Improved multifidelity Monte Carlo estimators based on normalizing flows and dimensionality reduction techniques.

We study the problem of multifidelity uncertainty propagation for computationally expensive models. In particular, we consider the general setting where the high-fidelity and low-fidelity models have a dissimilar parameterization both in terms of number of random inputs and their probability distributions, which can be either known in closed form or provided through samples. We derive novel multifidelity Monte Carlo estimators which rely on a shared subspace between the high-fidelity and low-fidelity models where the parameters follow the same probability distribution, i.e., a standard Gaussian. We build the shared space employing normalizing flows to map different probability distributions into a common one, together with linear and nonlinear dimensionality reduction techniques, active subspaces and autoencoders, respectively, which capture the subspaces where the models vary the most. We then compose the existing low-fidelity model with these transformations and construct modified models with an increased correlation with the high-fidelity model, which therefore yield multifidelity estimators with reduced variance. A series of numerical experiments illustrate the properties and advantages of our approaches.

Global sensitivity analysis with multifidelity Monte Carlo and polynomial chaos expansion for vascular haemodynamics.

Computational models of the cardiovascular system are increasingly used for the diagnosis, treatment, and prevention of cardiovascular disease. Before being used for translational applications, the predictive abilities of these models need to be thoroughly demonstrated through verification, validation, and uncertainty quantification. When results depend on multiple uncertain inputs, sensitivity analysis is typically the first step required to separate relevant from unimportant inputs, and is key to determine an initial reduction on the problem dimensionality that will significantly affect the cost of all downstream analysis tasks. For computationally expensive models with numerous uncertain inputs, sample-based sensitivity analysis may become impractical due to the substantial number of model evaluations it typically necessitates. To overcome this limitation, we consider recently proposed Multifidelity Monte Carlo estimators for Sobol' sensitivity indices, and demonstrate their applicability to an idealized model of the common carotid artery. Variance reduction is achieved combining a small number of three-dimensional fluid-structure interaction simulations with affordable one- and zero-dimensional reduced-order models. These multifidelity Monte Carlo estimators are compared with traditional Monte Carlo and polynomial chaos expansion estimates. Specifically, we show consistent sensitivity ranks for both bi- (1D/0D) and tri-fidelity (3D/1D/0D) estimators, and superior variance reduction compared to traditional single-fidelity Monte Carlo estimators for the same computational budget. As the computational burden of Monte Carlo estimators for Sobol' indices is significantly affected by the problem dimensionality, polynomial chaos expansion is found to have lower computational cost for idealized models with smooth stochastic response.

A probabilistic neural twin for treatment planning in peripheral pulmonary artery stenosis.

The substantial computational cost of high-fidelity models in numerical hemodynamics has, so far, relegated their use mainly to offline treatment planning. New breakthroughs in data-driven architectures and optimization techniques for fast surrogate modeling provide an exciting opportunity to overcome these limitations, enabling the use of such technology for time-critical decisions. We discuss an application to the repair of multiple stenosis in peripheral pulmonary artery disease through either transcatheter pulmonary artery rehabilitation or surgery, where it is of interest to achieve desired pressures and flows at specific locations in the pulmonary artery tree, while minimizing the risk for the patient. Since different degrees of success can be achieved in practice during treatment, we formulate the problem in probability, and solve it through a sample-based approach. We propose a new offline-online pipeline for probabilistic real-time treatment planning which combines offline assimilation of boundary conditions, model reduction, and training dataset generation with online estimation of marginal probabilities, possibly conditioned on the degree of augmentation observed in already repaired lesions. Moreover, we propose a new approach for the parametrization of arbitrarily shaped vascular repairs through iterative corrections of a zero-dimensional approximant. We demonstrate this pipeline for a diseased model of the pulmonary artery tree available through the Vascular Model Repository.

Oral probiotic therapy improves motor function in a rodent model of sensorimotor stroke.

Ischemic stroke is a debilitating neurological disease with few effective therapeutics. Previous work has shown that oral probiotic treatment prior to stroke can attenuate cerebral infarction and neuroinflammation, highlighting the gut-microbiota-brain axis as a novel therapeutic target. Whether a more clinically relevant, post-stroke, administration of probiotics can improve stroke outcomes is unknown. In this study, we examined the effect of post-stroke oral probiotic therapy on motor behavior in the pre-clinical mouse endothelin-1 (ET-1) model of sensorimotor stroke. We found that post-stroke oral probiotic therapy with Cerebiome® (Lallemand, Montreal, Canada), containing B. longum R0175 and L. helveticus R0052, improved functional recovery and changed the composition of the post-stroke gut microbiota. Interestingly, oral Cerebiome® administration did not result in alterations of lesion volume or the number of CD8+/Iba1+ cells in the injured tissue. Overall, these findings suggest that probiotic treatment following injury can improve sensorimotor function.

