Physical Exercise Transforms the Topography and Increases the Invulnerability of the Symptom Network of Depression-Anxiety in the Elderly.

BACKGROUND AND OBJECTIVES: Depression and anxiety often co-occur and have worse impacts on the elderly when experienced simultaneously. Although physical exercise may alleviate depression and anxiety, how it affects the specific symptoms is not fully understood. METHODS: A total of 8884 participants was selected from the 2018 CLHLS database. The 10-item Center for Epidemiologic Studies Depression Scale (CESD-10) and the Generalized Anxiety Disorder Scale-7 (GAD-7) were used to assess depression and anxiety, respectively. Participants were divided into the exercise and the nonexercise groups using propensity score matching to minimize the influence of confounding variables. Depression-anxiety symptom networks were constructed, and network indexes were computed for each group, based on various packages of R. By computing network connectivity, invulnerability simulation was used to investigate the role of physical exercise in network robustness. RESULTS: Both groups had D3 (sad mood), A4 (trouble relaxing) and A2 (uncontrollably worry) as central symptoms. In the exercise group, A1 (nervousness), A3 (too much worry) and D1 (bothered by little things) were the strongest bridge nodes. In the nonexercise group, A1 (nervousness), D1 (bothered by little things) and A4 (trouble relaxing) played this role. Participation in physical exercise decreased the centrality of D9 (cannot get doing) but increased the centrality of A3 (too much worry). Furthermore, the exercise group had higher network invulnerability than the nonexercise group under random attack conditions. CONCLUSIONS: Physical exercise affected core symptoms of depression-anxiety and the interactions of symptoms. Targeting central or bridge nodes may be an effective intervention for alleviating the comorbidity. Increased network invulnerability manifested the positive effects of physical exercise.

Bridging the relationship between physical exercise and mental health in adolescents based on network analysis.

Although physical exercise has been recommended as a useful means of enhancing the mental health of adolescents, the exact mechanisms through which physical exercise plays a role are unclear. Both physical exercise and mental health are complex concepts with multiple facets, and traditional methods may constrain the manifestations of their mapping relationships. This research aimed to find the bridging connections between physical exercise and mental health. Mental health and physical exercise behaviors were assessed using the Symptom Checklist 90 (SCL-90) and the Adolescent Physical Activity Questionnaire (PAQ-A) in 9072 Chinese adolescents, respectively. Network analysis was utilized to construct the mental health-physical exercise network and to analyze the relationships between individual physical exercise behaviors and mental health symptoms. Core and bridging nodes were identified based on expected influence (EI) and bridge expected influence (BEI). Gender differences were also examined. The results revealed specific and distinct pathways between physical exercise and mental health (e.g., winter sports-obsessive-compulsive symptoms, winter sports-phobia). For both males and females, anxiety, depression, interpersonal sensitivity, ball sports, and evening activity were the most central symptoms/behaviors, reflecting their relative significance in their respective associations. The nodes with the highest BEI were obsessive-compulsive symptoms and physical education, showing negative associations with nodes in the other community. Furthermore, in the male group, somatization and winter sports stood out as the most positive bridge nodes. Conversely, in the female group, interpersonal sensitivity and sports games were the most positive bridge nodes. These findings illuminate the pathways linking physical exercise and mental health, supporting the implementation of physical exercise in a more elaborate way.

A Bayesian network model to predict neoplastic risk for patients with gallbladder polyps larger than 10 mm based on preoperative ultrasound features.

BACKGROUND: Polyp size of 10 mm is insufficient to discriminate neoplastic and non-neoplastic risk in patients with gallbladder polyps (GPs). The aim of the study is to develop a Bayesian network (BN) prediction model to identify neoplastic polyps and create more precise criteria for surgical indications in patients with GPs lager than 10 mm based on preoperative ultrasound features. METHODS: A BN prediction model was established and validated based on the independent risk variables using data from 759 patients with GPs who underwent cholecystectomy from January 2015 to August 2022 at 11 tertiary hospitals in China. The area under receiver operating characteristic curves (AUCs) were used to evaluate the predictive ability of the BN model and current guidelines, and Delong test was used to compare the AUCs. RESULTS: The mean values of polyp cross-sectional area (CSA), long, and short diameter of neoplastic polyps were higher than those of non-neoplastic polyps (P < 0.0001). Independent neoplastic risk factors for GPs included single polyp, polyp CSA ≥ 85 mm 2, fundus with broad base, and medium echogenicity. The accuracy of the BN model established based on the above independent variables was 81.88% and 82.35% in the training and testing sets, respectively. Delong test also showed that the AUCs of the BN model was better than that of JSHBPS, ESGAR, US-reported, and CCBS in training and testing sets, respectively (P < 0.05). CONCLUSION: A Bayesian network model was accurate and practical for predicting neoplastic risk in patients with gallbladder polyps larger than 10 mm based on preoperative ultrasound features.

