A Comparative Study of Biomechanical Simulators in Deformable Registration of Brain Tumor Images

Simulating the brain tissue deformation caused by tumor growth has been found to aid the deformable registration of brain tumor images. In this paper, we evaluate the impact that different biomechanical simulators have on the accuracy of deformable registration. We use two alternative frameworks for biomechanical simulations of mass effect in 3-D magnetic resonance (MR) brain images. The first one is based on a finite-element model of nonlinear elasticity and unstructured meshes using the commercial software package ABAQUS. The second one employs incremental linear elasticity and regular grids in a fictitious domain method. In practice, biomechanical simulations via the second approach may be at least ten times faster. Landmarks error and visual examination of the coregistered images indicate that the two alternative frameworks for biomechanical simulations lead to comparable results of deformable registration. Thus, the computationally less expensive biomechanical simulator offers a practical alternative for registration purposes

Biased efficacy estimates in phase-III dengue vaccine trials due to heterogeneous exposure and differential detectability of primary infections across trial arms.

Vaccine efficacy (VE) estimates are crucial for assessing the suitability of dengue vaccine candidates for public health implementation, but efficacy trials are subject to a known bias to estimate VE toward the null if heterogeneous exposure is not accounted for in the analysis of trial data. In light of many well-characterized sources of heterogeneity in dengue virus (DENV) transmission, our goal was to estimate the potential magnitude of this bias in VE estimates for a hypothetical dengue vaccine. To ensure that we realistically modeled heterogeneous exposure, we simulated city-wide DENV transmission and vaccine trial protocols using an agent-based model calibrated with entomological and epidemiological data from long-term field studies in Iquitos, Peru. By simulating a vaccine with a true VE of 0.8 in 1,000 replicate trials each designed to attain 90% power, we found that conventional methods underestimated VE by as much as 21% due to heterogeneous exposure. Accounting for the number of exposures in the vaccine and placebo arms eliminated this bias completely, and the more realistic option of including a frailty term to model exposure as a random effect reduced this bias partially. We also discovered a distinct bias in VE estimates away from the null due to lower detectability of primary DENV infections among seronegative individuals in the vaccinated group. This difference in detectability resulted from our assumption that primary infections in vaccinees who are seronegative at baseline resemble secondary infections, which experience a shorter window of detectable viremia due to a quicker immune response. This resulted in an artefactual finding that VE estimates for the seronegative group were approximately 1% greater than for the seropositive group. Simulation models of vaccine trials that account for these factors can be used to anticipate the extent of bias in field trials and to aid in their interpretation

tumor interaction biophysical models for medical image registration

Abstract. State-of-the art registration algorithms are based on the minimization of an image similarity functional that is regularized by adding a penalty term on the deformation map. The penalty function typically represents a smoothness regularization. In this article, we propose a constrained optimization formulation in which the image similarity functional is coupled to a biophysical model. Such a formulation is pertinent when the data has been generated by imaging tissue that undergoes deformations due to an actual biophysical phenomenon. Such is the case of co-registering tumor-bearing brain images from the same individual. We present an approximate model that couples tumor growth with the mechanical deformations of the surrounding brain tissue. We consider primary brain tumors, in particular gliomas. Glioma growth is modeled by a reaction-advection-diffusion PDE, with a two-way coupling with the underlying tissue elastic deformation. Tumor bulk, infiltration and subsequent mass-effects are not regarded separately, but captured by the model itself in the course of its evolution. Our formulation allows for updating the tumor diffusion coefficient following structural displacements caused by tumor growth/infiltration. Our forward problem implementation builds on the PETSc library (Argonne National Laboratory). Our reformulation results in a very small parameter space and we use the derivative-free optimization library APPSPACK (Sandia National Laboratories). We test the forward model and the optimization framework by using landmark-based similarity functions and by applying it to brain tumor data from clinical and animal studies. State-of-the-art registration algorithms fail in such problems due to excessive deformations. We compare our results with previous work in our group and we observed ‘ up to 50 % improvement in landmark deformation prediction. We present preliminary validation results in which we were able to reconstruct deformation fields using four degrees of freedom. Our study demonstrates the validity of our formulation and points to the need for richer datasets and fast optimization algorithms. Key words. simulations Medical image registration, tumor growth, deformable registration, soft-tissu

Digital Object Identifier Mathematical Biology Simulating complex tumor dynamics from avascular to vascular growth using a general level-set method

Abstract. A comprehensive continuum model of solid tumor evolution and development is investigated in detail numerically, both under the assumption of spherical symmetry and for arbitrary two-dimensional growth. The level set approach is used to obtain solutions for a recently developed multi-cell transport model formulated as a moving boundary problem for the evolution of the tumor. The model represents both the avascular and the vascular phase of growth, and is able to simulate when the transition occurs; progressive formation of a necrotic core and a rim structure in the tumor during the avascular phase are also captured. In terms of transport processes, the interaction of the tumor with the surrounding tissue is realistically incorporated. The two-dimensional simulation results are presented for different initial configurations. The computational framework, based on a Cartesian mesh/narrow band level-set method, can be applied to similar models that require the solution of coupled advection-diffusion equations with a moving boundary inside a fixed domain. The solution algorithm is designed so that extension to three-dimensional simulations is straightforward

An analysis of factors associated with influenza, pneumoccocal, Tdap, and herpes zoster vaccine uptake in the US adult population and corresponding inter-state variability

Despite longstanding recommendations for routine vaccination against influenza; pneumococcal; tetanus, diphtheria, acellular pertussis (Tdap); and herpes zoster (HZ) among the United States general adult population, vaccine uptake remains low. Understanding factors that influence adult vaccination and coverage variability beyond the national level are important steps toward developing targeted strategies for increasing vaccination coverage. A retrospective analysis was conducted using data from the Behavioral Risk Factor Surveillance System (2011–2014). Multivariable logistic regression modeling was employed to identify individual factors associated with vaccination (socio-demographics, health status, healthcare utilization, state of residence) and generate adjusted vaccination coverage and compliance estimates nationally and by state. Results indicated that multiple characteristics were consistently associated with a higher likelihood of vaccination across all four vaccines, including female sex, increased educational attainment, and annual household income. Model-adjusted vaccination coverage estimates varied widely by state, with inter-state variability for the most recent year of data as follows: influenza (aged ≥18 years) 30.2–49.5%; pneumococcal (aged ≥65 years) 64.0–74.7%; Tdap (aged ≥18 years) 18.7–46.6%; and HZ (aged ≥60 years) 21.3–42.9%. Model-adjusted compliance with age-appropriate recommendations across vaccines was low and also varied by state: influenza+Tdap (aged 18–59 years) 7.9–24.7%; influenza+Tdap+HZ (aged 60–64 years) 4.1–14.4%; and influenza+Tdap+HZ+pneumococcal (aged ≥65 years) 3.0–18.3%. In summary, after adjusting for individual characteristics associated with vaccination, substantial heterogeneity across states remained, suggesting that other local factors (e.g. state policies) may be impacting adult vaccines uptake. Further research is needed to understand such factors, focusing on differences between states with high versus low vaccination coverage