Outcome of liver cancer patients with SARS-CoV-2 infection: An International, Multicentre, Cohort Study.

BACKGROUND & AIMS: Information about the impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in patients with liver cancer is lacking. This study characterizes the outcomes and mortality risk in this population. METHODS: Multicentre retrospective, cross-sectional, international study of liver cancer patients with SARS-CoV-2 infection registered between February and December 2020. Clinical data at SARS-CoV-2 diagnosis and outcomes were registered. RESULTS: Two hundred fifty patients from 38 centres were included, 218 with hepatocellular carcinoma (HCC) and 32 with intrahepatic cholangiocarcinoma (iCCA). The median age was 66.5 and 64.5 years, and 84.9% and 21.9% had cirrhosis in the HCC and iCCA cohorts respectively. Patients had advanced cancer stage at SARS-CoV-2 diagnosis in 39.0% of the HCC and 71.9% of the iCCA patients. After a median follow-up of 7.20 (IQR: 1.84-11.24) months, 100 (40%) patients have died, 48% of the deaths were SARS-CoV-2-related. Forty (18.4%) HCC patients died within 30-days. The death rate increase was significantly different according to the BCLC stage (6.10% [95% CI 2.24-12.74], 11.76% [95% CI 4.73-22.30], 20.69% [95% CI 11.35-31.96] and 34.52% [95% CI 17.03-52.78] for BCLC 0/A, B, C and D, respectively; p = .0017). The hazard ratio was 1.45 (95% CI 0.49-4.31; p = .5032) in BCLC-B versus 0/A, and 3.13 (95% CI 1.29-7.62; p = .0118) in BCLC-C versus 0/A in the competing risk Cox regression model. Nineteen out of 32 iCCA (59.4%) died, and 12 deaths were related to SARS-CoV-2 infection. CONCLUSIONS: This is the largest cohort of liver cancer patients infected with SARS-CoV-2. It characterizes the 30-day mortality risk of SARS-CoV-2 infected patients with HCC during this period.

Geometric Uncertainty in Patient-Specific Cardiovascular Modeling with Convolutional Dropout Networks.

We propose a novel approach to generate samples from the conditional distribution of patient-specific cardiovascular models given a clinically aquired image volume. A convolutional neural network architecture with dropout layers is first trained for vessel lumen segmentation using a regression approach, to enable Bayesian estimation of vessel lumen surfaces. This network is then integrated into a path-planning patient-specific modeling pipeline to generate families of cardiovascular models. We demonstrate our approach by quantifying the effect of geometric uncertainty on the hemodynamics for three patient-specific anatomies, an aorto-iliac bifurcation, an abdominal aortic aneurysm and a sub-model of the left coronary arteries. A key innovation introduced in the proposed approach is the ability to learn geometric uncertainty directly from training data. The results show how geometric uncertainty produces coefficients of variation comparable to or larger than other sources of uncertainty for wall shear stress and velocity magnitude, but has limited impact on pressure. Specifically, this is true for anatomies characterized by small vessel sizes, and for local vessel lesions seen infrequently during network training.

Predictive Modeling of Secondary Pulmonary Hypertension in Left Ventricular Diastolic Dysfunction.

Diastolic dysfunction is a common pathology occurring in about one third of patients affected by heart failure. This condition may not be associated with a marked decrease in cardiac output or systemic pressure and therefore is more difficult to diagnose than its systolic counterpart. Compromised relaxation or increased stiffness of the left ventricle induces an increase in the upstream pulmonary pressures, and is classified as secondary or group II pulmonary hypertension (2018 Nice classification). This may result in an increase in the right ventricular afterload leading to right ventricular failure. Elevated pulmonary pressures are therefore an important clinical indicator of diastolic heart failure (sometimes referred to as heart failure with preserved ejection fraction, HFpEF), showing significant correlation with associated mortality. However, accurate measurements of this quantity are typically obtained through invasive catheterization and after the onset of symptoms. In this study, we use the hemodynamic consistency of a differential-algebraic circulation model to predict pulmonary pressures in adult patients from other, possibly non-invasive, clinical data. We investigate several aspects of the problem, including the ability of model outputs to represent a sufficiently wide pathologic spectrum, the identifiability of the model's parameters, and the accuracy of the predicted pulmonary pressures. We also find that a classifier using the assimilated model parameters as features is free from the problem of missing data and is able to detect pulmonary hypertension with sufficiently high accuracy. For a cohort of 82 patients suffering from various degrees of heart failure severity, we show that systolic, diastolic, and wedge pulmonary pressures can be estimated on average within 8, 6, and 6 mmHg, respectively. We also show that, in general, increased data availability leads to improved predictions.

