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PURPOSE A semiautomated pipeline for the collection and curation of free-text and imaging real-world data (RWD) was developed to quantify cancer treatment outcomes in large-scale retrospective real-world studies. The objectives of this article are to illustrate the challenges of RWD extraction, to demonstrate approaches for quality assurance, and to showcase the potential of RWD for precision oncology. METHODS We collected data from patients with advanced melanoma receiving immune checkpoint inhibitors at the Lausanne University Hospital. Cohort selection relied on semantically annotated electronic health records and was validated using process mining. The selected imaging examinations were segmented using an automatic commercial software prototype. A postprocessing algorithm enabled longitudinal lesion identification across imaging time points and consensus malignancy status prediction. Resulting data quality was evaluated against expert-annotated ground-truth and clinical outcomes obtained from radiology reports. RESULTS The cohort included 108 patients with melanoma and 465 imaging examinations (median, 3; range, 1-15 per patient). Process mining was used to assess clinical data quality and revealed the diversity of care pathways encountered in a real-world setting. Longitudinal postprocessing greatly improved the consistency of image-derived data compared with single time point segmentation results (classification precision increased from 53% to 86%). Image-derived progression-free survival resulting from postprocessing was comparable with the manually curated clinical reference (median survival of 286 v 336 days, P = .89). CONCLUSION We presented a general pipeline for the collection and curation of text- and image-based RWD, together with specific strategies to improve reliability. We showed that the resulting disease progression measures match reference clinical assessments at the cohort level, indicating that this strategy has the potential to unlock large amounts of actionable retrospective real-world evidence from clinical records.

Background: Glioblastoma is the deadliest, yet most common, brain tumor in adults, with poor survival and response to aggressive therapy. Therapeutic failure results from a number of causes inherent to these tumors. Imaging, computational, and drug delivery approaches can aid in the quest to access and kill each tumor cell in patients. One factor, interstitial fluid flow, is a driving force therapeutic delivery. However, convective and diffusive transport mechanisms are un-der-studied. In this study, we examine the application of a novel image analysis method to meas-ure fluid flow and diffusion in glioblastoma patients with MRI and compare to patient outcomes. Methods: Building on a prior imaging methodology tested and validated in vitro, in silico and in preclinical models of disease, here we apply our analysis method to archival patient data from the Ivy GAP dataset. Results: We characterize interstitial fluid flow and diffusion patterns in patients. We find strong correlations between flow rates measured within tumors and in the surrounding parenchymal space, where we hypothesized that velocities would be higher. Looking at overall magnitudes, there is significant correlation with both age and survival in this patient cohort. Additionally, we find that tumor size nor resection significantly alter the velocity magnitude. Last, we map the flow pathways in patient tumors and find variability in degree of directionality that we hypothesize in future studies may lead to information concerning treatment, invasive spread, and progression. Conclusions: Analysis of standard DCE-MRI in patients with glioblastoma offers more infor-mation regarding transport within and around tumor, can be measured post-resection and mag-nitudes correlate with patient prognosis.

Background: Glioblastoma (GBM) is the deadliest and most common brain tumor in adults, with poor survival and response to aggressive therapy. Limited access of drugs to tumor cells is one reason for such grim clinical outcomes. A driving force for therapeutic delivery is interstitial fluid flow (IFF), both within the tumor and in the surrounding brain parenchyma. However, convective and diffusive transport mechanisms are understudied. In this study, we examined the application of a novel image analysis method to measure fluid flow and diffusion in GBM patients. Methods: Here, we applied an imaging methodology that had been previously tested and validated in vitro, in silico, and in preclinical models of disease to archival patient data from the Ivy Glioblastoma Atlas Project (GAP) dataset. The analysis required the use of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which is readily available in the database. The analysis results, which consisted of IFF flow velocity and diffusion coefficients, were then compared to patient outcomes such as survival. Results: We characterized IFF and diffusion patterns in patients. We found strong correlations between flow rates measured within tumors and in the surrounding parenchymal space, where we hypothesized that velocities would be higher. Analyzing overall magnitudes indicated a significant correlation with both age and survival in this patient cohort. Additionally, we found that neither tumor size nor resection significantly altered the velocity magnitude. Lastly, we mapped the flow pathways in patient tumors and found a variability in the degree of directionality that we hypothesize may lead to information concerning treatment, invasive spread, and progression in future studies. Conclusions: An analysis of standard DCE-MRI in patients with GBM offers more information regarding IFF and transport within and around the tumor, shows that IFF is still detected post-resection, and indicates that velocity magnitudes correlate with patient prognosis. Published version

