GPU-accelerated compartmental modeling analysis of DCE-MRI data from glioblastoma patients treated with bevacizumab.
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ABSTRACT:
SUBMITTER: Hsu YH
PROVIDER: S-EPMC4364976 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Glioblastoma (GBM) is a highly angiogenic malignancy, and its abundant, aberrant neovascularization is closely related to the proliferation and invasion of tumor cells. However, anti-angiogenesis combined with standard radio-/chemo-therapy produces little improvement in treatment outcomes. Determining the reason for treatment failure is pivotal for GBM treatment. Here, histopathological analysis and dynamic contrast-enhanced MRI (DCE-MRI) were used to explore the effects of temozolomide (TMZ) and bevacizumab (BEV) on GBM neovascularization patterns in an orthotopic U87MG mouse model at 1, 3 and 6 days after treatment. We found that the amount of vascular mimicry (VM) significantly increased 6 days after BEV treatment. TMZ inhibited neovascularization at an early stage, but the microvessel density (MVD) and transfer coefficient (Ktrans) derived from DCE-MRI increased 6 days after treatment. TMZ and BEV combination therapy slightly prolonged the inhibitory effect on tumor microvessels. Sprouting angiogenesis was positively correlated with Ktrans in all treatment groups. The increase in VM after BEV administration and the increase in MVD and Ktrans after TMZ administration may be responsible for treatment resistance. Ktrans holds great potential as an imaging biomarker for indicating the variation in sprouting angiogenesis during drug treatment for GBM.
| S-EPMC5654943 | biostudies-literature
Project description:Motivation:The alignment of bisulfite-treated DNA sequences (BS-seq reads) to a large genome involves a significant computational burden beyond that required to align non-bisulfite-treated reads. In the analysis of BS-seq data, this can present an important performance bottleneck that can be mitigated by appropriate algorithmic and software-engineering improvements. One strategy is to modify the read-alignment algorithms by integrating the logic related to BS-seq alignment, with the goal of making the software implementation amenable to optimizations that lead to higher speed and greater sensitivity than might otherwise be attainable. Results:We evaluated this strategy using Arioc, a short-read aligner that uses GPU (general-purpose graphics processing unit) hardware to accelerate computationally-expensive programming logic. We integrated the BS-seq computational logic into both GPU and CPU code throughout the Arioc implementation. We then carried out a read-by-read comparison of Arioc's reported alignments with the alignments reported by well-known CPU-based BS-seq read aligners. With simulated reads, Arioc's accuracy is equal to or better than the other read aligners we evaluated. With human sequencing reads, Arioc's throughput is at least 10 times faster than existing BS-seq aligners across a wide range of sensitivity settings. Availability and implementation:The Arioc software is available for download at https://github.com/RWilton/Arioc. It is released under a BSD open-source license. Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC6061805 | biostudies-literature
Project description:PurposeTo investigate the ability of potential imaging biomarkers based on 18F-AlF-NOTA-PRGD2 positron emission tomography/computed tomography (18F-RGD PET/CT) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) imaging to predict the response to bevacizumab combined with conventional therapy in postoperative newly diagnosed glioblastoma.MethodsTwenty patients with newly diagnosed with glioblastoma after surgery were prospectively enrolled to receive bevacizumab plus conventional concurrent radiotherapy and temozolomide (CCRT). 18F-RGD PET/CT and DCE-MRI were performed at baseline, week 3, and week 10 for each patient. Statistical methods included the analysis of variance (ANOVA), Kaplan-Meier method and Cox proportional hazard analysis.ResultsAll patients completed CCRT plus bevacizumab therapy without interruption. The median follow-up time was 33.9 months (95% confidence interval [CI], 28.3-39.5 months). The median progression-free survival (PFS) and overall survival (OS) was 9.66 months (95% CI, 6.20-13.12 months) and 15.89 months (95% CI, 13.89-17.78), respectively. Treatment was generally well tolerated, and there were no Treatment emergent adverse events (TEAEs) with a toxicity grade equal to or exceeding 3 or that led to termination of treatment or patient death.Over the treatment interval of bevacizumab therapy from week 3 to week 10, patients with a large decrease of SUVmean was associated with a better PFS with a hazard ratio (HR) of 6.562, 95% CI (1.318-32.667), p=0.022. According to Kaplan-Meier analysis, patients with a decrease in the SUVmean of more than 0.115 on 18F-RGD PET/CT had a longer PFS than those with a decrease in the SUVmean of 0.115 or less (12.25 months vs.7.46 months, p=0.009). For OS, only a small decrease of Ktrans was also found to have certain prognostic value (HR=0.986, 95% CI (0.975-0.998), p=0.023). Patients with a decrease in Ktrans larger than 37.03 (min-1) on DCE-MRI had worse OS than those with a decrease in Ktrans of 37.03 (min-1) or less (15.93 months vs. 26.42 months, p=0.044).Conclusion18F-RGD PET/CT and DCE-MRI may be valuable in evaluating the response of glioblastoma to treatment with the combination of bevacizumab and CCRT, with a greater decrease in SUVmean predicting better PFS as well as a small decrease in Ktrans predicting improved OS.
