@article{Virostko2018,

title = {Dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted magnetic resonance imaging for predicting the response of locally advanced breast cancer to neoadjuvant therapy: a meta-analysis},

author = {J Virostko and A Hainline and H Kang and LR Arlinghaus and RG Abramson and SL Barnes and JD Blume and S Avery and D Patt and B Goodgame and TE Yankeelov and AG Sorace},

doi = {10.1117/1.JMI.5.1.011011},

year  = {2018},

date = {2018-01-01},

journal = {J Med Imaging (Bellingham)},

volume = {5},

number = {1},

abstract = {This meta-analysis assesses the prognostic value of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) performed during neoadjuvant therapy (NAT) of locally advanced breast cancer. A systematic literature search was conducted to identify studies of quantitative DCE-MRI and DW-MRI performed during breast cancer NAT that report the sensitivity and specificity for predicting pathological complete response (pCR). Details of the study population and imaging parameters were extracted from each study for subsequent meta-analysis. Metaregression analysis, subgroup analysis, study heterogeneity, and publication bias were assessed. Across 10 studies that met the stringent inclusion criteria for this meta-analysis (out of 325 initially identified studies), we find that MRI had a pooled sensitivity of 0.91 [95% confidence interval (CI), 0.80 to 0.96] and specificity of 0.81(95% CI, 0.68 to 0.89) when adjusted for covariates. Quantitative DCE-MRI exhibits greater specificity for predicting pCR than semiquantitative DCE-MRI ([Formula: see text]). Quantitative DCE-MRI and DW-MRI are able to predict, early in the course of NAT, the eventual response of breast tumors, with a high level of specificity and sensitivity. However, there is a high degree of heterogeneity in published studies highlighting the lack of standardization in the field.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yankeelov2018d,

title = {Toward uniform implementation of parametric map Digital Imaging and Communication in Medicine standard in multisite quantitative diffusion imaging studies},

author = {Malyarenko D, Fedorov A, Bell L, Prah M, Hectors S, Arlinghaus L, Muzi M, Solaiyappan M, Jacobs M, Fung M, Shukla-Dave A, McManus K, Boss M, Taouli B, Yankeelov TE, Quarles CC, Schmainda K, Chenevert TL, Newitt DC},

doi = {10.1117/1.JMI.5.1.011006},

year  = {2018},

date = {2018-01-01},

journal = {J Med Imaging (Bellingham)},

volume = {5},

number = {1},

abstract = {This paper reports on results of a multisite collaborative project launched by the MRI subgroup of Quantitative Imaging Network to assess current capability and provide future guidelines for generating a standard parametric diffusion map Digital Imaging and Communication in Medicine (DICOM) in clinical trials that utilize quantitative diffusion-weighted imaging (DWI). Participating sites used a multivendor DWI DICOM dataset of a single phantom to generate parametric maps (PMs) of the apparent diffusion coefficient (ADC) based on two models. The results were evaluated for numerical consistency among models and true phantom ADC values, as well as for consistency of metadata with attributes required by the DICOM standards. This analysis identified missing metadata descriptive of the sources for detected numerical discrepancies among ADC models. Instead of the DICOM PM object, all sites stored ADC maps as DICOM MR objects, generally lacking designated attributes and coded terms for quantitative DWI modeling. Source-image reference, model parameters, ADC units and scale, deemed important for numerical consistency, were either missing or stored using nonstandard conventions. Guided by the identified limitations, the DICOM PM standard has been amended to include coded terms for the relevant diffusion models. Open-source software has been developed to support conversion of site-specific formats into the standard representation},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yankeelov2018e,

title = {Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network},

author = {Newitt DC, Malyarenko D, Chenevert TL, Quarles CC, Bell L, Fedorov A, Fennessy F, Jacobs MA, Solaiyappan M, Hectors S, Taouli B, Muzi M, Kinahan PE, Schmainda KM, Prah MA, Taber EN, Kroenke C, Huang W, Arlinghaus LR, Yankeelov TE, Cao Y, Aryal M, Yen YF, Kalpathy-Cramer J, Shukla-Dave A, Fung M, Liang J, Boss M, Hylton N},

doi = {10.1117/1.JMI.5.1.011003},

year  = {2018},

date = {2018-01-01},

journal = {J Med Imaging (Bellingham)},

volume = {5},

number = {1},

abstract = {Diffusion weighted MRI has become ubiquitous in many areas of medicine, including cancer diagnosis and treatment response monitoring. Reproducibility of diffusion metrics is essential for their acceptance as quantitative biomarkers in these areas. We examined the variability in the apparent diffusion coefficient (ADC) obtained from both postprocessing software implementations utilized by the NCI Quantitative Imaging Network and online scan time-generated ADC maps. Phantom and in vivo breast studies were evaluated for two ([Formula: see text]) and four ([Formula: see text]) [Formula: see text]-value diffusion metrics. Concordance of the majority of implementations was excellent for both phantom ADC measures and in vivo [Formula: see text], with relative biases [Formula: see text] ([Formula: see text]) and [Formula: see text] (phantom [Formula: see text]) but with higher deviations in ADC at the lowest phantom ADC values. In vivo [Formula: see text] concordance was good, with typical biases of [Formula: see text] to 3% but higher for online maps. Multiple b-value ADC implementations were separated into two groups determined by the fitting algorithm. Intergroup mean ADC differences ranged from negligible for phantom data to 2.8% for [Formula: see text] in vivo data. Some higher deviations were found for individual implementations and online parametric maps. Despite generally good concordance, implementation biases in ADC measures are sometimes significant and may be large enough to be of concern in multisite studies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{MR2018,