While targeted therapies exist for human epidermal growth factor receptor 2 positive (HER2+) breast cancer, HER2+ patients do not always respond to therapy. We present the results of utilizing a biophysical mathematical model to predict tumor response for two HER2+ breast cancer patients treated with the same therapeutic regimen but who achieved different treatment outcomes. Quantitative data from magnetic resonance imaging (MRI) and 64Cu-DOTA-Trastuzumab positron emission tomography (PET) are used to estimate tumor density, perfusion, and distribution of HER2-targeted antibodies for each individual patient. MRI and PET data are collected prior to therapy, and follow-up MRI scans are acquired at a midpoint in therapy. Given these data types, we align the data sets to a common image space to enable model calibration. Once the model is parameterized with these data, we forecast treatment response with and without HER2-targeted therapy. By incorporating targeted therapy into the model, the resulting predictions are able to distinguish between the two different patient responses, increasing the difference in tumor volume change between the two patients by >40%. This work provides a proof-of-concept strategy for processing and integrating PET and MRI modalities into a predictive, clinical-mathematical framework to provide patient-specific predictions of HER2+ treatment response.

Chimeric antigen receptor (CAR) T-cell therapy has shown promise in the treatment of hematological cancers and is currently being investigated for solid tumors including high-grade glioma brain tumors. There is a desperate need to quantitatively study the factors that contribute to the efficacy of CAR T-cell therapy in solid tumors. In this work we use a mathematical model of predator-prey dynamics to explore the kinetics of CAR T-cell killing in glioma: the Chimeric Antigen Receptor t-cell treatment Response in GliOma (CARRGO) model. The model includes rates of cancer cell proliferation, CAR T-cell killing, CAR T-cell proliferation and exhaustion, and CAR T-cell persistence. We use patient-derived and engineered cancer cell lines with an in vitro real-time cell analyzer to parameterize the CARRGO model. We observe that CAR T-cell dose correlates inversely with the killing rate and correlates directly with the net rate of proliferation and exhaustion. This suggests that at a lower dose of CAR T-cells, individual T-cells kill more cancer cells but become more exhausted as compared to higher doses. Furthermore, the exhaustion rate was observed to increase significantly with tumor growth rate and was dependent on level of antigen expression. The CARRGO model highlights nonlinear dynamics involved in CAR T-cell therapy and provides novel insights into the kinetics of CAR T-cell killing. The model suggests that CAR T-cell treatment may be tailored to individual tumor characteristics including tumor growth rate and antigen level to maximize therapeutic benefit.Statement of SignificanceWe utilize a mathematical model to deconvolute the nonlinear contributions of CAR T-cell proliferation and exhaustion to predict therapeutic efficacy and dependence on CAR T-cell dose and target antigen levels.

While targeted therapies exist for human epidermal growth factor receptor 2 positive (HER2 +) breast cancer, HER2 + patients do not always respond to therapy. We present the results of utilizing a biophysical mathematical model to predict tumor response for two HER2 + breast cancer patients treated with the same therapeutic regimen but who achieved different treatment outcomes. Quantitative data from magnetic resonance imaging (MRI) and 64Cu-DOTA-trastuzumab positron emission tomography (PET) are used to estimate tumor density, perfusion, and distribution of HER2-targeted antibodies for each individual patient. MRI and PET data are collected prior to therapy, and follow-up MRI scans are acquired at a midpoint in therapy. Given these data types, we align the data sets to a common image space to enable model calibration. Once the model is parameterized with these data, we forecast treatment response with and without HER2-targeted therapy. By incorporating targeted therapy into the model, the resulting predictions are able to distinguish between the two different patient responses, increasing the difference in tumor volume change between the two patients by > 40%. This work provides a proof-of-concept strategy for processing and integrating PET and MRI modalities into a predictive, clinical-mathematical framework to provide patient-specific predictions of HER2 + treatment response.

