2019
|
Pineda F Wu C, Hormuth DA 2nd Quantitative analysis of vascular properties derived from ultrafast DCE-MRI to discriminate malignant and benign breast tumors. Journal Article In: Magn Reson Med, vol. 81, no. 3, pp. 2147-2160, 2019. @article{C2018,
title = {Quantitative analysis of vascular properties derived from ultrafast DCE-MRI to discriminate malignant and benign breast tumors.},
author = {Wu C, Pineda F, Hormuth DA 2nd, Karczmar GS, Yankeelov TE.},
doi = {10.1002/mrm.27529},
year = {2019},
date = {2019-03-01},
journal = {Magn Reson Med},
volume = {81},
number = {3},
pages = {2147-2160},
abstract = {PURPOSE:
We propose a novel methodology to integrate morphological and functional information of tumor-associated vessels to assist in the diagnosis of suspicious breast lesions.
THEORY AND METHODS:
Ultrafast, fast, and high spatial resolution DCE-MRI data were acquired on 15 patients with suspicious breast lesions. Segmentation of the vasculature from the surrounding tissue was performed by applying a Hessian filter to the enhanced image to generate a map of the probability for each voxel to belong to a vessel. Summary measures were generated for vascular morphology, as well as the inputs and outputs of vessels physically connected to the tumor. The ultrafast DCE-MRI data was analyzed by a modified Tofts model to estimate the bolus arrival time, Ktrans (volume transfer coefficient), and vp (plasma volume fraction). The measures were compared between malignant and benign lesions via the Wilcoxon test, and then incorporated into a logistic ridge regression model to assess their combined diagnostic ability.
RESULTS:
A total of 24 lesions were included in the study (13 malignant and 11 benign). The vessel count, Ktrans , and vp showed significant difference between malignant and benign lesions (P = 0.009, 0.034, and 0.010, area under curve [AUC] = 0.76, 0.63, and 0.70, respectively). The best multivariate logistic regression model for differentiation included the vessel count and bolus arrival time (AUC = 0.91).
CONCLUSION:
This study provides preliminary evidence that combining quantitative characterization of morphological and functional features of breast vasculature may provide an accurate means to diagnose breast cancer.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
PURPOSE:
We propose a novel methodology to integrate morphological and functional information of tumor-associated vessels to assist in the diagnosis of suspicious breast lesions.
THEORY AND METHODS:
Ultrafast, fast, and high spatial resolution DCE-MRI data were acquired on 15 patients with suspicious breast lesions. Segmentation of the vasculature from the surrounding tissue was performed by applying a Hessian filter to the enhanced image to generate a map of the probability for each voxel to belong to a vessel. Summary measures were generated for vascular morphology, as well as the inputs and outputs of vessels physically connected to the tumor. The ultrafast DCE-MRI data was analyzed by a modified Tofts model to estimate the bolus arrival time, Ktrans (volume transfer coefficient), and vp (plasma volume fraction). The measures were compared between malignant and benign lesions via the Wilcoxon test, and then incorporated into a logistic ridge regression model to assess their combined diagnostic ability.
RESULTS:
A total of 24 lesions were included in the study (13 malignant and 11 benign). The vessel count, Ktrans , and vp showed significant difference between malignant and benign lesions (P = 0.009, 0.034, and 0.010, area under curve [AUC] = 0.76, 0.63, and 0.70, respectively). The best multivariate logistic regression model for differentiation included the vessel count and bolus arrival time (AUC = 0.91).
CONCLUSION:
This study provides preliminary evidence that combining quantitative characterization of morphological and functional features of breast vasculature may provide an accurate means to diagnose breast cancer. |
Saksena SD Bloom MJ, Swain GP The effects of IKK-beta inhibition on early NF-kappa-B activation and transcription of downstream genes Journal Article In: Cell Signal., vol. 55, pp. 17-25, 2019. @article{MJ2018,
title = {The effects of IKK-beta inhibition on early NF-kappa-B activation and transcription of downstream genes},
author = {Bloom MJ, Saksena SD, Swain GP, Behar MS, Yankeelov TE, Sorace AG},
doi = {10.1016/j.cellsig.2018.12.004},
year = {2019},
date = {2019-03-01},
journal = {Cell Signal.},
volume = {55},
pages = {17-25},
abstract = {Small molecule approaches targeting the nuclear factor kappa B (NF-kB) pathway, a regulator of inflammation, have thus far proven unsuccessful in the clinic in part due to the complex pleiotropic nature of this network. Downstream effects depend on multiple factors including stimulus-specific temporal patterns of NF-kB activity. Despite considerable advances, genome-level impact of changes in temporal NF-kB activity caused by inhibitors and their stimulus dependency remains unexplored. This study evaluates the effects of pathway inhibitors on early NF-κB activity and downstream gene transcription. 3T3 fibroblasts were treated with SC-514, an inhibitor targeted to the NF-kB pathway, prior to stimulation with interleukin 1 beta (IL-1β) or tumor necrosis factor alpha (TNF-α). Stimulus induced NF-κB activation was quantified using immunofluorescence imaging over 90-minutes and gene expression tracked over 6-hours using mRNA TagSeq. When stimulated with IL-1β or TNF-α, significant differences (P < 0.05, two-way ANOVA), were observed in the temporal profiles of NF-κB activation between treated and untreated cells. Increasing numbers of differentially expressed genes (P < 0.01) were observed at higher inhibitor concentrations. Individual gene expression profiles varied in an inhibitor concentration and stimulus-dependent manner. The results in this study demonstrate small molecule inhibitors acting on pleiotropic pathway components can alter signal dynamics in a stimulus-dependent manner and affect gene response in complex ways.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Small molecule approaches targeting the nuclear factor kappa B (NF-kB) pathway, a regulator of inflammation, have thus far proven unsuccessful in the clinic in part due to the complex pleiotropic nature of this network. Downstream effects depend on multiple factors including stimulus-specific temporal patterns of NF-kB activity. Despite considerable advances, genome-level impact of changes in temporal NF-kB activity caused by inhibitors and their stimulus dependency remains unexplored. This study evaluates the effects of pathway inhibitors on early NF-κB activity and downstream gene transcription. 3T3 fibroblasts were treated with SC-514, an inhibitor targeted to the NF-kB pathway, prior to stimulation with interleukin 1 beta (IL-1β) or tumor necrosis factor alpha (TNF-α). Stimulus induced NF-κB activation was quantified using immunofluorescence imaging over 90-minutes and gene expression tracked over 6-hours using mRNA TagSeq. When stimulated with IL-1β or TNF-α, significant differences (P < 0.05, two-way ANOVA), were observed in the temporal profiles of NF-κB activation between treated and untreated cells. Increasing numbers of differentially expressed genes (P < 0.01) were observed at higher inhibitor concentrations. Individual gene expression profiles varied in an inhibitor concentration and stimulus-dependent manner. The results in this study demonstrate small molecule inhibitors acting on pleiotropic pathway components can alter signal dynamics in a stimulus-dependent manner and affect gene response in complex ways. |
Gomez A Johnson K, Burton J. White D Directional inconsistency between Response Evaluation Criteria in Solid Tumors (RECIST) time to progression and response speed and depth Journal Article In: Eur J Cancer, vol. 18, pp. 31469-2, 2019. @article{K2019,
title = {Directional inconsistency between Response Evaluation Criteria in Solid Tumors (RECIST) time to progression and response speed and depth},
author = {Johnson K, Gomez A, Burton J. White D, Chakravarty A, Schmid A, Bottino D.},
doi = {10.1016/j.ejca.2018.11.008},
year = {2019},
date = {2019-02-06},
journal = {Eur J Cancer},
volume = {18},
pages = {31469-2},
abstract = {AIM:
We seek to characterize how faster tumour shrinkage rate (k) can lead to paradoxically shorter Response Evaluation Criteria in Solid Tumors (RECIST) time to progression ('TTP20' - tumour size exceeding its minimum by 5 mm and 20%) [1] and, therefore, progression-free survival (PFS). Specifically, we investigate under what conditions this paradoxical behaviour occurs, what fraction of patients satisfy these conditions, whether this phenomenon can invert population-level PFS hazard ratio, and consistency of an alternative time-to-event benefit metric with k.
METHODS:
We use a mathematical model treating tumour burden as decreasing drug-sensitive and increasing drug-resistant cell subpopulations. We fit this model to data from several clinical trials with different indications [2]. We simulated a more effective treatment and recorded whether patients' TTP20 increased or decreased. We performed a study-level analysis to compare the relationship of speed and depth of response with TTP20 for both the administered 'control' and simulated 'more effective' drug. We propose and test an alternative benefit metric: the model-projected time that tumour size reaches 120% of baseline (TTB120).
RESULTS:
Depending on indication, 3-27% of patients are estimated to have a paradoxically inverse relationship between k and TTP20. Simulated head-to-head studies show that TTP20-based PFS can favour the less effective drug. In contrast, TTB120 always favours the more effective drug.
CONCLUSION:
We demonstrate the paradoxical behaviour of RECIST TTP20 - as an exemplar of percent-change-from-nadir based cancer progression criterion - both in theory and in observed patient data at the individual and trial level. We propose an alternative tumour size-based criterion (TTB120) that is directionally consistent with tumour shrinkage rate.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
AIM:
We seek to characterize how faster tumour shrinkage rate (k) can lead to paradoxically shorter Response Evaluation Criteria in Solid Tumors (RECIST) time to progression ('TTP20' - tumour size exceeding its minimum by 5 mm and 20%) [1] and, therefore, progression-free survival (PFS). Specifically, we investigate under what conditions this paradoxical behaviour occurs, what fraction of patients satisfy these conditions, whether this phenomenon can invert population-level PFS hazard ratio, and consistency of an alternative time-to-event benefit metric with k.
