Learning a single-hidden layer feedforward neural network using a rank correlation-based strategy with application to high dimensional gene expression and proteomic spectra datasets in cancer detection.

Methods based on microarrays (MA), mass spectrometry (MS), and machine learning (ML) algorithms have evolved rapidly in recent years, allowing for early detection of several types of cancer. A pitfall of these approaches, however, is the overfitting of data due to large number of attributes and small number of instances -- a phenomenon known as the 'curse of dimensionality'. A potentially fruitful idea to avoid this drawback is to develop algorithms that combine fast computation with a filtering module for the attributes. The goal of this paper is to propose a statistical strategy to initiate the hidden nodes of a single-hidden layer feedforward neural network (SLFN) by using both the knowledge embedded in data and a filtering mechanism for attribute relevance. In order to attest its feasibility, the proposed model has been tested on five publicly available high-dimensional datasets: breast, lung, colon, and ovarian cancer regarding gene expression and proteomic spectra provided by cDNA arrays, DNA microarray, and MS. The novel algorithm, called adaptive SLFN (aSLFN), has been compared with four major classification algorithms: traditional ELM, radial basis function network (RBF), single-hidden layer feedforward neural network trained by backpropagation algorithm (BP-SLFN), and support vector-machine (SVM). Experimental results showed that the classification performance of aSLFN is competitive with the comparison models.

Variable selection in Logistic regression model with genetic algorithm.

Variable or feature selection is one of the most important steps in model specification. Especially in the case of medical-decision making, the direct use of a medical database, without a previous analysis and preprocessing step, is often counterproductive. In this way, the variable selection represents the method of choosing the most relevant attributes from the database in order to build a robust learning models and, thus, to improve the performance of the models used in the decision process. In biomedical research, the purpose of variable selection is to select clinically important and statistically significant variables, while excluding unrelated or noise variables. A variety of methods exist for variable selection, but none of them is without limitations. For example, the stepwise approach, which is highly used, adds the best variable in each cycle generally producing an acceptable set of variables. Nevertheless, it is limited by the fact that it commonly trapped in local optima. The best subset approach can systematically search the entire covariate pattern space, but the solution pool can be extremely large with tens to hundreds of variables, which is the case in nowadays clinical data. Genetic algorithms (GA) are heuristic optimization approaches and can be used for variable selection in multivariable regression models. This tutorial paper aims to provide a step-by-step approach to the use of GA in variable selection. The R code provided in the text can be extended and adapted to other data analysis needs.

Boosting backpropagation algorithm by stimulus-sampling: Application in computer-aided medical diagnosis.

Neural networks (NNs), in general, and multi-layer perceptron (MLP), in particular, represent one of the most efficient classifiers among the machine learning (ML) algorithms. Inspired by the stimulus-sampling paradigm, it is plausible to assume that the association of stimuli with the neurons in the output layer of a MLP can increase its performance. The stimulus-sampling process is assumed memoryless (Markovian), in the sense that the choice of a particular stimulus at a certain step, conditioned by the whole prior evolution of the learning process, depends only on the network's answer at the previous step. This paper proposes a novel learning technique, by enhancing the standard backpropagation algorithm performance with the aid of a stimulus-sampling procedure applied to the output neurons. The network uses the observable behavior that varies throughout the training process by stimulating the correct answers through corresponding rewards/penalties assigned to the output neurons. The proposed model has been applied in computer-aided medical diagnosis using five real-life breast cancer, colon cancer, diabetes, thyroid, and fetal heartbeat databases. The statistical comparison to well-established ML algorithms proved beyond doubt its efficiency and robustness.

A hybrid genetic algorithm-queuing multi-compartment model for optimizing inpatient bed occupancy and associated costs.