| S-EPMC9459034 | biostudies-literature
Project description:Multi-scale modeling plays an important role in understanding the structure and biological functionalities of large biomolecular complexes. In this paper, we present an efficient computational framework to construct multi-scale models from atomic resolution data in the Protein Data Bank (PDB), which is accelerated by multi-core CPU and programmable Graphics Processing Units (GPU). A multi-level summation of Gaus-sian kernel functions is employed to generate implicit models for biomolecules. The coefficients in the summation are designed as functions of the structure indices, which specify the structures at a certain level and enable a local resolution control on the biomolecular surface. A method called neighboring search is adopted to locate the grid points close to the expected biomolecular surface, and reduce the number of grids to be analyzed. For a specific grid point, a KD-tree or bounding volume hierarchy is applied to search for the atoms contributing to its density computation, and faraway atoms are ignored due to the decay of Gaussian kernel functions. In addition to density map construction, three modes are also employed and compared during mesh generation and quality improvement to generate high quality tetrahedral meshes: CPU sequential, multi-core CPU parallel and GPU parallel. We have applied our algorithm to several large proteins and obtained good results.
| S-EPMC3858848 | biostudies-literature
Project description:The purpose of this study was to assess the impact of bevacizumab alone and in combination with cytotoxic therapy on tumour vasculature in osteosarcoma (OS) using DCE-MRI.Six DCE-MRI and three (18)F-FDG PET examinations were scheduled in 42 subjects with newly diagnosed OS to monitor the response to antiangiogenic therapy alone and in combination with cytotoxic therapy before definitive surgery (week 10). Serial DCE-MRI parameters (K(trans), v(p), and v(e)) were examined for correlation with FDG-PET (SUV(max)) and association with drug exposure, and evaluated with clinical outcome.K(trans) (P=0.041) and v(p) (P=0.001) significantly dropped from baseline at 24 h after the first dose of bevacizumab alone, but returned to baseline by 72?h. Greater exposure to bevacizumab was correlated with larger decreases in v(p) at day 5 (P=0.04) and week 10 (P=0.02). A lower K(trans) at week 10 was associated with greater percent necrosis (P=0.024) and longer event-free survival (P=0.034).This is the first study to demonstrate significant changes of the plasma volume fraction and vascular leakage in OS with bevacizumab alone. The combination of demonstrated associations between drug exposure and imaging metrics, and imaging metrics and patient survival during neoadjuvant therapy, provides a compelling rationale for larger studies using DCE-MRI to assess vascular effects of therapy in OS.
| S-EPMC4815789 | biostudies-literature
Project description:PurposeTo ascertain whether complex dynamic contrast enhanced (DCE) MRI tracer kinetic models are supported by data acquired in the clinic and to determine the consequences of limited contrast-to-noise.MethodsGenerically representative in silico and clinical (cervical cancer) DCE-MRI data were examined. Bayesian model selection evaluated support for four compartmental DCE-MRI models: the Tofts model (TM), Extended Tofts model, Compartmental Tissue Uptake model (CTUM), and Two-Compartment Exchange model.ResultsComplex DCE-MRI models were more sensitive to noise than simpler models with respect to both model selection and parameter estimation. Indeed, as contrast-to-noise decreased, complex DCE models became less probable and simpler models more probable. The less complex TM and CTUM were the optimal models for the DCE-MRI data acquired in the clinic. [In cervical tumors, Ktrans, Fp, and PS increased after radiotherapy (P = 0.004, 0.002, and 0.014, respectively)].ConclusionCaution is advised when considering application of complex DCE-MRI kinetic models to data acquired in the clinic. It follows that data-driven model selection is an important prerequisite to DCE-MRI analysis. Model selection is particularly important when high-order, multiparametric models are under consideration. (Parameters obtained from kinetic modeling of cervical cancer clinical DCE-MRI data showed significant changes at an early stage of radiotherapy.) Magn Reson Med 77:1329-1339, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
| S-EPMC5548456 | biostudies-literature
Project description:BackgroundBisulfite sequencing is a powerful tool for profiling genomic methylation, an epigenetic modification critical in the understanding of cancer, psychiatric disorders, and many other conditions. Raw data generated by whole genome bisulfite sequencing (WGBS) requires several computational steps before it is ready for statistical analysis, and particular care is required to process data in a timely and memory-efficient manner. Alignment to a reference genome is one of the most computationally demanding steps in a WGBS workflow, taking several hours or even days with commonly used WGBS-specific alignment software. This naturally motivates the creation of computational workflows that can utilize GPU-based alignment software to greatly speed up the bottleneck step. In addition, WGBS produces raw data that is large and often unwieldy; a lack of memory-efficient representation of data by existing pipelines renders WGBS impractical or impossible to many researchers.ResultsWe present BiocMAP, a Bioconductor-friendly methylation analysis pipeline consisting of two modules, to address the above concerns. The first module performs computationally-intensive read alignment using Arioc, a GPU-accelerated short-read aligner. Since GPUs are not always available on the same computing environments where traditional CPU-based analyses are convenient, the second module may be run in a GPU-free environment. This module extracts and merges DNA methylation proportions-the fractions of methylated cytosines across all cells in a sample at a given genomic site. Bioconductor-based output objects in R utilize an on-disk data representation to drastically reduce required main memory and make WGBS projects computationally feasible to more researchers.ConclusionsBiocMAP is implemented using Nextflow and available at http://research.libd.org/BiocMAP/ . To enable reproducible analysis across a variety of typical computing environments, BiocMAP can be containerized with Docker or Singularity, and executed locally or with the SLURM or SGE scheduling engines. By providing Bioconductor objects, BiocMAP's output can be integrated with powerful analytical open source software for analyzing methylation data.