title = {Tunable Collagen Microfluidic Platform to Study Nanoparticle Transport in the Tumor Microenvironment},

author = {DeWitt MR, Rylander MN},

doi = {10.1007/978-1-4939-8661-3_12},

year  = {2018},

date = {2018-01-01},

journal = {Methods Mol Biol. },

volume = {1831},

pages = {159-178},

abstract = {This chapter describes the motivation and protocol for creating a perfused 3D microfluidic in vitro platform representative of the tumor microenvironment to study nanoparticle transport. The cylindrical vascularized tumor platform described consists of a central endothelialized microchannel surrounded by a collagen hydrogel matrix containing cancer cells. This system can be employed to investigate key nanoparticle transport events in the tumor such as extravasation, diffusion within the extracellular matrix, and nanoparticle uptake. This easily manufactured tumor platform can be used for novel nanoparticle refinement focused on optimizing nanoparticle features such as size, shape, and functionalization method. This can yield ideal nanoparticles with properties that facilitate increased transport within the tumor microenvironment, leading to more effective nanoparticle-based treatments for cancer including nanoparticle-based drug delivery systems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hormuth2018,

title = {Biophysical Modeling of In Vivo Glioma Response After Whole-Brain Radiation Therapy in a Murine Model of Brain Cancer},

author = {Hormuth DA, Weis JA, Barnes SL, Miga MI, Quaranta V, Yankeelov TE},

doi = {https://doi.org/10.1016/j.ijrobp.2017.12.004},

year  = {2017},

date = {2017-12-13},

journal = {International Journal of Radiation Oncology Biology Physics},

abstract = {Purpose

To develop and investigate a set of biophysical models based on a mechanically coupled reaction-diffusion model of the spatiotemporal evolution of tumor growth after radiation therapy.



Methods and Materials

Post–radiation therapy response is modeled using a cell death model (Md), a reduced proliferation rate model (Mp), and cell death and reduced proliferation model (Mdp). To evaluate each model, rats (n = 12) with C6 gliomas were imaged with diffusion-weighted magnetic resonance imaging (MRI) and contrast-enhanced MRI at 7 time points over 2 weeks. Rats received either 20 or 40 Gy between the third and fourth imaging time point. Diffusion-weighted MRI was used to estimate tumor cell number within enhancing regions in contrast-enhanced MRI data. Each model was fit to the spatiotemporal evolution of tumor cell number from time point 1 to time point 5 to estimate model parameters. The estimated model parameters were then used to predict tumor growth at the final 2 imaging time points. The model prediction was evaluated by calculating the error in tumor volume estimates, average surface distance, and voxel-based cell number.



Results

For both the rats treated with either 20 or 40 Gy, significantly lower error in tumor volume, average surface distance, and voxel-based cell number was observed for the Mdp and Mp models compared with the Md model. The Mdp model fit, however, had significantly lower sum squared error compared with the Mp and Md models.



Conclusions

The results of this study indicate that for both doses, the Mp and Mdp models result in accurate predictions of tumor growth, whereas the Md model poorly describes response to radiation therapy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Brock2017b,

title = {Precision Oncology: Between Vaguely Right and Precisely Wrong},

author = {A Brock, S Huang},

doi = {10.1158/0008-5472},

year  = {2017},

date = {2017-12-01},

journal = {Cancer research},

volume = {77},

number = {23},

pages = {6473-6479},

publisher = {American Association for Cancer Research},

abstract = {Precision Oncology seeks to identify and target the mutation that drives a tumor. Despite its straightforward rationale, concerns about its effectiveness are mounting. What is the biological explanation for the "imprecision?" First, Precision Oncology relies on indiscriminate sequencing of genomes in biopsies that barely represent the heterogeneous mix of tumor cells. Second, findings that defy the orthodoxy of oncogenic "driver mutations" are now accumulating: the ubiquitous presence of oncogenic mutations in silent premalignancies or the dynamic switching without mutations between various cell phenotypes that promote progression. Most troublesome is the observation that cancer cells that survive treatment still will have suffered cytotoxic stress and thereby enter a stem cell–like state, the seeds for recurrence. The benefit of “precision targeting” of mutations is inherently limited by this counterproductive effect. These findings confirm that there is no precise linear causal relationship between tumor genotype and phenotype, a reminder of logician Carveth Read's caution that being vaguely right may be preferable to being precisely wrong. An open-minded embrace of the latest inconvenient findings indicating nongenetic and "imprecise" phenotype dynamics of tumors as summarized in this review will be paramount if Precision Oncology is ultimately to lead to clinical benefits.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lima2017b,

title = {Selection and Validation of Predictive Models of Radiation Effects on Tumor Growth Based on Noninvasive Imaging Data},