PURPOSE: Explore how efficient intelligent decision support systems, both easily understandable and straightforwardly implemented, can help modern hospital managers to optimize both bed occupancy and utilization costs. METHODS AND MATERIALS: This paper proposes a hybrid genetic algorithm-queuing multi-compartment model for the patient flow in hospitals. A finite capacity queuing model with phase-type service distribution is combined with a compartmental model, and an associated cost model is set up. An evolutionary-based approach is used for enhancing the ability to optimize both bed management and associated costs. In addition, a "What-if analysis" shows how changing the model parameters could improve performance while controlling costs. The study uses bed-occupancy data collected at the Department of Geriatric Medicine - St. George's Hospital, London, period 1969-1984, and January 2000. RESULTS: The hybrid model revealed that a bed-occupancy exceeding 91%, implying a patient rejection rate around 1.1%, can be carried out with 159 beds plus 8 unstaffed beds. The same holding and penalty costs, but significantly different bed allocations (156 vs. 184 staffed beds, and 8 vs. 9 unstaffed beds, respectively) will result in significantly different costs (£755 vs. £1172). Moreover, once the arrival rate exceeds 7 patient/day, the costs associated to the finite capacity system become significantly smaller than those associated to an Erlang B queuing model (£134 vs. £947). CONCLUSION: Encoding the whole information provided by both the queuing system and the cost model through chromosomes, the genetic algorithm represents an efficient tool in optimizing the bed allocation and associated costs. The methodology can be extended to different medical departments with minor modifications in structure and parameterization.

Improving hospital bed occupancy and resource utilization through queuing modeling and evolutionary computation.

Scarce healthcare resources require carefully made policies ensuring optimal bed allocation, quality healthcare service, and adequate financial support. This paper proposes a complex analysis of the resource allocation in a hospital department by integrating in the same framework a queuing system, a compartmental model, and an evolutionary-based optimization. The queuing system shapes the flow of patients through the hospital, the compartmental model offers a feasible structure of the hospital department in accordance to the queuing characteristics, and the evolutionary paradigm provides the means to optimize the bed-occupancy management and the resource utilization using a genetic algorithm approach. The paper also focuses on a "What-if analysis" providing a flexible tool to explore the effects on the outcomes of the queuing system and resource utilization through systematic changes in the input parameters. The methodology was illustrated using a simulation based on real data collected from a geriatric department of a hospital from London, UK. In addition, the paper explores the possibility of adapting the methodology to different medical departments (surgery, stroke, and mental illness). Moreover, the paper also focuses on the practical use of the model from the healthcare point of view, by presenting a simulated application.

Error-correction learning for artificial neural networks using the Bayesian paradigm. Application to automated medical diagnosis.

Automated medical diagnosis models are now ubiquitous, and research for developing new ones is constantly growing. They play an important role in medical decision-making, helping physicians to provide a fast and accurate diagnosis. Due to their adaptive learning and nonlinear mapping properties, the artificial neural networks are widely used to support the human decision capabilities, avoiding variability in practice and errors based on lack of experience. Among the most common learning approaches, one can mention either the classical back-propagation algorithm based on the partial derivatives of the error function with respect to the weights, or the Bayesian learning method based on posterior probability distribution of weights, given training data. This paper proposes a novel training technique gathering together the error-correction learning, the posterior probability distribution of weights given the error function, and the Goodman-Kruskal Gamma rank correlation to assembly them in a Bayesian learning strategy. This study had two main purposes; firstly, to develop anovel learning technique based on both the Bayesian paradigm and the error back-propagation, and secondly,to assess its effectiveness. The proposed model performance is compared with those obtained by traditional machine learning algorithms using real-life breast and lung cancer, diabetes, and heart attack medical databases. Overall, the statistical comparison results indicate that thenovellearning approach outperforms the conventional techniques in almost all respects.

Evolutionary strategy to develop learning-based decision systems. Application to breast cancer and liver fibrosis stadialization.

