The elucidation of resources pertaining to the Chimonanthus praecox varieties and the establishment of a fingerprint serve as crucial underpinnings for advancing scientific inquiry and industrial progress in relation to C. praecox. Employing the SSR molecular marker technology, an exploration of the genetic diversity of 175 C. praecox varieties (lines) in the Yanling region was conducted, and an analysis of the genetic diversity among these varieties was carried out using the UPDM clustering method in NTSYSpc 2.1 software. We analyzed the genetic structure of 175 germplasm using Structure v2.3.3 software based on a Bayesian model. General linear model (GLM) association was utilized to analyze traits and markers. The genetic diversity analysis revealed a mean number of alleles (Na) of 6.857, a mean expected heterozygosity (He) of 0.496 3, a mean observed heterozygosity (Ho) of 0.503 7, a mean genetic diversity index of Nei՚s of 0.494 9, and a mean Shannon information index of 0.995 8. These results suggest that the C. praecox population in Yanling exhibits a rich genetic diversity. Additionally, the population structure and the UPDM clustering were examined. In the GLM model, a total of fifteen marker loci exhibited significant (P < 0.05) association with eight phenotypic traits, with the explained phenotypic variation ranging from 14.90% to 36.03%. The construction of fingerprints for C. praecox varieties (lines) was accomplished by utilizing eleven primer pairs with the highest polymorphic information content, resulting in the analysis of 175 SSR markers. The present study offers a thorough examination of the genetic diversity and SSR molecular markers of C. praecox in Yanling, and establishes a fundamental germplasm repository of C. praecox, thereby furnishing theoretical underpinnings for the selection and cultivation of novel and superior C. praecox varieties, varietal identification, and resource preservation and exploitation.
Bayes Theorem ; Biomarkers ; Phenotype ; Cluster Analysis ; Genetic Variation

BACKGROUND: Large disparities exist in liver cancer burden trends across countries but are poorly understood. We aimed to investigate the global trajectories of liver cancer burden, explore the driving forces, and predict future trends. METHODS: Data on the liver cancer burden in 204 countries and territories from 1990 to 2019 were extracted from the Global Burden of Disease Study. The age-standardized incidence rate (ASIR) and age-standardized mortality rate (ASMR) trajectories were defined using growth mixture models. Five major risk factors contributing to changes in the ASIR or ASMR and socioeconomic determinants were explored using the identified trajectories. A Bayesian age-period-cohort model was used to predict future trends through 2035. RESULTS: Three trajectories of liver cancer burden were identified: increasing, stable, and decreasing groups. Almost half of the American countries were classified in the decreasing group (48.6% for ASIR and ASMR), and the increasing group was the most common in the European region (ASIR, 49.1%; ASMR, 37.7%). In the decreasing group, the decrease of liver cancer due to hepatitis B contributed 63.4% and 60.4% of the total decreases in ASIR and ASMR, respectively. The increase of liver cancer due to alcohol use, hepatitis C, and hepatitis B contributed the most to the increase in the increasing group (30.8%, 31.1%, and 24.2% for ASIR; 33.7%, 30.2%, and 22.2% for ASMR, respectively). The increasing group was associated with a higher sociodemographic index, gross domestic product per capita, health expenditure per capita, and universal health coverage (all P <0.05). Significant variations in disease burden are predicted to continue through 2035, with a disproportionate burden in the decreasing group. CONCLUSION Global disparities were observed in liver cancer burden trajectories. Hepatitis B, alcohol use, and hepatitis C were identified as driving forces in different regions.