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 degrees +/- 1.8 degrees for glenoid version and 2.1 degrees +/- 2.0 degrees 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 Al (version) and 132 (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 arthmplasty. (C) 2018 Journal of Shoulder and Elbow Surgery Board of Trustees. All rights reserved.

Chimeric antigen receptor (CAR) T-cell therapy is an emerging targeted immunotherapy which has shown success in liquid cancers such as leukemias. CAR T-cells are also being used for the treatment of solid tumors such as glioblastoma, which is a primary brain tumor. Ongoing phase I trials have been designed to evaluate CAR T-cell dosing, scheduling, and route of administration in order to understand and improve the efficacy of CAR T-cell therapy. A better understanding of factors leading to the success of CAR T-cell immunotherapy for solid tumors will be necessary to improve outcomes for patients with solid tumors and to advance the field of CAR T-cell immuno-oncology. Here we use mathematical model to explore factors in determining a successful response to CAR T-cell therapy: proliferation, persistence, and killing capacity of CAR T-cells. Using a novel in vitro experimental apparatus, we are able to measure the density of cancer cells over several days in 15 minute interval time resolution. This highly temporally resolved data provides a unique opportunity to confidently estimate parameters of the model and to provide insights into the dynamics of CAR T-cell proliferation, persistence, and killing capacity. Furthermore we explore the relationship between these factor with CAR T-cell dose level. We will show results from experiments using patient-derived cancer cell lines as well as cancer cells engineered to express specific levels of the target antigen (IL13Rα2) to quantitatively evaluate the roles of proliferation, persistence, and killing in cells with different levels of antigen expression. We will discuss the interpretation of the model parameters and demonstrate the clinical value of this analysis through an application of CAR T-cell treatment tailored to the dynamics of an individual patient’s cancer growth rate.

Interstitial fluid flow around brain tumors is increased above normal levels. This force has been shown to drive tumor cell invasion in brain tumors and alter the extracellular matrix, vasculature, immune and cellular niches in non-nervous tissues in ways that promote tumor progression [1]. Here, by applying our dynamic contrast enhanced MRI method and analysis [2], we measured interstitial fluid flow in multiple patient cell line-derived xenograft models of glioma in mice (n=6–8 per group, four different lines). The range of interstitial velocity magnitudes is similar across these models (range from 0.1–2.6 micron/s), though intratumoral variability is high. We examined tumors by immunohistochemistry including tumor cells (proliferating cells, ki67; stem-like cells, Nestin and Sox2; apoptotic cells, Caspase 3/7), the cellular microenvironment (astrocytes, GFAP and GLAST; microglia/macrophages, Iba-1 and CX3CR1; blood vessels, CD31; neurons, NeuN and Neurofilament; oligodendrocytes, OSP1), and of the extracellular space (hyaluronan, HABP; tenascin C; white matter tracts, fluoromyelin; cytokines). Fluid flow pathways and rates are dictated by anatomical features of the brain in these models (i.e. blood vessels and white matter tracts). Blood vessel density did not significantly correlate with regions of higher velocities. Based on our observations we performed in vitro experiments in a 3D tissue engineered model of the glioma microenvironment [3] to assess invasion within the range of measured flow rates. Velocities were controlled using either microfluidic pump driven flow or pressure driven flow and cellular outcomes were assessed using flow cytometry and immunocytochemistry of the 3D gel system. The results of these studies will be discussed. 1) Munson, Shieh, Cancer Management Research (2014) 6: 317; 2) Kingsmore, Vaccari, Abler, et al. APL Bioengineering (In press); 3) Harris, Yuan, Munson, Methods (2018) 134:20–31.

Glioblastoma (GBM) is the most frequent malignant brain tumour in adults. Its growth is characterized by infiltration of surrounding healthy tissue, and the formation of a necrotic core. GBM presents with varying degree of mass-effect which results in healthy-tissue deformation, midline shift or herniation. Biomechanical forces, such as those resulting from displacive tumour growth, shape the tumour environment, contribute to tumour progression and may affect treatment response and outcome.To investigate the role of tumour mass-effect for tumour evolution, we have previously developed a mechanically-coupled reaction-diffusion model that captures three dominant aspects of macroscopic GBM growth: (a) tumour cell proliferation, (b) the diffuse invasion of the growing tumour into surrounding healthy tissue, and (c) the resulting mass effect.Here we present an implementation of this model in FENICS and first steps towards an image-based optimization approach, based on dolphin-adjoint, to estimate patient-specific parameters from clinical magnetic-resonance imaging (MRI).

