2017
|
Khalaf, Ahmed M; Fuentes, David T; Ahmed, Kareem; Abdel-Wahab, Reham; Hassan, Manal; Kaseb, Ahmed Omar; Hazle, John D; Elsayes, Khaled M "Quantitative CT imaging features for hepatocellular carcinoma (HCC) with b-catenin (CTNNB1) gene mutation" Journal Article In: Journal of Clinical Oncology, vol. 35, no. 4, pp. 253-253, 2017. @article{Fuentes2016b,
title = {"Quantitative CT imaging features for hepatocellular carcinoma (HCC) with b-catenin (CTNNB1) gene mutation"},
author = {Ahmed M Khalaf and David T Fuentes and Kareem Ahmed and Reham Abdel-Wahab and Manal Hassan and Ahmed Omar Kaseb and John D Hazle and Khaled M Elsayes},
url = {http://ascopubs.org/doi/abs/10.1200/JCO.2017.35.4_suppl.253},
year = {2017},
date = {2017-02-01},
journal = {Journal of Clinical Oncology},
volume = {35},
number = {4},
pages = {253-253},
publisher = {American Society of Clinical Oncology},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
JA, Weis; MI, Miga; TE, Yankeelov "Three-dimensional Image-based Mechanical Modeling for Predicting the Response of Breast Cancer to Neoadjuvant Therapy" Journal Article In: Computer Methods in Applied Mechanics and Engineering, vol. 314, pp. 494-512, 2017. @article{Yankeelov2016b,
title = {"Three-dimensional Image-based Mechanical Modeling for Predicting the Response of Breast Cancer to Neoadjuvant Therapy"},
author = {Weis JA and Miga MI and Yankeelov TE},
url = {http://www.sciencedirect.com/science/article/pii/S0045782516310167?via%3Dihub},
doi = {10.1016/j.cma.2016.08.024},
year = {2017},
date = {2017-02-01},
journal = {Computer Methods in Applied Mechanics and Engineering},
volume = {314},
pages = {494-512},
abstract = {The use of quantitative medical imaging data to initialize and constrain mechanistic mathematical models of tumor growth has demonstrated a compelling strategy for predicting therapeutic response. More specifically, we have demonstrated a data-driven framework for prediction of residual tumor burden following neoadjuvant therapy in breast cancer that uses a biophysical mathematical model combining reaction-diffusion growth/therapy dynamics and biomechanical effects driven by early time point imaging data. Whereas early work had been based on a limited dimensionality reduction (two-dimensional planar modeling analysis) to simplify the numerical implementation, in this work, we extend our framework to a fully volumetric, three-dimensional biophysical mathematical modeling approach in which parameter estimates are generated by an inverse problem based on the adjoint state method for numerical efficiency. In an in silico performance study, we show accurate parameter estimation with error less than 3% as compared to ground truth. We apply the approach to patient data from a patient with pathological complete response and a patient with residual tumor burden and demonstrate technical feasibility and predictive potential with direct comparisons between imaging data observation and model predictions of tumor cellularity and volume. Comparisons to our previous two-dimensional modeling framework reflect enhanced model prediction of residual tumor burden through the inclusion of additional imaging slices of patient-specific data.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The use of quantitative medical imaging data to initialize and constrain mechanistic mathematical models of tumor growth has demonstrated a compelling strategy for predicting therapeutic response. More specifically, we have demonstrated a data-driven framework for prediction of residual tumor burden following neoadjuvant therapy in breast cancer that uses a biophysical mathematical model combining reaction-diffusion growth/therapy dynamics and biomechanical effects driven by early time point imaging data. Whereas early work had been based on a limited dimensionality reduction (two-dimensional planar modeling analysis) to simplify the numerical implementation, in this work, we extend our framework to a fully volumetric, three-dimensional biophysical mathematical modeling approach in which parameter estimates are generated by an inverse problem based on the adjoint state method for numerical efficiency. In an in silico performance study, we show accurate parameter estimation with error less than 3% as compared to ground truth. We apply the approach to patient data from a patient with pathological complete response and a patient with residual tumor burden and demonstrate technical feasibility and predictive potential with direct comparisons between imaging data observation and model predictions of tumor cellularity and volume. Comparisons to our previous two-dimensional modeling framework reflect enhanced model prediction of residual tumor burden through the inclusion of additional imaging slices of patient-specific data. |
Rocha, Heber L; Almeida, Regina C; Resende, Anna Claudia M; Lima, Ernesto ABF "MODELAGEM HÍBRIDA EM TRÊS ESCALAS PARA O CRESCIMENTO TUMORAL" Journal Article In: Revista Interdisciplinar de Pesquisa em Engenharia-RIPE, vol. 2, no. 11, pp. 61-74, 2017. @article{Lima2017,
title = {"MODELAGEM HÍBRIDA EM TRÊS ESCALAS PARA O CRESCIMENTO TUMORAL"},
author = {Heber L Rocha and Regina C Almeida and Anna Claudia M Resende and Ernesto ABF Lima},
year = {2017},
date = {2017-01-10},
journal = {Revista Interdisciplinar de Pesquisa em Engenharia-RIPE},
volume = {2},
number = {11},
pages = {61-74},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Joyce, MH; Rodriguez, Adan; Brock, Amy "A biomimetic model of tumor-macrophage interactions" Journal Article In: Bioengineering, 2017. @article{Brock2017,
title = {"A biomimetic model of tumor-macrophage interactions"},
author = {MH Joyce and Adan Rodriguez and Amy Brock},
year = {2017},
date = {2017-01-01},
journal = {Bioengineering},
abstract = {Reciprocal signaling between tumor cells and their complex microenvironment is a critical determinant of disease progression. Here we develop a spheroid co-culture system to model the in vivo interaction of macrophage cells with mammary tumor cell populations. Analysis of mammary tumors derived from a progressive series of genetically-matched C3-SV40-TAg cancer cell lines revealed differential recruitment of macrophage. Cells derived from a metastatic tumors (C3-SV40-TAg M6C) recruited significantly fewer macrophage than tumor cells derived from a mammary carcinoma (C3-SV40-TAg M6) or from hyperplastic mammary tissue (C3-SV40-TAg M28). Conventional 2D co-culture of the same tumor cells with macrophage failed to mimic the differential recognition and engulfment of the cell line panel. However, co-culture in an alginate gel system revealed differential macrophage engulfment, mimicking the interactions observed in vivo.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Reciprocal signaling between tumor cells and their complex microenvironment is a critical determinant of disease progression. Here we develop a spheroid co-culture system to model the in vivo interaction of macrophage cells with mammary tumor cell populations. Analysis of mammary tumors derived from a progressive series of genetically-matched C3-SV40-TAg cancer cell lines revealed differential recruitment of macrophage. Cells derived from a metastatic tumors (C3-SV40-TAg M6C) recruited significantly fewer macrophage than tumor cells derived from a mammary carcinoma (C3-SV40-TAg M6) or from hyperplastic mammary tissue (C3-SV40-TAg M28). Conventional 2D co-culture of the same tumor cells with macrophage failed to mimic the differential recognition and engulfment of the cell line panel. However, co-culture in an alginate gel system revealed differential macrophage engulfment, mimicking the interactions observed in vivo. |
Woodall, Ryan T.; Eldridge, Stephanie L.; Sorace, Anna G.; Yankeelov, Thomas E. "Abstract A23: A finite element model of perfusion and diffusion within tumors based on dynamic contrast enhanced magnetic resonance imaging" Journal Article In: Cancer Research, vol. 77, no. 2, pp. Supplement, 2017. @article{Woodall2017,
title = {"Abstract A23: A finite element model of perfusion and diffusion within tumors based on dynamic contrast enhanced magnetic resonance imaging"},
author = {Ryan T. Woodall and Stephanie L. Eldridge and Anna G. Sorace and Thomas E. Yankeelov},
url = {http://cancerres.aacrjournals.org/content/77/2_Supplement/A23.short},
doi = {10.1158/1538-7445.EPSO16-A23},
year = {2017},
date = {2017-01-01},
journal = {Cancer Research},
volume = {77},
number = {2},
pages = {Supplement},
