Daniel Abler

MR-FLIP: A new method that combines FLIP with anatomical information for the spatial compliance assessment of the anal sphincter muscles.

Continence results from a complex interplay between anal canal (AC) muscles and sensorimotor feedback mechanisms. The passive ability of the AC to withstand opening pressure – its compliance – has recently been shown to correlate with continence. A functional lumen imaging probe (FLIP) is used to assess AC compliance, although it provides no anatomical information. Therefore, assessment of the compliance specific anatomical structures has not been possible, and the anatomical position of critical functional zones remains unknown. In addition, the FLIP technique assumes a circular orifice cross‐section, which has not been shown for the AC. To address these shortcomings, a technique combining FLIP with a medical imaging modality is needed.

MR-FLIP: a new method that combines a functional lumen imaging probe with anatomical information for spatial compliance assessment of the anal sphincter muscles

Continence results from a complex interplay between anal canal (AC) muscles and sensorimotor feedback mechanisms. The passive ability of the AC to withstand opening pressure – its compliance – has recently been shown to correlate with continence. A functional lumen imaging probe (FLIP) is used to assess AC compliance, although it provides no anatomical information. Therefore, assessment of the compliance specific anatomical structures has not been possible, and the anatomical position of critical functional zones remains unknown. In addition, the FLIP technique assumes a circular orifice cross‐section, which has not been shown for the AC. To address these shortcomings, a technique combining FLIP with a medical imaging modality is needed.

Evaluation of a Mechanically Coupled Reaction–Diffusion Model for Macroscopic Brain Tumor Growth

The macroscopic growth of brain tumors has been studied by means of different computational modeling approaches. Glioblastoma multiforme (GBM) is the most common malignant type and is commonly modeled as a reaction–diffusion type system, accounting for its invasive growth pattern. Purely biomechanical models have been proposed to represent the mass effect caused by the growing tumor, but only a few models consider mass effect and tissue invasion effects in a single 3D model. We report first results of a comparative study that evaluates the ability of a simple computational model to reproduce the shape of pathologies found in patients. GBM invasion into brain tissue and the mechanical interaction between tumor and healthy tissue components are simulated using the finite element method (FEM). Cell proliferation and invasion are modeled as a reaction–diffusion process; simulation of the mechanic interaction relies on a linear elastic material model. Both are coupled by relating the local increase in tumor cell concentration to the generation of isotropic strain in the corresponding tissue element. The model accounts for multiple brain regions with values for proliferation, isotropic diffusion, and mechanical properties derived from literature. Tumors were seeded at multiple locations in FEM models derived from publicly available human brain atlases. Simulation results for a given tumor volume were compared to patient images. Simulated tumors showed a more symmetric growth pattern compared to their real counterparts. Resulting levels of tumor invasiveness were in agreement with simulation parameters and tumor-induced pressures of realistic magnitude were found.

Anatomy and mechanical properties of the anal sphincter muscles in healthy senior volunteers.

A large proportion of age‐related fecal incontinence is attributed to weakness or degeneration of the muscles composing the anal continence organ. However, the individual role of these muscles and their functional interplay remain poorly understood.

A statistical shape model to predict the premorbid glenoid cavity

BACKGROUND This study proposes a method for inferring the premorbid glenoid shape and orientation of scapulae affected by glenohumeral osteoarthritis (OA) to inform restorative surgery. METHODS A statistical shape model (SSM) built from 64 healthy scapulae was used to reconstruct the premorbid glenoid shape based on anatomic features that are considered unaffected by OA. First, the method was validated on healthy scapulae by quantifying the accuracy of the predicted shape in terms of surface distance, glenoid version, and inclination. The SSM-based reconstruction was then applied to 30 OA scapulae. Glenoid version and inclination were measured fully automatically and compared between the original OA glenoids, SSM-based glenoid reconstructions, and healthy scapulae. RESULTS Validation on healthy scapulae showed a root-mean-square surface distance between original and predicted glenoids of 1.0 ± 0.2 mm. The prediction error was 2.3° ± 1.8° for glenoid version and 2.1° ± 2.0° for inclination. When applied to an OA dataset, SSM-based reconstruction restored average glenoid version and inclination to values similar to the healthy situation. No differences were observed between average orientation values measured on SSM-based reconstructed and healthy scapulae (P ≥ .10). However, the average orientation of the reconstructed premorbid glenoid differed from the average orientation of OA glenoids for Walch classes A1 (version) and B2 (version, inclination, and medialization). CONCLUSION The proposed SSM can predict the premorbid glenoid cavity of healthy scapulae with millimeter accuracy. This technique has the potential to reconstruct the premorbid glenoid cavity shape, as it was prior to OA, and thus to guide the positioning of glenoid implants in total shoulder arthroplasty.