METHODS:
We use a mathematical model treating tumour burden as decreasing drug-sensitive and increasing drug-resistant cell subpopulations. We fit this model to data from several clinical trials with different indications [2]. We simulated a more effective treatment and recorded whether patients' TTP20 increased or decreased. We performed a study-level analysis to compare the relationship of speed and depth of response with TTP20 for both the administered 'control' and simulated 'more effective' drug. We propose and test an alternative benefit metric: the model-projected time that tumour size reaches 120% of baseline (TTB120).
RESULTS:
Depending on indication, 3-27% of patients are estimated to have a paradoxically inverse relationship between k and TTP20. Simulated head-to-head studies show that TTP20-based PFS can favour the less effective drug. In contrast, TTB120 always favours the more effective drug.
CONCLUSION:
We demonstrate the paradoxical behaviour of RECIST TTP20 - as an exemplar of percent-change-from-nadir based cancer progression criterion - both in theory and in observed patient data at the individual and trial level. We propose an alternative tumour size-based criterion (TTB120) that is directionally consistent with tumour shrinkage rate. |
Ghousifam N Ozkan A, Hoopes PJ In Vitro Vascularized Liver and Tumor Tissue Microenvironments on a Chip for Dynamic Determination of Nanoparticle Transport and Toxicity Journal Article In: Biotechnol Bioeng., 2019. @article{A2019,
title = {In Vitro Vascularized Liver and Tumor Tissue Microenvironments on a Chip for Dynamic Determination of Nanoparticle Transport and Toxicity},
author = {Ozkan A, Ghousifam N, Hoopes PJ, Rylander MN},
doi = {10.1002/bit.26919.},
year = {2019},
date = {2019-01-13},
journal = { Biotechnol Bioeng.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Woodall R Syed AK, Whisenant JG Characterizing Trastuzumab-Induced Alterations in Intratumoral Heterogeneity with Quantitative Imaging and Immunohistochemistry in HER2+ Breast Cancer Journal Article In: Neoplasia, vol. 21, no. 1, pp. 17-29, 2019. @article{AK2018,
title = {Characterizing Trastuzumab-Induced Alterations in Intratumoral Heterogeneity with Quantitative Imaging and Immunohistochemistry in HER2+ Breast Cancer},
author = {Syed AK, Woodall R, Whisenant JG, Yankeelov TE, Sorace AG.},
doi = {10.1016/j.neo.2018.10.008},
year = {2019},
date = {2019-01-01},
journal = {Neoplasia},
volume = {21},
number = {1},
pages = {17-29},
abstract = {The purpose of this study is to investigate imaging and histology-based measurements of intratumoral heterogeneity to evaluate early treatment response to targeted therapy in a murine model of HER2+ breast cancer. BT474 tumor-bearing mice (N = 30) were treated with trastuzumab or saline and imaged longitudinally with either dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) or 18F-fluoromisonidazole (FMISO) positron emission tomography (PET). At the imaging study end point (day 4 for MRI or 7 for PET), each tumor was excised for immunohistochemistry analysis. Voxel-based histogram analysis was performed on imaging-derived parametric maps (i.e., Ktrans and ve from DCE-MRI, SUV from 18F-FMISO-PET) of the tumor region of interest to measure heterogeneity. Image processing and histogram analysis of whole tumor slice immunohistochemistry data were performed to validate the in vivo imaging findings. Trastuzumab-treated tumors had increased heterogeneity in quantitative imaging measures of cellularity (ve), with a mean Kolmogorov-Smirnov (K-S) distance of 0.32 (P = .05) between baseline and end point distributions. Trastuzumab-treated tumors had increased vascular heterogeneity (Ktrans) and decreased hypoxic heterogeneity (SUV), with a mean K-S distance of 0.42 (P < .01) and 0.46 (P = .047), respectively, between baseline and study end points. These observations were validated by whole-slice immunohistochemistry analysis with mean interquartile range of CD31 distributions of 1.72 for treated and 0.95 for control groups (P = .02). Quantitative longitudinal changes in tumor cellular and vascular heterogeneity in response to therapy may provide evidence for early prediction of response and guide therapy for patients with HER2+ breast cancer.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The purpose of this study is to investigate imaging and histology-based measurements of intratumoral heterogeneity to evaluate early treatment response to targeted therapy in a murine model of HER2+ breast cancer. BT474 tumor-bearing mice (N = 30) were treated with trastuzumab or saline and imaged longitudinally with either dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) or 18F-fluoromisonidazole (FMISO) positron emission tomography (PET). At the imaging study end point (day 4 for MRI or 7 for PET), each tumor was excised for immunohistochemistry analysis. Voxel-based histogram analysis was performed on imaging-derived parametric maps (i.e., Ktrans and ve from DCE-MRI, SUV from 18F-FMISO-PET) of the tumor region of interest to measure heterogeneity. Image processing and histogram analysis of whole tumor slice immunohistochemistry data were performed to validate the in vivo imaging findings. Trastuzumab-treated tumors had increased heterogeneity in quantitative imaging measures of cellularity (ve), with a mean Kolmogorov-Smirnov (K-S) distance of 0.32 (P = .05) between baseline and end point distributions. Trastuzumab-treated tumors had increased vascular heterogeneity (Ktrans) and decreased hypoxic heterogeneity (SUV), with a mean K-S distance of 0.42 (P < .01) and 0.46 (P = .047), respectively, between baseline and study end points. These observations were validated by whole-slice immunohistochemistry analysis with mean interquartile range of CD31 distributions of 1.72 for treated and 0.95 for control groups (P = .02). Quantitative longitudinal changes in tumor cellular and vascular heterogeneity in response to therapy may provide evidence for early prediction of response and guide therapy for patients with HER2+ breast cancer. |
2018
|
Lima EABF Jarrett AM, Hormuth DA Mathematical models of tumor cell proliferation: A review of the literature. Journal Article In: Expert Rev Anticancer Ther., vol. 18, no. 12, pp. 1271-1286, 2018. @article{AM2018b,
title = {Mathematical models of tumor cell proliferation: A review of the literature.},
author = {Jarrett AM, Lima EABF, Hormuth DA, McKenna MT, Feng X, Ekrut DA, Resende ACM, Brock A, Yankeelov TE.},
doi = {10.1080/14737140.2018},
year = {2018},
date = {2018-12-01},
journal = {Expert Rev Anticancer Ther.},
volume = {18},
number = {12},
pages = {1271-1286},
abstract = {A defining hallmark of cancer is aberrant cell proliferation. Efforts to understand the generative properties of cancer cells span all biological scales: from genetic deviations and alterations of metabolic pathways to physical stresses due to overcrowding, as well as the effects of therapeutics and the immune system. While these factors have long been studied in the laboratory, mathematical and computational techniques are being increasingly applied to help understand and forecast tumor growth and treatment response. Advantages of mathematical modeling of proliferation include the ability to simulate and predict the spatiotemporal development of tumors across multiple experimental scales. Central to proliferation modeling is the incorporation of available biological data and validation with experimental data. Areas covered: We present an overview of past and current mathematical strategies directed at understanding tumor cell proliferation. We identify areas for mathematical development as motivated by available experimental and clinical evidence, with a particular emphasis on emerging, non-invasive imaging technologies. Expert commentary: The data required to legitimize mathematical models are often difficult or (currently) impossible to obtain. We suggest areas for further investigation to establish mathematical models that more effectively utilize available data to make informed predictions on tumor cell proliferation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A defining hallmark of cancer is aberrant cell proliferation. Efforts to understand the generative properties of cancer cells span all biological scales: from genetic deviations and alterations of metabolic pathways to physical stresses due to overcrowding, as well as the effects of therapeutics and the immune system. While these factors have long been studied in the laboratory, mathematical and computational techniques are being increasingly applied to help understand and forecast tumor growth and treatment response. Advantages of mathematical modeling of proliferation include the ability to simulate and predict the spatiotemporal development of tumors across multiple experimental scales. Central to proliferation modeling is the incorporation of available biological data and validation with experimental data. Areas covered: We present an overview of past and current mathematical strategies directed at understanding tumor cell proliferation. We identify areas for mathematical development as motivated by available experimental and clinical evidence, with a particular emphasis on emerging, non-invasive imaging technologies. Expert commentary: The data required to legitimize mathematical models are often difficult or (currently) impossible to obtain. We suggest areas for further investigation to establish mathematical models that more effectively utilize available data to make informed predictions on tumor cell proliferation. |
Woodall R Syed AK, Whisenant JG Characterizing Trastuzumab-Induced Alterations in Intratumoral Heterogeneity with Quantitative Imaging and Immunohistochemistry in HER2+ Breast Cancer Journal Article In: Neoplasia, vol. 21, no. 1, pp. 17-29, 2018. @article{AK2018b,
title = {Characterizing Trastuzumab-Induced Alterations in Intratumoral Heterogeneity with Quantitative Imaging and Immunohistochemistry in HER2+ Breast Cancer},
author = {Syed AK, Woodall R, Whisenant JG, Yankeelov TE, Sorace AG},
doi = {10.1016/j.neo.2018.10.008},
year = {2018},
date = {2018-11-22},
journal = {Neoplasia},
volume = {21},
number = {1},
pages = {17-29},