abstract = {Quantitative analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data provides estimates of physiological parameters that characterize tissue volume fractions, blood flow, and vascular permeability. While it provides a reasonable description of well-vascularized tissue, the standard models do not include the effects of intra-voxel diffusion, which is hypothesized to play a significant role in distributing contrast agent in poorly vascularized voxels1. This mechanism has been studied in silico using simulated tissue regions, but the results have yet to be tested with experimental data. We hypothesize that explicit incorporation of the contrast agent diffusion into the standard models is required to accurately model delivery and retention of the contrast agent within poorly vascularized regions. To investigate the effect of the diffusion of contrast agent within a tumor, nude athymic mice are subcutaneously implanted with BT474 cancer cells, and tumors develop for 4-6 weeks prior to performing DCE-MRI. Following imaging, the tumors are extracted, sectioned, and stained for vascularity (CD31) and viability/cellularity (H&E). Stained central slice sections were digitized in high resolution, segmented in MATLAB (Natick, MA), and a finite element model (FEM) was developed. A population arterial input function (AIF)2 serves as the source of contrast agent delivery into the FEM at the vascular boundaries, while the diffusion equation distributes the contrast agent through the extracellular space. The distribution of contrast agent over time within an imaging voxel is converted from concentration to MR signal intensity, and fit to the extended Tofts model3, providing estimates of vascular permeability and perfusion (Ktrans), extravascular extracellular volume fraction (ve), and vascular volume fraction (vp). These values are then compared to the true tissue volume fractions, determined by tissue segmentation, and an assigned (reasonable) set of Ktrans values. In order to verify the accuracy of the histologically segmented FEM, the simulated DCE-MRI signal is compared to in vivo experimental DCE-MRI scans of the same tumor. Comparison is completed by registering DCE-MRI voxels to simulated voxel domains, and directly measuring the difference between simulated and experimental signal intensities. Using a Ktrans of 0.4 min-1 and a diffusion constant of 3 x 10-5 mm2/s, the extended Tofts model poorly predicts tissue properties, with predicted Ktrans errors ranging from -95% to 68%, ve errors ranging -80% to 13%, and vp errors ranging 32% to 381%. Notably, necrotic regions of the tumor are subject to the highest level of error in the Tofts approximation of these parameters. Each of the maximum absolute prediction errors listed above occur in voxels with a vp value below 0.5% and ve above 80%. In contrast, the Tofts model can more accurately predict vp when the true value is above 5%, and most accurately predicts ve when its true value is below 70%. These results indicate that the lack of a diffusion term in the extended Tofts model can lead to significant errors in the estimates of physiological properties of tumor tissue in vivo. This is especially notable in regions with both a low vp and high ve, where the tissue is necrotic and poorly perfused. These results call for the inclusion of a diffusive term in the analysis of DCE-MRI in order to accurately model and understand contrast agent perfusion in a tumor. By including such a term, the diagnostic and predictive power of Ktrans ¸ vp, and ve, could be further improved. Our ongoing efforts for this research include further refining histology segmentation and model validation by comparing both simulated and measured DCE-MRI signal intensities.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Quantitative analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data provides estimates of physiological parameters that characterize tissue volume fractions, blood flow, and vascular permeability. While it provides a reasonable description of well-vascularized tissue, the standard models do not include the effects of intra-voxel diffusion, which is hypothesized to play a significant role in distributing contrast agent in poorly vascularized voxels1. This mechanism has been studied in silico using simulated tissue regions, but the results have yet to be tested with experimental data. We hypothesize that explicit incorporation of the contrast agent diffusion into the standard models is required to accurately model delivery and retention of the contrast agent within poorly vascularized regions. To investigate the effect of the diffusion of contrast agent within a tumor, nude athymic mice are subcutaneously implanted with BT474 cancer cells, and tumors develop for 4-6 weeks prior to performing DCE-MRI. Following imaging, the tumors are extracted, sectioned, and stained for vascularity (CD31) and viability/cellularity (H&E). Stained central slice sections were digitized in high resolution, segmented in MATLAB (Natick, MA), and a finite element model (FEM) was developed. A population arterial input function (AIF)2 serves as the source of contrast agent delivery into the FEM at the vascular boundaries, while the diffusion equation distributes the contrast agent through the extracellular space. The distribution of contrast agent over time within an imaging voxel is converted from concentration to MR signal intensity, and fit to the extended Tofts model3, providing estimates of vascular permeability and perfusion (Ktrans), extravascular extracellular volume fraction (ve), and vascular volume fraction (vp). These values are then compared to the true tissue volume fractions, determined by tissue segmentation, and an assigned (reasonable) set of Ktrans values. In order to verify the accuracy of the histologically segmented FEM, the simulated DCE-MRI signal is compared to in vivo experimental DCE-MRI scans of the same tumor. Comparison is completed by registering DCE-MRI voxels to simulated voxel domains, and directly measuring the difference between simulated and experimental signal intensities. Using a Ktrans of 0.4 min-1 and a diffusion constant of 3 x 10-5 mm2/s, the extended Tofts model poorly predicts tissue properties, with predicted Ktrans errors ranging from -95% to 68%, ve errors ranging -80% to 13%, and vp errors ranging 32% to 381%. Notably, necrotic regions of the tumor are subject to the highest level of error in the Tofts approximation of these parameters. Each of the maximum absolute prediction errors listed above occur in voxels with a vp value below 0.5% and ve above 80%. In contrast, the Tofts model can more accurately predict vp when the true value is above 5%, and most accurately predicts ve when its true value is below 70%. These results indicate that the lack of a diffusion term in the extended Tofts model can lead to significant errors in the estimates of physiological properties of tumor tissue in vivo. This is especially notable in regions with both a low vp and high ve, where the tissue is necrotic and poorly perfused. These results call for the inclusion of a diffusive term in the analysis of DCE-MRI in order to accurately model and understand contrast agent perfusion in a tumor. By including such a term, the diagnostic and predictive power of Ktrans ¸ vp, and ve, could be further improved. Our ongoing efforts for this research include further refining histology segmentation and model validation by comparing both simulated and measured DCE-MRI signal intensities. |
2016
|
Oden, J. T.; Lima, E. A. B. F.; Almeida, R. C.; Feng, Y. S.; Rylander, M. N.; Fuentes, D.; Faghihi, D.; Rahman, M. M.; DeWitt, M.; Gadde, M.; Zhou, J. C. "Toward predictive multiscale modeling of vascular tumor growth" Journal Article In: Archives of Computational Methods in Engineering, vol. 23, no. 4, pp. 735-779, 2016. @article{Oden2016,
title = {"Toward predictive multiscale modeling of vascular tumor growth"},
author = {Oden, J. T. and Lima, E. A. B. F. and Almeida, R. C. and Feng, Y. S. and Rylander, M. N. and Fuentes, D. and Faghihi, D. and Rahman, M. M. and DeWitt, M. and Gadde, M. and Zhou, J. C..},
url = {https://link.springer.com/article/10.1007/s11831-015-9156-x},
doi = {10.1007/s11831-015-9156-x},
year = {2016},
date = {2016-12-01},
journal = {Archives of Computational Methods in Engineering},
volume = {23},
number = {4},
pages = {735-779},
publisher = {Springer Netherlands},