Humans ; Bayes Theorem ; Liver Neoplasms ; Risk Factors ; Hepatitis C/complications* ; Hepatitis B ; Hepacivirus ; Incidence

Using machine learning algorithms to construct an early prediction model of brucellosis to improve the diagnosis efficiency of Brucellosis. This study was a case-control study. 2 381 brucellosis patients from Beijing Ditan Hospital affiliated to Capital Medical University were retrospectively collected as case group, and healthy people from Beijing Chaoyang Hospital affiliated to Capital Medical University were collected as control group from May 9, 2011 to November 29, 2021. The relevant clinical information and full blood count results of 13 257 data were collected and five algorithms of machine learning were used to construct an early predication model of brucellosis by using machine learning: random forest, Naive Bayes, decision tree, logistic regression and support vector machine;14 074 data (2 143 cases incase group and 11 931 cases in control group) were used to establish the early predication model of brucellosis, and 1 564 (238 cases in case group and 1 326 cases in control group) data were used to test the predication efficiency of the brucellosis model. The results showed that the support vector machine algorithm has the best predication performance by comparing the five machine learning models. The area under receiver curve (AUC) of receiver operating characteristic (ROC) was 0.991, and the accuracy, precision, specificity and Recall were 95.6%, 95.5%, 95.4% and 95.9%, respectively. Based on the SHAP plot, platelet distribution width (PDW) and basophil relative value (BASO%) results were low, and men with high coefficient of variation (R-CV), erythrocyte hemoglobin concentration (MCHC), and platelet volume (MPV) were predicted to be at high risk of brucellosis. Platelet distribution width (PDW) contributed the most to the prediction model, followed by red blood cell distribution width coefficient of variation (R-CV). In conclusion, the establishment of a high-precision early predication method of brucellosis based on machine learning may be of great significance for the early detection and treatment of brucellosis patients.
Male ; Humans ; Retrospective Studies ; Case-Control Studies ; Bayes Theorem ; Algorithms ; Machine Learning

Objective: Based on the diagnostic model established and validated by the machine learning algorithm, to investigate the value of seven tumor-associated autoantibodies (TAABs), namely anti-p53, PGP9.5, SOX2, GAGE7, GBU4-5, MAGEA1 and CAGE antibodies in the diagnosis of non-small cell lung cancer (NSCLC) and to differentiate between NSCLC and benign lung nodules. Methods: This was a retrospective study of clinical cases. Model building queue: a total of 227 primary patients who underwent radical lung cancer surgery in the Department of Thoracic Surgery, Shengjing Hospital of China Medical University, from November 2018 to June 2021 were collected as the NSCLC group, and 120 cases of benign lung nodules, 122 cases of pneumonia and 120 healthy individuals were selected as the control groups. External validation queue: a total of 100 primary patients who underwent radical lung cancer surgery in the Department of Thoracic Surgery, Shengjing Hospital of China Medical University, from May 2022 to December 2022 were collected as the NSCLC group, and 36 cases of benign lung nodules, 32 cases of pneumonia and 44 healthy individuals were selected as the control groups. In addition, NSCLC was divided into early (stage 0-ⅠB) and mid-to-late (stage ⅡA-ⅢB) subgroups. The levels of 7-TAABs were detected by enzyme immunoassay, and serum concentrations of CEA and CYFRA21-1 were detected by electrochemiluminescence. Four machine learning algorithms, XGBoost, Lasso logistic regression, Naïve Bayes, and Support Vector Machine are used to establish classification models. And the best performance model was chosen based on evaluation metrics and a multi-indicator combination model was established. In addition, an online risk evaluation tool was generated to assist clinical applications. Results: Except for p53, the levels of rest six TAABs, CEA and CYFRA21-1 were significantly higher in the NSCLC group (P<0.05). Serum levels of anti-SOX2 [1.50 (0.60, 10.85) U/ml vs. 0.8 (0.20, 2.10) U/ml, Z=2.630, P<0.05] and MAGEA1 antibodies [0.20 (0.10, 0.43) U/ml vs. 0.10 (0.10, 0.20) U/ml, Z=2.289, P<0.05], CEA [3.13 (2.12, 5.64) ng/ml vs. 2.11 (1.25, 3.09) ng/ml, Z=3.970, P<0.05] and CYFRA21-1 [4.31(2.37, 7.14) ng/ml vs. 2.53(1.92, 3.48) ng/ml, Z=3.959, P<0.05] were significantly higher in patients with mid-to late-stage NSCLC than in early stages. XGBoost model was used to establish a multi-indicator combined detection model (after removing p53). 