. We employ machine learning to uncover novel genomic dependencies of this process and validate the role of the centrosomal gene CEP89 as a modulator of proliferation and G0 arrest capacity. Lastly, we demonstrate that G0 arrest underlies unfavourable responses to various therapies exploiting cell cycle, kinase signalling and epigenetic mechanisms in single-cell data. CONCLUSIONS: We propose a G0 arrest transcriptional signature that is linked with therapeutic resistance and can be used to further study and clinically track this state

DISTINCT MOLECULAR AND MORPHOLOGICAL SUBCIRCUITS OF THE SUBPLATE NEURONS

Subplate neurons (SPNs) are a population of neurons in the mammalian cerebral cortex that exist predominantly in the prenatal and early postnatal period. Loss of SPNs prevents the functional maturation of the cerebral cortex. SPNs receive subcortical input from the thalamus and relay this information to the developing cortical plate and thereby can influence cortical activity in a feed-forward manner. Little is known about potential feedback projections from the cortical plate to SPN. SPNs are also a heterogeneous population in terms of molecular and morphological identity. And the functional role of the different subpopulation of SPN remains poorly defined. This is mainly due to the lack of tools- i.e. transgenic lines and reporters to target and manipulate the SPNs at different stages of development. Hence the functional significance of the molecular diversity remains unexplored. In this study, we used a combination of genetic, molecular, anatomical and physiological approaches to address these questions and also to identify and characterize transgenic `tools' to manipulate the SPN. We identified and characterized a set of reporters and transgenic lines expressing Cre recombinase or green fluorescent protein with different levels of specificity in the subplate (SP). Using these transgenic driver lines and specific antibodies, we find that defined SPNs project to the main thalamo-recipient layers - L4 and L1 - and the spatial pattern of SPN projections to layer 4 is related to the spatial pattern of thalamo-cortical projections. However different subclasses have distinct patterns of projections with respect to the thalamic afferents. While certain subclasses have been shown to project locally, we observe that certain cell types of SPN also extend long-range projections to different thalamic nuclei. Thus molecularly defined SPN cell types are differentially integrated into the thalamo-cortical and intra-cortical connectivity. We also find a laminar difference in intra-cortical connectivity of the SPN. The first class of SPNs receives inputs from only deep cortical layers, while the second class of SPNs receives inputs from deep as well as superficial layers including layer 4 and are located more superficially. These superficial cortical inputs to SPNs emerge in the second postnatal week. Taken together, we demonstrate the presence of distinct laminar and molecular circuits in the developing subplate and characterize yet another level of heterogeneity of this population

Doctor of Philosophy

dissertationThe overall objective of this project is to develop methods that can help us to understand the movement of drugs and carriers along their routes inside solid tumors. The origins and current paradigm of targeted drug delivery offer a lot of promising strategies. However, the carriers often struggle with challenges in optimizing their own characteristics against that of the tumor's. Ultimately, they struggle with translation into the clinical setting. It is apparent that solid tumors pose a unique challenge in drug delivery. Many drug carrier characteristics are designed to take advantage of the pathophysiology of the tumor environment. However, this passive delivery and accumulation is constrained to partial distribution within the tumor. Many uncertainties remain regarding how nanoparticles enter and travel through the tumor environment. The barriers to intratumoral distribution are still currently being probed. The research herein identified transport barriers using human fibroid tumors known to have impaired drug transport. After perfusing human uteri containing fibroids with stains, probe distribution was found to correlate with features of the pathophysiology such as blood vessel characteristics, tissue and collagen density, interstitial fluid pressure, and solid stress. Methods, including custom MATLAB code, were developed to analyze the spatiotemporal distribution of two uniquely fluorescent nanoparticle doses in xenograft mice. It shows how three-dimensional distance measurements of nanoparticles from nearest blood vessels are more precise than two-dimensional measurements. Colocalization analysis on the fluorescent signals showed the two different doses (administered hours apart from each other) did not accumulate in the same locations with the tumor. Furthermore, intravital imaging showed that some vessels of the tumor would only provide access to the first dose of nanoparticles. Future work suggests further analysis of multidose interdependence and implementing these methods to screen strategies in the literature of modifying drug carriers and the tumor environment to improve intratumoral distribution of cancer drugs. The more understanding we have of the solid tumor environment and its barriers, the better we can navigate treatments to reach the tumor

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

Multimodality and multi-parametric imaging in abdominal oncology:current and future strategies to harnessing the complementary value of PET/CT and MRI

Medical imaging is essential for the diagnosis, treatment and follow-up of patients with cancer. Combinations of different, complementary imaging modalities are increasingly being used: multimodal imaging. This thesis describes recent developments and expected innovations in research (and application of) combined PET/CT and MRI in patients with abdominal cancer. To this end, the effect of integrated assessment of PET/CT and MRI scans was investigated. This resulted in a different result in 1 in 9 patients, as well as a positive effect on the confidence in the results. As a next step, the value of quantitative parameters from PET/CT and MRI was assessed, to predict the treatment outcome of patients with cancer of the rectum, uterine cervix or anus. This value appears to be limited, but the findings from conventional, visual image assessment, does contribute to the prediction