abstract = {New directions in medical and biomedical sciences have gradually emerged over recent years that will change the way diseases are diagnosed and treated and are leading to the redirection of medicine toward patient-specific treatments. We refer to these new approaches for studying biomedical systems as predictive medicine, a new version of medical science that involves the use of advanced computer models of biomedical phenomena, high-performance computing, new experimental methods for model data calibration, modern imaging technologies, cutting-edge numerical algorithms for treating large stochastic systems, modern methods for model selection, calibration, validation, verification, and uncertainty quantification, and new approaches for drug design and delivery, all based on predictive models. The methodologies are designed to study events at multiple scales, from genetic data, to sub-cellular signaling mechanisms, to cell interactions, to tissue physics and chemistry, to organs in living human subjects. The present document surveys work on the development and implementation of predictive models of vascular tumor growth, covering aspects of what might be called modeling-and-experimentally based computational oncology. The work described is that of a multi-institutional team, centered at ICES with strong participation by members at M. D. Anderson Cancer Center and University of Texas at San Antonio. This exposition covers topics on signaling models, cell and cell-interaction models, tissue models based on multi-species mixture theories, models of angiogenesis, and beginning work of drug effects. A number of new parallel computer codes for implementing finite-element methods, multi-level Markov Chain Monte Carlo sampling methods, data classification methods, stochastic PDE solvers, statistical inverse algorithms for model calibration and validation, models of events at different spatial and temporal scales is presented. Importantly, new methods for model selection in the presence of uncertainties fundamental to predictive medical science, are described which are based on the notion of Bayesian model plausibilities. Also, as part of this general approach, new codes for determining the sensitivity of model outputs to variations in model parameters are described that provide a basis for assessing the importance of model parameters and controlling and reducing the number of relevant model parameters. Model specific data is to be accessible through careful and model-specific platforms in the Tumor Engineering Laboratory. We describe parallel computer platforms on which large-scale calculations are run as well as specific time-marching algorithms needed to treat stiff systems encountered in some phase-field mixture models. We also cover new non-invasive imaging and data classification methods that provide in vivo data for model validation. The study concludes with a brief discussion of future work and open challenges},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
New directions in medical and biomedical sciences have gradually emerged over recent years that will change the way diseases are diagnosed and treated and are leading to the redirection of medicine toward patient-specific treatments. We refer to these new approaches for studying biomedical systems as predictive medicine, a new version of medical science that involves the use of advanced computer models of biomedical phenomena, high-performance computing, new experimental methods for model data calibration, modern imaging technologies, cutting-edge numerical algorithms for treating large stochastic systems, modern methods for model selection, calibration, validation, verification, and uncertainty quantification, and new approaches for drug design and delivery, all based on predictive models. The methodologies are designed to study events at multiple scales, from genetic data, to sub-cellular signaling mechanisms, to cell interactions, to tissue physics and chemistry, to organs in living human subjects. The present document surveys work on the development and implementation of predictive models of vascular tumor growth, covering aspects of what might be called modeling-and-experimentally based computational oncology. The work described is that of a multi-institutional team, centered at ICES with strong participation by members at M. D. Anderson Cancer Center and University of Texas at San Antonio. This exposition covers topics on signaling models, cell and cell-interaction models, tissue models based on multi-species mixture theories, models of angiogenesis, and beginning work of drug effects. A number of new parallel computer codes for implementing finite-element methods, multi-level Markov Chain Monte Carlo sampling methods, data classification methods, stochastic PDE solvers, statistical inverse algorithms for model calibration and validation, models of events at different spatial and temporal scales is presented. Importantly, new methods for model selection in the presence of uncertainties fundamental to predictive medical science, are described which are based on the notion of Bayesian model plausibilities. Also, as part of this general approach, new codes for determining the sensitivity of model outputs to variations in model parameters are described that provide a basis for assessing the importance of model parameters and controlling and reducing the number of relevant model parameters. Model specific data is to be accessible through careful and model-specific platforms in the Tumor Engineering Laboratory. We describe parallel computer platforms on which large-scale calculations are run as well as specific time-marching algorithms needed to treat stiff systems encountered in some phase-field mixture models. We also cover new non-invasive imaging and data classification methods that provide in vivo data for model validation. The study concludes with a brief discussion of future work and open challenges |
Malyarenko, Dariya I; Wilmes, Lisa J; Arlinghaus, Lori R; Jacobs, Michael A; Huang, Wei; Helmer, Karl G; Taouli, Bachir; Yankeelov, Thomas E; Newitt, David; Chenevert, Thomas L "QIN DAWG Validation of Gradient Nonlinearity Bias Correction Workflow for Quantitative Diffusion-Weighted Imaging in Multicenter Trials" Journal Article In: Tomography, vol. 2, no. 4, pp. 396-405, 2016. @article{Yankeelov2016,
title = {"QIN DAWG Validation of Gradient Nonlinearity Bias Correction Workflow for Quantitative Diffusion-Weighted Imaging in Multicenter Trials"},
author = {Dariya I Malyarenko and Lisa J Wilmes and Lori R Arlinghaus and Michael A Jacobs and Wei Huang and Karl G Helmer and Bachir Taouli and Thomas E Yankeelov and David Newitt and Thomas L Chenevert},
url = {http://www.tomography.org/media/vol2/issue4/pdf/tomo-02-396.pdf},
year = {2016},
date = {2016-12-01},
journal = {Tomography},
volume = {2},
number = {4},
pages = {396-405},
abstract = {Previous research has shown that system-dependent gradient nonlinearity (GNL) introduces a significant spatial bias (nonuniformity) in apparent diffusion coefficient (ADC) maps. Here, the feasibility of centralized retrospective system-specific correction of GNL bias for quantitative diffusion-weighted imaging (DWI) in multisite clinical trials is demonstrated across diverse scanners independent of the scanned object. Using corrector maps generated from system characterization by ice-water phantom measurement completed in the previous project phase, GNL bias correction was performed for test ADC measurements from an independent DWI phantom (room temperature agar) at two offset locations in the bore. The precomputed three-dimensional GNL correctors were retrospectively applied to test DWI scans by the central analysis site. The correction was blinded to reference DWI of the agar phantom at magnet isocenter where the GNL bias is negligible. The performance was evaluated from changes in ADC region of interest histogram statistics before and after correction with respect to the unbiased reference ADC values provided by sites. Both absolute error and nonuniformity of the ADC map induced by GNL (median, 12%; range, 35% to 10%) were substantially reduced by correction (7-fold in median and 3-fold in range). The residual ADC nonuniformity errors were attributed to measurement noise and other non-GNL sources. Correction of systematic GNL bias resulted in a 2-fold decrease in technical variability across scanners (down to site temperature range). The described validation of GNL bias correction marks progress toward implementation of this technology in multicenter trials that utilize quantitative DWI.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Previous research has shown that system-dependent gradient nonlinearity (GNL) introduces a significant spatial bias (nonuniformity) in apparent diffusion coefficient (ADC) maps. Here, the feasibility of centralized retrospective system-specific correction of GNL bias for quantitative diffusion-weighted imaging (DWI) in multisite clinical trials is demonstrated across diverse scanners independent of the scanned object. Using corrector maps generated from system characterization by ice-water phantom measurement completed in the previous project phase, GNL bias correction was performed for test ADC measurements from an independent DWI phantom (room temperature agar) at two offset locations in the bore. The precomputed three-dimensional GNL correctors were retrospectively applied to test DWI scans by the central analysis site. The correction was blinded to reference DWI of the agar phantom at magnet isocenter where the GNL bias is negligible. The performance was evaluated from changes in ADC region of interest histogram statistics before and after correction with respect to the unbiased reference ADC values provided by sites. Both absolute error and nonuniformity of the ADC map induced by GNL (median, 12%; range, 35% to 10%) were substantially reduced by correction (7-fold in median and 3-fold in range). The residual ADC nonuniformity errors were attributed to measurement noise and other non-GNL sources. Correction of systematic GNL bias resulted in a 2-fold decrease in technical variability across scanners (down to site temperature range). The described validation of GNL bias correction marks progress toward implementation of this technology in multicenter trials that utilize quantitative DWI.