author = {Lima EABF, Oden JT, Wohlmuth B, Shahmoradi A, Hormuth DA 2nd, Yankeelov TE, Scarabosio L, Horger T},

doi = {doi: 10.1016/j.cma.2017.08.009},

year  = {2017},

date = {2017-12-01},

journal = {Comput Methods Appl Mech Eng},

volume = {327},

pages = {277-305},

abstract = {The use of mathematical and computational models for reliable predictions of tumor growth and decline in living organisms is one of the foremost challenges in modern predictive science, as it must cope with uncertainties in observational data, model selection, model parameters, and model inadequacy, all for very complex physical and biological systems. In this paper, large classes of parametric models of tumor growth in vascular tissue are discussed including models for radiation therapy. Observational data is obtained from MRI of a murine model of glioma and observed over a period of about three weeks, with X-ray radiation administered 14.5 days into the experimental program. Parametric models of tumor proliferation and decline are presented based on the balance laws of continuum mixture theory, particularly mass balance, and from accepted biological hypotheses on tumor growth. Among these are new model classes that include characterizations of effects of radiation and simple models of mechanical deformation of tumors. The Occam Plausibility Algorithm (OPAL) is implemented to provide a Bayesian statistical calibration of the model classes, 39 models in all, as well as the determination of the most plausible models in these classes relative to the observational data, and to assess model inadequacy through statistical validation processes. Discussions of the numerical analysis of finite element approximations of the system of stochastic, nonlinear partial differential equations characterizing the model classes, as well as the sampling algorithms for Monte Carlo and Markov chain Monte Carlo (MCMC) methods employed in solving the forward stochastic problem, and in computing posterior distributions of parameters and model plausibilities are provided. The results of the analyses described suggest that the general framework developed can provide a useful approach for predicting tumor growth and the effects of radiation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gadde2017,

title = {Three Dimensional In Vitro Tumor Platforms for Cancer Discovery},

author = {M Gadde, D Marrinan, RJ Michna, MN Rylander},

doi = {https://doi.org/10.1007/978-3-319-60511-1_5},

year  = {2017},

date = {2017-10-21},

journal = {Tumor Organoids},

pages = {71-94},

abstract = {Traditional experimental platforms to study cancer biology consist of two-dimensional (2D) cell culture systems and animal models. Although 2D cell cultures have yielded fundamental insights into cancer biology, they do not provide a physiologically representative three-dimensional (3D) volume for cell attachment and infiltration. These systems also cannot recapitulate critical features of the tumor microenvironment including hemodynamics, matrix mechanics, cellular crosstalk, and matrix interactions in a dynamic manner, or impose chemical and mechanical gradients. While animal models provide physiologic fidelity, they can be highly variable and cost prohibitive for extensive biological investigation and therapeutic optimization. Furthermore, the interplay of many different microenvironmental variables, such as growth factors, immune reaction, and stromal interactions, make it difficult to isolate the effect of a specific stimulus on cell response using animal models. Due to these limitations, 3D in vitro tumor models have recently emerged as valuable tools for the study of cancer progression as these systems have the ability to overcome many of the limitations of static 2D monolayers and mammalian systems. Initial 3D in vitro models have consisted of static 3D co-culture platforms and have been successful in providing a deeper insight compared to animal and static 2D systems. However, the majority of these existing systems lack the presence of physiological flow, a pivotal stimuli in tumor growth and metastasis and important consideration for transport of diagnostic or therapeutic agents. In order to consider the influence of flow on cancer progression microfluidic platforms are being widely used. The integration of microfluidic technology and microfabrication techniques with tumor biology has resulted in complex 3D microfluidic platforms capable of investigating various key stages in cancer evolution including angiogenesis and metastasis. 3D microfluidic platforms are able to provide a physiologically representative tumor environment while allowing for dynamic monitoring and simultaneous control of multiple factors such as cellular and extracellular matrix composition, fluid velocity and wall shear stress, and both biochemical and mechanical gradients.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yankeelov2017b,

title = {Dual Src and EGFR inhibition in combination with gemcitabine in advanced pancreatic cancer: phase I results},

author = {Dana B Cardin, Laura W Goff, Emily Chan, Jennifer G Whisenant, G Dan Ayers, Naoko Takebe, Lori R Arlinghaus, Thomas E Yankeelov, Jordan Berlin, Nipun Merchant},