6-TAABs combined with CYFRA21-1 was the best combination model for the diagnosis of NSCLC and early NSCLC. The optimal diagnostic thresholds were 0.410, 0.701 and 0.744, and the AUC was 0.828, 0.757 and 0.741, respectively (NSCLC vs. control, NSCLC vs. benign lung nodules, early NSCLC vs. benign lung nodules) in model building queue, and the AUC was 0.760, 0.710 and 0.660, respectively (NSCLC vs. control, NSCLC vs. benign lung nodules, early NSCLC vs. benign lung nodules) in external validation queue. Conclusion: In the diagnosis of NSCLC, 6-TAABs is superior to that of traditional tumor markers CEA and CYFRA21-1, and can compensate for the shortcomings of traditional tumor markers. For the differential diagnosis of NSCLC and benign lung nodule, "6-TAABs+CYFRA21-1" is the most cost-effective combination, and plays an important role in prevention and screening for early lung cancer.
Humans ; Carcinoma, Non-Small-Cell Lung/surgery* ; Lung Neoplasms/diagnosis* ; Retrospective Studies ; Autoantibodies ; Bayes Theorem ; Tumor Suppressor Protein p53 ; Carcinoembryonic Antigen ; Antigens, Neoplasm ; Biomarkers, Tumor ; Algorithms ; Pneumonia

This study used machine learning techniques combined with routine blood cell analysis parameters to build preliminary prediction models, helping differentiate patients with depression from healthy controls, or patients with anxiety. A multicenter study was performed by collecting blood cell analysis data of Beijing Chaoyang Hospital and the First Hospital of Hebei Medical University from 2020 to 2021. Machine learning techniques, including support vector machine, decision tree, naïve Bayes, random forest and multi-layer perceptron were explored to establish a prediction model of depression. The results showed that based on the blood cell analysis results of healthy controls and depression group, the accuracy of prediction model reached as high as 0.99, F1 was 0.975. Receiver operating characteristic curve area and average accuracy were 0.985 and 0.967, respectively. Platelet parameters contributed mostly to depression prediction model. While, to random forest differential diagnosis model based on the data from depression and anxiety groups, prediction accuracy reached 0.68 and AUC 0.622. Age, platelet parameters, and average volume of red blood cells contributed the most to the model. In conclusion, the study researched on the prediction model of depression by exploring blood cell analysis parameters, revealing that machine learning models were more objective in the evaluation of mental illness.
Humans ; Depression ; Bayes Theorem ; Machine Learning ; Support Vector Machine ; Blood Cell Count

OBJECTIVE: To measure the burden of hepatitis B-associated diseases in China from 1990 to 2019, and to predict its changes from 2020 to 2030. METHODS: The age-standardized prevalence, incidence, mortality and disability-adjusted life years (DALY) rate of hepatitis B-associated diseases in China from 1990 to 2019 were extracted from the Global Burden of Disease 2019 (GBD 2019) data resources, and the trends in burdens of hepatitis B-associated diseases were evaluated from 1990 to 2019 using estimated annual percentage change (EAPC) and annual percent change (APC). In addition, the changes in the burden of hepatitis B-associated diseases were predicted in China from 2020 to 2023 using the Bayesian model. RESULTS: The overall incidence of hepatitis B-associated diseases reduced from 2 725.98/10⁵ in 1990 to 1 397.31/10⁵ in 2019 in China [estimated annual percentage change (EAPC) = -2.35%, 95% confidential interval (CI): (-2.58%, -2.13%)], with a reduction in the prevalence from 12 239.53/10⁵ in 1990 to 6 566.12/10⁵ in 2019 [EAPC = -2.34%, 95% CI: (-2.54%, -2.14%)], a reduction