| S-EPMC10498615 | biostudies-literature
Project description:Many scientific problems such as classifier training or medical image reconstruction can be expressed as minimization of differentiable real-valued cost functions and solved with iterative gradient-based methods. Adjoint algorithmic differentiation (AAD) enables automated computation of gradients of such cost functions implemented as computer programs. To backpropagate adjoint derivatives, excessive memory is potentially required to store the intermediate partial derivatives on a dedicated data structure, referred to as the "tape". Parallelization is difficult because threads need to synchronize their accesses during taping and backpropagation. This situation is aggravated for many-core architectures, such as Graphics Processing Units (GPUs), because of the large number of light-weight threads and the limited memory size in general as well as per thread. We show how these limitations can be mediated if the cost function is expressed using GPU-accelerated vector and matrix operations which are recognized as intrinsic functions by our AAD software. We compare this approach with naive and vectorized implementations for CPUs. We use four increasingly complex cost functions to evaluate the performance with respect to memory consumption and gradient computation times. Using vectorization, CPU and GPU memory consumption could be substantially reduced compared to the naive reference implementation, in some cases even by an order of complexity. The vectorization allowed usage of optimized parallel libraries during forward and reverse passes which resulted in high speedups for the vectorized CPU version compared to the naive reference implementation. The GPU version achieved an additional speedup of 7.5 ± 4.4, showing that the processing power of GPUs can be utilized for AAD using this concept. Furthermore, we show how this software can be systematically extended for more complex problems such as nonlinear absorption reconstruction for fluorescence-mediated tomography.
| S-EPMC4772124 | biostudies-literature
Project description:MotivationMathematical modelling is central to systems and synthetic biology. Using simulations to calculate statistics or to explore parameter space is a common means for analysing these models and can be computationally intensive. However, in many cases, the simulations are easily parallelizable. Graphics processing units (GPUs) are capable of efficiently running highly parallel programs and outperform CPUs in terms of raw computing power. Despite their computational advantages, their adoption by the systems biology community is relatively slow, since differences in hardware architecture between GPUs and CPUs complicate the porting of existing code.ResultsWe present a Python package, cuda-sim, that provides highly parallelized algorithms for the repeated simulation of biochemical network models on NVIDIA CUDA GPUs. Algorithms are implemented for the three popular types of model formalisms: the LSODA algorithm for ODE integration, the Euler-Maruyama algorithm for SDE simulation and the Gillespie algorithm for MJP simulation. No knowledge of GPU computing is required from the user. Models can be specified in SBML format or provided as CUDA code. For running a large number of simulations in parallel, up to 360-fold decrease in simulation runtime is attained when compared to single CPU implementations.Availabilityhttp://cuda-sim.sourceforge.net/
| S-EPMC3051321 | biostudies-literature
Project description:PurposePrevious work has shown that combining dynamic contrast-enhanced (DCE)-MRI and oxygen-enhanced (OE)-MRI binary enhancement maps can identify tumor hypoxia. The current work proposes a novel, data-driven method for mapping tissue oxygenation and perfusion heterogeneity, based on clustering DCE/OE-MRI data.MethodsDCE-MRI and OE-MRI were performed on nine U87 (glioblastoma) and seven Calu6 (non-small cell lung cancer) murine xenograft tumors. Area under the curve and principal component analysis features were calculated and clustered separately using Gaussian mixture modelling. Evaluation metrics were calculated to determine the optimum feature set and cluster number. Outputs were quantitatively compared with a previous non data-driven approach.ResultsThe optimum method located six robustly identifiable clusters in the data, yielding tumor region maps with spatially contiguous regions in a rim-core structure, suggesting a biological basis. Mean within-cluster enhancement curves showed physiologically distinct, intuitive kinetics of enhancement. Regions of DCE/OE-MRI enhancement mismatch were located, and voxel categorization agreed well with the previous non data-driven approach (Cohen's kappa = 0.61, proportional agreement = 0.75).ConclusionThe proposed method locates similar regions to the previous published method of binarization of DCE/OE-MRI enhancement, but renders a finer segmentation of intra-tumoral oxygenation and perfusion. This could aid in understanding the tumor microenvironment and its heterogeneity. Magn Reson Med 79:2236-2245, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
| S-EPMC5836865 | biostudies-literature