url = {https://doi.org/10.1007/s10637-017-0519-z},

issn = {0167-6997},

year  = {2017},

date = {2017-10-09},

journal = {Investigational New Drugs},

pages = {1-9},

abstract = {Pancreatic adenocarcinoma remains a major therapeutic challenge, as the poor (<8%) 5-year survival rate has not improved over the last three decades. Our previous preclinical data showed cooperative attenuation of pancreatic tumor growth when dasatinib (Src inhibitor) was added to erlotinib (EGFR inhibitor) and gemcitabine. Thus, this study was designed to determine the maximum-tolerated dose of the triplet combination. Standard 3 + 3 dose escalation was used, starting with daily oral doses of 70 mg dasatinib and 100 mg erlotinib with gemcitabine on days 1, 8, and 15 (800 mg/m2) of a 28-day cycle (L0). Nineteen patients were enrolled, yet 18 evaluable for dose-limiting toxicities (DLTs). One DLT observed at L0, however dasatinib was reduced to 50 mg (L−1) given side effects observed in the first two patients. At L−1, a DLT occurred in 1/6 patients and dose was re-escalated to L0, where zero DLTs reported in next four patients. Dasatinib was escalated to 100 mg (L1) where 1/6 patients experienced a DLT. Although L1 was tolerable, dose escalation was stopped as investigators felt L1 was within the optimal therapeutic window. Most frequent toxicities were anemia (89%), elevated aspartate aminotransferase (79%), fatigue (79%), nausea (79%), elevated alanine aminotransferase (74%), lymphopenia (74%), leukopenia (74%), neutropenia (63%), and thrombocytopenia (63%), most Grade 1/2. Stable disease as best response was observed in 69% (9/13). Median progression-free and overall survival was 3.6 and 8 months, respectively. Dasatinib, erlotinib, and gemcitabine was safe with manageable side effects, and with encouraging preliminary clinical activity in advanced pancreatic cancer.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yankeelov2017b,

title = {CCR7 modulates the generation of thymic regulatory T cells by altering the composition of the thymic dendritic cell compartment},

author = {Zicheng Hu, Yu Li, Annemarie Van Nieuwenhuijze, Hilary J Selden, Angela M Jarrett, Anna G Sorace, Thomas E Yankeelov, Adrian Liston, Lauren IR Ehrlich},

url = {https://doi.org/10.1016/j.celrep.2017.09.016},

year  = {2017},

date = {2017-10-03},

journal = {Cell reports},

volume = {21},

number = {1},

pages = {168-180},

publisher = {Cell Press},

abstract = {Upon recognition of auto-antigens, thymocytes are negatively selected or diverted to a regulatory T cell (Treg) fate. CCR7 is required for negative selection of auto-reactive thymocytes in the thymic medulla. Here, we describe an unanticipated contribution of CCR7 to intrathymic Treg generation. Ccr7−/− mice have increased Treg cellularity because of a hematopoietic but non-T cell autonomous CCR7 function. CCR7 expression by thymic dendritic cells (DCs) promotes survival of mature Sirpα− DCs. Thus, CCR7 deficiency results in apoptosis of Sirpα− DCs, which is counterbalanced by expansion of immature Sirpα+ DCs that efficiently induce Treg generation. CCR7 deficiency results in enhanced intrathymic generation of Tregs at the neonatal stage and in lymphopenic adults, when Treg differentiation is critical for establishing self-tolerance. Together, these results reveal a complex function for CCR7 in thymic tolerance induction, where CCR7 not only promotes negative selection but also governs intrathymic Treg generation via non-thymocyte intrinsic mechanisms.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sorace2017b,

title = {Imaging Considerations and Interprofessional Opportunities in the Care of Breast Cancer Patients in the Neoadjuvant Setting},

author = {Sorace AG and Harvey S and Syed A and Yankeelov TE},

url = {http://www.sciencedirect.com/science/article/pii/S0749208117300748},

doi = {10.1016/j.soncn.2017.08.008},

year  = {2017},

date = {2017-09-15},

journal = {Seminars in Oncology Nursing},

abstract = {OBJECTIVE:

To discuss standard-of-care and emerging imaging techniques employed for screening and detection, diagnosis and staging, monitoring response to therapy, and guiding cancer treatments.

DATA SOURCES:

Published journal articles indexed in the National Library of Medicine database and relevant websites.

CONCLUSION:

Imaging plays a fundamental role in the care of cancer patients and specifically, breast cancer patients in the neoadjuvant setting, providing an excellent opportunity for interprofessional collaboration between oncologists, researchers, radiologists, and oncology nurses. Quantitative imaging strategies to assess cellular, molecular, and vascular characteristics within the tumor is needed to better evaluate initial diagnosis and treatment response.

IMPLICATIONS FOR NURSING PRACTICE:

Nurses caring for patients in all settings must continue to seek education on emerging imaging techniques. Oncology nurses provide education about the test, ensure the patient has appropriate pre-testing instructions, and manage patient expectations about timing of results availability.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Barnes2017,

title = {DCE- and DW-MRI as early imaging biomarkers of treatment response in a preclinical model of triple negative breast cancer},

author = {Barnes SL and Sorace AG and Whisenant JG and McIntyre JO and Kang and Yankeelov TE},

url = {http://onlinelibrary.wiley.com/doi/10.1002/nbm.3799/abstract?systemMessage=Wiley+Online+Library+will+be+unavailable+on+Saturday+7th+Oct+from+03.00+EDT+%2F+08%3A00+BST+%2F+12%3A30+IST+%2F+15.00+SGT+to+08.00+EDT+%2F+13.00+BST+%2F+17%3A30+IST+%2F+20.00+SGT+and+Sunday+8th+Oct+from+03.00+EDT+%2F+08%3A00+BST+%2F+12%3A30+IST+%2F+15.00+SGT+to+06.00+EDT+%2F+11.00+BST+%2F+15%3A30+IST+%2F+18.00+SGT+for+essential+maintenance.+Apologies+for+the+inconvenience+caused+.},