in the mortality from 24.67/10⁵ in 1990 to 8.07/10⁵ in 2019 [EAPC = -4.92%, 95% CI: (-5.37%, -4.47%)], and a reduction in the DALY rate from 793.38/10⁵ in 1990 to 247.71/10⁵ in 2019 [(EAPC = -5.15%, 95% CI: (-5.64%, -4.66%)]. The DALY rate of hepatitis B-associated diseases were mainly attributed to liver cancer, and the DALY rate of hepatitis B-associated diseases appeared a tendency towards a rise in China from 2012 to 2019 [APC = 1.30%, 95% CI: (0.16%, 2.45%)]. The overall burden of hepatitis Bassociated diseases was higher in males than in females, and the DALY rate of hepatitis B-associated diseases increased with age, with the greatest DALY rate seen among patients at ages of 50 to 69 years. The overall incidence of hepatitis B-associated diseases was projected to be 866.79/10⁵ in China in 2030, with the greatest incidence seen in acute hepatitis B (854.87/10⁵), and the burden of hepatitis B-associated diseases was predicted to decline in China from 2020 to 2030; however, the burden of liver disease was projected to appear a tendency towards a rise. CONCLUSIONS The burden of hepatitis B-associated diseases appears an overall tendency towards a decline in China from 1990 to 2030; however, the burden of liver cancer appears a tendency towards aggravation. Early diagnosis and treatment of liver cancer should be given a high priority.
Male ; Female ; Humans ; Middle Aged ; Aged ; Bayes Theorem ; Quality-Adjusted Life Years ; Hepatitis B/epidemiology* ; Liver Neoplasms/epidemiology* ; China/epidemiology* ; Incidence

OBJECTIVE: To measure the burden of hepatitis C-associated diseases in China from 1990 to 2019, and to predict its changes from 2020 to 2044, so as to provide insights into formulation of the targeted hepatitis C control strategy. METHODS: The total burden due to hepatitis C-associated diseases in China from 1990 to 2019 were extracted from the Global Burden of Disease 2019 (GBD 2019) data resources, and the trends in age-standardized prevalence, incidence, mortality and disability-adjusted life years (DALYs) rate of hepatitis C-associated acute hepatitis C (AHC), chronic liver diseases (CLD) and liver cancer in China from 1990 to 2019 were evaluated in China from 1990 to 2019 using estimated annual percentage change (EAPC). In addition, the changes in the burden of hepatitis C-associated diseases were predicted in China from 2020 to 2044 using a Bayesian model. RESULTS: The prevalence, incidence, mortality and DALY rate of hepatitis C-associated diseases all appeared an overall tendency towards a decline in China from 1990 to 2019 (EAPC = -2.64%, -2.24%, -3.81% and -3.90%, respectively); however, there was a minor rise in the incidence and prevalence of hepatitis C-associated diseases from 2015 to 2019. The overall prevalence of hepatitis C-associated diseases reduced from 2 152.7/10⁵ in 1990 to 1 254.1/10⁵ in 2019 in China, with a reduction of 41.7%. The overall incidence reduced from 87.9/10⁵ in 1990 to 55.0/10⁵ in 2019 in China, with a reduction of 37.4%, and the highest incidence was seen for AHC, followed by CLD and liver cancer. The overall mortality and DALY rate of hepatitis C-associated diseases was 4.0/10⁵ and 100.8/10⁵ in China from 1990 to 2019, with CLD showing the largest contributions to the gross mortality and DALY. The mortality and DALY rate of hepatitis C-associated diseases were 5.5/10⁵ and 142.4/10⁵ among men in China in 2019, which were both much higher than among women (2.8/10⁵ and 60.3/10⁵, respectively), and the overall prevalence (1 604.9/10⁵), mortality (30.2/10⁵) and DALYs (437.1/10⁵) of hepatitis C-associated diseases were all highest among patients at ages of 70 years and older, and the highest incidence was seen among patients at ages of 0 to 9 years (167.3/10⁵). The incidence of hepatitis C-associated diseases was predicted to rise in China from 2020 to 2044; however, the DALY rate was projected to appear a tendency towards a decline. CONCLUSIONS Although the burden of hepatitis C-associated diseases showed a tendency towards a decline in China from 1990 to 2019, the burden remained high, and was predicted to slightly rise from 2020 to 2044. High attention should be paid to screening of hepatitis C among infants and treatment among adults.