Coupled disease-vaccination behavior dynamic analysis and its application in COVID-19 pandemic.

Predicting the evolutionary dynamics of the COVID-19 pandemic is a complex challenge. The complexity increases when the vaccination process dynamic is also considered. In addition, when applying a voluntary vaccination policy, the simultaneous behavioral evolution of individuals who decide whether and when to vaccinate must be included. In this paper, a coupled disease-vaccination behavior dynamic model is introduced to study the coevolution of individual vaccination strategies and infection spreading. We study disease transmission by a mean-field compartment model and introduce a non-linear infection rate that takes into account the simultaneity of interactions. Besides, the evolutionary game theory is used to investigate the contemporary evolution of vaccination strategies. Our findings suggest that sharing information with the entire population about the negative and positive consequences of infection and vaccination is beneficial as it boosts behaviors that can reduce the final epidemic size. Finally, we validate our transmission mechanism on real data from the COVID-19 pandemic in France.

A Bayesian Network Prediction Model for Microvascular Invasion in Patients with Intrahepatic Cholangiocarcinoma: A Multi-institutional Study.

BACKGROUND: Microvascular invasion (MVI) has been reported to be an independent prognostic factor of recurrence and poor overall survival in patients with intrahepatic cholangiocarcinoma (ICC). This study aimed to explore the preoperative independent risk factors of MVI and establish a Bayesian network (BN) prediction model to provide a reference for surgical diagnosis and treatment. METHODS: A total of 531 patients with ICC who underwent radical resection between 2010 and 2018 were used to establish and validate a BN model for MVI. The BN model was established based on the preoperative independent variables. The ROC curves and confusion matrix were used to assess the performance of the model. RESULTS: MVI was an independent risk factor for relapse-free survival (RFS) (P < 0.05). MVI has a correlation with postoperative recurrence, early recurrence (< 6 months), median RFS and median overall survival (all P < 0.05). The preoperative independent risk variables of MVI included obstructive jaundice, prognostic nutritional index, CA19-9, tumor size, and major vascular invasion, which were used to establish the BN model. The AUC of the BN model was 78.92% and 83.01%, and the accuracy was 70.85% and 77.06% in the training set and testing set, respectively. CONCLUSION: The BN model established based on five independent risk variables for MVI is an effective and practical model for predicting MVI in patients with ICC.

A Bayesian network prediction model for gallbladder polyps with malignant potential based on preoperative ultrasound.

BACKGROUND: It is important to identify gallbladder polyps (GPs) with malignant potential and avoid unnecessary cholecystectomy by constructing prediction model. The aim of the study is to develop a Bayesian network (BN) prediction model for GPs with malignant potential in a long diameter of 8-15 mm based on preoperative ultrasound. METHODS: The independent risk factors for GPs with malignant potential were screened by χ2 test and Logistic regression model. Prediction model was established and validated using data from 1296 patients with GPs who underwent cholecystectomy from January 2015 to December 2019 at 11 tertiary hospitals in China. A BN model was established based on the independent risk variables. RESULTS: Independent risk factors for GPs with malignant potential included age, number of polyps, polyp size (long diameter), polyp size (short diameter), and fundus. The BN prediction model identified relationships between polyp size (long diameter) and three other variables [polyp size (short diameter), fundus and number of polyps]. Each variable was assigned scores under different status and the probabilities of GPs with malignant potential were classified as [0-0.2), [0.2-0.5), [0.5-0.8) and [0.8-1] according to the total points of [- 337, - 234], [- 197, - 145], [- 123, - 108], and [- 62,500], respectively. The AUC was 77.38% and 75.13%, and the model accuracy was 75.58% and 80.47% for the BN model in the training set and testing set, respectively. CONCLUSION: A BN prediction model was accurate and practical for predicting GPs with malignant potential patients in a long diameter of 8-15 mm undergoing cholecystectomy based on preoperative ultrasound.