doi = {10.1002/nbm.3799},

year  = {2017},

date = {2017-09-15},

journal = {NMR In Biomedicine},

abstract = {This work evaluates quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) parameters as early biomarkers of response in a preclinical model of triple negative breast cancer (TNBC). The standard Tofts' model of DCE-MRI returns estimates of the volume transfer constant (Ktrans ) and the extravascular extracellular volume fraction (ve ). DW-MRI returns estimates of the apparent diffusion coefficient (ADC). Mice (n = 38) were injected subcutaneously with MDA-MB-231. Tumors were grown to approximately 275 mm3 and sorted into the following groups: saline controls, low-dose Abraxane (15 mg/kg) and high-dose Abraxane (25 mg/kg). Animals were imaged at days zero, one and three. On day three, tumors were extracted for immunohistochemistry. The positive percentage change in ADC on day one was significantly higher in both treatment groups relative to the control group (p < 0.05). In addition, the positive percentage change in Ktrans was significantly higher than controls (p < 0.05) on day one for the high-dose group and on days one and three for the low-dose group. The percentage change in tumor volume was significantly different between the high-dose and control groups on day three (p = 0.006). Histology confirmed differences at day three through reduced numbers of proliferating cells (Ki67 staining) in the high-dose group (p = 0.03) and low-dose group (p = 0.052) compared with the control group. Co-immunofluorescent staining of vascular maturity [using von Willebrand Factor (vWF) and α-smooth muscle actin (α-SMA)] indicated significantly higher vascular maturation in the low-dose group compared with the controls on day three (p = 0.03), and trending towards significance in the high-dose group compared with controls on day three (p = 0.052). These results from quantitative imaging with histological validation indicate that ADC and Ktrans have the potential to serve as early biomarkers of treatment response in murine studies of TNBC.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yankeelov2017b,

title = {Accuracy, repeatability, and interplatform reproducibility of T1 quantification methods used for DCE-MRI: Results from a multicenter phantom study},

author = {Bane O and Hectors SJ and Wagner M and Arlinghaus LL and Aryal MP and Cao Y and Chenevert TL and Fennessy F and Huang W and Hylton NM and Kalpathy-Cramer J and Keenan KE and Malyarenko DI and Mulkern RV and Newitt DC and Russek SE and Stupic KF and Tudorica A and Wilmes LJ and Yankeelov TE and Yen YF and Boss MA and Taouli B},

url = {http://onlinelibrary.wiley.com/doi/10.1002/mrm.26903/abstract?systemMessage=Wiley+Online+Library+will+be+unavailable+on+Saturday+7th+Oct+from+03.00+EDT+%2F+08%3A00+BST+%2F+12%3A30+IST+%2F+15.00+SGT+to+08.00+EDT+%2F+13.00+BST+%2F+17%3A30+IST+%2F+20.00+SGT+and+Sunday+8th+Oct+from+03.00+EDT+%2F+08%3A00+BST+%2F+12%3A30+IST+%2F+15.00+SGT+to+06.00+EDT+%2F+11.00+BST+%2F+15%3A30+IST+%2F+18.00+SGT+for+essential+maintenance.+Apologies+for+the+inconvenience+caused+.},

doi = {10.1002/mrm.26903},

year  = {2017},

date = {2017-09-14},

journal = {Magnetic Resonance in Medicine},

abstract = {PURPOSE:

To determine the in vitro accuracy, test-retest repeatability, and interplatform reproducibility of T1 quantification protocols used for dynamic contrast-enhanced MRI at 1.5 and 3 T.

METHODS:

A T1 phantom with 14 samples was imaged at eight centers with a common inversion-recovery spin-echo (IR-SE) protocol and a variable flip angle (VFA) protocol using seven flip angles, as well as site-specific protocols (VFA with different flip angles, variable repetition time, proton density, and Look-Locker inversion recovery). Factors influencing the accuracy (deviation from reference NMR T1 measurements) and repeatability were assessed using general linear mixed models. Interplatform reproducibility was assessed using coefficients of variation.

RESULTS:

For the common IR-SE protocol, accuracy (median error across platforms = 1.4-5.5%) was influenced predominantly by T1 sample (P < 10-6 ), whereas test-retest repeatability (median error = 0.2-8.3%) was influenced by the scanner (P < 10-6 ). For the common VFA protocol, accuracy (median error = 5.7-32.2%) was influenced by field strength (P = 0.006), whereas repeatability (median error = 0.7-25.8%) was influenced by the scanner (P < 0.0001). Interplatform reproducibility with the common VFA was lower at 3 T than 1.5 T (P = 0.004), and lower than that of the common IR-SE protocol (coefficient of variation 1.5T: VFA/IR-SE = 11.13%/8.21%, P = 0.028; 3 T: VFA/IR-SE = 22.87%/5.46%, P = 0.001). Among the site-specific protocols, Look-Locker inversion recovery and VFA (2-3 flip angles) protocols showed the best accuracy and repeatability (errors < 15%).

