BACKGROUND Portal hypertension(PHT),primarily induced by cirrhosis,manifests severe symptoms impacting patient survival.Although transjugular intrahepatic portosystemic shunt(TIPS)is a critical intervention for managi...BACKGROUND Portal hypertension(PHT),primarily induced by cirrhosis,manifests severe symptoms impacting patient survival.Although transjugular intrahepatic portosystemic shunt(TIPS)is a critical intervention for managing PHT,it carries risks like hepatic encephalopathy,thus affecting patient survival prognosis.To our knowledge,existing prognostic models for post-TIPS survival in patients with PHT fail to account for the interplay among and collective impact of various prognostic factors on outcomes.Consequently,the development of an innovative modeling approach is essential to address this limitation.AIM To develop and validate a Bayesian network(BN)-based survival prediction model for patients with cirrhosis-induced PHT having undergone TIPS.METHODS The clinical data of 393 patients with cirrhosis-induced PHT who underwent TIPS surgery at the Second Affiliated Hospital of Chongqing Medical University between January 2015 and May 2022 were retrospectively analyzed.Variables were selected using Cox and least absolute shrinkage and selection operator regression methods,and a BN-based model was established and evaluated to predict survival in patients having undergone TIPS surgery for PHT.RESULTS Variable selection revealed the following as key factors impacting survival:age,ascites,hypertension,indications for TIPS,postoperative portal vein pressure(post-PVP),aspartate aminotransferase,alkaline phosphatase,total bilirubin,prealbumin,the Child-Pugh grade,and the model for end-stage liver disease(MELD)score.Based on the above-mentioned variables,a BN-based 2-year survival prognostic prediction model was constructed,which identified the following factors to be directly linked to the survival time:age,ascites,indications for TIPS,concurrent hypertension,post-PVP,the Child-Pugh grade,and the MELD score.The Bayesian information criterion was 3589.04,and 10-fold cross-validation indicated an average log-likelihood loss of 5.55 with a standard deviation of 0.16.The model’s accuracy,precision,recall,and F1 score were 0.90,0.92,0.97,and 0.95 respectively,with the area under the receiver operating characteristic curve being 0.72.CONCLUSION This study successfully developed a BN-based survival prediction model with good predictive capabilities.It offers valuable insights for treatment strategies and prognostic evaluations in patients having undergone TIPS surgery for PHT.展开更多
BACKGROUND Gallbladder cancer(GBC)is the most common malignant tumor of the biliary system,and is often undetected until advanced stages,making curative surgery unfeasible for many patients.Curative surgery remains th...BACKGROUND Gallbladder cancer(GBC)is the most common malignant tumor of the biliary system,and is often undetected until advanced stages,making curative surgery unfeasible for many patients.Curative surgery remains the only option for long-term survival.Accurate postsurgical prognosis is crucial for effective treatment planning.tumor-node-metastasis staging,which focuses on tumor infiltration,lymph node metastasis,and distant metastasis,limits the accuracy of prognosis.Nomograms offer a more comprehensive and personalized approach by visually analyzing a broader range of prognostic factors,enhancing the precision of treatment planning for patients with GBC.AIM A retrospective study analyzed the clinical and pathological data of 93 patients who underwent radical surgery for GBC at Peking University People's Hospital from January 2015 to December 2020.Kaplan-Meier analysis was used to calculate the 1-,2-and 3-year survival rates.The log-rank test was used to evaluate factors impacting prognosis,with survival curves plotted for significant variables.Single-factor analysis revealed statistically significant differences,and multivariate Cox regression identified independent prognostic factors.A nomogram was developed and validated with receiver operating characteristic curves and calibration curves.Among 93 patients who underwent radical surgery for GBC,30 patients survived,accounting for 32.26%of the sample,with a median survival time of 38 months.The 1-year,2-year,and 3-year survival rates were 83.87%,68.82%,and 53.57%,respectively.Univariate analysis revealed that carbohydrate antigen 19-9 expre-ssion,T stage,lymph node metastasis,histological differentiation,surgical margins,and invasion of the liver,ex-trahepatic bile duct,nerves,and vessels(P≤0.001)significantly impacted patient prognosis after curative surgery.Multivariate Cox regression identified lymph node metastasis(P=0.03),histological differentiation(P<0.05),nerve invasion(P=0.036),and extrahepatic bile duct invasion(P=0.014)as independent risk factors.A nomogram model with a concordance index of 0.838 was developed.Internal validation confirmed the model's consistency in predicting the 1-year,2-year,and 3-year survival rates.CONCLUSION Lymph node metastasis,tumor differentiation,extrahepatic bile duct invasion,and perineural invasion are independent risk factors.A nomogram based on these factors can be used to personalize and improve treatment strategies.展开更多