Male ; Adult ; Infant ; Humans ; Female ; Bayes Theorem ; Quality-Adjusted Life Years ; Hepatitis C/epidemiology* ; Liver Neoplasms/epidemiology* ; China/epidemiology* ; Incidence

OBJECTIVE: To explore lung ultrasound radiomics features which related to extravascular lung water index (EVLWI), and to predict EVLWI in critically ill patients based on lung ultrasound radiomics combined with machine learning and validate its effectiveness. METHODS: A retrospective case-control study was conducted. The lung ultrasound videos and pulse indicated continuous cardiac output (PiCCO) monitoring results of critically ill patients admitted to the department of critical care medicine of the First Affiliated Hospital of Guangxi Medical University from November 2021 to October 2022 were collected, and randomly divided into training set and validation set at 8:2. The corresponding images from lung ultrasound videos were obtained to extract radiomics features. The EVLWI measured by PiCCO was regarded as the "gold standard", and the radiomics features of training set was filtered through statistical analysis and LASSO algorithm. Eight machine learning models were trained using filtered radiomics features including random forest (RF), extreme gradient boost (XGBoost), decision tree (DT), Naive Bayes (NB), multi-layer perceptron (MLP), K-nearest neighbor (KNN), support vector machine (SVM), and Logistic regression (LR). Receiver operator characteristic curve (ROC curve) was plotted to evaluate the predictive performance of models on EVLWI in the validation set. RESULTS: A total of 151 samples from 30 patients were enrolled (including 906 lung ultrasound videos and 151 PiCCO monitoring results), 120 in the training set, and 31 in the validation set. There were no statistically significant differences in main baseline data including gender, age, body mass index (BMI), mean arterial pressure (MAP), central venous pressure (CVP), heart rate (HR), cardiac index (CI), cardiac function index (CFI), stroke volume index (SVI), global end diastolic volume index (GEDVI), systemic vascular resistance index (SVRI), pulmonary vascular permeability index (PVPI) and EVLWI. The overall EVLWI range in 151 PiCCO monitoring results was 3.7-25.6 mL/kg. Layered analysis showed that both datasets had EVLWI in the 7-15 mL/kg interval, and there was no statistically significant difference in EVLWI distribution. Two radiomics features were selected by using LASSO algorithm, namely grayscale non-uniformity (weight was -0.006 464) and complexity (weight was -0.167 583), and they were used for modeling. ROC curve analysis showed that the MLP model had better predictive performance. The area under the ROC curve (AUC) of the prediction validation set EVLWI was higher than that of RF, XGBoost, DT, KNN, LR, SVM, NB models (0.682 vs. 0.658, 0.657, 0.614, 0.608, 0.596, 0.557, 0.472). CONCLUSIONS The gray level non-uniformity and complexity of lung ultrasound were the most correlated radiomics features with EVLWI monitored by PiCCO. The MLP model based on gray level non-uniformity and complexity of lung ultrasound can be used for semi-quantitative prediction of EVLWI in critically ill patients.