The purpose of this paper is twofold: first, to propose an evolutionary-based method for building a decision model and, second, to assess and validate the model's performance using five different real-world medical datasets (breast cancer and liver fibrosis) by comparing it with state-of-the-art machine learning techniques. The evolutionary-inspired approach has been used to develop the learning-based decision model in the following manner: the hybridization of algorithms has been considered as "crossover", while the development of new variants which can be thought of as "mutation". An appropriate hierarchy of the component algorithms was established based on a statistically built fitness measure. A synergetic decision-making process, based on a weighted voting system, involved the collaboration between the selected algorithms in making the final decision. Well-established statistical performance measures and comparison tests have been extensively used to design and implement the model. Finally, the proposed method has been tested on five medical datasets, out of which four publicly available, and contrasted with state-of-the-art techniques, showing its efficiency in supporting the medical decision-making process.

Multicenter randomized controlled trial comparing the performance of 22 gauge versus 25 gauge EUS-FNA needles in solid masses.

BACKGROUND AND AIMS: Few randomized studies have assessed the clinical performance of 25-gauge (25G) needles compared with 22-gauge (22G) needles during endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) biopsy of intra-abdominal lesions. We aimed to compare the diagnostic yield, as well as performance characteristics of 22G versus 25G EUS biopsy needles by determining their diagnostic capabilities, the number of needle passes as well as cellularity of aspirated tissue specimen. METHODS: The study is a prospective, randomized, multicenter study. Patients were referred between January 2009 and January 2010 for diagnostic EUS including EUS-guided FNA of different lesions adjacent to the upper GI tract. All patients were randomized to EUS-FNA performed with either a 22G or 25G aspiration needle. RESULTS: EUS-FNA was performed in 135 patients (62 patients with a 22G needle). Sensitivity and specificity of the 22G needle was 94.1% and 95.8%, respectively, and for the 25G needle 94.1% and 100%, respectively. Investigators reported better visualization and performance for the 22G needle compared to the 25G (p < 0.0001). The number of tissue slides obtained was higher for the 22G needle during the second and third needle passes (p < 0.05). We did not observe significant differences between the number and preservation status of obtained cells (p > 0.05). CONCLUSIONS: A significant difference was found between the two types of needles in terms of reduced visualization of the 25G needle and suboptimal performance rating. However, this did not impact on overall results since both needles were equally successful in terms of a high diagnostic yield and overall accuracy.

Efficacy of an artificial neural network-based approach to endoscopic ultrasound elastography in diagnosis of focal pancreatic masses.

BACKGROUND & AIMS: By using strain assessment, real-time endoscopic ultrasound (EUS) elastography provides additional information about a lesion's characteristics in the pancreas. We assessed the accuracy of real-time EUS elastography in focal pancreatic lesions using computer-aided diagnosis by artificial neural network analysis. METHODS: We performed a prospective, blinded, multicentric study at of 258 patients (774 recordings from EUS elastography) who were diagnosed with chronic pancreatitis (n = 47) or pancreatic adenocarcinoma (n = 211) from 13 tertiary academic medical centers in Europe (the European EUS Elastography Multicentric Study Group). We used postprocessing software analysis to compute individual frames of elastography movies recorded by retrieving hue histogram data from a dynamic sequence of EUS elastography into a numeric matrix. The data then were analyzed in an extended neural network analysis, to automatically differentiate benign from malignant patterns. RESULTS: The neural computing approach had 91.14% training accuracy (95% confidence interval [CI], 89.87%-92.42%) and 84.27% testing accuracy (95% CI, 83.09%-85.44%). These results were obtained using the 10-fold cross-validation technique. The statistical analysis of the classification process showed a sensitivity of 87.59%, a specificity of 82.94%, a positive predictive value of 96.25%, and a negative predictive value of 57.22%. Moreover, the corresponding area under the receiver operating characteristic curve was 0.94 (95% CI, 0.91%-0.97%), which was significantly higher than the values obtained by simple mean hue histogram analysis, for which the area under the receiver operating characteristic was 0.85. CONCLUSIONS: Use of the artificial intelligence methodology via artificial neural networks supports the medical decision process, providing fast and accurate diagnoses.