|
BF, Kurland; S, Aggarwal; TE, Yankeelov; ER, Gerstner; JM, Mountz; H, Linden; EF, Jones; KL, Bodeker; JM, Buatti "Accrual Patterns for Clinical Studies Involving Quantitative Imaging: Results of an NCI Quantitative Imaging Network (QIN) Survey" Journal Article In: Tomography, vol. 2, no. 4, pp. 276-282, 2016. @article{Yankeelov2016b,
title = {"Accrual Patterns for Clinical Studies Involving Quantitative Imaging: Results of an NCI Quantitative Imaging Network (QIN) Survey"},
author = {Kurland BF and Aggarwal S and Yankeelov TE and Gerstner ER and Mountz JM and Linden H and Jones EF and Bodeker KL and Buatti JM},
url = {http://digitalpub.tomography.org/i/763956-vol-2-no-4-dec-2016/45?m4=},
doi = {10.18383/j.tom.2016.00169},
year = {2016},
date = {2016-12-01},
journal = {Tomography},
volume = {2},
number = {4},
pages = {276-282},
abstract = {Patient accrual is essential for the success of oncology clinical trials. Recruitment for trials involving the development of quantitative imaging biomarkers may face different challenges than treatment trials. This study surveyed investigators and study personnel for evaluating accrual performance and perceived barriers to accrual and for soliciting solutions to these accrual challenges that are specific to quantitative imaging-based trials. Responses for 25 prospective studies were received from 12 sites. The median percent annual accrual attained was 94.5% (range, 3%-350%). The most commonly selected barrier to recruitment (n = 11/25, 44%) was that "patients decline participation," followed by "too few eligible patients" (n = 10/25, 40%). In a forced choice for the single greatest recruitment challenge, "too few eligible patients" was the most common response (n = 8/25, 32%). Quantitative analysis and qualitative responses suggested that interactions among institutional, physician, and patient factors contributed to accrual success and challenges. Multidisciplinary collaboration in trial design and execution is essential to accrual success, with attention paid to ensuring and communicating potential trial benefits to enrolled and future patients.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Patient accrual is essential for the success of oncology clinical trials. Recruitment for trials involving the development of quantitative imaging biomarkers may face different challenges than treatment trials. This study surveyed investigators and study personnel for evaluating accrual performance and perceived barriers to accrual and for soliciting solutions to these accrual challenges that are specific to quantitative imaging-based trials. Responses for 25 prospective studies were received from 12 sites. The median percent annual accrual attained was 94.5% (range, 3%-350%). The most commonly selected barrier to recruitment (n = 11/25, 44%) was that "patients decline participation," followed by "too few eligible patients" (n = 10/25, 40%). In a forced choice for the single greatest recruitment challenge, "too few eligible patients" was the most common response (n = 8/25, 32%). Quantitative analysis and qualitative responses suggested that interactions among institutional, physician, and patient factors contributed to accrual success and challenges. Multidisciplinary collaboration in trial design and execution is essential to accrual success, with attention paid to ensuring and communicating potential trial benefits to enrolled and future patients. |
LR, Arlinghaus; RD, Dortch; JG, Whisenant; H, Kang; RG, Abramson; TE, Yankeelov "Quantitative Magnetization Transfer Imaging of the Breast at 3.0 T: Reproducibility in Healthy Volunteers" Journal Article In: Tomography, vol. 2, no. 4, pp. 260-266, 2016. @article{Yankeelov2016b,
title = {"Quantitative Magnetization Transfer Imaging of the Breast at 3.0 T: Reproducibility in Healthy Volunteers"},
author = {Arlinghaus LR and Dortch RD and Whisenant JG and Kang H and Abramson RG and Yankeelov TE},
url = {http://digitalpub.tomography.org/i/763956-vol-2-no-4-dec-2016/29?m4=},
doi = {10.18383/j.tom.2016.00142},
year = {2016},
date = {2016-12-01},
journal = {Tomography},
volume = {2},
number = {4},
pages = {260-266},
abstract = {Quantitative magnetization transfer magnetic resonance imaging provides a means for indirectly detecting changes in the macromolecular content of tissue noninvasively. A potential application is the diagnosis and assessment of treatment response in breast cancer; however, before quantitative magnetization transfer imaging can be reliably used in such settings, the technique's reproducibility in healthy breast tissue must be established. Thus, this study aims to establish the reproducibility of the measurement of the macromolecular-to-free water proton pool size ratio (PSR) in healthy fibroglandular (FG) breast tissue. Thirteen women with no history of breast disease were scanned twice within a single scanning session, with repositioning between scans. Eleven women had appreciable FG tissue for test-retest measurements. Mean PSR values for the FG tissue ranged from 9.5% to 16.7%. The absolute value of the difference between 2 mean PSR measurements for each volunteer ranged from 0.1% to 2.1%. The 95% confidence interval for the mean difference was ±0.75%, and the repeatability value was 2.39%. These results indicate that the expected measurement variability would be ±0.75% for a cohort of a similar size and would be ±2.39% for an individual, suggesting that future studies of change in PSR in patients with breast cancer are feasible.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Quantitative magnetization transfer magnetic resonance imaging provides a means for indirectly detecting changes in the macromolecular content of tissue noninvasively. A potential application is the diagnosis and assessment of treatment response in breast cancer; however, before quantitative magnetization transfer imaging can be reliably used in such settings, the technique's reproducibility in healthy breast tissue must be established. Thus, this study aims to establish the reproducibility of the measurement of the macromolecular-to-free water proton pool size ratio (PSR) in healthy fibroglandular (FG) breast tissue. Thirteen women with no history of breast disease were scanned twice within a single scanning session, with repositioning between scans. Eleven women had appreciable FG tissue for test-retest measurements. Mean PSR values for the FG tissue ranged from 9.5% to 16.7%. The absolute value of the difference between 2 mean PSR measurements for each volunteer ranged from 0.1% to 2.1%. The 95% confidence interval for the mean difference was ±0.75%, and the repeatability value was 2.39%. These results indicate that the expected measurement variability would be ±0.75% for a cohort of a similar size and would be ±2.39% for an individual, suggesting that future studies of change in PSR in patients with breast cancer are feasible. |
JG, Whisenant; RD, Dortch; W, Grissom; H, Kang; LR, Arlinghaus; TE, Yankeelov "Bloch-Siegert B1-Mapping Improves Accuracy and Precision of Longitudinal Relaxation Measurements in the Breast at 3 T" Journal Article In: Tomography, vol. 2, no. 4, pp. 250-259, 2016. @article{Yankeelov2016b,
title = {"Bloch-Siegert B1-Mapping Improves Accuracy and Precision of Longitudinal Relaxation Measurements in the Breast at 3 T"},
author = {Whisenant JG and Dortch RD and Grissom W and Kang H and Arlinghaus LR and Yankeelov TE},
url = {http://digitalpub.tomography.org/i/763956-vol-2-no-4-dec-2016/19?m4=},
doi = {10.18383/j.tom.2016.00133},
year = {2016},
date = {2016-12-01},
journal = {Tomography},
volume = {2},
number = {4},
pages = {250-259},
abstract = {Variable flip angle (VFA) sequences are a popular method of calculating T1 values, which are required in a quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). B1 inhomogeneities are substantial in the breast at 3 T, and these errors negatively impact the accuracy of the VFA approach, thus leading to large errors in the DCE-MRI parameters that could limit clinical adoption of the technique. This study evaluated the ability of Bloch-Siegert B1 mapping to improve the accuracy and precision of VFA-derived T1 measurements in the breast. Test-retest MRI sessions were performed on 16 women with no history of breast disease. T1 was calculated using the VFA sequence, and B1 field variations were measured using the Bloch-Siegert methodology. As a gold standard, inversion recovery (IR) measurements of T1 were performed. Fibroglandular tissue and adipose tissue from each breast were segmented using the IR images, and the mean T1 was calculated for each tissue. Accuracy was evaluated by percent error (%err). Reproducibility was assessed via the 95% confidence interval (CI) of the mean difference and repeatability coefficient (r). After B1 correction, %err significantly (P < .001) decreased from 17% to 8.6%, and the 95% CI and r decreased from ±94 to ±38 milliseconds and from 276 to 111 milliseconds, respectively. Similar accuracy and reproducibility results were observed in the adipose tissue of the right breast and in both tissues of the left breast. Our data show that Bloch-Siegert B1 mapping improves accuracy and precision of VFA-derived T1 measurements in the breast.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Variable flip angle (VFA) sequences are a popular method of calculating T1 values, which are required in a quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). B1 inhomogeneities are substantial in the breast at 3 T, and these errors negatively impact the accuracy of the VFA approach, thus leading to large errors in the DCE-MRI parameters that could limit clinical adoption of the technique. This study evaluated the ability of Bloch-Siegert B1 mapping to improve the accuracy and precision of VFA-derived T1 measurements in the breast. Test-retest MRI sessions were performed on 16 women with no history of breast disease. T1 was calculated using the VFA sequence, and B1 field variations were measured using the Bloch-Siegert methodology. As a gold standard, inversion recovery (IR) measurements of T1 were performed. Fibroglandular tissue and adipose tissue from each breast were segmented using the IR images, and the mean T1 was calculated for each tissue. Accuracy was evaluated by percent error (%err). Reproducibility was assessed via the 95% confidence interval (CI) of the mean difference and repeatability coefficient (r). After B1 correction, %err significantly (P < .001) decreased from 17% to 8.6%, and the 95% CI and r decreased from ±94 to ±38 milliseconds and from 276 to 111 milliseconds, respectively. Similar accuracy and reproducibility results were observed in the adipose tissue of the right breast and in both tissues of the left breast. Our data show that Bloch-Siegert B1 mapping improves accuracy and precision of VFA-derived T1 measurements in the breast. |
JM, Williams; LR, Arlinghaus; SD, Rani; MD, Shone; VG, Abramson; P, Pendyala; AB, Chakravarthy; WJ, Gorge; JG, Knowland; RK, Lattanze; SR, Perrin; CW, Scarantino; DW, Townsend; RG, Abramson; TE, Yankeelov "Towards real-time topical detection and characterization of FDG dose infiltration prior to PET imaging" Journal Article In: European Journal of Nuclear Medicine and Molecular Imaging, vol. 43, no. 13, pp. 2374–2380, 2016. @article{Yankeelov2016b,
title = {"Towards real-time topical detection and characterization of FDG dose infiltration prior to PET imaging"},
author = {Williams JM and Arlinghaus LR and Rani SD and Shone MD and Abramson VG and Pendyala P and Chakravarthy AB and Gorge WJ and Knowland JG and Lattanze RK and Perrin SR and Scarantino CW and Townsend DW and Abramson RG and Yankeelov TE},
url = {https://link.springer.com/article/10.1007%2Fs00259-016-3477-3},
doi = {10.1007/s00259-016-3477-3},
year = {2016},
date = {2016-12-01},
journal = {European Journal of Nuclear Medicine and Molecular Imaging},
volume = {43},
number = {13},
pages = {2374–2380},
abstract = {PURPOSE:
To dynamically detect and characterize 18F-fluorodeoxyglucose (FDG) dose infiltrations and evaluate their effects on positron emission tomography (PET) standardized uptake values (SUV) at the injection site and in control tissue.