abstract = {The purpose of this study is to investigate imaging and histology-based measurements of intratumoral heterogeneity to evaluate early treatment response to targeted therapy in a murine model of HER2+ breast cancer. BT474 tumor-bearing mice (N = 30) were treated with trastuzumab or saline and imaged longitudinally with either dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) or 18F-fluoromisonidazole (FMISO) positron emission tomography (PET). At the imaging study end point (day 4 for MRI or 7 for PET), each tumor was excised for immunohistochemistry analysis. Voxel-based histogram analysis was performed on imaging-derived parametric maps (i.e., Ktrans and ve from DCE-MRI, SUV from 18F-FMISO-PET) of the tumor region of interest to measure heterogeneity. Image processing and histogram analysis of whole tumor slice immunohistochemistry data were performed to validate the in vivo imaging findings. Trastuzumab-treated tumors had increased heterogeneity in quantitative imaging measures of cellularity (ve), with a mean Kolmogorov-Smirnov (K-S) distance of 0.32 (P = .05) between baseline and end point distributions. Trastuzumab-treated tumors had increased vascular heterogeneity (Ktrans) and decreased hypoxic heterogeneity (SUV), with a mean K-S distance of 0.42 (P < .01) and 0.46 (P = .047), respectively, between baseline and study end points. These observations were validated by whole-slice immunohistochemistry analysis with mean interquartile range of CD31 distributions of 1.72 for treated and 0.95 for control groups (P = .02). Quantitative longitudinal changes in tumor cellular and vascular heterogeneity in response to therapy may provide evidence for early prediction of response and guide therapy for patients with HER2+ breast cancer.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The purpose of this study is to investigate imaging and histology-based measurements of intratumoral heterogeneity to evaluate early treatment response to targeted therapy in a murine model of HER2+ breast cancer. BT474 tumor-bearing mice (N = 30) were treated with trastuzumab or saline and imaged longitudinally with either dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) or 18F-fluoromisonidazole (FMISO) positron emission tomography (PET). At the imaging study end point (day 4 for MRI or 7 for PET), each tumor was excised for immunohistochemistry analysis. Voxel-based histogram analysis was performed on imaging-derived parametric maps (i.e., Ktrans and ve from DCE-MRI, SUV from 18F-FMISO-PET) of the tumor region of interest to measure heterogeneity. Image processing and histogram analysis of whole tumor slice immunohistochemistry data were performed to validate the in vivo imaging findings. Trastuzumab-treated tumors had increased heterogeneity in quantitative imaging measures of cellularity (ve), with a mean Kolmogorov-Smirnov (K-S) distance of 0.32 (P = .05) between baseline and end point distributions. Trastuzumab-treated tumors had increased vascular heterogeneity (Ktrans) and decreased hypoxic heterogeneity (SUV), with a mean K-S distance of 0.42 (P < .01) and 0.46 (P = .047), respectively, between baseline and study end points. These observations were validated by whole-slice immunohistochemistry analysis with mean interquartile range of CD31 distributions of 1.72 for treated and 0.95 for control groups (P = .02). Quantitative longitudinal changes in tumor cellular and vascular heterogeneity in response to therapy may provide evidence for early prediction of response and guide therapy for patients with HER2+ breast cancer. |
Deatherage D Al'Khafaji AM, Brock A Control of Lineage-Specific Gene Expression by Functionalized gRNA Barcodes Journal Article In: ACS Synth Biol., vol. 7, no. 10, pp. 2468-2474, 2018. @article{AM2018c,
title = {Control of Lineage-Specific Gene Expression by Functionalized gRNA Barcodes},
author = {Al'Khafaji AM, Deatherage D, Brock A},
doi = {10.1021/acssynbio.8b00105},
year = {2018},
date = {2018-10-19},
journal = {ACS Synth Biol.},
volume = {7},
number = {10},
pages = {2468-2474},
abstract = {Lineage tracking delivers essential quantitative insight into dynamic, probabilistic cellular processes, such as somatic tumor evolution and differentiation. Methods for high diversity lineage quantitation rely on sequencing a population of DNA barcodes. However, manipulation of specific individual lineages is not possible with this approach. To address this challenge, we developed a functionalized lineage tracing tool, Control of Lineages by Barcode Enabled Recombinant Transcription (COLBERT), that enables high diversity lineage tracing and lineage-specific manipulation of gene expression. This modular platform utilizes expressed barcode gRNAs to both track cell lineages and direct lineage-specific gene expression.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Lineage tracking delivers essential quantitative insight into dynamic, probabilistic cellular processes, such as somatic tumor evolution and differentiation. Methods for high diversity lineage quantitation rely on sequencing a population of DNA barcodes. However, manipulation of specific individual lineages is not possible with this approach. To address this challenge, we developed a functionalized lineage tracing tool, Control of Lineages by Barcode Enabled Recombinant Transcription (COLBERT), that enables high diversity lineage tracing and lineage-specific manipulation of gene expression. This modular platform utilizes expressed barcode gRNAs to both track cell lineages and direct lineage-specific gene expression. |
Ghousifam N Lima EABF, Ozkan A Calibration of Multi-Parameter Models of Avascular Tumor Growth Using Time Resolved Microscopy Data Journal Article In: Sci Rep. , vol. 8, no. 1, 2018. @article{EABF2018,
title = {Calibration of Multi-Parameter Models of Avascular Tumor Growth Using Time Resolved Microscopy Data},
author = {Lima EABF, Ghousifam N, Ozkan A, Oden JT, Shahmoradi A, Rylander MN, Wohlmuth B, Yankeelov TE.},
doi = {10.1038/s41598-018-32347-9},
year = {2018},
date = {2018-09-28},
journal = {Sci Rep. },
volume = {8},
number = {1},
abstract = {Two of the central challenges of using mathematical models for predicting the spatiotemporal development of tumors is the lack of appropriate data to calibrate the parameters of the model, and quantitative characterization of the uncertainties in both the experimental data and the modeling process itself. We present a sequence of experiments, with increasing complexity, designed to systematically calibrate the rates of apoptosis, proliferation, and necrosis, as well as mobility, within a phase-field tumor growth model. The in vitro experiments characterize the proliferation and death of human liver carcinoma cells under different initial cell concentrations, nutrient availabilities, and treatment conditions. A Bayesian framework is employed to quantify the uncertainties in model parameters. The average difference between the calibration and the data, across all time points is between 11.54% and 14.04% for the apoptosis experiments, 7.33% and 23.30% for the proliferation experiments, and 8.12% and 31.55% for the necrosis experiments. The results indicate the proposed experiment-computational approach is generalizable and appropriate for step-by-step calibration of multi-parameter models, yielding accurate estimations of model parameters related to rates of proliferation, apoptosis, and necrosis.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Two of the central challenges of using mathematical models for predicting the spatiotemporal development of tumors is the lack of appropriate data to calibrate the parameters of the model, and quantitative characterization of the uncertainties in both the experimental data and the modeling process itself. We present a sequence of experiments, with increasing complexity, designed to systematically calibrate the rates of apoptosis, proliferation, and necrosis, as well as mobility, within a phase-field tumor growth model. The in vitro experiments characterize the proliferation and death of human liver carcinoma cells under different initial cell concentrations, nutrient availabilities, and treatment conditions. A Bayesian framework is employed to quantify the uncertainties in model parameters. The average difference between the calibration and the data, across all time points is between 11.54% and 14.04% for the apoptosis experiments, 7.33% and 23.30% for the proliferation experiments, and 8.12% and 31.55% for the necrosis experiments. The results indicate the proposed experiment-computational approach is generalizable and appropriate for step-by-step calibration of multi-parameter models, yielding accurate estimations of model parameters related to rates of proliferation, apoptosis, and necrosis. |
Bloom MJ Jarrett AM, Godfrey W Mathematical modelling of trastuzumab-induced immune response in an in vivo murine model of HER2+ breast cancer Journal Article In: Math Med Biol., 2018. @article{AM2018,
title = {Mathematical modelling of trastuzumab-induced immune response in an in vivo murine model of HER2+ breast cancer},
author = {Jarrett AM, Bloom MJ, Godfrey W, Syed AK, Ekrut DA, Ehrlich LI, Yankeelov TE, Sorace AG.},
doi = {10.1093/imammb/dqy014},
year = {2018},
date = {2018-09-19},
journal = {Math Med Biol.},
abstract = {The goal of this study is to develop an integrated, mathematical-experimental approach for understanding the interactions between the immune system and the effects of trastuzumab on breast cancer that overexpresses the human epidermal growth factor receptor 2 (HER2+). A system of coupled, ordinary differential equations was constructed to describe the temporal changes in tumour growth, along with intratumoural changes in the immune response, vascularity, necrosis and hypoxia. The mathematical model is calibrated with serially acquired experimental data of tumour volume, vascularity, necrosis and hypoxia obtained from either imaging or histology from a murine model of HER2+ breast cancer. Sensitivity analysis shows that model components are sensitive for 12 of 13 parameters, but accounting for uncertainty in the parameter values, model simulations still agree with the experimental data. Given theinitial conditions, the mathematical model predicts an increase in the immune infiltrates over time in the treated animals. Immunofluorescent staining results are presented that validate this prediction by showing an increased co-staining of CD11c and F4/80 (proteins expressed by dendritic cells and/or macrophages) in the total tissue for the treated tumours compared to the controls ($p < 0.03$). We posit that the proposed mathematical-experimental approach can be used to elucidate driving interactions between the trastuzumab-induced responses in the tumour and the immune system that drive the stabilization of vasculature while simultaneously decreasing tumour growth-conclusions revealed by the mathematical model that were not deducible from the experimental data alone.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The goal of this study is to develop an integrated, mathematical-experimental approach for understanding the interactions between the immune system and the effects of trastuzumab on breast cancer that overexpresses the human epidermal growth factor receptor 2 (HER2+). A system of coupled, ordinary differential equations was constructed to describe the temporal changes in tumour growth, along with intratumoural changes in the immune response, vascularity, necrosis and hypoxia. The mathematical model is calibrated with serially acquired experimental data of tumour volume, vascularity, necrosis and hypoxia obtained from either imaging or histology from a murine model of HER2+ breast cancer. Sensitivity analysis shows that model components are sensitive for 12 of 13 parameters, but accounting for uncertainty in the parameter values, model simulations still agree with the experimental data. Given theinitial conditions, the mathematical model predicts an increase in the immune infiltrates over time in the treated animals. Immunofluorescent staining results are presented that validate this prediction by showing an increased co-staining of CD11c and F4/80 (proteins expressed by dendritic cells and/or macrophages) in the total tissue for the treated tumours compared to the controls ($p < 0.03$). We posit that the proposed mathematical-experimental approach can be used to elucidate driving interactions between the trastuzumab-induced responses in the tumour and the immune system that drive the stabilization of vasculature while simultaneously decreasing tumour growth-conclusions revealed by the mathematical model that were not deducible from the experimental data alone. |