CONCLUSIONS:

The VFA protocols with 2 to 3 flip angles optimized for different applications achieved acceptable balance of extensive spatial coverage, accuracy, and repeatability in T1 quantification (errors < 15%). Further optimization in terms of flip-angle choice for each tissue application, and the use of B1 correction, are needed to improve the robustness of VFA protocols for T1 mapping. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yankeelov2017b,

title = {A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations},

author = {Ger RB and Mohamed ASR and Awan MJ and Ding Y and Li K and Fave XJ and Beers AL and Driscoll B and Elhalawani H and Hormuth DA 2nd and Houdt PJV and He R and Zhou S and Mathieu KB and Li H and Coolens C and Chung C and Bankson JA and Huang W and Wang J and Sandulache VC and Lai SY and Howell RM and Stafford RJ and Yankeelov TE and Heide UAV and Frank SJ and Barboriak DP and Hazle JD and Court LE and Kalpathy-Cramer J and Fuller CD},

url = {https://www.nature.com/articles/s41598-017-11554-w},

doi = {10.1038/s41598-017-11554-w},

year  = {2017},

date = {2017-09-11},

journal = {Scientific Reports},

volume = {7},

abstract = {Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides quantitative metrics (e.g. Ktrans, ve) via pharmacokinetic models. We tested inter-algorithm variability in these quantitative metrics with 11 published DCE-MRI algorithms, all implementing Tofts-Kermode or extended Tofts pharmacokinetic models. Digital reference objects (DROs) with known Ktrans and ve values were used to assess performance at varying noise levels. Additionally, DCE-MRI data from 15 head and neck squamous cell carcinoma patients over 3 time-points during chemoradiotherapy were used to ascertain Ktrans and ve kinetic trends across algorithms. Algorithms performed well (less than 3% average error) when no noise was present in the DRO. With noise, 87% of Ktrans and 84% of ve algorithm-DRO combinations were generally in the correct order. Low Krippendorff's alpha values showed that algorithms could not consistently classify patients as above or below the median for a given algorithm at each time point or for differences in values between time points. A majority of the algorithms produced a significant Spearman correlation in ve of the primary gross tumor volume with time. Algorithmic differences in Ktrans and ve values over time indicate limitations in combining/comparing data from distinct DCE-MRI model implementations. Careful cross-algorithm quality-assurance must be utilized as DCE-MRI results may not be interpretable using differing software.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yankeelov2017,

title = {Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast},

author = {Wang D and Arlinghaus LR and Yankeelov TE and Yang X and Smith DS},

url = {https://www.hindawi.com/journals/ijbi/2017/7835749/},

doi = {10.1155/2017/7835749},

year  = {2017},

date = {2017-08-28},

journal = {International Journal of Biomedical Imaging},

abstract = {PURPOSE:

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is used in cancer imaging to probe tumor vascular properties. Compressed sensing (CS) theory makes it possible to recover MR images from randomly undersampled k-space data using nonlinear recovery schemes. The purpose of this paper is to quantitatively evaluate common temporal sparsity-promoting regularizers for CS DCE-MRI of the breast.

METHODS:

We considered five ubiquitous temporal regularizers on 4.5x retrospectively undersampled Cartesian in vivo breast DCE-MRI data: Fourier transform (FT), Haar wavelet transform (WT), total variation (TV), second-order total generalized variation (TGV α2), and nuclear norm (NN). We measured the signal-to-error ratio (SER) of the reconstructed images, the error in tumor mean, and concordance correlation coefficients (CCCs) of the derived pharmacokinetic parameters Ktrans (volume transfer constant) and ve (extravascular-extracellular volume fraction) across a population of random sampling schemes.

RESULTS:

NN produced the lowest image error (SER: 29.1), while TV/TGV α2 produced the most accurate Ktrans (CCC: 0.974/0.974) and ve (CCC: 0.916/0.917). WT produced the highest image error (SER: 21.8), while FT produced the least accurate Ktrans (CCC: 0.842) and ve (CCC: 0.799).

CONCLUSION:

TV/TGV α2 should be used as temporal constraints for CS DCE-MRI of the breast.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Barnes2017b,

title = {A Predictive Mathematical Modeling Approach for the Study of Doxorubicin Treatment in Triple Negative Breast Cancer},

author = {McKenna MT and Weis JA and Barnes SL and Tyson DR and Miga MI and Quaranta V and Yankeelov TE},

url = {https://www.nature.com/articles/s41598-017-05902-z},

doi = {10.1038/s41598-017-05902-z},

year  = {2017},

date = {2017-07-18},

journal = {Scientific Reports},

volume = {7},

number = {1},

pages = {5725},

abstract = {Doxorubicin forms the basis of chemotherapy regimens for several malignancies, including triple negative breast cancer (TNBC). Here, we present a coupled experimental/modeling approach to establish an in vitro pharmacokinetic/pharmacodynamic model to describe how the concentration and duration of doxorubicin therapy shape subsequent cell population dynamics. This work features a series of longitudinal fluorescence microscopy experiments that characterize (1) doxorubicin uptake dynamics in a panel of TNBC cell lines, and (2) cell population response to doxorubicin over 30 days. We propose a treatment response model, fully parameterized with experimental imaging data, to describe doxorubicin uptake and predict subsequent population dynamics. We found that a three compartment model can describe doxorubicin pharmacokinetics, and pharmacokinetic parameters vary significantly among the cell lines investigated. The proposed model effectively captures population dynamics and translates well to a predictive framework. In a representative cell line (SUM-149PT) treated for 12 hours with doxorubicin, the mean percent errors of the best-fit and predicted models were 14% (±10%) and 16% (±12%), which are notable considering these statistics represent errors over 30 days following treatment. More generally, this work provides both a template for studies quantitatively investigating treatment response and a scalable approach toward predictions of tumor response in vivo.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Feng2018b,