METHODS:
Investigational gamma scintillation sensors were topically applied to patients with locally advanced breast cancer scheduled to undergo limited whole-body FDG-PET as part of an ongoing clinical study. Relative to the affected breast, sensors were placed on the contralateral injection arm and ipsilateral control arm during the resting uptake phase prior to each patient's PET scan. Time-activity curves (TACs) from the sensors were integrated at varying intervals (0-10, 0-20, 0-30, 0-40, and 30-40 min) post-FDG and the resulting areas under the curve (AUCs) were compared to SUVs obtained from PET.
RESULTS:
In cases of infiltration, observed in three sensor recordings (30 %), the injection arm TAC shape varied depending on the extent and severity of infiltration. In two of these cases, TAC characteristics suggested the infiltration was partially resolving prior to image acquisition, although it was still apparent on subsequent PET. Areas under the TAC 0-10 and 0-20 min post-FDG were significantly different in infiltrated versus non-infiltrated cases (Mann-Whitney, p < 0.05). When normalized to control, all TAC integration intervals from the injection arm were significantly correlated with SUVpeak and SUVmax measured over the infiltration site (Spearman ρ ≥ 0.77, p < 0.05). Receiver operating characteristic (ROC) analyses, testing the ability of the first 10 min of post-FDG sensor data to predict infiltration visibility on the ensuing PET, yielded an area under the ROC curve of 0.92.
CONCLUSIONS:
Topical sensors applied near the injection site provide dynamic information from the time of FDG administration through the uptake period and may be useful in detecting infiltrations regardless of PET image field of view. This dynamic information may also complement the static PET image to better characterize the true extent of infiltrations.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
PURPOSE:
To dynamically detect and characterize 18F-fluorodeoxyglucose (FDG) dose infiltrations and evaluate their effects on positron emission tomography (PET) standardized uptake values (SUV) at the injection site and in control tissue.
METHODS:
Investigational gamma scintillation sensors were topically applied to patients with locally advanced breast cancer scheduled to undergo limited whole-body FDG-PET as part of an ongoing clinical study. Relative to the affected breast, sensors were placed on the contralateral injection arm and ipsilateral control arm during the resting uptake phase prior to each patient's PET scan. Time-activity curves (TACs) from the sensors were integrated at varying intervals (0-10, 0-20, 0-30, 0-40, and 30-40 min) post-FDG and the resulting areas under the curve (AUCs) were compared to SUVs obtained from PET.
RESULTS:
In cases of infiltration, observed in three sensor recordings (30 %), the injection arm TAC shape varied depending on the extent and severity of infiltration. In two of these cases, TAC characteristics suggested the infiltration was partially resolving prior to image acquisition, although it was still apparent on subsequent PET. Areas under the TAC 0-10 and 0-20 min post-FDG were significantly different in infiltrated versus non-infiltrated cases (Mann-Whitney, p < 0.05). When normalized to control, all TAC integration intervals from the injection arm were significantly correlated with SUVpeak and SUVmax measured over the infiltration site (Spearman ρ ≥ 0.77, p < 0.05). Receiver operating characteristic (ROC) analyses, testing the ability of the first 10 min of post-FDG sensor data to predict infiltration visibility on the ensuing PET, yielded an area under the ROC curve of 0.92.
CONCLUSIONS:
Topical sensors applied near the injection site provide dynamic information from the time of FDG administration through the uptake period and may be useful in detecting infiltrations regardless of PET image field of view. This dynamic information may also complement the static PET image to better characterize the true extent of infiltrations.