BACKGROUND Hepatocellular carcinoma(HCC)is the most common primary liver malignancy with a rising incidence worldwide.The prognosis of HCC patients after radical resection remains poor.Radiomics is a novel machine lea...BACKGROUND Hepatocellular carcinoma(HCC)is the most common primary liver malignancy with a rising incidence worldwide.The prognosis of HCC patients after radical resection remains poor.Radiomics is a novel machine learning method that extracts quantitative features from medical images and provides predictive information of cancer,which can assist with cancer diagnosis,therapeutic decision-making and prognosis improvement.AIM To develop and validate a contrast-enhanced computed tomography-based radiomics model for predicting the overall survival(OS)of HCC patients after radical hepatectomy.METHODS A total of 150 HCC patients were randomly divided into a training cohort(n=107)and a validation cohort(n=43).Radiomics features were extracted from the entire tumour lesion.The least absolute shrinkage and selection operator algorithm was applied for the selection of radiomics features and the construction of the radiomics signature.Univariate and multivariate Cox regression analyses were used to identify the independent prognostic factors and develop the predictive nomogram,incorporating clinicopathological characteristics and the radiomics signature.The accuracy of the nomogram was assessed with the concordance index,receiver operating characteristic(ROC)curve and calibration curve.The clinical utility was evaluated by decision curve analysis(DCA).Kaplan–Meier methodology was used to compare the survival between the low-and high-risk subgroups.RESULTS In total,seven radiomics features were selected to construct the radiomics signature.According to the results of univariate and multivariate Cox regression analyses,alpha-fetoprotein(AFP),neutrophil-to-lymphocyte ratio(NLR)and radiomics signature were included to build the nomogram.The C-indices of the nomogram in the training and validation cohorts were 0.736 and 0.774,respectively.ROC curve analysis for predicting 1-,3-,and 5-year OS confirmed satisfactory accuracy[training cohort,area under the curve(AUC)=0.850,0.791 and 0.823,respectively;validation cohort,AUC=0.905,0.884 and 0.911,respectively].The calibration curve analysis indicated a good agreement between the nomogram-prediction and actual survival.DCA curves suggested that the nomogram had more benefit than traditional staging system models.Kaplan-Meier survival analysis indicated that patients in the low-risk group had longer OS and disease-free survival(all P<0.0001).CONCLUSION The nomogram containing the radiomics signature,NLR and AFP is a reliable tool for predicting the OS of HCC patients.展开更多
BACKGROUND Investigating molecular biomarkers that accurately predict prognosis is of considerable clinical significance.Accumulating evidence suggests that long noncoding ribonucleic acids(lncRNAs)are frequently aber...BACKGROUND Investigating molecular biomarkers that accurately predict prognosis is of considerable clinical significance.Accumulating evidence suggests that long noncoding ribonucleic acids(lncRNAs)are frequently aberrantly expressed in colorectal cancer(CRC).AIM To elucidate the prognostic function of multiple lncRNAs serving as biomarkers in CRC.METHODS We performed lncRNA expression profiling using the lncRNA mining approach in large CRC cohorts from The Cancer Genome Atlas(TCGA)database.Receiver operating characteristic analysis was performed to identify the optimal cutoff point at which patients could be classified into the high-risk or low-risk groups.Based on the Cox coefficient of the individual lncRNAs,we identified a ninelncRNA signature that was associated with the survival of CRC patients in the training set(n=175).The prognostic value of this nine-lncRNA signature was validated in the testing set(n=174)and TCGA set(n=349).The prognostic models,consisting of these nine CRC-specific lncRNAs,performed well for risk stratification in the testing set and TCGA set.Time-dependent receiver operating characteristic analysis indicated that this predictive model had good performance.RESULTS Multivariate Cox regression and stratification analysis demonstrated that this nine-lncRNA signature was independent of other clinical features in predicting overall survival.Functional enrichment analysis of Kyoto Encyclopedia of Genes and Genomes pathways and Gene Ontology terms further indicated that these nine prognostic lncRNAs were closely associated with carcinogenesis-associated pathways and biological functions in CRC.CONCLUSION A nine-lncRNA expression signature was identified and validated that could improve the prognosis prediction of CRC,thereby providing potential prognostic biomarkers and efficient therapeutic targets for patients with CRC.展开更多
Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)scans.These factors present significant challenges for MRI-based segmentation,a...Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)scans.These factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma progression.This study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation accuracy.ResMHA-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention mechanisms.This powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range dependencies.By doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor boundaries.We rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 datasets.Notably,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse datasets.Furthermore,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset size.Radiomic features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival prediction.This model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing methods.This ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient survival.Importantly,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.展开更多
Background:Due to the high heterogeneity among hepatocellular carcinoma(HCC)patients receiving transarterial chemoembolization(TACE),the prognosis of patients varies significantly.The decisionmaking on the initiation ...Background:Due to the high heterogeneity among hepatocellular carcinoma(HCC)patients receiving transarterial chemoembolization(TACE),the prognosis of patients varies significantly.The decisionmaking on the initiation and/or repetition of TACE under different liver functions is a matter of concern in clinical practice.Thus,we aimed to develop a prediction model for TACE candidates using risk stratification based on varied liver function.Methods:A total of 222 unresectable HCC patients who underwent TACE as their only treatment were included in this study.Cox proportional hazards regression was performed to select the independent risk factors and establish a predictive model for the overall survival(OS).The model was validated in patients with different Child-Pugh class and compared to previous TACE scoring systems.Results:The five independent risk factors,including alpha-fetoprotein(AFP)level,maximal tumor size,the increase of albumin-bilirubin(ALBI)grade score,tumor response,and the increase of aspartate aminotransferase(AST),were used to build a prognostic model(ASARA).In the training and validation cohorts,the OS of patients with ASARA score≤2 was significantly higher than that of patients with ASARA score>2(P<0.001,P=0.006,respectively).The ASARA model and its modified version“AS(ARA)”can effectively distinguish the OS(P<0.001,P=0.004)between patients with Child-Pugh class A and B,and the C-index was 0.687 and 0.706,respectively.For repeated TACE,the ASARA model was superior to Assessment for Retreatment with TACE(ART)and ALBI grade,maximal tumor size,AFP,and tumor response(ASAR)among Child-Pugh class A patients.For the first TACE,the performance of AS(ARA)was better than that of modified hepatoma arterial-embolization prognostic(mHAP),mHAP3,and ASA(R)models among Child-Pugh class B patients.Conclusions:The ASARA scoring system is valuable in the decision-making of TACE repetition for HCC patients,especially Child-Pugh class A patients.The modified AS(ARA)can be used to screen the ideal candidate for TACE initiation in Child-Pugh class B patients with poor liver function.展开更多
Gliomas,the most prevalent primary brain tumors,require accurate segmentation for diagnosis and risk assess-ment.In this paper,we develop a novel deep learning-based method,the Dynamic Hierarchical Attention for Impro...Gliomas,the most prevalent primary brain tumors,require accurate segmentation for diagnosis and risk assess-ment.In this paper,we develop a novel deep learning-based method,the Dynamic Hierarchical Attention for Improved Segmentation and Survival Prognosis(DHA-ISSP)model.The DHA-ISSP model combines a three-band 3D convolutional neural network(CNN)U-Net architecture with dynamic hierarchical attention mechanisms,enabling precise tumor segmentation and survival prediction.The DHA-ISSP model captures fine-grained details and contextual information by leveraging attention mechanisms at multiple levels,enhancing segmentation accuracy.By achieving remarkable results,our approach surpasses 369 competing teams in the 2020 Multimodal Brain Tumor Segmentation Challenge.With a Dice similarity coefficient of 0.89 and a Hausdorff distance of 4.8 mm,the DHA-ISSP model demonstrates its effectiveness in accurately segmenting brain tumors.We also extract radio mic characteristics from the segmented tumor areas using the DHA-ISSP model.By applying cross-validation of decision trees to the selected features,we identify crucial predictors for glioma survival,enabling personalized treatment strategies.Utilizing the DHA-ISSP model and the desired features,we assess patients’overall survival and categorize survivors into short,mid,in addition to long survivors.The proposed work achieved impressive performance metrics,including the highest accuracy of 0.91,precision of 0.84,recall of 0.92,F1 score of 0.88,specificity of 0.94,sensitivity of 0.92,area under the curve(AUC)value of 0.96,and the lowest mean absolute error value of 0.09 and mean squared error value of 0.18.These results clearly demonstrate the superiority of the proposed system in accurately segmenting brain tumors and predicting survival outcomes,highlighting its significant merit and potential for clinical applications.展开更多
