A comparison of Bayesian and non-linear regression methods for robust estimation of pharmacokinetics in DCE-MRI and how it affects cancer diagnosis

The aim of this work is to compare Bayesian Inference for nonlinear models with commonly used traditional non-linear regression (NR) algorithms for estimating tracer kinetics in Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI). The algorithms are compared in terms of accuracy, and reproducibility under different initialization settings. Further it is investigated how a more robust estimation of tracer kinetics affects cancer diagnosis. The derived tracer kinetics from the Bayesian algorithm were validated against traditional NR algorithms (i.e. Levenberg-Marquardt, simplex) in terms of accuracy on a digital DCE phantom and in terms of goodness-of-fit (Kolmogorov-Smirnov test) on ROI-based concentration time courses from two different patient cohorts. The first cohort consisted of 76 men, 20 of whom had significant peripheral zone prostate cancer (any cancer-core-length (CCL) with Gleason>3+3 or any-grade with CCL>=4mm) following transperineal template prostate mapping biopsy. The second cohort consisted of 9 healthy volunteers and 24 patients with head and neck squamous cell carcinoma. The diagnostic ability of the derived tracer kinetics was assessed with receiver operating characteristic area under curve (ROC AUC) analysis. The Bayesian algorithm accurately recovered the ground-truth tracer kinetics for the digital DCE phantom consistently improving the Structural Similarity Index (SSIM) across the 50 different initializations compared to NR. For optimized initialization, Bayesian did not improve significantly the fitting accuracy on both patient cohorts, and it only significantly improved the ve ROC AUC on the HN population from ROC AUC=0.56 for the simplex to ROC AUC=0.76. For both cohorts, the values and the diagnostic ability of tracer kinetic parameters estimated with the Bayesian algorithm weren't affected by their initialization. To conclude, the Bayesian algorithm led to a more accurate and reproducible quantification of tracer kinetic parameters in DCE-MRI, improving their ROC-AUC and decreasing their dependence on initialization settings

Bayesian pharmacokinetic modeling of dynamic contrast-enhanced magnetic resonance imaging: validation and application

Tracer-kinetic analysis of dynamic contrast-enhanced magnetic resonance imaging data is commonly performed with the well-known Tofts model and nonlinear least squares (NLLS) regression. This approach yields point estimates of model parameters, uncertainty of these estimates can be assessed e.g. by an additional bootstrapping analysis. Here, we present a Bayesian probabilistic modeling approach for tracer-kinetic analysis with a Tofts model, which yields posterior probability distributions of perfusion parameters and therefore promises a robust and information-enriched alternative based on a framework of probability distributions. In this manuscript, we use the quantitative imaging biomarkers alliance (QIBA) Tofts phantom to evaluate the Bayesian tofts model (BTM) against a bootstrapped NLLS approach. Furthermore, we demonstrate how Bayesian posterior probability distributions can be employed to assess treatment response in a breast cancer DCE-MRI dataset using Cohen's d. Accuracy and precision of the BTM posterior distributions were validated and found to be in good agreement with the NLLS approaches, and assessment of therapy response with respect to uncertainty in parameter estimates was found to be excellent. In conclusion, the Bayesian modeling approach provides an elegant means to determine uncertainty via posterior distributions within a single step and provides honest information about changes in parameter estimates

A comparison of Bayesian and non-linear regression methods for robust estimation of pharmacokinetics in DCE-MRI and how it affects cancer diagnosis