|
Hall, Matthew S; Alisafaei, Farid; Ban, Ehsan; Feng, Xinzeng; Hui, Chung-Yuen; Shenoy, Vivek B; Wu, Mingming "Fibrous nonlinear elasticity enables positive mechanical feedback between cells and ECMs" Journal Article In: Proceedings of the National Academy of Sciences, vol. 113, no. 49, pp. 14043-14048, 2016. @article{Feng2016,
title = {"Fibrous nonlinear elasticity enables positive mechanical feedback between cells and ECMs"},
author = {Matthew S Hall and Farid Alisafaei and Ehsan Ban and Xinzeng Feng and Chung-Yuen Hui and Vivek B Shenoy and Mingming Wu},
url = {http://www.pnas.org/content/113/49/14043.short},
year = {2016},
date = {2016-10-26},
journal = {Proceedings of the National Academy of Sciences},
volume = {113},
number = {49},
pages = {14043-14048},
publisher = {National Acad Sciences},
abstract = {In native states, animal cells of many types are supported by a fibrous network that forms the main structural component of the ECM. Mechanical interactions between cells and the 3D ECM critically regulate cell function, including growth and migration. However, the physical mechanism that governs the cell interaction with fibrous 3D ECM is still not known. In this article, we present single-cell traction force measurements using breast tumor cells embedded within 3D collagen matrices. We recreate the breast tumor mechanical environment by controlling the microstructure and density of type I collagen matrices. Our results reveal a positive mechanical feedback loop: cells pulling on collagen locally align and stiffen the matrix, and stiffer matrices, in return, promote greater cell force generation and a stiffer cell body. Furthermore, cell force transmission distance increases with the degree of strain-induced fiber alignment and stiffening of the collagen matrices. These findings highlight the importance of the nonlinear elasticity of fibrous matrices in regulating cell–ECM interactions within a 3D context, and the cell force regulation principle that we uncover may contribute to the rapid mechanical tissue stiffening occurring in many diseases, including cancer and fibrosis.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In native states, animal cells of many types are supported by a fibrous network that forms the main structural component of the ECM. Mechanical interactions between cells and the 3D ECM critically regulate cell function, including growth and migration. However, the physical mechanism that governs the cell interaction with fibrous 3D ECM is still not known. In this article, we present single-cell traction force measurements using breast tumor cells embedded within 3D collagen matrices. We recreate the breast tumor mechanical environment by controlling the microstructure and density of type I collagen matrices. Our results reveal a positive mechanical feedback loop: cells pulling on collagen locally align and stiffen the matrix, and stiffer matrices, in return, promote greater cell force generation and a stiffer cell body. Furthermore, cell force transmission distance increases with the degree of strain-induced fiber alignment and stiffening of the collagen matrices. These findings highlight the importance of the nonlinear elasticity of fibrous matrices in regulating cell–ECM interactions within a 3D context, and the cell force regulation principle that we uncover may contribute to the rapid mechanical tissue stiffening occurring in many diseases, including cancer and fibrosis. |
H Shinohara K Inoue, M Behar "Oscillation dynamics underlie functional switching of NF-κB for B-cell activation" Journal Article In: NPJ systems biology and applications, vol. 2, pp. 16024, 2016. @article{Behar2016,
title = {"Oscillation dynamics underlie functional switching of NF-κB for B-cell activation"},
author = {K Inoue, H Shinohara, M Behar, N Yumoto, G Tanaka, A Hoffmann, K Aihara, M Okada-Hatakeyama},
doi = {10.1038/npjsba.2016.24},
year = {2016},
date = {2016-10-20},
journal = {NPJ systems biology and applications},
volume = {2},
pages = {16024},
abstract = {Transcription factor nuclear factor kappa B (NF-κB) shows cooperative switch-like activation followed by prolonged oscillatory nuclear translocation in response to extracellular stimuli. These dynamics are important for activation of the NF-κB transcriptional machinery, however, NF-κB activity regulated by coordinated actions of these dynamics has not been elucidated at the system level. Using a variety of B cells with artificially rewired NF-κB signaling networks, we show that oscillations and switch-like activation of NF-κB can be dissected and that, under some conditions, these two behaviors are separated upon antigen receptor activation. Comprehensive quantitative experiments and mathematical analysis showed that the functional role of switch activation in the NF-κB system is to overcome transient IKK (IκB kinase) activity to amplify nuclear translocation of NF-κB, thereby inducing the prolonged NF-κB oscillatory behavior necessary for target gene expression and B-cell activation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Transcription factor nuclear factor kappa B (NF-κB) shows cooperative switch-like activation followed by prolonged oscillatory nuclear translocation in response to extracellular stimuli. These dynamics are important for activation of the NF-κB transcriptional machinery, however, NF-κB activity regulated by coordinated actions of these dynamics has not been elucidated at the system level. Using a variety of B cells with artificially rewired NF-κB signaling networks, we show that oscillations and switch-like activation of NF-κB can be dissected and that, under some conditions, these two behaviors are separated upon antigen receptor activation. Comprehensive quantitative experiments and mathematical analysis showed that the functional role of switch activation in the NF-κB system is to overcome transient IKK (IκB kinase) activity to amplify nuclear translocation of NF-κB, thereby inducing the prolonged NF-κB oscillatory behavior necessary for target gene expression and B-cell activation. |
JP, O'Connor; EO, Aboagye; JE, Adams; HJ, Aerts; SF, Barrington; AJ, Beer; R, Boellaard; SE, Bohndiek; M, Brady; G, Brown; DL, Buckley; TL, Chenevert; LP, Clarke; S, Collette; GJ, Cook; deSouza NM,; JC, Dickson; C, Dive; JL, Evelhoch; C, Faivre-Finn; FA, Gallagher; FJ, Gilbert; RJ, Gillies; V, Goh; JR, Griffiths; AM, Groves; S, Halligan; AL, Harris; DJ, Hawkes; OS, Hoekstra; EP, Huang; BF, Hutton; EF, Jackson; GC, Jayson; A, Jones; DM, Koh; D, Lacombe; P, Lambin; N, Lassau; MO, Leach; TY, Lee; EL, Leen; JS, Lewis; Y, Liu; MF, Lythgoe; P, Manoharan; RJ, Maxwell; KA, Miles; B, Morgan; S, Morris; T, Ng; AR, Padhani; GJ, Parker; M, Partridge; AP, Pathak; AC, Peet; S, Punwani; AR, Reynolds; SP, Robinson; Soloviev D Shankar LK Sharma RA, Stroobants S "Imaging biomarker roadmap for cancer studies" Journal Article In: Nature Reviews Clinical Oncology, vol. 14, pp. 169–186, 2016. @article{Yankeelov2017b,
title = {"Imaging biomarker roadmap for cancer studies"},
author = {O'Connor JP and Aboagye EO and Adams JE and Aerts HJ and Barrington SF and Beer AJ and Boellaard R and Bohndiek SE and Brady M and Brown G and Buckley DL and Chenevert TL and Clarke LP and Collette S and Cook GJ and deSouza NM and Dickson JC and Dive C and Evelhoch JL and Faivre-Finn C and Gallagher FA and Gilbert FJ and Gillies RJ and Goh V and Griffiths JR and Groves AM and Halligan S and Harris AL and Hawkes DJ and Hoekstra OS and Huang EP and Hutton BF and Jackson EF and Jayson GC and Jones A and Koh DM and Lacombe D and Lambin P and Lassau N and Leach MO and Lee TY and Leen EL and Lewis JS and Liu Y and Lythgoe MF and Manoharan P and Maxwell RJ and Miles KA and Morgan B and Morris S and Ng T and Padhani AR and Parker GJ and Partridge M and Pathak AP and Peet AC and Punwani S and Reynolds AR and Robinson SP and Shankar LK Sharma RA, Soloviev D, Stroobants S, Sullivan DC, Taylor SA, Tofts PS, Tozer GM, van Herk M, Walker-Samuel S, Wason J, Williams KJ, Workman P, Yankeelov TE, Brindle KM, McShane LM, Jackson A, Waterton JC},
url = {https://www.nature.com/nrclinonc/journal/v14/n3/full/nrclinonc.2016.162.html},
doi = {10.1038/nrclinonc.2016.162},
year = {2016},
date = {2016-10-11},
journal = {Nature Reviews Clinical Oncology},
volume = {14},
pages = {169–186},
abstract = {maging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing 'translational gaps' through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored 'roadmap'. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
maging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing 'translational gaps' through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored 'roadmap'. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use. |
Lima, E. A. B. F.; Oden, J. T.; Hormuth II, D. A.; Yankeelov, T. E.; Almeida, R. C. "Selection, calibration, and validation of models of tumor growth. Mathematical Models and Methods in Applied Sciences" Journal Article In: Mathematical Models and Methods in Applied Sciences, vol. 26, no. 12, pp. 2341-2368, 2016. @article{Lima2016,
title = {"Selection, calibration, and validation of models of tumor growth. Mathematical Models and Methods in Applied Sciences"},
author = {Lima, E. A. B. F. and Oden, J. T. and Hormuth II, D. A. and Yankeelov, T. E. and Almeida, R. C..},