Combined contrast-enhanced power Doppler and real-time sonoelastography performed during EUS, used in the differential diagnosis of focal pancreatic masses (with videos).

BACKGROUND: Contrast-enhanced power Doppler (CEPD) and real-time sonoelastography (RTSE) performed during EUS were previously described to be useful for the differential diagnosis between chronic pseudotumoral pancreatitis and pancreatic cancer. OBJECTIVE: To prospectively assess the accuracy of the combination of CEPD and RTSE to differentiate pancreatic focal masses. DESIGN: Cross-sectional feasibility study. SETTING: A tertiary-care academic referral center. PATIENTS: The study group included 54 patients with chronic pancreatitis (n = 21) and pancreatic adenocarcinoma (n = 33). INTERVENTIONS: Both imaging methods (CEPD and RTSE) were performed sequentially during the same EUS examination. Power Doppler mode examination was performed after intravenous injection of a second-generation contrast agent (2.4 mL of SonoVue), and the data were digitally recorded, comprising both the early arterial phase and venous/late phase. Three 10-second sonoelastographic videos were also digitally recorded that included the focal mass and the surrounding pancreatic parenchyma. Postprocessing analyses based on specially designed software were used to analyze the CEPD and RTSE videos. A power Doppler vascularity index was used to characterize CEPD videos, the values being averaged during a 10-second video in the venous phase. Hue histogram analysis was used to characterize RTSE videos, with the mean hue histogram values being also averaged during a 10-second video. MAIN OUTCOME MEASUREMENTS: To differentiate chronic pancreatitis and pancreatic cancer. RESULTS: The sensitivity, specificity, and accuracy of combined information provided by CEPD and RTSE to differentiate hypovascular hard masses suggestive of pancreatic carcinoma were 75.8%, 95.2%, and 83.3%, respectively, with a positive predictive value and negative predictive value of 96.2% and 71.4%, respectively. LIMITATION: A single-center, average size of study population. CONCLUSIONS: A combination of CEPD and RTSE performed during EUS seems to be a promising method that allows characterization and differentiation of focal pancreatic masses.

Real-time sono-elastography in the diagnosis of diffuse liver diseases.

AIM: To analyze whether computer-enhanced dynamic analysis of elastography movies is able to better characterize and differentiate between different degrees of liver fibrosis. METHODS: The study design was prospective. A total of 132 consecutive patients with chronic liver diseases and healthy volunteers were examined by transabdominal ultrasound elastography. All examinations were done by two doctors. RESULTS: Due to the limitations of the method, we obtained high-quality elastography information in only 73.48% of the patients. The kappa-means clustering method was applied to assess the inter-observer diagnosis variability, which showed good variability values in accordance with the experience of ultrasound examination of every observer. Cohen's kappa test indicated a moderate agreement between the study observers (kappa = 0.4728). Furthermore, we compared the way the two observers clustered the patients, using the test for comparing two proportions (t value, two-sided test). There was no statistically significant difference between the two physicians, regardless of the patients' real status. CONCLUSION: Transabdominal real-time elastography is certainly a very useful method in depicting liver hardness, although it is incompletely tested in large multicenter studies.

Randomized controlled trial of endoscopic ultrasound-guided fine-needle sampling with or without suction for better cytological diagnosis.

