Characteristics of symptoms and development of psychological status in late Chinese adolescence.

BACKGROUND: Many late adolescents experience a state of psychological sub-health, requiring early recognition and intervention. This study aims to assess the psychological state of late Chinese adolescents and uncover developmental trend of mental health through network analysis. METHOD: We analyzed data from 9072 Chinese high school adolescents in Shandong Province surveyed in 2020-2021, and divided them into the normal, the suspected, and the abnormal groups based on Symptom Checklist 90 (SCL-90) scores. Network analysis was employed to identify the core symptoms and bridge symptoms across different states. RESULTS: Anxiety and depression were the most central symptoms, without gender differences. Core symptoms, network structure, and network invulnerability varied across different psychological states. The abnormal group exhibited the highest value of natural connectivity, followed by the suspected and normal groups. This pattern extended to bridge networks. While not meeting diagnostic criteria, the suspected group demonstrated abnormalities in network edge invariance and global strength invariance. LIMITATIONS: The cross-sectional design cannot establish causality, and biases in self-report measurements cannot be ignored. CONCLUSION: Compared to traditional scale indicators, network structural characteristics may be a more sensitive assessment indicator.

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.

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.

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.

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.