Boosting Antitumor Response by Costimulatory Strategies Driven to 4-1BB and OX40 T-cell Receptors.

Immunotherapy explores several strategies to enhance the host immune system's ability to detect and eliminate cancer cells. The use of antibodies that block immunological checkpoints, such as anti-programed death 1/programed death 1 ligand and cytotoxic T-lymphocyte-associated protein 4, is widely recognized to generate a long-lasting antitumor immune response in several types of cancer. Evidence indicates that the elimination of tumors by T cells is the key for tumor control. It is well known that costimulatory and coinhibitory pathways are critical regulators in the activation of T cells. Besides blocking checkpoints inhibitors, the agonistic signaling on costimulatory molecules also plays an important role in T-cell activation and antitumor response. Therefore, molecules driven to costimulatory pathways constitute promising targets in cancer therapy. The costimulation of tumor necrosis factor superfamily receptors on lymphocytes surface may transduce signals that control the survival, proliferation, differentiation, and effector functions of these immune cells. Among the members of the tumor necrosis factor receptor superfamily, there are 4-1BB and OX40. Several clinical studies have been carried out targeting these molecules, with agonist monoclonal antibodies, and preclinical studies exploring their ligands and other experimental approaches. In this review, we discuss functional aspects of 4-1BB and OX40 costimulation, as well as the progress of its application in immunotherapies.

NDP-MSH treatment recovers marginal lungs during ex vivo lung perfusion (EVLP).

The increasing use of marginal lungs for transplantation encourages novel approaches to improve graft quality. Melanocortins and their receptors (MCRs) exert multiple beneficial effects in pulmonary inflammation. We tested the idea that treatment with the synthetic α-melanocyte-stimulating hormone analogue [Nle4,D-Phe7]-α-MSH (NDP-MSH) during ex vivo lung perfusion (EVLP) could exert positive influences in lungs exposed to different injuries. Rats were assigned to one of the following protocols (N = 10 each): 1) ischemia/reperfusion (IR) or 2) cardiac death (CD) followed by ex vivo perfusion. NDP-MSH treatment was performed in five rats of each protocol before lung procurement and during EVLP. Pulmonary function and perfusate concentration of gases, electrolytes, metabolites, nitric-oxide, mediators, and cells were assessed throughout EVLP. ATP content and specific MCR expression were investigated in perfused lungs and in biopsies collected from rats in resting conditions (Native, N = 5). NDP-MSH reduced the release of inflammatory mediators in perfusates of both the IR and the CD groups. Treatment was likewise associated with a lesser amount of leukocytes (IR: p = 0.034; CD: p = 0.002) and reduced lactate production (IR: p = 0.010; CD: p = 0.008). In lungs exposed to IR injury, the NDP-MSH group showed increased ATP content (p = 0.040) compared to controls. In CD lungs, a significant improvement of vascular (p = 0.002) and airway (Ppeak: p < 0.001, compliance: p < 0.050, pO2: p < 0.001) parameters was observed. Finally, the expression of MC1R and MC5R was detected in both native and ex vivo-perfused lungs. The results indicate that NDP-MSH administration preserves lung function through broad positive effects on multiple pathways and suggest that exploitation of the melanocortin system during EVLP could improve reconditioning of marginal lungs before transplantation.

Development of recombinant human granulocyte colony-stimulating factor (nartograstim) production process in Escherichia coli compatible with industrial scale and with no antibiotics in the culture medium.