CHIC – A Multi-scale Modelling Platform for in-silico Oncology

Models of normal physiology and disease are necessary in cancer research and clinical practice to optimally exploit the available (pre)clinical multi-scale and multi-modality data. Relevant models often cover multiple spatio-temporal scales and require automated access to heterogeneous and confidential data, making their development, validation and deployment challenging. The CHIC (Computational Horizons in Cancer) [1] project develops computational models for the cancer domain, as well as tools, services and a secure infrastructure for model and data access, and reuse. The architecture is designed to support the creation of complex disease models (hypermodels) by composition of reusable component models (hypomodels). It aims to provide individualized answers to concrete clinical questions by patient-specific parametrization of disease-specific hyper-models. We introduce the CHIC project and illustrate its approach to multi-scale cancer modelling by coupled execution of two component models operating on distinct spatial scales: OncoSimulator (OS): a spatially discrete model of cancer cell proliferation and treatment effect in function of tumour, treatment and patient-specific parameters [2], implemented as cellular automaton model, Bio-mechanical Simulator (BMS): a macroscopic continuum model of mechanical effects caused by tumour expansion in patient-specific anatomy, implemented as finite element model, based on [3]. Both component models exchange information about the spatial distribution of cancer cells and mechanical pressure in order to simulate the evolution of tumour volume and shape. Latter is achieved by correcting simple spherical growth (OS) by mechanically induced growth anisotropy (BMS). Results are demonstrated on the clinical example of Glioblastoma Multiforme. CHIC is working towards an extensible platform for in-silico oncology with a set of reusable component models at its core, covering sub-cellular, cellular and super-cellular scales. Viability of infrastructure and composite hyper-models is being evaluated against clinical questions in the treatment of Nephroblastoma, Glioblastoma and Non-small Cell Lung Cancer.

CHIC-CDR: A repository for managing multi-modality clinical data and its application to in-silico oncology

A remains an important causative factor in developing several type of childhood cancers, particularly childhood lumphomas and other lymphoproliferative disorder. Also post diagnosis and treatment for any childhood malignancy, an autoimmune disorder may result in 40% of the cases. Hence this association is very complex. There need to develop various guidelines to be able to screen for these autoimmune disorder in cancer survivors or current clear strategie to reduce the risk. This paper will analyze and address the strength of this association more in detail and would reccommend a suitable screening tool.Background: Non-Hodgkins lymphoma is an aggressive malignant disease in children and adoles- cents. Although it is the fourth most common malignancy in Saudi children as reported in Saudi cancer registry, less information is available about pediatric Non-Hodgkin lymphoma and its out- come in Saudi Arabia. Study Objectives: To provide demographic data, disease characteristics, treatment protocol, toxicity and outcome of treatment in children & adolescents with Non-Hodg- kins lymphoma treated at KFMC. This study will form base line for future studies about pediatric Non-Hodgkins lymphoma in KFMC, which may help to improve outcome for children with cancer in Saudi Arabia. Study Patients and Method: We retrospectively analyzed 28 children and adoles- cents diagnosed to have Non-Hodgkins lymphoma at KFMC between December 2006 and Decem- ber 2013, followed-up through June 2014. Results: Of the 28 patients, 10 (35.7%) girls and 18 (64.3%) boys, the male-to-female ratio was 1.8; 1. The median age at time of diagnosis was 6.4 years old (range 2.0 to 13.0 years old). The majority of patients (64.3%) were aged between 5 and 12 years old. Burkitts lymphoma BL/BLL was the most common pathological subtype (60.7%), and DLBCL was the second most common subtype (21.4%). Abdominal and Retroperitoneal in- volvement was the most common primary site (78.6%) including the ileocaecal region. Most of the children presented with advanced Stage III and IV (75%), Cytogenetic study which screens specif- * Corresponding author.

Component Model for Macroscopic Tumour Biomechanics

The CHIC (Computational Horizons in Cancer) [1] project develops computational models for the cancer domain, as well as a secure infrastructure for data and model access, and reuse. It addresses challenges related to the development, validation and maintenance of multi-scale models by proposing the creation of complex disease models as composition of reusable component models. We present a versatile component model for the simulation of bio-mechanical aspects of macroscopic tumor growth. The model computes mechanical stresses and strains, resulting from tumor growth or shrinkage in a patient-specific anatomy, from a map of cancer cell concentration. In iterative coupled execution with other component models, its output can be used, for example, to guide the directionality of tumour expansion [2], or to simulate the effect of increased pressure on blood perfusion. Simulation of the bio-mechanic interaction relies on the finite element method (FEM); it is based on a hyper-elastic material model, as well as organ-specific boundary conditions and material properties. A pre-processing pipeline has been developed to automate the configuration process. In combination with automatic segmentation tools, this pipeline permits rapid generation of patient-specific FEM models for personalized simulations, including the assignment of suitable material parameters and boundary conditions from simple configuration options. Model and pre-processing pipeline are implemented using Open Source libraries and software packages (CGAL, VTK, FEBio). The model can be parametrised easily for different organs and body sites of interests; it has been applied to the simulation of kidney, lung and brain cancers in the context of CHIC.

Mechanically coupled Reaction-Diffusion Model of Macroscopic Brain Tumour Growth

Brain tumours represent a rare but serious medical condition, with glioblastoma multiforme (GBM) being the most frequent malignant histological type. These tumours are characterized by invasive growth, infiltrating surrounding healthy tissue, and poor long term prognosis with 5-y survival rates below 3% [1]. Growth and dynamics of brain tumours, and GBM in particular, have been studied extensively by means of different computational modelling approaches. Most macroscopic models of spatial tumour evolution within a patient-specific anatomy have been based either on reaction-diffusion models, e.g. [2], accounting for the invasive growth of GBM, or on purely mechanical models, e.g. [3], simulating the mass-effect caused by a growing solid tumour. Few models, such as [4, 5], consider both effects in a single 3D model in order to better understand disease progression and to support personalized treatments.