GBMs present with different growth phenotypes, ranging from invasive lesions without notable mass-effect to strongly displacing lesions that induce mechanical stresses and result in healthy-tissue deformation, midline shift or herniation. Biomechanical forces, such as those resulting from displacive tumor growth, are recognized to shape the tumor environment and to contribute to tumor progression. We therefore expect that biomechanical forces exerted by lesions on the brain parenchyma have implications on the biophysical level, and that they may affect treatment response and outcome. To better understand the role of biomechanics in the formation of different GBM phenotypes we started developing a framework for the predictive mathematical modeling of mechanical tumor-healthy tissue interaction on the macroscopic level. The tumor’s mass-effect is represented by a solid-mechanics model of brain tissue that computes tumor-induced strain based on local tumor cell concentration. The framework allows to seed tumors at multiple locations in a human brain atlas. It simulates tumor evolution over time and across different brain regions using literature-based parameter estimates for tumor cell proliferation, as well as isotropic motility, and mechanical tissue properties. Despite its simplicity, the mathematical model yielded realistic estimates of the mechanical impact of a growing tumor on intra-cranial pressure. However, comparison to publicly available GBM imaging data showed that asymmetric shapes could not be reproduced by isotropic growth assumptions. Here we present and evaluate an extended version of this mechanically-coupled reaction-diffusion model that takes into account tissue anisotropies based on MRI diffusion tensor imaging (MR-DTI). Structural anisotropies in brain tissue have been found to affect the directionality of tumor cell migration and are critical to mechanical behavior. This makes them likely to play a role also in the development of GBM phenotypes.

BACKGROUND 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. METHODS This study employs a novel technique based on the combination of MR imaging and FLIP measurements (MR-FLIP) to obtain anatomical and mechanical information simultaneously. Unlike previous methods used to assess the mechanics of the continence organ, MR-FLIP allows inter-individual comparisons and statistical analysis of the sphincter morpho-mechanical parameters. The anatomy as well as voluntary and involuntary mechanical properties of the anal continence organ were characterized in 20 healthy senior volunteers. RESULTS Results showed that the external anal sphincter (EAS) forms a funnel-like shape with wall thickness increasing by a factor of 2.5 from distal (6 ± 0 mm) to proximal (15 ± 3 mm). Both voluntary and involuntary mechanical properties in this region correlate strongly with the thickness of the muscle. The positions of least compliance and maximal orifice closing were both located toward the proximal EAS end. In addition, maximal contraction during squeeze maneuvers was reached after 2 s, but high muscle fatigue was measured during a 7 s holding phase, corresponding to about 60% loss of the energy produced by the muscles during the contraction phase. CONCLUSIONS This work reports baseline parameters describing the morpho-mechanical condition of the sphincter muscle of healthy elderly volunteers. New parameters were also proposed to quantify the active properties of the muscles based on the mechanical energy associated with muscle contraction and fatigue. This information could be used to assess patients suffering from AI or for the design of novel implants.

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.

Aim 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. Method We implemented a new research method (MR-FLIP) that combines FLIP with MR imaging. Twenty healthy volunteers underwent MR-FLIP and conventional FLIP assessment. MR-FLIP was validated by comparison with FLIP results. Anatomical markers were identified, and the cross-sectional shape of the orifice was investigated. Results MR-FLIP provides compliance measurements identical to those obtained by conventional FLIP. Anatomical analysis revealed that the least compliant AC zone was located at the proximal end of the external anal sphincter (EAS). The cross-sectional shape of the AC was found to deviate only slightly from circularity in healthy volunteers. Conclusion The proposed method is equivalent to classical FLIP. It establishes for the first time direct mapping between local tissue compliance and anatomical structure, which is key to gaining novel insights into (in)continence. In addition, MR-FLIP provides a tool for better understanding conventional FLIP measurements in the AC by quantifying its limitations and assumptions.