Lu C Joyce MH, James ER Phenotypic Basis for Matrix Stiffness-Dependent Chemoresistance of Breast Cancer Cells to Doxorubicin Journal Article In: Front Oncol., vol. 5, no. 8, pp. 337, 2018. @article{MH2018,
title = {Phenotypic Basis for Matrix Stiffness-Dependent Chemoresistance of Breast Cancer Cells to Doxorubicin},
author = {Joyce MH, Lu C, James ER, Hegab R, Allen SC, Suggs LJ, Brock A},
doi = {10.3389/fonc.2018.00337},
year = {2018},
date = {2018-09-05},
journal = {Front Oncol.},
volume = {5},
number = {8},
pages = {337},
abstract = {The persistence of drug resistant cell populations following chemotherapeutic treatment is a significant challenge in the clinical management of cancer. Resistant subpopulations arise via both cell intrinsic and extrinsic mechanisms. Extrinsic factors in the microenvironment, including neighboring cells, glycosaminoglycans, and fibrous proteins impact therapy response. Elevated levels of extracellular fibrous proteins are associated with tumor progression and cause the surrounding tissue to stiffen through changes in structure and composition of the extracellular matrix (ECM). We sought to determine how this progressively stiffening microenvironment affects the sensitivity of breast cancer cells to chemotherapeutic treatment. MDA-MB-231 triple negative breast carcinoma cells cultured in a 3D alginate-based hydrogel system displayed a stiffness-dependent response to the chemotherapeutic doxorubicin. MCF7 breast carcinoma cells cultured in the same conditions did not exhibit this stiffness-dependent resistance to the drug. This differential therapeutic response was coordinated with nuclear translocation of YAP, a marker of mesenchymal differentiation. The stiffness-dependent response was lost when cells were transferred from 3D to monolayer cultures, suggesting that endpoint ECM conditions largely govern the response to doxorubicin. To further examine this response, we utilized a platform capable of dynamic ECM stiffness modulation to allow for a change in matrix stiffness over time. We found that MDA-MB-231 cells have a stiffness-dependent resistance to doxorubicin and that duration of exposure to ECM stiffness is sufficient to modulate this response. These results indicate the need for additional tools to integrate mechanical stiffness with therapeutic response and inform decisions for more effective use of chemotherapeutics in the clinic.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The persistence of drug resistant cell populations following chemotherapeutic treatment is a significant challenge in the clinical management of cancer. Resistant subpopulations arise via both cell intrinsic and extrinsic mechanisms. Extrinsic factors in the microenvironment, including neighboring cells, glycosaminoglycans, and fibrous proteins impact therapy response. Elevated levels of extracellular fibrous proteins are associated with tumor progression and cause the surrounding tissue to stiffen through changes in structure and composition of the extracellular matrix (ECM). We sought to determine how this progressively stiffening microenvironment affects the sensitivity of breast cancer cells to chemotherapeutic treatment. MDA-MB-231 triple negative breast carcinoma cells cultured in a 3D alginate-based hydrogel system displayed a stiffness-dependent response to the chemotherapeutic doxorubicin. MCF7 breast carcinoma cells cultured in the same conditions did not exhibit this stiffness-dependent resistance to the drug. This differential therapeutic response was coordinated with nuclear translocation of YAP, a marker of mesenchymal differentiation. The stiffness-dependent response was lost when cells were transferred from 3D to monolayer cultures, suggesting that endpoint ECM conditions largely govern the response to doxorubicin. To further examine this response, we utilized a platform capable of dynamic ECM stiffness modulation to allow for a change in matrix stiffness over time. We found that MDA-MB-231 cells have a stiffness-dependent resistance to doxorubicin and that duration of exposure to ECM stiffness is sufficient to modulate this response. These results indicate the need for additional tools to integrate mechanical stiffness with therapeutic response and inform decisions for more effective use of chemotherapeutics in the clinic. |
Johnson KE Howard GR, Rodriguez Ayala A A multi-state model of chemoresistance to characterize phenotypic dynamics in breast cancer Journal Article In: Sci Rep., vol. 8, no. 1, 2018. @article{GR2018,
title = {A multi-state model of chemoresistance to characterize phenotypic dynamics in breast cancer},
author = {Howard GR, Johnson KE, Rodriguez Ayala A, Yankeelov TE, Brock A.},
doi = {10.1038/s41598-018-30467-w},
year = {2018},
date = {2018-08-13},
journal = {Sci Rep.},
volume = {8},
number = {1},
abstract = {The development of resistance to chemotherapy is a major cause of treatment failure in breast cancer. While mathematical models describing the dynamics of resistant cancer cell subpopulations have been proposed, experimental validation has been difficult due to the complex nature of resistance that limits the ability of a single phenotypic marker to sufficiently identify the drug resistant subpopulations. We address this problem with a coupled experimental/modeling approach to reveal the composition of drug resistant subpopulations changing in time following drug exposure. We calibrate time-resolved drug sensitivity assays to three mathematical models to interrogate the models' ability to capture drug response dynamics. The Akaike information criterion was employed to evaluate the three models, and it identified a multi-state model incorporating the role of population heterogeneity and cellular plasticity as the optimal model. To validate the model's ability to identify subpopulation composition, we mixed different proportions of wild-type MCF-7 and MCF-7/ADR resistant cells and evaluated the corresponding model output. Our blinded two-state model was able to estimate the proportions of cell types with an R-squared value of 0.857. To the best of our knowledge, this is the first work to combine experimental time-resolved drug sensitivity data with a mathematical model of resistance development.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The development of resistance to chemotherapy is a major cause of treatment failure in breast cancer. While mathematical models describing the dynamics of resistant cancer cell subpopulations have been proposed, experimental validation has been difficult due to the complex nature of resistance that limits the ability of a single phenotypic marker to sufficiently identify the drug resistant subpopulations. We address this problem with a coupled experimental/modeling approach to reveal the composition of drug resistant subpopulations changing in time following drug exposure. We calibrate time-resolved drug sensitivity assays to three mathematical models to interrogate the models' ability to capture drug response dynamics. The Akaike information criterion was employed to evaluate the three models, and it identified a multi-state model incorporating the role of population heterogeneity and cellular plasticity as the optimal model. To validate the model's ability to identify subpopulation composition, we mixed different proportions of wild-type MCF-7 and MCF-7/ADR resistant cells and evaluated the corresponding model output. Our blinded two-state model was able to estimate the proportions of cell types with an R-squared value of 0.857. To the best of our knowledge, this is the first work to combine experimental time-resolved drug sensitivity data with a mathematical model of resistance development. |
Barnes SL Woodall RT, Hormuth DA 2nd The effects of intravoxel contrast agent diffusion on the analysis of DCE-MRI data in realistic tissue domains Journal Article In: Magn Reson Med, vol. 80, no. 1, pp. 330-340, 2018. @article{Woodall2018,
title = {The effects of intravoxel contrast agent diffusion on the analysis of DCE-MRI data in realistic tissue domains},
author = {Woodall RT, Barnes SL, Hormuth DA 2nd, Sorace AG, Quarles CC, Yankeelov TE},
doi = {10.1002/mrm.26995},
year = {2018},
date = {2018-07-01},
journal = {Magn Reson Med},
volume = {80},
number = {1},
pages = {330-340},
abstract = {PURPOSE:
Quantitative evaluation of dynamic contrast enhanced MRI (DCE-MRI) allows for estimating perfusion, vessel permeability, and tissue volume fractions by fitting signal intensity curves to pharmacokinetic models. These compart mental models assume rapid equilibration of contrast agent within each voxel. However, there is increasing evidence that this assumption is violated for small molecular weight gadolinium chelates. To evaluate the error introduced by this invalid assumption, we simulated DCE-MRI experiments with volume fractions computed from entire histological tumor cross-sections obtained from murine studies.
METHODS:
A 2D finite element model of a diffusion-compensated Tofts-Kety model was developed to simulate dynamic T1 signal intensity data. Digitized histology slices were segmented into vascular (vp ), cellular and extravascular extracellular (ve ) volume fractions. Within this domain, Ktrans (the volume transfer constant) was assigned values from 0 to 0.5 min-1 . A representative signal enhancement curve was then calculated for each imaging voxel and the resulting simulated DCE-MRI data analyzed by the extended Tofts-Kety model.
RESULTS:
Results indicated parameterization errors of -19.1% ± 10.6% in Ktrans , -4.92% ± 3.86% in ve , and 79.5% ± 16.8% in vp for use of Gd-DTPA over 4 tumor domains.