The granulocyte colony-stimulating factor (G-CSF) is a hematopoietic cytokine that has important clinical applications for treating neutropenia. Nartograstim is a recombinant variant of human G-CSF. Nartograstim has been produced in Escherichia coli as inclusion bodies (IB) and presents higher stability and biological activity than the wild type of human G-CSF because of its mutations. We developed a production process of nartograstim in a 10-L bioreactor using auto-induction or chemically defined medium. After cell lysis, centrifugation, IB washing, and IB solubilization, the following three refolding methods were evaluated: diafiltration, dialysis, and direct dilution in two refolding buffers. Western blot and SDS-PAGE confirmed the identity of 18.8-kDa bands as nartograstim in both cultures. The auto-induction medium produced 1.17 g/L and chemically defined medium produced 0.95 g/L. The dilution method yielded the highest percentage of refolding (99%). After refolding, many contaminant proteins precipitated during pH adjustment to 5.2, increasing purity from 50 to 78%. After applying the supernatant to cation exchange chromatography (CEC), nartograstim recovery was low and the purity was 87%. However, when the refolding solution was applied to anion exchange chromatography followed by CEC, 91%-98% purity and 2.2% recovery were obtained. The purification process described in this work can be used to obtain nartograstim with high purity, structural integrity, and the expected biological activity. KEY POINTS: • Few papers report the final recovery of the purification process from inclusion bodies. • The process developed led to high purity and reasonable recovery compared to literature. • Nartograstim biological activity was demonstrated in mice using a neutropenia model.

Preclinical models of idiosyncratic drug-induced liver injury (iDILI): Moving towards prediction

Idiosyncratic drug-induced liver injury (iDILI) encompasses the unexpected harms that prescription and non-prescription drugs, herbal and dietary supplements can cause to the liver. iDILI remains a major public health problem and a major cause of drug attrition. Given the lack of biomarkers for iDILI prediction, diagnosis and prognosis, searching new models to predict and study mechanisms of iDILI is necessary. One of the major limitations of iDILI preclinical assessment has been the lack of correlation between the markers of hepatotoxicity in animal toxicological studies and clinically significant iDILI. Thus, major advances in the understanding of iDILI susceptibility and pathogenesis have come from the study of well-phenotyped iDILI patients. However, there are many gaps for explaining all the complexity of iDILI susceptibility and mechanisms. Therefore, there is a need to optimize preclinical human

The effects of clinically-derived parametric data uncertainty in patient-specific coronary simulations with deformable walls.

Cardiovascular simulations are increasingly used for noninvasive diagnosis of cardiovascular disease, to guide treatment decisions, and in the design of medical devices. Quantitative assessment of the variability of simulation outputs due to input uncertainty is a key step toward further integration of cardiovascular simulations in the clinical workflow. In this study, we present uncertainty quantification in computational models of the coronary circulation to investigate the effect of uncertain parameters, including coronary pressure waveform, intramyocardial pressure, morphometry exponent, and the vascular wall Young's modulus. We employ a left coronary artery model with deformable vessel walls, simulated via an Arbitrary-Lagrangian-Eulerian framework for fluid-structure interaction, with a prescribed inlet pressure and open-loop lumped parameter network outlet boundary conditions. Stochastic modeling of the uncertain inputs is determined from intra-coronary catheterization data or gathered from the literature. Uncertainty propagation is performed using several approaches including Monte Carlo, Quasi Monte Carlo sampling, stochastic collocation, and multi-wavelet stochastic expansion. Variabilities in the quantities of interest, including branch pressure, flow, wall shear stress, and wall deformation are assessed. We find that uncertainty in inlet pressures and intramyocardial pressures significantly affect all resulting QoIs, while uncertainty in elastic modulus only affects the mechanical response of the vascular wall. Variability in the morphometry exponent used to distribute the total downstream vascular resistance to the single outlets, has little effect on coronary hemodynamics or wall mechanics. Finally, we compare convergence behaviors of statistics of QoIs using several uncertainty propagation methods on three model benchmark problems and the left coronary simulations. From the simulation results, we conclude that the multi-wavelet stochastic expansion shows superior accuracy and performance against Quasi Monte Carlo and stochastic collocation methods.

Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics.

Standard approaches for uncertainty quantification in cardiovascular modeling pose challenges due to the large number of uncertain inputs and the significant computational cost of realistic three-dimensional simulations. We propose an efficient uncertainty quantification framework utilizing a multilevel multifidelity Monte Carlo (MLMF) estimator to improve the accuracy of hemodynamic quantities of interest while maintaining reasonable computational cost. This is achieved by leveraging three cardiovascular model fidelities, each with varying spatial resolution to rigorously quantify the variability in hemodynamic outputs. We employ two low-fidelity models (zero- and one-dimensional) to construct several different estimators. Our goal is to investigate and compare the efficiency of estimators built from combinations of these two low-fidelity model alternatives and our high-fidelity three-dimensional models. We demonstrate this framework on healthy and diseased models of aortic and coronary anatomy, including uncertainties in material property and boundary condition parameters. Our goal is to demonstrate that for this application it is possible to accelerate the convergence of the estimators by utilizing a MLMF paradigm. Therefore, we compare our approach to single fidelity Monte Carlo estimators and to a multilevel Monte Carlo approach based only on three-dimensional simulations, but leveraging multiple spatial resolutions. We demonstrate significant, on the order of 10 to 100 times, reduction in total computational cost with the MLMF estimators. We also examine the differing properties of the MLMF estimators in healthy versus diseased models, as well as global versus local quantities of interest. As expected, global quantities such as outlet pressure and flow show larger reductions than local quantities, such as those relating to wall shear stress, as the latter rely more heavily on the highest fidelity model evaluations. Similarly, healthy models show larger reductions than diseased models. In all cases, our workflow coupling Dakota's MLMF estimators with the SimVascular cardiovascular modeling framework makes uncertainty quantification feasible for constrained computational budgets.