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 (hyper-models) by composition of reusable component models (hypo-models). 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). 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. [1] http://www.chic-vph.eu/ [2] Stamatakos, G., 2011. In silico oncology: PART I Clinically oriented cancer multilevel modeling based on discrete event simulation. In: Deisboeck, T., Stamatakos, G. (Eds.), Multiscale Cancer Modeling. Chapman & Hall/CRC, Boca Raton, Florida,USA. [3] C. P. May, E. Kolokotroni, G. S. Stamatakos, and P. Büchler, ‘Coupling biomechanics to a cellular level model: An approach to patient-specific image driven multi-scale and multi-physics tumor simulation’, Progress in Biophysics and Molecular Biology, vol. 107, no. 1, pp. 193–199, Oct. 2011.

Biomechanical forces are known to affect tumor growth and evolution [1]. Likewise, tumor growth drives physical changes in the micro-environment that affect tissue solid and fluid mechanics. Tumor mass effect, resulting from rapid tumor cell proliferation, has been shown to be prognostic for poor outcome in glioblastoma (GBM) patients and to be associated with the expression of gene signatures consistent with proliferative growth phenotype [2]. Similarly, elevated interstitial fluid flow (IFF) has been shown to drive GBM invasion [3]. This study investigates the relationship between tumor mass effect, diffusion, perfusion and IFF in GBM using anatomical (pre- and post-contrast T1 weighted, T2/FLAIR) and quantitative MR imaging (Dynamic Contrast Enhanced (DCE) MRI, and Diffusion Weighted Imaging (DWI)). We use data from 39 patients from the Ivy Glioblastoma Atlas Project (Ivy GAP)[4] which provides matched imaging, ISH, RNA, gene expression and clinical data over the course of treatment. We analyze pre-operative anatomic imaging data to determine the tumor-induced mass effect in each patient using quantitative measures such as ‘Lateral ventricle displacement’ [2]. Perfusion and diffusion measures are derived from pre-operative DCE and DWI imaging. Additionally, we estimate IFF velocities in the tumor region using DCE imaging data in combination with a computational model of fluid flow [5]. We will report the results of quantitative imaging analysis in relation to tumor mass effect and examine correlations with biological tumor characteristics and treatment outcome. We further investigate our findings in patient-derived xenograft models of GBM. References: [1] R.K. Jain et al. Annu. Rev. Biomed. Eng., 2014, 16, 321–346. [2] T.C. Steed et al. Scientific Reports, 2018, 8, 2827. [3] K.M. Kingsmore et al. Integr. Biol., 2016, 8 1246-1260 [4] N. Shah et al. Data from Ivy GAP. The Cancer Imaging Archive 2016. [5] K.M. Kingsmore et al. APL Bioengineering (In press).

Chimeric antigen receptor (CAR) T-cell therapy is a promising emerging area of immunotherapy for the treatment of recurrent glioblastoma. In this therapy, T-cells are genetically modified to target tumor-specific antigens. Efficacy of CAR T-cell therapy depends on several factors, including CAR T-cell proliferation, persistence, and tumor cell killing capacity. Here we use a mathematical model to investigate the role of these three factors in determining response of recurrent GBM to this novel immunotherapy. Additionally, the impact of dosing and scheduling of CAR T-cell infusions on the therapeutic response is explored. We model the interaction between the two cell populations (cancer cells and CAR T-cells) using an ordinary differential equation based formalism. The growth and death of cancer cells are simulated as rates of proliferation and interaction between cancer cells and CAR T-cells, respectively. Biological data was used to parameterize the model. Analysis of the dynamics of interaction between cancer cells and CAR T-cells was performed to determine the maximum efficacy of a single and multiple doses of CAR T-cells. Our mathematical model and analysis shows that a critical parameter for the success of CAR T-cell therapy is the ratio of cancer cell proliferation to the killing capacity of the CAR T-cells. We quantify the dose level of CAR T-cells required to eliminate the cancer cell population. Furthermore, we use the mathematical model to predict the time to progression for specific dose levels, which may help in optimizing and scheduling multiple doses of CAR T-cell monotherapy. We compare the mathematical results with patient data from an ongoing dose-escalation study (NCT02208362) using central memory derived IL13Rα2-targeted CAR T-cell therapy for recurrent glioblastoma.