url = {http://www.worldscientific.com/doi/pdf/10.1142/S021820251650055X},
doi = {10.1142/S021820251650055X},
year = {2016},
date = {2016-10-03},
journal = {Mathematical Models and Methods in Applied Sciences},
volume = {26},
number = {12},
pages = {2341-2368},
publisher = {World Scientific Publishing Company},
abstract = {This paper presents general approaches for addressing some of the most important issues in predictive computational oncology concerned with developing classes of predictive models of tumor growth. First, the process of developing mathematical models of vascular tumors evolving in the complex, heterogeneous, macroenvironment of living tissue; second, the selection of the most plausible models among these classes, given relevant observational data; third, the statistical calibration and validation of models in these classes, and finally, the prediction of key Quantities of Interest (QOIs) relevant to patient survival and the effect of various therapies. The most challenging aspects of this endeavor is that all of these issues often involve confounding uncertainties: in observational data, in model parameters, in model selection, and in the features targeted in the prediction. Our approach can be referred to as “model agnostic” in that no single model is advocated; rather, a general approach that explores powerful mixture-theory representations of tissue behavior while accounting for a range of relevant biological factors is presented, which leads to many potentially predictive models. Then representative classes are identified which provide a starting point for the implementation of OPAL, the Occam Plausibility Algorithm (OPAL) which enables the modeler to select the most plausible models (for given data) and to determine if the model is a valid tool for predicting tumor growth and morphology (in vivo). All of these approaches account for uncertainties in the model, the observational data, the model parameters, and the target QOI. We demonstrate these processes by comparing a list of models for tumor growth, including reaction–diffusion models, phase-fields models, and models with and without mechanical deformation effects, for glioma growth measured in murine experiments. Examples are provided that exhibit quite acceptable predictions of tumor growth in laboratory animals while demonstrating successful implementations of OPAL.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper presents general approaches for addressing some of the most important issues in predictive computational oncology concerned with developing classes of predictive models of tumor growth. First, the process of developing mathematical models of vascular tumors evolving in the complex, heterogeneous, macroenvironment of living tissue; second, the selection of the most plausible models among these classes, given relevant observational data; third, the statistical calibration and validation of models in these classes, and finally, the prediction of key Quantities of Interest (QOIs) relevant to patient survival and the effect of various therapies. The most challenging aspects of this endeavor is that all of these issues often involve confounding uncertainties: in observational data, in model parameters, in model selection, and in the features targeted in the prediction. Our approach can be referred to as “model agnostic” in that no single model is advocated; rather, a general approach that explores powerful mixture-theory representations of tissue behavior while accounting for a range of relevant biological factors is presented, which leads to many potentially predictive models. Then representative classes are identified which provide a starting point for the implementation of OPAL, the Occam Plausibility Algorithm (OPAL) which enables the modeler to select the most plausible models (for given data) and to determine if the model is a valid tool for predicting tumor growth and morphology (in vivo). All of these approaches account for uncertainties in the model, the observational data, the model parameters, and the target QOI. We demonstrate these processes by comparing a list of models for tumor growth, including reaction–diffusion models, phase-fields models, and models with and without mechanical deformation effects, for glioma growth measured in murine experiments. Examples are provided that exhibit quite acceptable predictions of tumor growth in laboratory animals while demonstrating successful implementations of OPAL. |
Deng, Wei; Xu, Yan; Chen, Wenchun; Paul, David S; Syed, Anum K; Dragovich, Matthew A; Liang, Xin; Zakas, Philip; Berndt, Michael C; Paola, Jorge Di; Ware, Jerry; Lanza, Francois; Doering, Christopher B; Bergmeier, Wolfgang; Zhang, X Frank; Li, Renhao "Platelet clearance via shear-induced unfolding of a membrane mechanoreceptor" Journal Article In: Nature Communications, vol. 7, 2016. @article{Syed2016b,
title = {"Platelet clearance via shear-induced unfolding of a membrane mechanoreceptor"},
author = {Wei Deng and Yan Xu and Wenchun Chen and David S Paul and Anum K Syed and Matthew A Dragovich and Xin Liang and Philip Zakas and Michael C Berndt and Jorge Di Paola and Jerry Ware and Francois Lanza and Christopher B Doering and Wolfgang Bergmeier and X Frank Zhang and Renhao Li},
url = {http://europepmc.org/articles/pmc5052631},
doi = {10.1038/ncomms12863},
year = {2016},
date = {2016-09-27},
journal = {Nature Communications},
volume = {7},
publisher = {Nature Research},
abstract = {Mechanisms by which blood cells sense shear stress are poorly characterized. In platelets, glycoprotein (GP)Ib–IX receptor complex has been long suggested to be a shear sensor and receptor. Recently, a relatively unstable and mechanosensitive domain in the GPIbα subunit of GPIb–IX was identified. Here we show that binding of its ligand, von Willebrand factor, under physiological shear stress induces unfolding of this mechanosensory domain (MSD) on the platelet surface. The unfolded MSD, particularly the juxtamembrane ‘Trigger' sequence therein, leads to intracellular signalling and rapid platelet clearance. These results illustrate the initial molecular event underlying platelet shear sensing and provide a mechanism linking GPIb–IX to platelet clearance. Our results have implications on the mechanism of platelet activation, and on the pathophysiology of von Willebrand disease and related thrombocytopenic disorders. The mechanosensation via receptor unfolding may be applicable for many other cell adhesion receptors.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Mechanisms by which blood cells sense shear stress are poorly characterized. In platelets, glycoprotein (GP)Ib–IX receptor complex has been long suggested to be a shear sensor and receptor. Recently, a relatively unstable and mechanosensitive domain in the GPIbα subunit of GPIb–IX was identified. Here we show that binding of its ligand, von Willebrand factor, under physiological shear stress induces unfolding of this mechanosensory domain (MSD) on the platelet surface. The unfolded MSD, particularly the juxtamembrane ‘Trigger' sequence therein, leads to intracellular signalling and rapid platelet clearance. These results illustrate the initial molecular event underlying platelet shear sensing and provide a mechanism linking GPIb–IX to platelet clearance. Our results have implications on the mechanism of platelet activation, and on the pathophysiology of von Willebrand disease and related thrombocytopenic disorders. The mechanosensation via receptor unfolding may be applicable for many other cell adhesion receptors. |
Chen, Wenchun; Liang, Xin; Syed, Anum K; Jessup, Paula; Church, William R; Ware, Jerry; Josephson, Cassandra D; Li, Renhao "Inhibiting GPIbα Shedding Preserves Post-Transfusion Recovery and Hemostatic Function of Platelets After Prolonged StorageHighlights" Journal Article In: Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 36, no. 9, pp. 1821-1828, 2016. @article{Syed2016b,
title = {"Inhibiting GPIbα Shedding Preserves Post-Transfusion Recovery and Hemostatic Function of Platelets After Prolonged StorageHighlights"},
author = {Wenchun Chen and Xin Liang and Anum K Syed and Paula Jessup and William R Church and Jerry Ware and Cassandra D Josephson and Renhao Li},
url = {http://atvb.ahajournals.org/content/36/9/1821.short},
doi = {https://doi.org/10.1161/ATVBAHA.116.307639},
year = {2016},
date = {2016-09-01},
journal = {Arteriosclerosis, Thrombosis, and Vascular Biology},
volume = {36},
number = {9},
pages = {1821-1828},
publisher = {American Heart Association, Inc},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Sharifi, Farrokh; Sooriyarachchi, Avinash C.; Altural, Hayriye; Montazami, Reza; Rylander, Marissa Nichole; Hashemi, Nastaran "Fiber Based Approaches as Medicine Delivery Systems" Journal Article In: ACS Biomater. Sci. Eng., vol. 2, no. 9, pp. 1411-1431, 2016. @article{Rylander2016,
title = {"Fiber Based Approaches as Medicine Delivery Systems"},
author = {Farrokh Sharifi and Avinash C. Sooriyarachchi and Hayriye Altural and Reza Montazami and Marissa Nichole Rylander and Nastaran Hashemi},
url = {http://pubs.acs.org/doi/abs/10.1021/acsbiomaterials.6b00281},
doi = {10.1021/acsbiomaterials.6b00281},
year = {2016},
date = {2016-07-19},
journal = {ACS Biomater. Sci. Eng.},
volume = {2},
number = {9},
pages = {1411-1431},
publisher = {American Chemical Society},