Humans ; Extravascular Lung Water/diagnostic imaging* ; Retrospective Studies ; Critical Illness ; Case-Control Studies ; Bayes Theorem ; China ; Lung/diagnostic imaging*

BACKGROUND: Patients with mass-forming pancreatitis (MFP) or pancreatic ductal adenocarcinoma (PDAC) presented similar clinical symptoms, but required different treatment approaches and had different survival outcomes. This meta-analysis aimed to compare the diagnostic performance of contrast-enhanced ultrasound (CEUS) and contrast-enhanced computed tomography (CECT) in differentiating MFP from PDAC. METHODS: A literature search was performed in the PubMed, EMBASE (Ovid), Cochrane Library (CENTRAL), China National Knowledge Infrastructure (CNKI), Weipu (VIP), and WanFang databases to identify original studies published from inception to August 20, 2021. Studies reporting the diagnostic performances of CEUS and CECT for differentiating MFP from PDAC were included. The meta-analysis was performed with Stata 15.0 software. The outcomes included the pooled sensitivity, specificity, positive likelihood ratio (+LR), negative likelihood ratio (-LR), diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC) curves of CEUS and CECT. Meta-regression was conducted to investigate heterogeneity. Bayesian network meta-analysis was conducted to indirectly compare the overall diagnostic performance. RESULTS: Twenty-six studies with 2115 pancreatic masses were included. The pooled sensitivity and specificity of CEUS for MFP were 82% (95% confidence interval [CI], 73%-88%; I2  = 0.00%) and 95% (95% CI, 90%-97%; I2  = 63.44%), respectively; the overall +LR, -LR, and DOR values were 15.12 (95% CI, 7.61-30.01), 0.19 (95% CI, 0.13-0.29), and 78.91 (95% CI, 30.94-201.27), respectively; and the area under the SROC curve (AUC) was 0.90 (95% CI, 0.87-92). However, the overall sensitivity and specificity of CECT were 81% (95% CI, 75-85%; I2  = 66.37%) and 94% (95% CI, 90-96%; I2  = 74.87%); the overall +LR, -LR, and DOR values were 12.91 (95% CI, 7.86-21.20), 0.21 (95% CI, 0.16-0.27), and 62.53 (95% CI, 34.45-113.51), respectively; and, the SROC AUC was 0.92 (95% CI, 0.90-0.94). The overall diagnostic accuracy of CEUS was comparable to that of CECT for the differential diagnosis of MFP and PDAC (relative DOR 1.26, 95% CI [0.42-3.83], P  > 0.05). CONCLUSIONS CEUS and CECT have comparable diagnostic performance for differentiating MFP from PDAC, and should be considered as mutually complementary diagnostic tools for suspected focal pancreatic lesions.
Humans ; Contrast Media ; Bayes Theorem ; Tomography, X-Ray Computed/methods* ; Pancreatic Neoplasms/diagnostic imaging* ; Carcinoma, Pancreatic Ductal/diagnostic imaging* ; Sensitivity and Specificity ; Pancreatitis/diagnostic imaging* ; Ultrasonography/methods*

BACKGROUND: The brain is a common metastatic site in patients with non-small cell lung cancer (NSCLC), resulting in a relatively poor prognosis. Systemic therapy with epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) is recommended as the first-line treatment for EGFR -mutated, advanced NSCLC patients. However, intracranial activity varies in different drugs. Thus, brain metastasis (BM) should be considered when choosing the treatment regimens. We conducted this network meta-analysis to explore the optimal first-line therapeutic schedule for advanced EGFR -mutated NSCLC patients with different BM statuses. METHODS: Randomized controlled trials focusing