LuAG:Ce single crystals with various activator concentrations were grown by the vertical Bridgman technique. Characterization of crystals was done in terms of actual doping level, macroscopic defects and degree of non-equivalent substitutions by Lu for Al in octahedral lattice sites. Scintillation measurements were performed using 2×2×8 mm3 shaped samples with Ce concentration in the range 0.05–0.55 at%. Essential improvement of performance was demonstrated in samples containing ≥0.2 at% of Ce; the light yield measured in LuAG:Ce (0.55 at%) was about 26000 ph/MeV, or close to that of LSO.

This report presents a proposal for a new slow extraction scheme for the Low Energy Ion Ring (LEIR) in the context of the feasibility study for a biomedical research facility at CERN. LEIR has to be maintained as a heavy ion accumulator ring for LHC and for fixed-target experiments with the SPS. In parallel to this on-going operation for physics experiments, an additional secondary use of LEIR for a biomedical research facility was proposed [Dosanjh2013, Holzscheiter2012, PHE2010]. This facility would complement the existing research beam-time available at other laboratories for studies related to ion beam therapy. The new slow extraction [Abler2013] is based on the third-integer resonance. The reference beam is composed of fully stripped carbon ions with extraction energies of 20-440 MeV/u, transverse physical emittances of 5-25 µm and momentum spreads of ±2-9•10-4. Two resonance driving mechanisms have been studied: the quadrupole-driven method and the RF-knockout technique. Both were made compatible with the tight aperture constraints imposed by operation as heavy ion accumulator. Tracking is performed with MADX-PTC [MADX, PTC]. The resonance is excited using an existing Pole-Face Winding. The dedicated devices to be installed in LEIR are an electrostatic septum (0.1 mm, 8.0 MV/m, 86 cm physical length), a thin magnetic septum (9 mm, 0.46 T, 90 cm physical length), a thick magnetic septum (22 mm, 0.7 T, 1.2 m physical length) and two new orbit correctors (0.2 T, 40 cm physical length).

Decision-making processes in medicine rely increasingly on modelling and simulation techniques; they are especially useful when combining evidence from multiple sources. Markov models are frequently used to synthesize the available evidence for such simulation studies, by describing disease and treatment progress, as well as associated factors such as the treatment's effects on a patient's life and the costs to society. When the same decision problem is investigated by multiple stakeholders, differing modelling assumptions are often applied, making synthesis and interpretation of the results difficult. This paper proposes a standardized approach towards the creation of Markov models. It introduces the notion of 'general Markov models', providing a common definition of the Markov models that underlie many similar decision problems, and develops a language for their specification. We demonstrate the application of this language by developing a general Markov model for adverse event analysis in radiotherapy and argue that the proposed method can automate the creation of Markov models from existing data. The approach has the potential to support the radiotherapy community in conducting systematic analyses involving predictive modelling of existing and upcoming radiotherapy data. We expect it to facilitate the application of modelling techniques in medical decision problems beyond the field of radiotherapy, and to improve the comparability of their results.

To make reliable, safe, and effective use of data outside the context of its collection, we require an adequate understanding of its meaning. In data-intensive science, as in many other applications of computing, this necessitates the association of each item of data with complex, detailed metadata. The most important, most useful piece of metadata is often a description of the form used in data acquisition. This paper discusses, with examples, the requirements for standard metamodels or languages for forms, sufficient for the automatic association of form data with a computable description of its semantics, and also for the automatic generation of form structures and completion workflows. It explains how form models in specific domains can be used to facilitate data sharing, and to improve data quality, and semantic interoperability.

The main objective of this contribution is to point out the potentialities of cerium doped LuAG single crystal as pixels and fibers. We first show that after optimization of growth conditions using Bridgman technology, this composition exhibits very good performances for scintillating applications (up to 26 000 photons/MeV). When grown with the micropulling down technology, fiber shapes can be obtained while the intrinsic performances are preserved. For the future high energy experiments requiring new detector concepts capable of delivering much richer informations about x- or gamma-ray energy deposition, unusual fiber shaped dense materials need to be developed. We demonstrate in this frame that cerium doped LuAG is a serious candidate for the next generation of ionizing radiation calorimeters.