A computational bio-chemo-mechanical model of in vivo tissue-engineered vascular graft development.

Stenosis is the primary complication of current tissue-engineered vascular grafts used in pediatric congenital cardiac surgery. Murine models provide considerable insight into the possible mechanisms underlying this situation, but they are not efficient for identifying optimal changes in scaffold design or therapeutic strategies to prevent narrowing. In contrast, computational modeling promises to enable time- and cost-efficient examinations of factors leading to narrowing. Whereas past models have been limited by their phenomenological basis, we present a new mechanistic model that integrates molecular- and cellular-driven immuno- and mechano-mediated contributions to in vivo neotissue development within implanted polymeric scaffolds. Model parameters are inferred directly from in vivo measurements for an inferior vena cava interposition graft model in the mouse that are augmented by data from the literature. By complementing Bayesian estimation with identifiability analysis and simplex optimization, we found optimal parameter values that match model outputs with experimental targets and quantify variability due to measurement uncertainty. Utility is illustrated by parametrically exploring possible graft narrowing as a function of scaffold pore size, macrophage activity, and the immunomodulatory cytokine transforming growth factor beta 1 (TGF-ß1). The model captures salient temporal profiles of infiltrating immune and synthetic cells and associated secretion of cytokines, proteases, and matrix constituents throughout neovessel evolution, and parametric studies suggest that modulating scaffold immunogenicity with early immunomodulatory therapies may reduce graft narrowing without compromising compliance.

Performance of preconditioned iterative linear solvers for cardiovascular simulations in rigid and deformable vessels.

Computing the solution of linear systems of equations is invariably the most time consuming task in the numerical solutions of PDEs in many fields of computational science. In this study, we focus on the numerical simulation of cardiovascular hemodynamics with rigid and deformable walls, discretized in space and time through the variational multiscale finite element method. We focus on three approaches: the problem agnostic generalized minimum residual (GMRES) and stabilized bi-conjugate gradient (BICGS) methods, and a recently proposed, problem specific, bi-partitioned (BIPN) method. We also perform a comparative analysis of several preconditioners, including diagonal, block-diagonal, incomplete factorization, multigrid, and resistance based methods. Solver performance and matrix characteristics (diagonal dominance, symmetry, sparsity, bandwidth and spectral properties) are first examined for an idealized cylindrical geometry with physiologic boundary conditions and then successively tested on several patient-specific anatomies representative of realistic cardiovascular simulation problems. Incomplete factorization preconditioners provide the best performance and results in terms of both strong and weak scalability. The BIPN method was found to outperform other methods in patient-specific models with rigid walls. In models with deformable walls, BIPN was outperformed by BICG with diagonal and Incomplete LU preconditioners.

Uncertainty quantification of simulated biomechanical stimuli in coronary artery bypass grafts.

Coronary artery bypass graft surgery (CABG) is performed on more than 400,000 patients annually in the U.S. However, saphenous vein grafts (SVGs) implanted during CABG exhibit poor patency compared to arterial grafts, with failure rates up to 40% within 10 years after surgery. Differences in mechanical stimuli are known to play a role in driving maladaptation and have been correlated with endothelial damage and thrombus formation. As these quantities are difficult to measure in vivo, multi-scale coronary models offer a way to quantify them, while accounting for complex coronary physiology. However, prior studies have primarily focused on deterministic evaluations, without reporting variability in the model parameters due to uncertainty. This study aims to assess confidence in multi-scale predictions of wall shear stress and wall strain while accounting for uncertainty in peripheral hemodynamics and material properties. Boundary condition distributions are computed by assimilating uncertain clinical data, while spatial variations of vessel wall stiffness are obtained through approximation by a random field. We developed a stochastic submodeling approach to mitigate the computational burden of repeated multi-scale model evaluations to focus exclusively on the bypass grafts. This produces a two-level decomposition of quantities of interest into submodel contributions and full model/submodel discrepancies. We leverage these two levels in the context of forward uncertainty propagation using a previously proposed multi-resolution approach. The time- and space-averaged wall shear stress is well estimated with a coefficient of variation of <35%, but ignorance about the spatial distribution on the wall elastic modulus and thickness lead to large variations in an objective measure of wall strain, with coefficients of variation up to 100%. Sensitivity analysis reveals how the interactions between the flow and material parameters contribute to output variability.