CONCLUSION:
These results indicate a need for revising the standard model of DCE-MRI to incorporate a correction for slow diffusion of contrast agent.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
PURPOSE:
Quantitative evaluation of dynamic contrast enhanced MRI (DCE-MRI) allows for estimating perfusion, vessel permeability, and tissue volume fractions by fitting signal intensity curves to pharmacokinetic models. These compart mental models assume rapid equilibration of contrast agent within each voxel. However, there is increasing evidence that this assumption is violated for small molecular weight gadolinium chelates. To evaluate the error introduced by this invalid assumption, we simulated DCE-MRI experiments with volume fractions computed from entire histological tumor cross-sections obtained from murine studies.
METHODS:
A 2D finite element model of a diffusion-compensated Tofts-Kety model was developed to simulate dynamic T1 signal intensity data. Digitized histology slices were segmented into vascular (vp ), cellular and extravascular extracellular (ve ) volume fractions. Within this domain, Ktrans (the volume transfer constant) was assigned values from 0 to 0.5 min-1 . A representative signal enhancement curve was then calculated for each imaging voxel and the resulting simulated DCE-MRI data analyzed by the extended Tofts-Kety model.
RESULTS:
Results indicated parameterization errors of -19.1% ± 10.6% in Ktrans , -4.92% ± 3.86% in ve , and 79.5% ± 16.8% in vp for use of Gd-DTPA over 4 tumor domains.
CONCLUSION:
These results indicate a need for revising the standard model of DCE-MRI to incorporate a correction for slow diffusion of contrast agent. |
JA Weis MT McKenna, A Brock Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer Journal Article In: Translational oncology, vol. 11, no. 3, pp. 732-742, 2018. @article{Brock2018,
title = {Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer},
author = {MT McKenna, JA Weis, A Brock, V Quaranta, TE Yankeelov},
doi = {https://doi.org/10.1016/j.tranon.2018.03.009},
year = {2018},
date = {2018-06-30},
journal = {Translational oncology},
volume = {11},
number = {3},
pages = {732-742},
publisher = {Elsevier},
abstract = {Medical oncology is in need of a mathematical modeling toolkit that can leverage clinically-available measurements to optimize treatment selection and schedules for patients. Just as the therapeutic choice has been optimized to match tumor genetics, the delivery of those therapeutics should be optimized based on patient-specific pharmacokinetic/pharmacodynamic properties. Under the current approach to treatment response planning and assessment, there does not exist an efficient method to consolidate biomarker changes into a holistic understanding of treatment response. While the majority of research on chemotherapies focus on cellular and genetic mechanisms of resistance, there are numerous patient-specific and tumor-specific measures that contribute to treatment response. New approaches that consolidate multimodal information into actionable data are needed. Mathematical modeling offers a solution to this problem. In this perspective, we first focus on the particular case of breast cancer to highlight how mathematical models have shaped the current approaches to treatment. Then we compare chemotherapy to radiation therapy. Finally, we identify opportunities to improve chemotherapy treatments using the model of radiation therapy. We posit that mathematical models can improve the application of anticancer therapeutics in the era of precision medicine. By highlighting a number of historical examples of the contributions of mathematical models to cancer therapy, we hope that this contribution serves to engage investigators who may not have previously considered how mathematical modeling can provide real insights into breast cancer therapy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Medical oncology is in need of a mathematical modeling toolkit that can leverage clinically-available measurements to optimize treatment selection and schedules for patients. Just as the therapeutic choice has been optimized to match tumor genetics, the delivery of those therapeutics should be optimized based on patient-specific pharmacokinetic/pharmacodynamic properties. Under the current approach to treatment response planning and assessment, there does not exist an efficient method to consolidate biomarker changes into a holistic understanding of treatment response. While the majority of research on chemotherapies focus on cellular and genetic mechanisms of resistance, there are numerous patient-specific and tumor-specific measures that contribute to treatment response. New approaches that consolidate multimodal information into actionable data are needed. Mathematical modeling offers a solution to this problem. In this perspective, we first focus on the particular case of breast cancer to highlight how mathematical models have shaped the current approaches to treatment. Then we compare chemotherapy to radiation therapy. Finally, we identify opportunities to improve chemotherapy treatments using the model of radiation therapy. We posit that mathematical models can improve the application of anticancer therapeutics in the era of precision medicine. By highlighting a number of historical examples of the contributions of mathematical models to cancer therapy, we hope that this contribution serves to engage investigators who may not have previously considered how mathematical modeling can provide real insights into breast cancer therapy. |
Weis JA McKenna MT, Quaranta V Variable Cell Line Pharmacokinetics Contribute to Non-Linear Treatment Response in Heterogeneous Cell Populations Journal Article In: Ann Biomed Eng, vol. 46, no. 6, pp. 899-911, 2018. @article{Yankeelov2018b,
title = {Variable Cell Line Pharmacokinetics Contribute to Non-Linear Treatment Response in Heterogeneous Cell Populations},
author = {McKenna MT, Weis JA, Quaranta V, Yankeelov TE},
doi = {10.1007/s10439-018-2001-2},
year = {2018},
date = {2018-06-01},
journal = {Ann Biomed Eng},
volume = {46},
number = {6},
pages = {899-911},
abstract = {We develop a combined experimental-mathematical framework to investigate heterogeneity in the context of breast cancer treated with doxorubicin. We engineer a cell line to over-express the multi-drug resistance 1 protein (MDR1), an ATP-dependent pump that effluxes intracellular drug. Co-culture experiments mixing the MDR1-overexpressing line with its parental line are evaluated via fluorescence microscopy. To quantify the impact of population heterogeneity on therapy response, these data are analyzed with a coupled pharmacokinetics/pharmacodynamics model. The proliferation and death rates of each line vary with co-culture condition (the relative fraction of each cell line at the time of seeding). For example, the death rate in the parental line under low-dose doxorubicin treatment is increased from 0.64 (± 0.22) × 10-2 to 1.46 (± 0.58) × 10-2 h-1 with increasing fractions of MDR1-overexpressing cells. The growth rate of the MDR1-overexpressing line increases 29% as its relative fraction is decreased. Simulations of the pharmacokinetics/pharmacodynamics model suggest increased efflux from MDR1-overexpressing cells contributes to the increased death rate in the parental cells. Experimentally, the death rate of parental cells is constant across co-culture conditions under co-treatment with an MDR1 inhibitor. These data indicate that intercellular pharmacokinetic variability should be considered in analyzing treatment response in heterogeneous populations.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We develop a combined experimental-mathematical framework to investigate heterogeneity in the context of breast cancer treated with doxorubicin. We engineer a cell line to over-express the multi-drug resistance 1 protein (MDR1), an ATP-dependent pump that effluxes intracellular drug. Co-culture experiments mixing the MDR1-overexpressing line with its parental line are evaluated via fluorescence microscopy. To quantify the impact of population heterogeneity on therapy response, these data are analyzed with a coupled pharmacokinetics/pharmacodynamics model. The proliferation and death rates of each line vary with co-culture condition (the relative fraction of each cell line at the time of seeding). For example, the death rate in the parental line under low-dose doxorubicin treatment is increased from 0.64 (± 0.22) × 10-2 to 1.46 (± 0.58) × 10-2 h-1 with increasing fractions of MDR1-overexpressing cells. The growth rate of the MDR1-overexpressing line increases 29% as its relative fraction is decreased. Simulations of the pharmacokinetics/pharmacodynamics model suggest increased efflux from MDR1-overexpressing cells contributes to the increased death rate in the parental cells. Experimentally, the death rate of parental cells is constant across co-culture conditions under co-treatment with an MDR1 inhibitor. These data indicate that intercellular pharmacokinetic variability should be considered in analyzing treatment response in heterogeneous populations. |
Moy AJ Feng X, Nguyen HTM Raman biophysical markers in skin cancer diagnosis Journal Article In: J Biomed Opt., vol. 23, no. 5, pp. 1-10, 2018. @article{Feng2018,
title = {Raman biophysical markers in skin cancer diagnosis},
author = {Feng X, Moy AJ, Nguyen HTM, Zhang Y, Zhang J, Fox MC, Sebastian KR, Reichenberg JS, Markey MK, Tunnell JW.},
doi = {10.1117/1.JBO.23.5.057002},
year = {2018},
date = {2018-05-01},
journal = {J Biomed Opt.},
volume = {23},
number = {5},
pages = {1-10},