Industrial gearbox fault diagnosis based on multi-scale convolutional neural networks and thermal imaging.

Infrared thermal technology plays a vital role in the health condition monitoring of gearbox. In the traditional infrared thermal technology-based methods, Gaussian pyramid is applied as the feature extraction approach, which has disadvantages of noise influence and information missing. Focus on such disadvantages, an improved multi-scale decomposition method combined with convolutional neural network is proposed to extract the fault features of the multi-scale infrared images in this paper. It can enlarge the data length at large scales, and thus reduce the fluctuations of feature values and reserve the fault information. The effectiveness of the proposed method is validated using the experiment infrared data of one industrial gearbox. Results demonstrate that our proposed method has the best performance comparing with five methods.

Comprehensive analysis of new prognostic signature based on ferroptosis-related genes in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is an aggressive tumor and the most common subtype of RCC. Ferroptosis is a novel form of regulated cell death, and ferroptosis-related genes (FRGs) have been associated with the prognosis of patients with certain cancers. However, the detailed prognostic correlation between FRGs and ccRCC has not yet been elucidated. To address this, the current study used The Cancer Genome Atlas (TCGA) dataset to explore 64 FRGs and determine their prognostic value in ccRCC. Results showed that 52 out of the 64 genes displayed significantly different expression levels in tumor tissue, and 35 out of the 52 differentially expressed genes (DEGs) were associated with overall survival. Subsequently, a four-gene prognostic signature (CD44, DPP4, NCOA4 and SLC7A11) was constructed and could successfully distinguish ccRCC patients with different prognosis in TCGA train and test sets. Furthermore, clinical ccRCC samples from our medical center were used to verify the application value of the new prognostic signature through immunohistochemistry and quantitative real-time polymerase chain reaction (qRT-PCR). Biological functional analysis implied that immune-related functions and pathways were enriched in the TCGA cohort and the immune status scores were significantly different between high- and low-risk sets. These results suggest that the four ferroptosis-related regulatory genes can act as reliable prognostic biomarkers of ccRCC, and might be exploited as potential targets of therapeutic strategies.

The Roles of CircRNAs in Bladder Cancer: Biomarkers, Tumorigenesis Drivers, and Therapeutic Targets.

Bladder cancer (BCa) is the most prevalent malignancy of the urinary system. Circular RNAs (circRNAs), a novel subtype of non-coding RNAs, play a crucial role in physiological and developmental processes. CircRNAs mainly function as regulators of splicing process and transcription, microRNA sponges, and protein brackets. Recent advances in understanding the pathogenesis of BCa have led to the identification of an abundance of dysregulated circRNAs associated with BCa. These aberrantly expressed circRNAs eventually lead to abnormalities in biological, genetic, and epigenetic information. In this review, we introduce the potential of circRNAs as biomarkers for BCa diagnosis and prognosis. Notably, diverse mechanisms have been proposed for circRNAs driving carcinogenesis, including increasing cell proliferation, promoting invasive and migratory capacity, enhancing endothelial-mesenchymal transition, sustaining stemness, and enabling resistance to chemotherapy. Importantly, a full understanding of circRNA mechanisms is needed to mine promising therapeutic approaches for targeting BCa. In this paper, we present the latest advances in circRNAs and systemically summarize the characteristics and mechanisms of circRNAs in BCa, providing potential perspectives for BCa treatment.

Does surgery benefit patients with oligometastatic or metastatic prostate cancer? - A retrospective cohort study and meta-analysis.

BACKGROUND: To evaluate long-term oncological outcomes of radical prostatectomy (RP) plus androgen deprivation therapy (ADT) in oligometastatic prostate cancer (PCa) patients. METHODS: Our study included oligometastatic PCa patients hospitalized between January 1, 2010 and December 31, 2015, who received ADT with or without RP. We evaluated survival by employing Kaplan-Meier methods, with log-rank tests and univariate and multivariate Cox regression analyses. A meta-analysis of previously published studies was additionally performed. RESULTS: The median follow-up times of both groups were 68.4 months (interquartile range = 56.5-85.0). In this cohort study, significant statistical difference in preoperative total prostate-specific antigen (tPSA; p = .121), clinical T stage (p = .115), and N stage (p = .394) were not found between the two groups. Meanwhile, the difference in overall survival (OS) between the two groups did not reach statistical significance (p = .649). A significant difference was not observed in castration-resistant prostate cancer (CRPC)-free survival between two groups as well (p = .183). Numbers of metastases might be an independent prognosis factor (p = .05) for OS, and postoperative tPSA is a risk predictor for CRPC-free survival (p = .032). A meta-analysis of four relevant studies demonstrated significant statistical difference in clinical improvement with RP plus ADT over ADT alone in OS survival (p < .001; hazard ratio [HR] = 0.51; 95% confidence interval [CI] = 0.38-0.69) instead of CRPC-free survival (p = .42; HR = 0.86; 95% CI = 0.59-1.24). CONCLUSION: The addition of RP to ADT for the treatment of oligometastatic PCa was associated with an improved OS instead of CRPC-free survival.