OBJECTIVE: Endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) is a highly accurate method to obtain specific diagnosis in various diseases. The optimal method of EUS-guided sampling of material for pathologic diagnosis has not been clearly established. The aim of our study was to compare two different techniques of EUS-guided sampling of solid masses, using either non-suction or suction with a 10-ml syringe. MATERIAL AND METHODS: Patients assessed during a 6-month period were randomized to three passes of EUS-guided sampling with suction (26 patients) or non-suction (26 patients). The samples were characterized for cellularity and bloodiness, with a final cytology diagnosis established blindly. The final diagnosis was reached either by EUS-FNA if malignancy was definite, or by surgery and/or clinical follow-up of a minimum of 6 months in the cases of non-specific benign lesions. RESULTS: EUS-guided fine-needle sampling with suction of solid masses increased the number of pathology slides (17.8+/-7.1 slides for suction as compared with 10.2+/-5.5 for non-suction, p=0.0001), without increasing the overall bloodiness of each sample. Sensitivity and the negative predictive values were higher when suction was applied, as compared to the non-suction group (85.7% as compared with 66.7%, p=0.05). CONCLUSIONS: This prospective randomized study showed that EUS-guided fine-needle sampling of solid masses using suction yields a higher number of slides without increasing bloodiness. Although, the proportion of target cells was relatively similar between the suction and non-suction sampling techniques, the sensitivity and negative predictive values of the procedure were significantly higher when suction was added.

Neural network analysis of dynamic sequences of EUS elastography used for the differential diagnosis of chronic pancreatitis and pancreatic cancer.

BACKGROUND: EUS elastography is a newly developed imaging procedure that characterizes the differences of hardness and strain between diseased and normal tissue. OBJECTIVE: To assess the accuracy of real-time EUS elastography in pancreatic lesions. DESIGN: Cross-sectional feasibility study. PATIENTS: The study group included, in total, 68 patients with normal pancreas (N = 22), chronic pancreatitis (N = 11), pancreatic adenocarcinoma (N = 32), and pancreatic neuroendocrine tumors (N = 3). A subgroup analysis of 43 cases with focal pancreatic masses was also performed. INTERVENTIONS: A postprocessing software analysis was used to examine the EUS elastography movies by calculating hue histograms of each individual image, data that were further subjected to an extended neural network analysis to differentiate benign from malignant patterns. MAIN OUTCOME MEASUREMENTS: To differentiate normal pancreas, chronic pancreatitis, pancreatic cancer, and neuroendocrine tumors. RESULTS: Based on a cutoff of 175 for the mean hue histogram values recorded on the region of interest, the sensitivity, specificity, and accuracy of differentiation of benign and malignant masses were 91.4%, 87.9%, and 89.7%, respectively. The positive and negative predictive values were 88.9% and 90.6%, respectively. Multilayer perceptron neural networks with both one and two hidden layers of neurons (3-layer perceptron and 4-layer perceptron) were trained to learn how to classify cases as benign or malignant, and yielded an excellent testing performance of 95% on average, together with a high training performance that equaled 97% on average. LIMITATION: A lack of the surgical standard in all cases. CONCLUSIONS: EUS elastography is a promising method that allows characterization and differentiation of normal pancreas, chronic pancreatitis, and pancreatic cancer. The currently developed methodology, based on artificial neural network processing of EUS elastography digitalized movies, enabled an optimal prediction of the types of pancreatic lesions. Future multicentric, randomized studies with adequate power will have to establish the clinical impact of this procedure for the differential diagnosis of focal pancreatic masses.

Dynamic analysis of EUS used for the differentiation of benign and malignant lymph nodes.

BACKGROUND: EUS elastography was reported to offer supplemental information that allows a better characterization of tissue, and that might enhance conventional EUS imaging. OBJECTIVE: Our purpose was to apply real-time elastography during EUS examinations and to assess the accuracy of the differentiation of benign versus malignant lymph nodes. DESIGN: Prospective cross-sectional feasibility study. SETTING: Department of Surgical Gastroenterology, Gentofte University Hospital, Hellerup, Denmark. PATIENTS: Patients diagnosed by EUS with cervical, mediastinal, or abdominal lymph nodes were included, with a total number of 78 lymph nodes examined. The final diagnosis of the type of lymph node was obtained by EUS-FNA cytologic analysis or by surgical pathologic examination and by a minimum 6 months of follow-up. INTERVENTIONS: Hue histogram analysis of the average images computed from EUS elastography movies was used to assess the color information inside the region of interest and to consequently differentiate benign and malignant lymph nodes. MAIN OUTCOME MEASUREMENTS: Differentiate between malignant and benign lymph nodes. RESULTS: By using mean hue histogram values, the sensitivity, specificity, and accuracy for the differential diagnosis were 85.4%, 91.9%, and 88.5%, respectively, on the basis of a cutoff level of 166 (middle of green-blue rainbow scale). The proposed method might be useful to avoid color perception errors, moving artifacts, or possible selection bias induced by analysis of still images. LIMITATIONS: Lack of the surgical standard in all cases. CONCLUSIONS: Computer-enhanced dynamic analysis based on hue histograms of the EUS elastography movies represents a promising method that allows the differential diagnosis of benign and malignant lymph nodes, offering complementary information added to conventional EUS imaging.