abstract = {Raman spectroscopy (RS) has demonstrated great potential for in vivo cancer screening; however, the biophysical changes that occur for specific diagnoses remain unclear. We recently developed an inverse biophysical skin cancer model to address this issue. Here, we presented the first demonstration of in vivo melanoma and nonmelanoma skin cancer (NMSC) detection based on this model. We fit the model to our previous clinical dataset and extracted the concentration of eight Raman active components in 100 lesions in 65 patients diagnosed with malignant melanoma (MM), dysplastic nevi (DN), basal cell carcinoma, squamous cell carcinoma, and actinic keratosis. We then used logistic regression and leave-one-lesion-out cross validation to determine the diagnostically relevant model components. Our results showed that the biophysical model captures the diagnostic power of the previously used statistical classification model while also providing the skin's biophysical composition. In addition, collagen and triolein were the most relevant biomarkers to represent the spectral variances between MM and DN, and between NMSC and normal tissue. Our work demonstrates the ability of RS to reveal the biophysical basis for accurate diagnosis of different skin cancers, which may eventually lead to a reduction in the number of unnecessary excisional skin biopsies performed.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Raman spectroscopy (RS) has demonstrated great potential for in vivo cancer screening; however, the biophysical changes that occur for specific diagnoses remain unclear. We recently developed an inverse biophysical skin cancer model to address this issue. Here, we presented the first demonstration of in vivo melanoma and nonmelanoma skin cancer (NMSC) detection based on this model. We fit the model to our previous clinical dataset and extracted the concentration of eight Raman active components in 100 lesions in 65 patients diagnosed with malignant melanoma (MM), dysplastic nevi (DN), basal cell carcinoma, squamous cell carcinoma, and actinic keratosis. We then used logistic regression and leave-one-lesion-out cross validation to determine the diagnostically relevant model components. Our results showed that the biophysical model captures the diagnostic power of the previously used statistical classification model while also providing the skin's biophysical composition. In addition, collagen and triolein were the most relevant biomarkers to represent the spectral variances between MM and DN, and between NMSC and normal tissue. Our work demonstrates the ability of RS to reveal the biophysical basis for accurate diagnosis of different skin cancers, which may eventually lead to a reduction in the number of unnecessary excisional skin biopsies performed. |
Hormuth Ii DA Jarrett AM, Barnes SL Incorporating drug delivery into an imaging-driven, mechanics-coupled reaction diffusion model for predicting the response of breast cancer to neoadjuvant chemotherapy: theory and preliminary clinical results Journal Article In: Phys Med Biol, 2018. @article{Jarrett2018,
title = {Incorporating drug delivery into an imaging-driven, mechanics-coupled reaction diffusion model for predicting the response of breast cancer to neoadjuvant chemotherapy: theory and preliminary clinical results},
author = {Jarrett AM, Hormuth Ii DA, Barnes SL, Feng X, Huang W, Yankeelov TE},
doi = {10.1088/1361-6560/aac040},
year = {2018},
date = {2018-04-26},
journal = {Phys Med Biol},
abstract = {Clinical methods for assessing tumor response to therapy are largely rudimentary, monitoring only temporal changes in tumor size. Our goal is to predict the response of breast tumors to therapy using a mathematical model that utilizes magnetic resonance imaging (MRI) data obtained non-invasively from individual patients. We extended a previously established, mechanically coupled, reaction-diffusion model for predicting tumor response initialized with patient-specific diffusion weighted MRI (DW-MRI) data by including the effects of chemotherapy drug delivery, which is estimated using dynamic contrast-enhanced (DCE-) MRI data. The extended, drug incorporated, model is initialized using patient-specific DW-MRI and DCE-MRI data. Data sets from five breast cancer patients were used-obtained before, after one cycle, and at mid-point of neoadjuvant chemotherapy. The DCE-MRI data was used to estimate spatiotemporal variations in tumor perfusion with the extended Kety-Tofts model. The physiological parameters derived from DCE-MRI were used to model changes in delivery of therapy drugs within the tumor for incorporation in the extended model. We simulated the original model and the extended model in both 2D and 3D and compare the results for this five-patient cohort. Preliminary results show reductions in the error of model predicted tumor cellularity and size compared to the experimentally-measured results for the third MRI scan when therapy was incorporated. Comparing the two models for agreement between the predicted total cellularity and the calculated total cellularity (from DW-MRI data) reveals an increased concordance correlation coefficient from 0.81 to 0.98 for the 2D analysis and 0.85 to 0.99 for the 3D analysis (p < 0.01 for each) when the extended model was used in place of the original model. This study demonstrates the plausibility of using DCE-MRI data as a means to estimate drug delivery on a patient-specific basis in predictive models and represents a step toward the goal of achieving individualized prediction of tumor response to therapy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Clinical methods for assessing tumor response to therapy are largely rudimentary, monitoring only temporal changes in tumor size. Our goal is to predict the response of breast tumors to therapy using a mathematical model that utilizes magnetic resonance imaging (MRI) data obtained non-invasively from individual patients. We extended a previously established, mechanically coupled, reaction-diffusion model for predicting tumor response initialized with patient-specific diffusion weighted MRI (DW-MRI) data by including the effects of chemotherapy drug delivery, which is estimated using dynamic contrast-enhanced (DCE-) MRI data. The extended, drug incorporated, model is initialized using patient-specific DW-MRI and DCE-MRI data. Data sets from five breast cancer patients were used-obtained before, after one cycle, and at mid-point of neoadjuvant chemotherapy. The DCE-MRI data was used to estimate spatiotemporal variations in tumor perfusion with the extended Kety-Tofts model. The physiological parameters derived from DCE-MRI were used to model changes in delivery of therapy drugs within the tumor for incorporation in the extended model. We simulated the original model and the extended model in both 2D and 3D and compare the results for this five-patient cohort. Preliminary results show reductions in the error of model predicted tumor cellularity and size compared to the experimentally-measured results for the third MRI scan when therapy was incorporated. Comparing the two models for agreement between the predicted total cellularity and the calculated total cellularity (from DW-MRI data) reveals an increased concordance correlation coefficient from 0.81 to 0.98 for the 2D analysis and 0.85 to 0.99 for the 3D analysis (p < 0.01 for each) when the extended model was used in place of the original model. This study demonstrates the plausibility of using DCE-MRI data as a means to estimate drug delivery on a patient-specific basis in predictive models and represents a step toward the goal of achieving individualized prediction of tumor response to therapy. |
Wu C Sorace AG, Barnes SL Repeatability, reproducibility, and accuracy of quantitative mri of the breast in the community radiology setting Journal Article In: J Magn Reson Imaging, 2018. @article{Sorace2018,
title = {Repeatability, reproducibility, and accuracy of quantitative mri of the breast in the community radiology setting},
author = {Sorace AG, Wu C, Barnes SL, Jarrett AM, Avery S, Patt D, Goodgame B, Luci JJ, Kang H, Abramson RG, Yankeelov TE, Virostko J},
doi = {10.1002/jmri.26011},
year = {2018},
date = {2018-03-23},
journal = {J Magn Reson Imaging},
abstract = {BACKGROUND:
Quantitative diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI) have the potential to impact patient care by providing noninvasive biological information in breast cancer.
PURPOSE/HYPOTHESIS:
To quantify the repeatability, reproducibility, and accuracy of apparent diffusion coefficient (ADC) and T1 -mapping of the breast in community radiology practices.
STUDY TYPE:
Prospective.
SUBJECTS/PHANTOM:
Ice-water DW-MRI and T1 gel phantoms were used to assess accuracy. Normal subjects (n = 3) and phantoms across three sites (one academic, two community) were used to assess reproducibility. Test-retest analysis at one site in normal subjects (n = 12) was used to assess repeatability.
FIELD STRENGTH/SEQUENCE:
3T Siemens Skyra MRI quantitative DW-MRI and T1 -mapping.
ASSESSMENT:
Quantitative DW-MRI and T1 -mapping parametric maps of phantoms and fibroglandular and adipose tissue of the breast.
STATISTICAL TESTS:
Average values of breast tissue were quantified and Bland-Altman analysis was performed to assess the repeatability of the MRI techniques, while the Friedman test assessed reproducibility.
RESULTS:
ADC measurements were reproducible across sites, with an average difference of 1.6% in an ice-water phantom and 7.0% in breast fibroglandular tissue. T1 measurements in gel phantoms had an average difference of 2.8% across three sites, whereas breast fibroglandular and adipose tissue had 8.4% and 7.5% average differences, respectively. In the repeatability study, we found no bias between first and second scanning sessions (P = 0.1). The difference between repeated measurements was independent of the mean for each MRI metric (P = 0.156, P = 0.862, P = 0.197 for ADC, T1 of fibroglandular tissue, and T1 of adipose tissue, respectively).
DATA CONCLUSION:
Community radiology practices can perform repeatable, reproducible, and accurate quantitative T1 -mapping and DW-MRI. This has the potential to dramatically expand the number of sites that can participate in multisite clinical trials and increase clinical translation of quantitative MRI techniques for cancer response assessment.
LEVEL OF EVIDENCE:
1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
BACKGROUND:
Quantitative diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI) have the potential to impact patient care by providing noninvasive biological information in breast cancer.
PURPOSE/HYPOTHESIS:
To quantify the repeatability, reproducibility, and accuracy of apparent diffusion coefficient (ADC) and T1 -mapping of the breast in community radiology practices.
STUDY TYPE:
Prospective.
SUBJECTS/PHANTOM:
Ice-water DW-MRI and T1 gel phantoms were used to assess accuracy. Normal subjects (n = 3) and phantoms across three sites (one academic, two community) were used to assess reproducibility. Test-retest analysis at one site in normal subjects (n = 12) was used to assess repeatability.
FIELD STRENGTH/SEQUENCE:
3T Siemens Skyra MRI quantitative DW-MRI and T1 -mapping.
ASSESSMENT:
Quantitative DW-MRI and T1 -mapping parametric maps of phantoms and fibroglandular and adipose tissue of the breast.
STATISTICAL TESTS:
Average values of breast tissue were quantified and Bland-Altman analysis was performed to assess the repeatability of the MRI techniques, while the Friedman test assessed reproducibility.
RESULTS:
ADC measurements were reproducible across sites, with an average difference of 1.6% in an ice-water phantom and 7.0% in breast fibroglandular tissue. T1 measurements in gel phantoms had an average difference of 2.8% across three sites, whereas breast fibroglandular and adipose tissue had 8.4% and 7.5% average differences, respectively. In the repeatability study, we found no bias between first and second scanning sessions (P = 0.1). The difference between repeated measurements was independent of the mean for each MRI metric (P = 0.156, P = 0.862, P = 0.197 for ADC, T1 of fibroglandular tissue, and T1 of adipose tissue, respectively).