Analysis of epidemic vaccination strategies on heterogeneous networks: Based on SEIRV model and evolutionary game.

Nowadays, vaccination is the most effective way to control the epidemic spreading. In this paper, an epidemic SEIRV (susceptible-exposed-infected-removed -vaccinated) model and an evolutionary game model are established to analyze the difference between mandatory vaccination method and voluntary vaccination method on heterogeneous networks. Firstly, we divide the population into four categories, including susceptible individuals, exposed individuals, infected individuals and removed individuals. Based on the mean field approximation theory, differential equations are developed to characterize the changes of the proportions of the four groups over time under mandatory vaccination. Then through the analysis of the differential equations, the disease-free equilibrium point (DFE) and the endemic disease equilibrium point (EDE) are obtained. Also, the basic reproduction number is obtained by the next-generation matrix method and the stability analysis of the equilibrium points is performed. Next, by considering factors such as vaccination cost, treatment cost and government subsidy rate, differential equations are established to represent the change of vaccination rate over time. By analyzing the final vaccination coverage rate, we can get the minimum vaccination cost to make infectious disease disappear. Finally, the Monte Carlo method is used for numerical simulation to verify the results obtained from the theoretical analysis. Using the SARS-Cov-2 pandemic data from Wuhan, China, the experimental results show that when the effectiveness rate of vaccination is 0.75, the vaccination cost is not higher than 0.886 so that the vaccination strategy can be spread among the population. If mandatory vaccination is adopted, the minimum vaccination rate is 0.146.

Optimal number of harvested lymph nodes for curatively resected gallbladder adenocarcinoma based on a Bayesian network model.

BACKGROUND AND OBJECTIVES: To identify the optimal range and the minimum number of lymph nodes (LNs) to be examined to maximize survival time of patients with curatively resected gallbladder adenocarcinoma (GBAC). METHODS: Data were collected from the surveillance, epidemiology, and end results database on patients with GBAC who underwent curative resection between 2004 and 2015. A Bayesian network (BN) model was constructed to identify the optimal range of harvested LNs. Model accuracy was evaluated using the confusion matrix and receiver operating characteristic (ROC) curve. RESULTS: A total of 1268 patients were enrolled in this study. Accuracy of the BN model was 72.82%, and the area under the curve of the ROC for the testing dataset was 78.49%. We found that at least seven LNs should be harvested to maximize survival time, and that the optimal count of harvested LNs was in the range of 7 to 10 overall, with an optimal range of 10 to 11 for N+ patients, 7 to 10 for stage T1-T2 patients, and 7 to 11 for stage T3-T4 patients. CONCLUSIONS: According to a BN model, at least seven LNs should be retrieved for GBAC with curative resection, with an overall optimal range of 7 to 10 harvested LNs.

Survival prediction for gallbladder carcinoma after curative resection: Comparison of nomogram and Bayesian network models.

BACKGROUND: In this study, we developed a nomogram and a Bayesian network (BN) model for prediction of survival in gallbladder carcinoma (GBC) patients following surgery and compared the performance of the two models. METHODS: Survival prediction models were established and validated using data from 698 patients with GBC who underwent curative-intent resection between 2008 and 2017 at one of six Chinese tertiary hospitals. Model construction and internal validation were performed using data from 381 patients at one hepatobiliary center, and external validation was then performed using data from 317 patients at the other five centers. A BN model and a nomogram model were constructed based on the independent prognostic variables. Performance of the BN and nomogram models was compared based on area under receiver operating characteristic curves (AUC), model accuracy, and a confusion matrix. RESULTS: Independent prognostic variables included age, pathological grade, liver infiltration, T stage, N stage, and margin. In internal validation, AUC was 84.14% and 78.22% for the BN and nomogram, respectively, and model accuracy was 75.65% and 72.17%, respectively. In external validation, AUC was 76.46% and 70.19% for the BN and nomogram, respectively, with model accuracy of 66.88% and 60.25%, respectively. Based on the confusion matrix, the nomogram had a higher true positive rate but a substantially lower true negative rate compared to the BN. CONCLUSION: A BN model was more accurate than a Cox regression-based nomogram for prediction of survival in GBC patients undergoing curative-intent resection.