Power Doppler endoscopic ultrasonography for the differential diagnosis between pancreatic cancer and pseudotumoral chronic pancreatitis.

OBJECTIVE: The accuracy of endoscopic ultrasonography (EUS) and EUS-guided fine-needle aspiration for the differential diagnosis of pancreatic masses is variable in the literature, being as low as 75% in some studies. The aim of the study was to assess the accuracy of power Doppler EUS for the differential diagnosis between pancreatic cancer and pseudotumoral chronic pancreatitis. METHODS: We included 42 consecutive patients with pancreatic tumor masses (27 men and 15 women) examined by EUS between January 2002 and August 2004. Endoscopic ultrasonographic procedures included power Doppler EUS as well as EUS-guided fine-needle aspiration in all patients. Final diagnosis of pancreatic cancer was confirmed in 29 patients on the basis of a combination of information provided by imaging tests, follow-up of at least 6 months, and laparotomy in 18 patients for diagnostic or palliative reasons. RESULTS: Sensitivity and specificity of the absence of power Doppler signals inside the suggestive pancreatic mass were 93% and 77%, respectively, with accuracy of 88%. Moreover, the addition of the information provided by the presence of peripancreatic collaterals improved the sensitivity and specificity to 97% and 92%, with accuracy of 95%. CONCLUSIONS: Power Doppler EUS provides useful information for the differential diagnosis of pancreatic masses. The results were in concordance with previous studies that showed a hypovascular pattern of pancreatic carcinoma, as well as the formation of collaterals in advanced cases due to the invasion of the splenic or portal veins. Further studies of dynamic EUS with contrast agents are necessary to better characterize pancreatic masses.

Stochastic modeling of the tumor volume assessment and growth patterns in hepatocellular carcinoma.

The growth pattern of hepatocellular carcinoma (HCC) arising from cirrhosis is variable and depends on the degree of differentiation and vascularization. Because growth is not constant in the natural history of HCC, prediction of subsequent growth rate based on tumor volume doubling time and correlation with histological and ultrasonographical characteristics at the moment of initial diagnosis are usually unreliable. The aim of our study was to assess the growth patterns of HCC with the aid of stochastic modeling. Thus, we included in our study 27 patients with histologically proven HCC, which had multiple (more than three)follow-up ultrasound studies in a six months interval. The patients did not receive any treatment during the observation period. HCC was visualized by computer aided ultrasound imaging, obtaining both the primary size quantification and the edge-detection enhancement. By a bi-cubic B-spline interpolation of points on the edges (3-D Bezier approximation) we approximated the surfaces shapes, and using the hit or miss Monte Carlo method we accurately estimate the tumor volume. Starting from the previous tumor volumes time series recorded during the first six months of evolution we applied both a linear, exponential and logarithmic smoothing to forecast the future size of the HCC tumor in the next six months. Our conclusion was that a dynamic forecasting model of HCC volumes could be very accurate for the assessment of tumor volume doubling time usually obtained by two discrete volume measurements of the tumor.

Immunohistochemical assessment of angiogenesis in primary hepatocellular carcinoma.