Objective: This study aimed to establish a method to predict the overall survival(OS) of patients with stage Ⅰ-Ⅲ colorectal cancer(CRC) through coupling radiomics analysis of CT images with the measurement of tumor ...Objective: This study aimed to establish a method to predict the overall survival(OS) of patients with stage Ⅰ-Ⅲ colorectal cancer(CRC) through coupling radiomics analysis of CT images with the measurement of tumor ecosystem diversification.Methods: We retrospectively identified 161 consecutive patients with stage Ⅰ-Ⅲ CRC who had underwent radical resection as a training cohort. A total of 248 patients were recruited for temporary independent validation as external validation cohort 1, with 103 patients from an external institute as the external validation cohort 2. CT image features to describe tumor spatial heterogeneity leveraging the measurement of diversification of tumor ecosystem, were extracted to build a marker, termed the EcoRad signature. Multivariate Cox regression was used to assess the EcoRad signature, with a prediction model constructed to demonstrate its incremental value to the traditional staging system for OS prediction.Results: The EcoRad signature was significantly associated with OS in the training cohort [hazard ratio(HR)=6.670;95% confidence interval(95% CI): 3.433-12.956;P<0.001), external validation cohort 1(HR=2.866;95% CI: 1.646-4.990;P<0.001) and external validation cohort 2(HR=3.342;95% CI: 1.289-8.663;P=0.002).Incorporating the EcoRad signature into the prediction model presented a higher prediction ability(P<0.001) with respect to the C-index(0.813, 95% CI: 0.804-0.822 in the training cohort;0.758, 95% CI: 0.751-0.765 in the external validation cohort 1;and 0.746, 95% CI: 0.722-0.770 in external validation cohort 2), compared with the reference model that only incorporated tumor, node, metastasis(TNM) system, as well as a better calibration,improved reclassification and superior clinical usefulness.Conclusions: This study establishes a method to measure the spatial heterogeneity of CRC through coupling radiomics analysis with measurement of diversification of the tumor ecosystem, and suggests that this approach could effectively predict OS and could be used as a supplement for risk stratification among stage Ⅰ-Ⅲ CRC patients.展开更多
Gastric cancer is the second leading cause of cancer deaths worldwide.Despite the great progress in the diagnosis and treatment of gastric cancer,the incidence and mortality rate of the disease in China are still rela...Gastric cancer is the second leading cause of cancer deaths worldwide.Despite the great progress in the diagnosis and treatment of gastric cancer,the incidence and mortality rate of the disease in China are still relatively high.The high mortality rate of gastric cancer may be related to its low early diagnosis rate and poor prognosis.Much research has been focused on improving the sensitivity and specificity of diagnostic tools for gastric cancer,in order to more accurately predict the survival times of gastric cancer patients.Taking appropriate treatment measures is the key to reducing the mortality rate of gastric cancer.In the past decade,artificial intelligence technology has been applied to various fields of medicine as a branch of computer science.This article discusses the application and research status of artificial intelligence in gastric cancer diagnosis and survival prediction.展开更多
With the rapid development of Open-Source(OS),more and more software projects are maintained and developed in the form of OS.These Open-Source projects depend on and influence each other,gradually forming a huge OS pr...With the rapid development of Open-Source(OS),more and more software projects are maintained and developed in the form of OS.These Open-Source projects depend on and influence each other,gradually forming a huge OS project network,namely an Open-Source Software ECOsystem(OSSECO).Unfortunately,not all OS projects in the open-source ecosystem can be healthy and stable in the long term,and more projects will go from active to inactive and gradually die.In a tightly connected ecosystem,the death of one project can potentially cause the collapse of the entire ecosystem network.How can we effectively prevent such situations from happening?In this paper,we first identify the basic project characteristics that affect the survival of OS projects at both project and ecosystem levels through the proportional hazards model.Then,we utilize graph convolutional networks based on the ecosystem network to extract the ecosystem environment characteristics of OS projects.Finally,we fuse basic project characteristics and environmental project characteristics and construct a Hybrid Structured Prediction Model(HSPM)to predict the OS project survival state.The experimental results show that HSPM significantly improved compared to the traditional prediction model.Our work can substantially assist OS project managers in maintaining their projects’health.It can also provide an essential reference for developers when choosing the right open-source project for their production activities.展开更多