Estimating survival benefit of adjuvant therapy based on a Bayesian network prediction model in curatively resected advanced gallbladder adenocarcinoma.

BACKGROUND: The factors affecting the prognosis and role of adjuvant therapy in advanced gallbladder carcinoma (GBC) after curative resection remain unclear. AIM: To provide a survival prediction model to patients with GBC as well as to identify the role of adjuvant therapy. METHODS: Patients with curatively resected advanced gallbladder adenocarcinoma (T3 and T4) were selected from the Surveillance, Epidemiology, and End Results database between 2004 and 2015. A survival prediction model based on Bayesian network (BN) was constructed using the tree-augmented naïve Bayes algorithm, and composite importance measures were applied to rank the influence of factors on survival. The dataset was divided into a training dataset to establish the BN model and a testing dataset to test the model randomly at a ratio of 7:3. The confusion matrix and receiver operating characteristic curve were used to evaluate the model accuracy. RESULTS: A total of 818 patients met the inclusion criteria. The median survival time was 9.0 mo. The accuracy of BN model was 69.67%, and the area under the curve value for the testing dataset was 77.72%. Adjuvant radiation, adjuvant chemotherapy (CTx), T stage, scope of regional lymph node surgery, and radiation sequence were ranked as the top five prognostic factors. A survival prediction table was established based on T stage, N stage, adjuvant radiotherapy (XRT), and CTx. The distribution of the survival time (>9.0 mo) was affected by different treatments with the order of adjuvant chemoradiotherapy (cXRT) > adjuvant radiation > adjuvant chemotherapy > surgery alone. For patients with node-positive disease, the larger benefit predicted by the model is adjuvant chemoradiotherapy. The survival analysis showed that there was a significant difference among the different adjuvant therapy groups (log rank, surgery alone vs CTx, P < 0.001; surgery alone vs XRT, P = 0.014; surgery alone vs cXRT, P < 0.001). CONCLUSION: The BN-based survival prediction model can be used as a decision-making support tool for advanced GBC patients. Adjuvant chemoradiotherapy is expected to improve the survival significantly for patients with node-positive disease.

Universal behavior of cascading failures in interdependent networks.

Catastrophic and major disasters in real-world systems, such as blackouts in power grids or global failures in critical infrastructures, are often triggered by minor events which originate a cascading failure in interdependent graphs. We present here a self-consistent theory enabling the systematic analysis of cascading failures in such networks and encompassing a broad range of dynamical systems, from epidemic spreading, to birth-death processes, to biochemical and regulatory dynamics. We offer testable predictions on breakdown scenarios, and, in particular, we unveil the conditions under which the percolation transition is of the first-order or the second-order type, as well as prove that accounting for dynamics in the nodes always accelerates the cascading process. Besides applying directly to relevant real-world situations, our results give practical hints on how to engineer more robust networked systems.

The Optimized Multi-Scale Permutation Entropy and Its Application in Compound Fault Diagnosis of Rotating Machinery.

Multi-scale permutation entropy (MPE) is a statistic indicator to detect nonlinear dynamic changes in time series, which has merits of high calculation efficiency, good robust ability, and independence from prior knowledge, etc. However, the performance of MPE is dependent on the parameter selection of embedding dimension and time delay. To complete the automatic parameter selection of MPE, a novel parameter optimization strategy of MPE is proposed, namely optimized multi-scale permutation entropy (OMPE). In the OMPE method, an improved Cao method is proposed to adaptively select the embedding dimension. Meanwhile, the time delay is determined based on mutual information. To verify the effectiveness of OMPE method, a simulated signal and two experimental signals are used for validation. Results demonstrate that the proposed OMPE method has a better feature extraction ability comparing with existing MPE methods.

Decision of surgical approach for advanced gallbladder adenocarcinoma based on a Bayesian network.