DATA CONCLUSION:
Community radiology practices can perform repeatable, reproducible, and accurate quantitative T1 -mapping and DW-MRI. This has the potential to dramatically expand the number of sites that can participate in multisite clinical trials and increase clinical translation of quantitative MRI techniques for cancer response assessment.
LEVEL OF EVIDENCE:
1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018. |
A. Shah † A.M. Jarrett, M. J. Bloom; Yankeelov, T. E. Towards a multi-scale model of combination targeted and cytotoxic therapy to evaluate treatment response in HER2+ breast cancer Presentation 01.03.2018. @misc{Jarrett2018b,
title = {Towards a multi-scale model of combination targeted and cytotoxic therapy to evaluate treatment response in HER2+ breast cancer},
author = {A.M. Jarrett, A. Shah †, M.J. Bloom, X. Feng, A.G. Sorace, and T.E. Yankeelov},
year = {2018},
date = {2018-03-01},
organization = {Interagency Modeling and Analysis Group (IMAG) Futures Meeting},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
|
Fahrenholtz S Mitchell D, MacLellan C A heterogeneous tissue model for treatment planning for magnetic resonance-guided laser interstitial thermal therapy Journal Article In: Int J Hyperthermia, 2018. @article{Fuentes2018,
title = {A heterogeneous tissue model for treatment planning for magnetic resonance-guided laser interstitial thermal therapy},
author = {Mitchell D, Fahrenholtz S, MacLellan C, Bastos D, Rao G, Prabhu S, Weinberg J, Hazle J, Stafford J, Fuentes D},
doi = {10.1080/02656736.2018.1429679},
year = {2018},
date = {2018-02-05},
journal = {Int J Hyperthermia},
abstract = {We evaluated a physics-based model for planning for magnetic resonance-guided laser interstitial thermal therapy for focal brain lesions. Linear superposition of analytical point source solutions to the steady-state Pennes bioheat transfer equation simulates laser-induced heating in brain tissue. The line integral of the photon attenuation from the laser source enables computation of the laser interaction with heterogeneous tissue. Magnetic resonance thermometry data sets (n = 31) were used to calibrate and retrospectively validate the model's thermal ablation prediction accuracy, which was quantified by the Dice similarity coefficient (DSC) between model-predicted and measured ablation regions (T > 57 °C). A Gaussian mixture model was used to identify independent tissue labels on pre-treatment anatomical magnetic resonance images. The tissue-dependent optical attenuation coefficients within these labels were calibrated using an interior point method that maximises DSC agreement with thermometry. The distribution of calibrated tissue properties formed a population model for our patient cohort. Model prediction accuracy was cross-validated using the population mean of the calibrated tissue properties. A homogeneous tissue model was used as a reference control. The median DSC values in cross-validation were 0.829 for the homogeneous model and 0.840 for the heterogeneous model. In cross-validation, the heterogeneous model produced a DSC higher than that produced by the homogeneous model in 23 of the 31 brain lesion ablations. Results of a paired, two-tailed Wilcoxon signed-rank test indicated that the performance improvement of the heterogeneous model over that of the homogeneous model was statistically significant (p < 0.01).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We evaluated a physics-based model for planning for magnetic resonance-guided laser interstitial thermal therapy for focal brain lesions. Linear superposition of analytical point source solutions to the steady-state Pennes bioheat transfer equation simulates laser-induced heating in brain tissue. The line integral of the photon attenuation from the laser source enables computation of the laser interaction with heterogeneous tissue. Magnetic resonance thermometry data sets (n = 31) were used to calibrate and retrospectively validate the model's thermal ablation prediction accuracy, which was quantified by the Dice similarity coefficient (DSC) between model-predicted and measured ablation regions (T > 57 °C). A Gaussian mixture model was used to identify independent tissue labels on pre-treatment anatomical magnetic resonance images. The tissue-dependent optical attenuation coefficients within these labels were calibrated using an interior point method that maximises DSC agreement with thermometry. The distribution of calibrated tissue properties formed a population model for our patient cohort. Model prediction accuracy was cross-validated using the population mean of the calibrated tissue properties. A homogeneous tissue model was used as a reference control. The median DSC values in cross-validation were 0.829 for the homogeneous model and 0.840 for the heterogeneous model. In cross-validation, the heterogeneous model produced a DSC higher than that produced by the homogeneous model in 23 of the 31 brain lesion ablations. Results of a paired, two-tailed Wilcoxon signed-rank test indicated that the performance improvement of the heterogeneous model over that of the homogeneous model was statistically significant (p < 0.01). |
DeGroot, AD; Busch, DJ; Hayden, CC; Mihelic, SA; Alpar, AT; Beha, M; Stachowiak, JC Biophysical Control of Receptor Recycling Using Engineered Ligands Journal Article In: Biophysical Journal, vol. 114, no. 3, pp. 463a, 2018. @article{Behar2018b,
title = {Biophysical Control of Receptor Recycling Using Engineered Ligands},
author = {AD DeGroot and DJ Busch and CC Hayden and SA Mihelic and AT Alpar and M Beha and JC Stachowiak},
doi = {https://doi.org/10.1016/j.bpj.2017.11.2555},
year = {2018},
date = {2018-02-02},
journal = {Biophysical Journal},
volume = {114},
number = {3},
pages = {463a},
publisher = {Elsevier},
abstract = {Receptor internalization by endocytosis regulates diverse cellular processes from the rate of nutrient uptake to the timescale of essential signaling events. The established view is that internalization is tightly controlled by specific protein binding interactions. However, recent work suggests that biophysical factors influence the process in ways that cannot be explained by biochemistry alone. Specifically, work from several groups suggests that increasing the steric bulk of receptors may inhibit their uptake by multiple types of trafficking vesicles.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Receptor internalization by endocytosis regulates diverse cellular processes from the rate of nutrient uptake to the timescale of essential signaling events. The established view is that internalization is tightly controlled by specific protein binding interactions. However, recent work suggests that biophysical factors influence the process in ways that cannot be explained by biochemistry alone. Specifically, work from several groups suggests that increasing the steric bulk of receptors may inhibit their uptake by multiple types of trafficking vesicles. |
RC Almeida HL Rocha, EABF Lima A hybrid three-scale model of tumor growth Journal Article In: Mathematical Models and Methods in Applied Sciences, vol. 28, no. 01, pp. 61-93, 2018, ISSN: 0218-2025. @article{Yankeelov2018,
title = {A hybrid three-scale model of tumor growth},
author = {HL Rocha, RC Almeida, EABF Lima, ACM Resende, JT Oden, TE Yankeelov},
url = {http://www.worldscientific.com/doi/abs/10.1142/S0218202518500021},
issn = {0218-2025},
year = {2018},
date = {2018-01-01},
journal = {Mathematical Models and Methods in Applied Sciences},
volume = {28},
number = {01},
pages = {61-93},
abstract = {Cancer results from a complex interplay of different biological, chemical, and physical phenomena that span a wide range of time and length scales. Computational modeling may help to unfold the role of multiple evolving factors that exist and interact in the tumor microenvironment. Understanding these complex multiscale interactions is a crucial step toward predicting cancer growth and in developing effective therapies. We integrate different modeling approaches in a multiscale, avascular, hybrid tumor growth model encompassing tissue, cell, and sub-cell scales. At the tissue level, we consider the dispersion of nutrients and growth factors in the tumor microenvironment, which are modeled through reaction–diffusion equations. At the cell level, we use an agent-based model (ABM) to describe normal and tumor cell dynamics, with normal cells kept in homeostasis and cancer cells differentiated into quiescent, proliferative, migratory, apoptotic, hypoxic, and necrotic states. Cell movement is driven by the balance of a variety of forces according to Newton’s second law, including those related to growth-induced stresses. Phenotypic transitions are defined by specific rule of behaviors that depend on microenvironment stimuli. We integrate in each cell/agent a branch of the epidermal growth factor receptor (EGFR) pathway. This pathway is modeled by a system of coupled nonlinear differential equations involving the mass laws of 20 molecules. The rates of change in the concentration of some key molecules trigger proliferation or migration advantage response. The bridge between cell and tissue scales is built through the reaction and source terms of the partial differential equations. Our hybrid model is built in a modular way, enabling the investigation of the role of different mechanisms at multiple scales on tumor progression. This strategy allows representing both the collective behavior due to cell assembly as well as microscopic intracellular phenomena described by signal transduction pathways. Here, we investigate the impact of some mechanisms associated with sustained proliferation on cancer progression. Speci- fically, we focus on the intracellular proliferation/migration-advantage-response driven by the EGFR pathway and on proliferation inhibition due to accumulation of growth-induced stresses. Simulations demonstrate that the model can adequately describe some complex mechanisms of tumor dynamics, including growth arrest in avascular tumors. Both the sub-cell model and growth-induced stresses give rise to heterogeneity in the tumor expansion and a rich variety of tumor behaviors.