title = {A fully coupled space-time multiscale modeling framework for predicting tumor growth},

author = {Rahman MM, Feng Y, Yankeelov TE, Oden JT},

doi = {10.1016/j.cma.2017.03.021},

year  = {2017},

date = {2017-06-15},

journal = {Comput Methods Appl Mech Eng},

volume = {320},

pages = {261-286},

abstract = {Most biological systems encountered in living organisms involve highly complex heterogeneous multi-component structures that exhibit different physical, chemical, and biological behavior at different spatial and temporal scales. The development of predictive mathematical and computational models of multiscale events in such systems is a major challenge in contemporary computational biomechanics, particularly the development of models of growing tumors in humans. The aim of this study is to develop a general framework for tumor growth prediction by considering major biological events at tissue, cellular, and subcellular scales. The key to developing such multiscale models is how to bridge spatial and temporal scales that range from 10-3 to 103 mm in space and from 10-6 to 107 s in time. In this paper, a fully coupled space-time multiscale framework for modeling tumor growth is developed. The framework consists of a tissue scale model, a model of cellular activities, and a subcellular transduction signaling pathway model. The tissue, cellular, and subcellular models in this framework are solved using partial differential equations for tissue growth, agent-based model for cellular events, and ordinary differential equations for signaling transduction pathway as a network at subcellular scale. The model is calibrated using experimental observations. Moreover, this model is biologically-driven from a signaling pathway, volumetrically-consistent between cellular and tissue scale in terms of tumor volume evolution in time, and a biophysically-sound tissue model that satisfies all conservation laws. The results show that the model is capable of predicting major characteristics of tumor growth such as the morphological instability, growth patterns of different cell phenotypes, compact regions of the higher cell density at the tumor region, and the reduction of growth rate due to drug delivery. The predicted treatment outcomes show a reduction in proliferation at different rates in response to different drug dosages. Moreover, the results of several 3D applications to tumor growth and the evolution of cellular and subcellular events are presented.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fuentes2016b,

title = {Performance Assessment for Brain MR Imaging Registration Methods},

author = {JS Lin and DT Fuentes and A Chandler and SS Prabhu and JS Weinberg and V Baladandayuthapani and JD Hazle and D Schellingerhout},

url = {http://www.ajnr.org/content/38/5/973},

doi = {https://doi.org/10.3174/ajnr.A5122},

year  = {2017},

date = {2017-05-01},

journal = {American Journal of Neuroradiology},

volume = {38},

number = {5},

pages = {973-980},

publisher = {American Journal of Neuroradiology},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fuentes2016b,

title = {Theoretical model for laser ablation outcome predictions in brain: calibration and validation on clinical MR thermometry images},

author = {Samuel John Fahrenholtz and Reza Madankan and Shabbar Danish and John Hazle and Roger Jason Stafford and David Fuentes},

url = {http://www.tandfonline.com/doi/abs/10.1080/02656736.2017.1319974},

doi = {http://dx.doi.org/10.1080/02656736.2017.1319974},

year  = {2017},

date = {2017-04-18},

journal = {International Journal of Hyperthermia},

pages = {1-28},

publisher = {Taylor & Francis},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hormuth2017,

title = {“A mechanically-coupled reaction diffusion model that incorporates intra-tumoral heterogeneity to predict in vivo glioma growth”},

author = {Hormuth DA and Weis JA and Barnes SL and Miga MI and Rericha EC and Quaranta V and Yankeelov TE.},

url = {http://rsif.royalsocietypublishing.org/content/14/128/20161010},

doi = {10.1098/rsif.2016.1010},

year  = {2017},

date = {2017-03-22},

journal = {Journal of the Royal Society Interface},

volume = {14},

number = {128},

pages = {20161010},

publisher = {The Royal Society},

abstract = {While gliomas have been extensively modelled with a reaction–diffusion (RD) type equation it is most likely an oversimplification. In this study, three mathematical models of glioma growth are developed and systematically investigated to establish a framework for accurate prediction of changes in tumour volume as well as intra-tumoural heterogeneity. Tumour cell movement was described by coupling movement to tissue stress, leading to a mechanically coupled (MC) RD model. Intra-tumour heterogeneity was described by including a voxel-specific carrying capacity (CC) to the RD model. The MC and CC models were also combined in a third model. To evaluate these models, rats (n = 14) with C6 gliomas were imaged with diffusion-weighted magnetic resonance imaging over 10 days to estimate tumour cellularity. Model parameters were estimated from the first three imaging time points and then used to predict tumour growth at the remaining time points which were then directly compared to experimental data. The results in this work demonstrate that mechanical–biological effects are a necessary component of brain tissue tumour modelling efforts. The results are suggestive that a variable tissue carrying capacity is a needed model component to capture tumour heterogeneity. Lastly, the results advocate the need for additional effort towards capturing tumour-to-tissue infiltration.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Behar2018c,