One of the main challenges for detectors at future high-energy collider experiments is high-precision measurement of hadrons and jet energy and momentum. Such measurement can be provided by the particle flow approach (PFA) that requires a complex highly segmented calorimeter system to identify and to track all particles in a jet. An alternative so-called dual-readout approach consists of simultaneously recording, in an active medium, scintillation light that is proportional to total energy deposit and Cerenkov light that is proportional to the electromagnetic part only, thus allowing extracting the electromagnetic fraction of the total shower energy on an event-by-event basis. The dual-readout method approach can be implemented using several techniques. The first method proposed uses a copper absorber structure containing two types of fibers: quartz fibers that produce Cerenkov light and plastic scintillating fibers that produce scintillation light. A second method proposed is based on the separation of scintillation and Cerenkov signal produced in homogeneous detector blocks made of scintillating crystals such as bismuth germanate (BGO), lead tungstate (PWO), or others. More recently, a third method has been proposed by our group that uses so-called "meta-crystals" consisting of both Ce-doped and undoped heavy crystal fibers of identical material. The undoped fibers behave as Cerenkov radiator, while Ce-doped fibers behave as scintillators. In this paper, we discuss advantages and inconveniences of all methods and present first results on methods 2 and 3.

One of the main challenges for detectors at future high energy collider experiments is high precision measurement of hadrons and jet energy and momentum. One possibility to enable such measurement is the particle flow approach (PFA) that requires a complex highly segmented calorimeter system to identify and to track all particles in a jet. An alternative so-called dual readout approach consists of simultaneously recording, in an active medium, scintillation light that is proportional to total energy deposit and Cerenkov light that is proportional to the electromagnetic part only, thus allowing extracting the electromagnetic fraction of the total shower energy on an event by event basis. A promising candidate to be used for dual readout calorimeter is Lutetium Aluminium Garnet Lu 3 Al 5 O 12 (LuAG). Its high density, relatively short radiation length and interaction length provide sufficient stopping power necessary for hadronic calorimetry at high energy colliders, the fundamental absorption edge at 177nm combined with a very large optical transmittance window and a high refraction index (n=1.84) make LuAG an excellent Cerenkov radiator. Moreover cerium doped LuAG (LuAG:Ce3+) has good scintillating performances in terms of light yield and decay time due to the efficient radiative recombination 5d-4f from Ce3+ ion. LuAG can be produced either in bulk crystal or as fiber crystal allowing to assess its performance in two different approaches for dual readout calorimetry. As bulk crystals Cerium doped LuAG can be used in the method based on the separation of scintillation and Cerenkov signal produced in homogeneous detector blocks. In form of fibers it can be used in the method using so-called meta-crystals consisting of both Ce doped and undoped LuAG crystal fibers. The undoped fibers behave as Cerenkov radiator while Ce-doped fibers behave as scintillators. This talk reviews the properties of LuAG material in view of its use as scintillator both for bulk and fiber scintillators.

High precision measurement of hadrons and jet energy and momentum is one of the main challenges for detectors at future high energy collider experiments. Currently, two approaches are considered to achieve this goal. In the particle flow approach (PFA), a complex highly segmented calorimeter system is required to identify and to track all particles in a jet. On the other hand the dual readout recording of both scintillation and Cerenkov light in an active medium allows extracting the electromagnetic fraction of the total shower energy on an event by event basis. Recent progress in heavy scintillating fiber crystal production methods offer interesting perspectives for the design of a homogeneous but finely structured calorimeter based on the dual readout principle. This paper reports on recent developments in the production of heavy scintillating crystal fibers. It presents a calorimeter concept based on such technology that allows for a simultaneous high precision measurement of the complete set of shower parameters such as the direction, the spatial distribution of the energy deposition and its composition in terms of electromagnetic, charged and neutral hadrons content.

A long x-ray pathway based on an x-ray back-diffraction cavity for coherent x-ray beam experiments is presented. In the present work, such a setup was tested and used for propagation-based x-ray phase contrast imaging (PBI). This setup showed to be useful for PBI purposes, with the advantage of being compact (3 m long) when compared with long x-ray synchrotron beamlines with dimensions from tens to hundreds of meters.