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Cancer results from a complex interplay of different biological, chemical, and physical phenomena that span a wide range of time and length scales. Computational modeling may help to unfold the role of multiple evolving factors that exist and interact in the tumor microenvironment. Understanding these complex multiscale interactions is a crucial step toward predicting cancer growth and in developing effective therapies. We integrate different modeling approaches in a multiscale, avascular, hybrid tumor growth model encompassing tissue, cell, and sub-cell scales. At the tissue level, we consider the dispersion of nutrients and growth factors in the tumor microenvironment, which are modeled through reaction–diffusion equations. At the cell level, we use an agent-based model (ABM) to describe normal and tumor cell dynamics, with normal cells kept in homeostasis and cancer cells differentiated into quiescent, proliferative, migratory, apoptotic, hypoxic, and necrotic states. Cell movement is driven by the balance of a variety of forces according to Newton’s second law, including those related to growth-induced stresses. Phenotypic transitions are defined by specific rule of behaviors that depend on microenvironment stimuli. We integrate in each cell/agent a branch of the epidermal growth factor receptor (EGFR) pathway. This pathway is modeled by a system of coupled nonlinear differential equations involving the mass laws of 20 molecules. The rates of change in the concentration of some key molecules trigger proliferation or migration advantage response. The bridge between cell and tissue scales is built through the reaction and source terms of the partial differential equations. Our hybrid model is built in a modular way, enabling the investigation of the role of different mechanisms at multiple scales on tumor progression. This strategy allows representing both the collective behavior due to cell assembly as well as microscopic intracellular phenomena described by signal transduction pathways. Here, we investigate the impact of some mechanisms associated with sustained proliferation on cancer progression. Speci- fically, we focus on the intracellular proliferation/migration-advantage-response driven by the EGFR pathway and on proliferation inhibition due to accumulation of growth-induced stresses. Simulations demonstrate that the model can adequately describe some complex mechanisms of tumor dynamics, including growth arrest in avascular tumors. Both the sub-cell model and growth-induced stresses give rise to heterogeneity in the tumor expansion and a rich variety of tumor behaviors. |
Partridge SC Sorace AG, Li X Distinguishing benign and malignant breast tumors: preliminary comparison of kinetic modeling approaches using multi-institutional dynamic contrast-enhanced MRI data from the International Breast MR Consortium 6883 trial Journal Article In: J Med Imaging (Bellingham), vol. 5, no. 1, 2018. @article{Sorace2018b,
title = {Distinguishing benign and malignant breast tumors: preliminary comparison of kinetic modeling approaches using multi-institutional dynamic contrast-enhanced MRI data from the International Breast MR Consortium 6883 trial},
author = {Sorace AG, Partridge SC, Li X, Virostko J, Barnes SL, Hippe DS, Huang W, Yankeelov TE},
doi = {10.1117/1.JMI.5.1.011019},
year = {2018},
date = {2018-01-01},
journal = {J Med Imaging (Bellingham)},
volume = {5},
number = {1},
abstract = {Comparative preliminary analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data collected in the International Breast MR Consortium 6883 multicenter trial was performed to distinguish benign and malignant breast tumors. Prebiopsy DCE-MRI data from 45 patients with suspicious breast lesions were obtained. Semiquantitative mean signal-enhancement ratio ([Formula: see text]) was calculated for all lesions, and quantitative pharmacokinetic, parameters [Formula: see text], [Formula: see text], and [Formula: see text], were calculated for the subset with available [Formula: see text] maps ([Formula: see text]). Diagnostic performance was estimated for DCE-MRI parameters and compared to standard clinical MRI assessment. Quantitative and semiquantitative metrics discriminated benign and malignant lesions, with receiver operating characteristic area under the curve (AUC) values of 0.71, 0.70, and 0.82 for [Formula: see text], [Formula: see text], and [Formula: see text], respectively ([Formula: see text]). At equal 94% sensitivity, the specificity and positive predictive value of [Formula: see text] (53% and 63%, respectively) and Ktrans (42% and 58%) were higher than clinical MRI assessment (32% and 54%). A multivariable model combining [Formula: see text] and clinical MRI assessment had an AUC value of 0.87. Quantitative pharmacokinetic and semiquantitative analyses of DCE-MRI improves discrimination of benign and malignant breast tumors, with our findings suggesting higher diagnostic accuracy using [Formula: see text]. [Formula: see text] has potential to help reduce unnecessary biopsies resulting from routine breast imaging.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Comparative preliminary analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data collected in the International Breast MR Consortium 6883 multicenter trial was performed to distinguish benign and malignant breast tumors. Prebiopsy DCE-MRI data from 45 patients with suspicious breast lesions were obtained. Semiquantitative mean signal-enhancement ratio ([Formula: see text]) was calculated for all lesions, and quantitative pharmacokinetic, parameters [Formula: see text], [Formula: see text], and [Formula: see text], were calculated for the subset with available [Formula: see text] maps ([Formula: see text]). Diagnostic performance was estimated for DCE-MRI parameters and compared to standard clinical MRI assessment. Quantitative and semiquantitative metrics discriminated benign and malignant lesions, with receiver operating characteristic area under the curve (AUC) values of 0.71, 0.70, and 0.82 for [Formula: see text], [Formula: see text], and [Formula: see text], respectively ([Formula: see text]). At equal 94% sensitivity, the specificity and positive predictive value of [Formula: see text] (53% and 63%, respectively) and Ktrans (42% and 58%) were higher than clinical MRI assessment (32% and 54%). A multivariable model combining [Formula: see text] and clinical MRI assessment had an AUC value of 0.87. Quantitative pharmacokinetic and semiquantitative analyses of DCE-MRI improves discrimination of benign and malignant breast tumors, with our findings suggesting higher diagnostic accuracy using [Formula: see text]. [Formula: see text] has potential to help reduce unnecessary biopsies resulting from routine breast imaging. |
Eldridge SL Hormuth DA 2nd, Weis JA Mechanically Coupled Reaction-Diffusion Model to Predict Glioma Growth: Methodological Details Journal Article In: Methods Mol Biol, vol. 1711, pp. 225-241, 2018. @article{Hormuth2018b,
title = {Mechanically Coupled Reaction-Diffusion Model to Predict Glioma Growth: Methodological Details},
author = {Hormuth DA 2nd, Eldridge SL, Weis JA, Miga MI, Yankeelov TE},
doi = {10.1007/978-1-4939-7493-1_11},
year = {2018},
date = {2018-01-01},
journal = {Methods Mol Biol},
volume = {1711},
pages = {225-241},
abstract = {Biophysical models designed to predict the growth and response of tumors to treatment have the potential to become a valuable tool for clinicians in care of cancer patients. Specifically, individualized tumor forecasts could be used to predict response or resistance early in the course of treatment, thereby providing an opportunity for treatment selection or adaption. This chapter discusses an experimental and modeling framework in which noninvasive imaging data is used to initialize and parameterize a subject-specific model of tumor growth. This modeling approach is applied to an analysis of murine models of glioma growth.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Biophysical models designed to predict the growth and response of tumors to treatment have the potential to become a valuable tool for clinicians in care of cancer patients. Specifically, individualized tumor forecasts could be used to predict response or resistance early in the course of treatment, thereby providing an opportunity for treatment selection or adaption. This chapter discusses an experimental and modeling framework in which noninvasive imaging data is used to initialize and parameterize a subject-specific model of tumor growth. This modeling approach is applied to an analysis of murine models of glioma growth. |
Hainline A Kang H, Arlinghaus LR Combining multiparametric MRI with receptor information to optimize prediction of pathologic response to neoadjuvant therapy in breast cancer: preliminary results Journal Article In: J Med Imaging (Bellingham), vol. 5, no. 1, 2018. @article{Yankeelov2018c,
title = {Combining multiparametric MRI with receptor information to optimize prediction of pathologic response to neoadjuvant therapy in breast cancer: preliminary results},
author = {Kang H, Hainline A, Arlinghaus LR, Elderidge S, Li X, Abramson VG, Chakravarthy AB, Abramson RG, Bingham B, Fakhoury K, Yankeelov TE},
doi = {10.1117/1.JMI.5.1.011015},
year = {2018},
date = {2018-01-01},
journal = {J Med Imaging (Bellingham)},
volume = {5},
number = {1},
abstract = {Pathologic complete response following neoadjuvant therapy (NAT) is used as a short-term surrogate marker of eventual outcome in patients with breast cancer. Analyzing voxel-level heterogeneity in MRI-derived parametric maps, obtained before and after the first cycle of NAT ([Formula: see text]), in conjunction with receptor status, may improve the predictive accuracy of tumor response to NAT. Toward that end, we incorporated two MRI-derived parameters, the apparent diffusion coefficient and efflux rate constant, with receptor status in a logistic ridge-regression model. The area under the curve (AUC) and Brier score of the model computed via 10-fold cross validation were 0.94 (95% CI: 0.85, 0.99) and 0.11 (95% CI: 0.06, 0.16), respectively. These two statistics strongly support the hypothesis that our proposed model outperforms the other models that we investigated (namely, models without either receptor information or voxel-level information). The contribution of the receptor information was manifested by an 8% to 15% increase in AUC and a 14% to 21% decrease in Brier score. These data indicate that combining multiparametric MRI with hormone receptor status has a high likelihood of improved prediction of pathologic response to NAT in breast cancer.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Pathologic complete response following neoadjuvant therapy (NAT) is used as a short-term surrogate marker of eventual outcome in patients with breast cancer. Analyzing voxel-level heterogeneity in MRI-derived parametric maps, obtained before and after the first cycle of NAT ([Formula: see text]), in conjunction with receptor status, may improve the predictive accuracy of tumor response to NAT. Toward that end, we incorporated two MRI-derived parameters, the apparent diffusion coefficient and efflux rate constant, with receptor status in a logistic ridge-regression model. The area under the curve (AUC) and Brier score of the model computed via 10-fold cross validation were 0.94 (95% CI: 0.85, 0.99) and 0.11 (95% CI: 0.06, 0.16), respectively. These two statistics strongly support the hypothesis that our proposed model outperforms the other models that we investigated (namely, models without either receptor information or voxel-level information). The contribution of the receptor information was manifested by an 8% to 15% increase in AUC and a 14% to 21% decrease in Brier score. These data indicate that combining multiparametric MRI with hormone receptor status has a high likelihood of improved prediction of pathologic response to NAT in breast cancer. |