title = {"Sequential rather than coincident molecular mechanisms govern the combinatorial control logic underlying pathogen-responsive gene expression programs"},

author = {CS Cheng, M Behar, GW Suryawanshi, KE Feldman, R Spreafico, A Hoffmann},

doi = {10.1016/j.cels.2017.01.012},

year  = {2017},

date = {2017-03-22},

journal = {Cell systems},

volume = {4},

number = {3},

pages = {330},

publisher = {NIH Public Access},

abstract = {Combinatorial control of gene expression is presumed to be mediated by molecular interactions between coincident transcription factors (TFs). While information on the genome-wide locations of TFs is available, the genes they regulate and whether they function combinatorially often remain open questions. Here, we developed a mechanistic, rather than statistical, modeling approach to elucidate TF control logic from gene expression data. Applying this approach to hundreds of genes in 85 datasets measuring the transcriptional responses of murine fibroblasts and macrophages to cytokines and pathogens, we found that stimulus-responsive TFs generally function sequentially in logical OR gates or singly. Logical AND gates were found between NFκB-responsive mRNA synthesis and MAPKp38-responsive control of mRNA half-life, but not between temporally coincident TFs. Our analyses identified the functional target genes of each of the pathogen-responsive TFs and prompts a revision of the conceptual underpinnings of combinatorial control of gene expression to include sequentially acting molecular mechanisms that govern mRNA synthesis and decay.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Behar2017,

title = {"Iterative modeling reveals evidence of sequential transcriptional control mechanisms"},

author = {CS Cheng, M Behar, GW Suryawanshi, KE Feldman, R Spreafico, A Hoffmann},

doi = {https://doi.org/10.1016/j.cels.2017.01.012},

year  = {2017},

date = {2017-03-22},

journal = {Cell Systems},

volume = {4},

number = {3},

pages = {330-343},

publisher = {Elsevier},

abstract = {Combinatorial control of gene expression is presumed to be mediated by molecular interactions between coincident transcription factors (TFs). While information on the genome-wide locations of TFs is available, the genes they regulate and whether they function combinatorially often remain open questions. Here, we developed a mechanistic, rather than statistical, modeling approach to elucidate TF control logic from gene expression data. Applying this approach to hundreds of genes in 85 datasets measuring the transcriptional responses of murine fibroblasts and macrophages to cytokines and pathogens, we found that stimulus-responsive TFs generally function sequentially in logical OR gates or singly. Logical AND gates were found between NF-κB-responsive mRNA synthesis and MAPKp38-responsive control of mRNA half-life, but not between temporally coincident TFs. Our analyses identified the functional target genes of each of the pathogen-responsive TFs and prompt a revision of the conceptual underpinnings of combinatorial control of gene expression to include sequentially acting molecular mechanisms that govern mRNA synthesis and decay.



},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fuentes2016b,

title = {"Referenceless Magnetic Resonance Temperature Imaging using Gaussian Process Modeling"},

author = {Joshua P Yung and David Fuentes and Christopher J MacLellan and Florian Maier and Yannis Liapis and John D Hazle and R Jason Stafford},

url = {http://onlinelibrary.wiley.com/doi/10.1002/mp.12231/full},

doi = {10.1002/mp.12231},

year  = {2017},

date = {2017-03-20},

journal = {Medical Physics},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sorace2017,

title = {"Abstract P4-02-02: Increased tumor perfusion following treatment with trastuzumab as measured by contrast-enhanced ultrasound"},

author = {AG Sorace and SL Barnes and CC Quarles and JO McIntyre and TE Yankeelov},

url = {http://cancerres.aacrjournals.org/content/77/4_Supplement/P4-02-02.short},

doi = {10.1158/1538-7445.SABCS16-P4-02-02},

year  = {2017},

date = {2017-02-15},

journal = {Cancer Research},

volume = {77},

number = {4 supplement},

pages = {P4-02-02-P4-02-02},

publisher = {American Association for Cancer Research},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sorace2016,

title = {"Quantitative [18F]FMISO PET Imaging Shows Reduction of Hypoxia Following Trastuzumab in a Murine Model of HER2+ Breast Cancer"},

author = {Sorace, A.G. and Syed, A.K. and Barnes, S.L. and Quarles, C.C. and Sanchez, V. and Kang, H. and Yankeelov, T.E.},

url = {https://link.springer.com/article/10.1007/s11307-016-0994-1},

doi = {10.1007/s11307-016-0994-1},

year  = {2017},

date = {2017-02-01},

journal = {Molecular Imaging and Biology},

volume = {19},

number = {1},

pages = {130-137},

publisher = {Springer US},

keywords = {},

pubstate = {published},

tppubtype = {article}

}