on EGFR-TKIs (alone or in combination) in advanced and EGFR -mutant NSCLC patients, who have not received systematic treatment, were systematically searched up to December 2021. We extracted and analyzed progression-free survival (PFS) and overall survival (OS). A network meta-analysis was performed with the Bayesian statistical model to determine the survival outcomes of all included therapy regimens using the R software. Hazard ratios (HRs) and 95% confidence intervals (CIs) were used to compare intervention measures, and overall rankings of therapies were estimated under the Bayesian framework. RESULTS: This analysis included 17 RCTs with 5077 patients and 12 therapies, including osimertinib + bevacizumab, aumolertinib, osimertinib, afatinib, dacomitinib, standards of care (SoC, including gefitinib, erlotinib, or icotinib), SoC + apatinib, SoC + bevacizumab, SoC + ramucirumab, SoC + pemetrexed based chemotherapy (PbCT), PbCT, and pemetrexed free chemotherapy (PfCT). For patients with BM, SoC + PbCT improved PFS compared with SoC (HR = 0.40, 95% CI: 0.17-0.95), and osimertinib + bevacizumab was most likely to rank first in PFS, with a cumulative probability of 34.5%, followed by aumolertinib, with a cumulative probability of 28.3%. For patients without BM, osimertinib + bevacizumab, osimertinib, aumolertinib, SoC + PbCT, dacomitinib, SoC + ramucirumab, SoC + bevacizumab, and afatinib showed superior efficacy compared with SoC (HR = 0.43, 95% CI: 0.20-0.90; HR = 0.46, 95% CI: 0.31-0.68; HR = 0.51, 95% CI: 0.34-0.77; HR = 0.50, 95% CI: 0.38-0.66; HR = 0.62, 95% CI: 0.43-0.89; HR = 0.64, 95% CI: 0.44-0.94; HR = 0.61, 95% CI: 0.48-0.76; HR = 0.71, 95% CI: 0.50-1.00), PbCT (HR = 0.29, 95% CI: 0.11-0.74; HR = 0.31, 95% CI: 0.15-0.62; HR = 0.34, 95% CI: 0.17-0.69; HR = 0.34, 95% CI: 0.18-0.64; HR = 0.42, 95% CI: 0.21-0.82; HR = 0.43, 95% CI: 0.22-0.87; HR = 0.41, 95% CI: 0.22-0.74; HR = 0.48, 95% CI: 0.31-0.75), and PfCT (HR = 0.14, 95% CI: 0.06-0.32; HR = 0.15, 95% CI: 0.09-0.26; HR = 0.17, 95% CI: 0.09-0.29; HR = 0.16, 95% CI: 0.10-0.26; HR = 0.20, 95% CI: 0.12-0.35; HR = 0.21, 95% CI: 0.12-0.39; HR = 0.20, 95% CI: 0.12-0.31; HR = 0.23, 95% CI: 0.16-0.34) in terms of PFS. And, SoC + apatinib showed relatively superior PFS when compared with PbCT (HR = 0.44, 95% CI: 0.22-0.92) and PfCT (HR = 0.21, 95% CI: 0.12-0.39), but similar PFS to SoC (HR = 0.65, 95% CI: 0.42-1.03). No statistical differences were observed for PFS in patients without BM between PbCT and SoC (HR = 1.49, 95% CI: 0.84-2.64), but both showed favorable PFS when compared with PfCT (PfCT vs. SoC, HR = 3.09, 95% CI: 2.06-4.55; PbCT vs. PfCT, HR = 0.14, 95% CI: 0.06-0.32). For patients without BM, osimertinib + bevacizumab was most likely to rank the first, with cumulative probabilities of 47.1%. For OS, SoC + PbCT was most likely to rank first in patients with and without BM, with cumulative probabilities of 46.8%, and 37.3%, respectively. CONCLUSION Osimertinib + bevacizumab is most likely to rank first in PFS in advanced EGFR -mutated NSCLC patients with or without BM, and SoC + PbCT is most likely to rank first in OS.
Humans ; Carcinoma, Non-Small-Cell Lung/metabolism* ; Afatinib/therapeutic use* ; Lung Neoplasms/metabolism* ; Bevacizumab/therapeutic use* ; Bayes Theorem ; Network Meta-Analysis ; Protein Kinase Inhibitors/therapeutic use* ; Pemetrexed/therapeutic use* ; ErbB Receptors/genetics* ; Brain Neoplasms/genetics* ; Mutation/genetics*