Light-actuated contactless macro motors exploiting Bénard-Marangoni convection.

Near-infrared light is commonly used to move small objects floating on water by exploiting the Bénard-Marangoni convection. This is because infrared light is absorbed well by water and the induced thermal gradients are responsible for the objects' motion. However, visible light was recently used to move macroscopic objects on the free liquid surfaces. In this work, we show the use of visible light to rotate symmetric millimeter-sized objects. Those objects represent light-driven macro motors that are able to work in a continuous or step-by-step mode. We studied light intensity's effects on our system's angular velocity and estimated the entire process's conversion efficiency.

Bayesian inference of constitutive model parameters from uncertain uniaxial experiments on murine tendons.

Constitutive models for biological tissue are typically formulated as a mixture of constituents and the overall response is then assembled by superposition or compatibility. This ensures the stress response of the biological tissue to be in the range of a given constitutive relationship, guaranteeing that at least one parameter combination exists so that an experimental response can be sufficiently well captured. Another, perhaps more challenging, problem is to use constitutive models as a proxy to infer the structure/function of a biological tissue from experiments. In other words, we determine the optimal set of parameters by solving an inverse problem and use these parameters to infer the integrity of the tissue constituents. In previous studies, we focused on the mechanical stress-stretch response of the murine patellar tendon at various age and healing timepoints and solved the inverse problem using three constitutive models, i.e., the Freed-Rajagopal, Gasser-Ogden-Holzapfel and Shearer in order of increasing microstructural detail. Herein, we extend this work by adopting a Bayesian perspective on parameter estimation and implement the constitutive relations in the tulip library for uncertainty analysis, critically analyzing parameter marginals, correlations, identifiability and sensitivity. Our results show the importance of investigating the variability of parameter estimates and that results from optimization may be misleading, particularly for models with many parameters inferred from limited experimental evidence. In our study, we show that different age and healing conditions do not correspond to statistically significant separation among the Gasser-Ogden-Holzapfel and Shearer model parameters, while the phenomenological Freed-Rajagopal model is instead characterized by better indentifiability and parameter learning. Use of the complete experimental observations rather than averaged stress-stretch responses appears to positively constrain inference and results appear to be invariant with respect to the scaling of the experimental uncertainty.

Experience with Sorafenib in 3 Hospitals in Sao Paulo.

INTRODUCTION AND AIM: Sorafenib has been the standard of care for first-line treatment of advanced hepatocellular carcinoma, a complex disease that affects an extremely heterogenous population. Thereby requiring multidisciplinary individualized treatment strategies that match the disease characteristics and the patients' specific needs. MATERIAL AND METHODS: Data for 175 patients who received sorafenib for hepatocellular carcinoma in three different hospitals in Sao Paulo, Brazil over a span of nine years were retrospectively analyzed. RESULTS: The median age was 62 years. Percentages of patients with Child-Pugh A, B and C liver cirrhosis were 61%, 31% and 5%, respectively. Approximately half of the patients had Barcelona Clinic Liver Cancer stage B disease, and the other half had stage C. The median treatment duration was 253 days. Sorafenib dose was reduced to 400 mg/day in 41% of the patients due to toxicity. Overall objective response rate as per Response Evaluation Criteria in Solid Tumors and its modified version was 39%. Patients who received transarterial chemoembolization (TACE) at any point during sorafenib therapy were significantly more likely to experience an objective response. After a median follow-up of 339 days, the median overall survival was 380 days. Child-Pugh cirrhosis, tumor response and concomitant chemoembolization were independent prognostic factors for overall survival in multivariate analysis. CONCLUSION: Our results suggest that, in experienced hands, sorafenib therapy may benefit carefully selected hepatocellular carcinoma patients for whom other therapies are initially contraindicated, including those patients with Child-Pugh B liver function and those patients who are subsequently treated with concomitant TACE.