The aim of this work is to compare Bayesian Inference for nonlinear models with commonly used traditional non-linear regression (NR) algorithms for estimating tracer kinetics in Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI). The algorithms are compared in terms of accuracy, and reproducibility under different initialization settings. Further it is investigated how a more robust estimation of tracer kinetics affects cancer diagnosis. The derived tracer kinetics from the Bayesian algorithm were validated against traditional NR algorithms (i.e. Levenberg-Marquardt, simplex) in terms of accuracy on a digital DCE phantom and in terms of goodness-of-fit (Kolmogorov-Smirnov test) on ROI-based concentration time courses from two different patient cohorts. The first cohort consisted of 76 men, 20 of whom had significant peripheral zone prostate cancer (any cancer-core-length (CCL) with Gleason > 3 + 3 or any-grade with CCL > = 4 mm) following transperineal template prostate mapping biopsy. The second cohort consisted of 9 healthy volunteers and 24 patients with head and neck squamous cell carcinoma. The diagnostic ability of the derived tracer kinetics was assessed with receiver operating characteristic area under curve (ROC AUC) analysis. The Bayesian algorithm accurately recovered the ground-truth tracer kinetics for the digital DCE phantom consistently improving the Structural Similarity Index (SSIM) across the 50 different initializations compared to NR. For optimized initialization, Bayesian did not improve significantly the fitting accuracy on both patient cohorts, and it only significantly improved the ve ROC AUC on the HN population from ROC AUC = 0.56 for the simplex to ROC AUC = 0.76. For both cohorts, the values and the diagnostic ability of tracer kinetic parameters estimated with the Bayesian algorithm weren’t affected by their initialization. To conclude, the Bayesian algorithm led to a more accurate and reproducible quantification of tracer kinetic parameters in DCE-MRI, improving their ROC-AUC and decreasing their dependence on initialization settings

The effect of environmental stress and selective glucocorticoid receptor modulators on chicken and human leukaemia cells

Glucocorticoids (GCs) play important functions in human physiology, and are commonly prescribed anti-inflammatory and immunosuppressive drugs. GCs are used in treatment of childhood acute lymphocytic leukemia (ALL), however resistance to therapy and side effects highlight the need for further research. Glucocorticoids exert their function through binding to intracellular protein glucocorticoid receptor (GR). It is believed that the desired apoptotic effect on cancer cells and anti-inflammatory properties of GCs are due to the GR’s mediated trans-repression function, and that genes positively regulated by GR may mediate unwanted GCs effects. Thus, this study aimed to investigate compounds that would potentially dissociate transcriptional activation from repression, minimize the side effects and GC resistance, towards improving childhood leukemia therapy. The recently developed selective GR modulator (SGRM) Compound A (CPDA) and synthetic GC dexamethasone (DEX) were used together with two “single ring” organic compounds; Tyramine (T) and Tyramine hydrochloride (THCl), as well as Compound B and Compound C, to assess their cytotoxic and anti-inflammatory effects. Molecular modelling has indicated that these compounds contact several residues similar to classical GCs. DEX, CPDA, T and THCL all show cytotoxic effect on GC sensitive and GC resistant ALL CEM-C7-14 and CEM-C1-15 cell lines respectively, as well as chicken derived leukemia cells DT40. Compound B and C showed growth stimulatory effects and were not studied further. Leukaemia cells proliferation was mostly inhibited by high doses and long incubation time, whereas combination of compound treatment with either high or low temperature interfered with this effect. All compounds had marginal growth inhibitory effect on proliferation of normal lung bronchial cells Beas-2b and MCF-C7, whereas T and THCL showed some stimulatory effect on HACAT cells proliferation. Compounds exerted selective and differential effects on cell cycle progression, apoptosis and caspase-8 enzyme activation. Normal peripheral blood mononuclear cells (PBMCs) were used to examine the cytotoxic effect on normal leukocytes. PBMCs were not significantly affected suggesting that tested compounds don’t have the growth suppressive effect on normal peripheral white blood cells. Cell type specific, anti-inflammatory action of studied compounds was measured by ROS, nitrite and cytokine production analysis. Evaluation of secretory cytokines IL-6 and IL-2 by ELISA has shown a cell specific regulation of these biomarkers of inflammation. Protein and gene expression of GR target genes and resistance markers was regulated in a drug and cell dependent manner. These data provided evidence of CPDA, T and THCL capability to inhibit leukemia cells proliferation and alter selected GR target genes expression. Thereby, these compounds show promising characteristics for drug development aiming to potential use in treatment of leukemia and inflammatory conditions