BACKGROUND: Primary hepatocellular carcinoma (HCC) is characterized by the presence of angiogenesis, which is necessary for tumor growth, progression and distant metastasis. Vascular endothelial growth factor (VEGF) is clearly expressed in HCC, in variable degrees as a function of differentiation and vascularisation. AIM: To assess angiogenesis in HCC, by means of immunohistochemical analysis of the expressions of VEGF. METHOD: Immunohistochemical techniques were performed on the samples obtained by ultrasound-guided liver biopsies or intraoperative biopsies, in 32 patients with HCC. RESULTS: Positive expression of VEGF was always observed in the extracellular matrix of portal tracts of the non-neoplastic or tumor areas (extra- and intranodular areas of HCC patients). VEGF was not expressed inside the hepatocytes of extranodular non-neoplastic areas of the patients with HCC. However, we did find positive reactions for VEGF inside the tumor hepatocytes in 34.38 % of the HCC patients. The difference between VEGF expression for the patients with poor and undifferentiated HCC as compared with moderate and well-differentiated HCC was statistically significant (P < 0.05). CONCLUSION: Our study demonstrated an increased VEGF expression in tumor hepatocytes, which progressed with the dedifferentiation of HCC. VEGF expression was always present in the extracellular matrix, supporting the hypothesis of paracrine activation of VEGF at the level of tumor stroma. Consequently, increased VEGF expression might be responsible for the activation of angiogenesis in HCC.

Using a queueing model to help plan bed allocation in a department of geriatric medicine.

By integrating queuing theory and compartmental models of flow we demonstrate how changing admission rates, length of stay and bed allocation influence bed occupancy, emptiness and rejection in departments of geriatric medicine. By extending the model to include waiting beds, we show how the provision of extra, emergency use, unstaffed, back up beds could improve performance while controlling costs. The model is applicable to all lengths of stay, admission rates and bed allocations. The results show why 10-15% bed emptiness is necessary to maintain service efficiency and demonstrate how unstaffed beds can serve to provide a more responsive and cost effective service. Further work is needed to test the validity and applicability of the model.

Hepatic arterial blood flow in large hepatocellular carcinoma with or without portal vein thrombosis: assessment by transcutaneous duplex Doppler sonography.

BACKGROUND: As liver cirrhosis progresses, the portal venous blood (PVBF) flow decreases, accompanied by an increase in hepatic arterial blood flow. Large hepatocellular carcinoma is a hypervascular tumour with a rapid growth, which seems to require an increase of the tumoral arterial blood flow. Furthermore, hepatocellular carcinoma is frequently associated with portal vein thrombosis, which subsequently impedes portal blood supply. METHODS: The purpose of our study was to estimate alterations in the hepatic arterial blood flow in large hepatocellular carcinomas occurring in liver cirrhosis, in comparison with liver cirrhosis and controls. Liver blood flow measurements were determined by duplex Doppler sonography in 47 patients with large hepatocellular carcinomas (13 with portal vein thrombosis and 34 without this thrombosis), 42 liver cirrhosis patients and 30 controls. The Doppler perfusion index was calculated as the ratio of hepatic arterial blood flow to total hepatic blood flow. RESULTS: The patients with liver cirrhosis had a significant increase of hepatic arterial blood flow as compared to controls (P < 0.001), accompanied by a significant reduction in PVBF (P < 0.005). As a result, the Doppler perfusion index was increased in patients with liver cirrhosis as compared to controls (P < 0.001). The hepatic arterial blood flow was increased in patients with hepatocellular carcinoma but without portal vein thrombosis as compared to the cirrhotic patients (P < 0.001), with a significant reduction of PVBF (P < 0.001). Hepatic arterial blood flow was also increased in patients with both hepatocellular carcinoma and portal vein thrombosis as compared to the patients without this thrombosis (P < 0.001). CONCLUSION: These results suggest that in large hepatocellular carcinomas there is a decreased PVBF, accompanied by an increased hepatic arterial blood flow. The hepatic arterial buffer response seems to be active in hepatocellular carcinomas and maintains liver perfusion to adequate levels.