The vacuum window and in-beam ion detection of a radiobiological endstation have been studied using the Monte Carlo particle transport code MCNPX 2.5.0, investigating the effects of different elements on an incident beam of 50 MeV protons. The scattering and energy spread as well as secondary particle generation are analysed. A 200 nm thick silicon nitride vacuum window was found to have the least effect on the incident beam, giving an energy spread of 5.01(8)× 10−4 MeV. Placing a silicon pixel detector before the sample caused scattering of the beam of around 15 μm in radius; too much to be used for experiments targeting individual nuclei.

Glioblastoma (GBM) is the most frequent malignant brain tumor in adults and presents with different growth phenotypes. We use a mechanically coupled reaction–diffusion model to study the influence of structural brain tissue anisotropy on tumor growth. Tumors were seeded at multiple locations in a human MR-DTI brain atlas and their spatiotemporal evolution was simulated using the Finite Element Method. We evaluated the impact of tissue anisotropy on the model’s ability to reproduce the aspherical shapes of real pathologies by comparing predicted lesions to publicly available GBM imaging data. The impact of tissue anisotropy on tumor shape was strongly location dependent and highest for tumors in brain regions with a single dominating white matter fiber direction, such as the corpus callosum. Despite strongly anisotropic growth assumptions, all simulated tumors remained more spherical than real lesions at the corresponding anatomic location and similar volume. These findings confirm previous simulation studies, suggesting that cell migration along WM fiber tracks is not a major determinant of tumor shape in the setting of reaction–diffusion-based tumor growth models and for most locations across the brain.

Gliomas are the most common malignant brain tumors in adults, with Glioblastoma (GBM) being the most agressive subtype. GBM is clinically evaluated with magnetic resonance imaging (MRI) and presents with different growth phenotypes, involving varying degrees of healthy tissue invasion and tumor induced herniation, also known as mass effect. GBM growth in the brain is frequently modeled as a reaction-diffusion process in which varying ratios of diffusion and proliferation coefficients mimic the observed spectrum of growth phenotypes ranging from nodal to diffuse. However, reaction-diffusion models alone are insufficient to explain tumor-induced mass effect on normal peripheral tissues, which is a critical clinical issue. We propose an analysis method and framework for estimating GBM growth properties (proliferation, invasiveness, displacive potential) from MRI data routinely collected in the clinical management of GBM. This framework accounts for the mass-effect of the growing tumor by assuming a coupling between local tumor-cell density and volumetric expansion of the tissue. We evaluate the reconstruction workflow on synthetic data that represents a range of realistic growth situations and levels of uncertainty. For most parameter combinations (90%) that correspond to tumors detectable by T1-weighted MRI, target parameters are recovered with a relative error of less than 15%.

Elevated tumor mass-effect is associated to poor prognosis in GBM [1,2]. However, tumor mass-effect is poorly quantified in clinical practice. Recently, Steed et al. [2] proposed ‘Lateral ventricle displacement’ (LVd), defined as the change in center-of-mass position of the lateral ventricles between an undeformed reference and the tumor-bearing anatomy, as quantitative imaging measure of mass-effect. They found that the magnitude of LVd in GBM patients can be associated with overall survival. These results show the clinical importance of tumor mass-effect in GBM, warranting robust clinical measures. This study characterizes image-derived estimates of tumor mass-effect by their ability to measure mass-effect accurately and reliably. We use a mathematical model to simulate tumor growth, which allows us to control and objectively quantify ‘mass-effect’ [3]. For given simulation parameters and growth location, we compute estimates of mass-effect from anatomical deformation during the growth process. We use multiple regression analysis to evaluate the ability of different estimates to explain the tumor’s objective mass-effect, measured by the tumor-induced pressure on the skull. Preliminary results from tumor growth simulations at 15 locations across a normal brain atlas confirm the potential of LVd as predictor of tumor mass-effect (R(2)=0.98). The maximum left-right displacement along the midline in a selected plane, a measure for ‘midline shift’, only explains a smaller percentage of mass-effect variability in the simulated population and strongly depends on the measurement plane (R(2)=0.20–0.85). We will report results of an extended study that applies our analysis approach to a large and diverse population of approximately 100 simulated tumors, by varying growth location and parameters related to the tumor’s proliferative and invasive potential. References: [1] Gamburg et al. IJROBP, 2000, 48, 5: 1359–62, [2] Steed et al. Scientific Reports, 2018, 8: 2827, [3] Abler et al. Neuro-Oncology, 2017, 19, suppl 6: vi245.