abstract = {The goal of drug delivery is to ensure that therapeutic molecules reach the intended target organ or tissue, such that the effectiveness of the drug is maximized. The efficiency of a drug delivery system greatly depends on the choice of drug carrier. Recently, there has been growing interest in using micro- and nanofibers for this purpose. The reasons for this growing interest include these materials’ high surface area to volume ratios, ease of fabrication, high mechanical properties, and desirable drug release profile. Here, we review developments in using these materials made by the most prevalent methods of fiber fabrication: electrospinning, microfluidics, wet spinning, rotary spinning, and self-assembly for drug delivery purposes. Additionally, we discuss the potential to use these fiber based systems in research and clinical applications.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The goal of drug delivery is to ensure that therapeutic molecules reach the intended target organ or tissue, such that the effectiveness of the drug is maximized. The efficiency of a drug delivery system greatly depends on the choice of drug carrier. Recently, there has been growing interest in using micro- and nanofibers for this purpose. The reasons for this growing interest include these materials’ high surface area to volume ratios, ease of fabrication, high mechanical properties, and desirable drug release profile. Here, we review developments in using these materials made by the most prevalent methods of fiber fabrication: electrospinning, microfluidics, wet spinning, rotary spinning, and self-assembly for drug delivery purposes. Additionally, we discuss the potential to use these fiber based systems in research and clinical applications. |
Sorace, Anna G; Quarles, C Chad; Sanchez, Violeta; Yankeelov, Thomas E "Decreased hypoxia in a HER2+ breast cancer model following trastuzumab therapy" Journal Article In: Cancer Research, vol. 76, no. 14 supplement, pp. 4237-4237, 2016. @article{Sorace2016b,
title = {"Decreased hypoxia in a HER2+ breast cancer model following trastuzumab therapy"},
author = {Anna G Sorace and C Chad Quarles and Violeta Sanchez and Thomas E Yankeelov},
url = {http://cancerres.aacrjournals.org/content/76/14_Supplement/4237.short},
doi = {10.1158/1538-7445.AM2016-4237},
year = {2016},
date = {2016-07-15},
journal = {Cancer Research},
volume = {76},
number = {14 supplement},
pages = {4237-4237},
publisher = {American Association for Cancer Research},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Feng, Xinzeng; Hu, Chung-Yuen "Force sensing using 3D displacement measurements in linear elastic bodies" Journal Article In: Computational Mechanics, vol. 58, no. 1, pp. 91-105, 2016. @article{Feng2016b,
title = {"Force sensing using 3D displacement measurements in linear elastic bodies"},
author = {Xinzeng Feng and Chung-Yuen Hu},
url = {https://link.springer.com/article/10.1007/s00466-016-1283-1},
doi = {10.1007/s00466-016-1283-1},
year = {2016},
date = {2016-07-01},
journal = {Computational Mechanics},
volume = {58},
number = {1},
pages = {91-105},
publisher = {Springer Berlin Heidelberg},
abstract = {In cell traction microscopy, the mechanical forces exerted by a cell on its environment is usually determined from experimentally measured displacement by solving an inverse problem in elasticity. In this paper, an innovative numerical method is proposed which finds the “optimal” traction to the inverse problem. When sufficient regularization is applied, we demonstrate that the proposed method significantly improves the widely used approach using Green’s functions. Motivated by real cell experiments, the equilibrium condition of a slowly migrating cell is imposed as a set of equality constraints on the unknown traction. Our validation benchmarks demonstrate that the numeric solution to the constrained inverse problem well recovers the actual traction when the optimal regularization parameter is used. The proposed method can thus be applied to study general force sensing problems, which utilize displacement measurements to sense inaccessible forces in linear elastic bodies with a priori constraints.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In cell traction microscopy, the mechanical forces exerted by a cell on its environment is usually determined from experimentally measured displacement by solving an inverse problem in elasticity. In this paper, an innovative numerical method is proposed which finds the “optimal” traction to the inverse problem. When sufficient regularization is applied, we demonstrate that the proposed method significantly improves the widely used approach using Green’s functions. Motivated by real cell experiments, the equilibrium condition of a slowly migrating cell is imposed as a set of equality constraints on the unknown traction. Our validation benchmarks demonstrate that the numeric solution to the constrained inverse problem well recovers the actual traction when the optimal regularization parameter is used. The proposed method can thus be applied to study general force sensing problems, which utilize displacement measurements to sense inaccessible forces in linear elastic bodies with a priori constraints. |
Tam, Alda L; Figueira, Tomas A; Gagea, Mihai; Ensor, Joe E; Dixon, Katherine; McWatters, Amanda; Gupta, Sanjay; Fuentes, David T "Irreversible Electroporation in the Epidural Space of the Porcine Spine: Effects on Adjacent Structures" Journal Article In: Radiology, vol. 281, no. 3, pp. 763-771, 2016. @article{Fuentes2016b,
title = {"Irreversible Electroporation in the Epidural Space of the Porcine Spine: Effects on Adjacent Structures"},
author = {Alda L Tam and Tomas A Figueira and Mihai Gagea and Joe E Ensor and Katherine Dixon and Amanda McWatters and Sanjay Gupta and David T Fuentes},
url = {http://pubs.rsna.org/doi/abs/10.1148/radiol.2016152688},
doi = {http://dx.doi.org/10.1148/radiol.2016152688},
year = {2016},
date = {2016-06-07},
journal = {Radiology},
volume = {281},
number = {3},
pages = {763-771},
publisher = {Radiological Society of North America},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Lin, J; Fuentes, D; Chandler, A; Hazle, J; Schellingerhout, D; MacLellan, C "TU‐H‐CAMPUS‐IeP3‐03: Validation of Image Registration Methods for Brain Magnetic Resonance Imaging" Journal Article In: Medical Physics, vol. 43, no. 6, pp. 3785-3785, 2016. @article{Fuentes2016,
title = {"TU‐H‐CAMPUS‐IeP3‐03: Validation of Image Registration Methods for Brain Magnetic Resonance Imaging"},
author = {J Lin and D Fuentes and A Chandler and J Hazle and D Schellingerhout and C MacLellan},
url = {http://onlinelibrary.wiley.com/doi/10.1118/1.4957696/full},
doi = {10.1118/1.4957696},
year = {2016},
date = {2016-06-01},
journal = {Medical Physics},
volume = {43},
number = {6},
pages = {3785-3785},
publisher = {American Association of Physicists in Medicine},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
MacLellan, C; Fuentes, D; Espinoza, H; Prabhu, S; Rao, G; Weinberg, J; Stafford, R "WE‐AB‐BRA‐04: Investigation of MRI Derived Thermal Dose Models" Journal Article In: Medical Physics, vol. 43, no. 6, pp. 3791-3792, 2016. @article{Fuentes2016b,
title = {"WE‐AB‐BRA‐04: Investigation of MRI Derived Thermal Dose Models"},
author = {C MacLellan and D Fuentes and H Espinoza and S Prabhu and G Rao and J Weinberg and R Stafford},
url = {http://onlinelibrary.wiley.com/doi/10.1118/1.4957733/full},
doi = {10.1118/1.4957733},
year = {2016},
date = {2016-06-01},
journal = {Medical Physics},
volume = {43},
number = {6},
pages = {3791-3792},
publisher = {American Association of Physicists in Medicine},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Madankan, R; MacLellan, C; Fahrenholtz, S; Weinberg, J; Rao, G; Hazle, J; Stafford, R; Fuentes, D "SU‐F‐J‐03: Treatment Planning for Laser Ablation Therapy in Presence of Heterogeneous Tissue: A Retrospective Study" Journal Article In: Medical Physics, vol. 43, no. 6, pp. 3406-3406, 2016. @article{Fuentes2016b,
title = {"SU‐F‐J‐03: Treatment Planning for Laser Ablation Therapy in Presence of Heterogeneous Tissue: A Retrospective Study"},
author = {R Madankan and C MacLellan and S Fahrenholtz and J Weinberg and G Rao and J Hazle and R Stafford and D Fuentes},
url = {http://onlinelibrary.wiley.com/doi/10.1118/1.4955911/full},
doi = {10.1118/1.4955911},
year = {2016},
date = {2016-06-01},
journal = {Medical Physics},
volume = {43},
number = {6},
pages = {3406-3406},
publisher = {American Association of Physicists in Medicine},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Fahrenholtz, SJ; Stafford, RJ; Madankan, R; Hazle, JD; Fuentes, D "SU‐F‐J‐02: Flexible Training of MR‐Guided Laser Ablation Models Via Global Optimization" Journal Article In: Medical Physics, vol. 43, no. 6, pp. 3405-3406, 2016. @article{Fuentes2016b,
title = {"SU‐F‐J‐02: Flexible Training of MR‐Guided Laser Ablation Models Via Global Optimization"},
author = {SJ Fahrenholtz and RJ Stafford and R Madankan and JD Hazle and D Fuentes},
url = {http://onlinelibrary.wiley.com/doi/10.1118/1.4955910/full},
doi = {10.1118/1.4955910},
year = {2016},
date = {2016-06-01},
journal = {Medical Physics},
volume = {43},
number = {6},
pages = {3405-3406},
publisher = {American Association of Physicists in Medicine},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