BACKGROUND AND OBJECTIVES: To determine whether radical resection can benefit patients with advanced gallbladder adenocarcinoma using a Bayesian network (BN) with clinical data. METHODS: In total, 362 patients who had undergone surgical treatment of gallbladder adenocarcinoma at a tertiary institute were evaluated to establish two BN models using a tree-augmented naïve Bayes algorithm. We then chose 250 patients with T3-4N0-2M0 stage gallbladder adenocarcinoma to test the posterior probability after the surgical type was taken into account. RESULTS: In total, 170 patients (≤7 months) and 137 patients (>7 months) were correctly classified in the median survival time model (accuracy, 84.81%), and 204 patients (≤12 months), 15 patients (12-36 months), 17 patients (36-60 months), and 34 patients (>60 months) were correctly classified in the 1-, 3-, and 5-year survival model (accuracy, 74.59%), respectively. Every posterior probability in the two models upregulated the ratio of the longer survival time and suggested a better prognosis for gallbladder adenocarcinoma that can be improved by R0 resection. CONCLUSIONS: These BN models indicate that stages T4 and N2 gallbladder adenocarcinoma are not contraindications for surgery and that R0 resection can improve survival in patients with advanced gallbladder adenocarcinoma.

Analysis of prognostic factors for survival after surgery for gallbladder cancer based on a Bayesian network.

The factors underlying prognosis for gallbladder cancer (GBC) remain unclear. This study combines the Bayesian network (BN) with importance measures to identify the key factors that influence GBC patient survival time. A dataset of 366 patients who underwent surgical treatment for GBC was employed to establish and test a BN model using BayesiaLab software. A tree-augmented naïve Bayes method was also used to mine relationships between factors. Composite importance measures were applied to rank the influence of factors on survival time. The accuracy of BN model was 81.15%. For patients with long survival time (>6 months), the true-positive rate of the model was 77.78% and the false-positive rate was 15.25%. According to the built BN model, the sex, age, and pathological type were independent factors for survival of GBC patients. The N stage, liver infiltration, T stage, M stage, and surgical type were dependent variables for survival time prediction. Surgical type and TNM stages were identified as the most significant factors for the prognosis of GBC based on the analysis results of importance measures.

Agent-based intelligent medical diagnosis system for patients.

BACKGROUND: According to the analysis of the challenges faced by the current public health circumstances such as the sharp increase in elderly patients, limited medical personnel, resources and technology, the agent-based intelligent medical diagnosis system for patients (AIMDS) is proposed in this research. OBJECTIVE: Based on advanced sensing technology and professional medical knowledge, the AIMDS can output the appropriate medical prescriptions and food prohibition when the physical signs and symptoms of the patient are inputted. METHODS: Three core modules are designed include sensing module, intuition-based fuzzy set theory/medical diagnosis module, and medical knowledge module. RESULTS: The result shows that the optimized prescription can reach the desired level, with great curative effect for patient disease, through a case study simulation. CONCLUSION: The presented AIMDS can integrate sensor technique and intelligent medical diagnosis methods to make an accurate diagnosis, resulting in three-type of optimized descriptions for patient selection.

Analysis of prognostic factors for survival after hepatectomy for hepatocellular carcinoma based on a bayesian network.

BACKGROUND: The prognosis of hepatocellular carcinoma (HCC) after hepatectomy involves many factors. Previous studies have evaluated the separate influences of single factors; few have considered the combined influence of various factors. This paper combines the Bayesian network (BN) with importance measures to identify key factors that have significant effects on survival time. METHODS: A dataset of 299 patients with HCC after hepatectomy was studied to establish a BN using a tree-augmented naïve Bayes algorithm that could mine relationships between factors. The composite importance measure was applied to rank the impact of factors on survival time. RESULTS: 124 patients (>10 months) and 77 patients (≤10 months) were correctly classified. The accuracy of BN model was 67.2%. For patients with long survival time (>10 months), the true-positive rate of the model was 83.22% and the false-positive rate was 48.67%. According to the model, the preoperative alpha fetoprotein (AFP) level and postoperative performance of transcatheter arterial chemoembolization (TACE) were independent factors for survival of HCC patients. The grade of preoperative liver function reflected the tendency for postoperative complications. Intraoperative blood loss, tumor size, portal vein tumor thrombosis (PVTT), time of clamping the porta hepatis, tumor number, operative method, and metastasis were dependent variables in survival time prediction. PVTT was considered the most significant for the prognosis of survival time. CONCLUSIONS: Using the BN and importance measures, PVTT was identified as the most significant predictor of survival time for patients with HCC after hepatectomy.