AIM: To investigate the relationship between osteopontin plasma concentrations and the severity of portal hypertension and to assess osteopontin prognostic value.METHODS: A cohort of 154 patients with confirmed liver ...AIM: To investigate the relationship between osteopontin plasma concentrations and the severity of portal hypertension and to assess osteopontin prognostic value.METHODS: A cohort of 154 patients with confirmed liver cirrhosis (112 ethylic, 108 men, age 34-72 years) were enrolled in the study. Hepatic venous pressure gradient (HVPG) measurement and laboratory and ultrasound examinations were carried out for all patients. HVPG was measured using a standard catheterization method with the balloon wedge technique. Osteopontin was measured using the enzyme-linked immunosorbent assay (ELISA) method in plasma. Patients were followed up with a specific focus on mortality. The control group consisted of 137 healthy age- and sex- matched individuals.RESULTS: The mean value of HVPG was 16.18 ± 5.6 mmHg. Compared to controls, the plasma levels of osteopontin in cirrhotic patients were significantly higher (P < 0.001). The plasma levels of osteopontin were positively related to HVPG (P = 0.0022, r = 0.25) and differed among the individual Child-Pugh groups of patients. The cut-off value of 80 ng/mL osteopontin distinguished patients with significant portal hypertension (HVPG above 10 mmHg) at 75% sensitivity and 63% specificity. The mean follow-up of patients was 3.7 ± 2.6 years. The probability of cumulative survival was 39% for patients with HVPG > 10 mmHg and 65% for those with HVPG ≤ 10 mmHg (P = 0.0086, odds ratio (OR), 2.92, 95% confidence interval (CI): 1.09-7.76). Osteopontin showed a similar prognostic value to HVPG. Patients with osteopontin values above 80 ng/mL had significantly lower cumulative survival compared to those with osteopontin ≤ 80 ng/mL (37% vs 56%, P = 0.00035; OR = 2.23, 95%CI: 1.06-4.68).CONCLUSION: Osteopontin is a non-invasive parameter of portal hypertension that distinguishes patients with clinically significant portal hypertension. It is a strong prognostic factor for survival.展开更多
Background:Breast cancer is one of the most common cancer in women and a proportion of patients experiences brain metastases with poor prognosis.The study aimed to construct a novel predictive clinical model to evalua...Background:Breast cancer is one of the most common cancer in women and a proportion of patients experiences brain metastases with poor prognosis.The study aimed to construct a novel predictive clinical model to evaluate the overall survival(OS)of patients with postoperative brain metastasis of breast cancer(BCBM)and validate its effectiveness.Methods:From 2010 to 2020,a total of 310 female patients with BCBM were diagnosed in The Affiliated Cancer Hospital of Xinjiang Medical University,and they were randomly assigned to the training cohort and the validation cohort.Data of another 173 BCBM patients were collected from the Surveillance,Epidemiology,and End Results Program(SEER)database as an external validation cohort.In the training cohort,the least absolute shrinkage and selection operator(LASSO)Cox regression model was used to determine the fundamental clinical predictive indicators and the nomogram was constructed to predict OS.The model capability was assessed using receiver operating characteristic,C-index,and calibration curves.Kaplan-Meier survival analysis was performed to evaluate clinical effectiveness of the risk stratification system in the model.The accuracy and prediction capability of the model were verified using the validation and SEER cohorts.Results:LASSO Cox regression analysis revealed that lymph node metastasis,molecular subtype,tumor size,chemotherapy,radiotherapy,and lung metastasis were statistically significantly correlated with BCBM.The C-indexes of the survival nomogram in the training,validation,and SEER cohorts were 0.714,0.710,and 0.670,respectively,which showed good prediction capability.The calibration curves demonstrated that the nomogram had great forecast precision,and a dynamic diagram was drawn to increase the maneuverability of the results.The Risk Stratification System showed that the OS of lowrisk patients was considerably better than that of high-risk patients(P<0.001).Conclusion:The nomogram prediction model constructed in this study has a good predictive value,which can effectively evaluate the survival rate of patients with postoperative BCBM.展开更多
Background： Restricted Boltzmann machines （RBMs） are endowed with the universal power of modeling （binary） joint distributions. Meanwhile, as a result of their confining network structure, training RBMs confronts...Background： Restricted Boltzmann machines （RBMs） are endowed with the universal power of modeling （binary） joint distributions. Meanwhile, as a result of their confining network structure, training RBMs confronts less difficulties when dealing with approximation and inference issues. But little work has been developed to fully exploit the capacity of these models to analyze cancer data, e.g., cancer genomic, transcriptomic, proteomic and epigenomic data. On the other hand, in the cancer data analysis task, the number of features/predictors is usually much larger than the sample size, which is known as the ＇~ 〉〉 N＂ problem and is also ubiquitous in other bioinformatics and computational biology fields. The ＂p 〉〉 N＂ problem puts the bias-variance trade-off in a more crucial place when designing statistical learning methods. However, to date, few RBM models have been particularly designed to address this issue. Methods： We propose a novel RBMs model, called elastic restricted Boltzmann machines （eRBMs）, which incorporates the elastic regularization term into the likelihood function, to balance the model complexity and sensitivity. Facilitated by the classic contrastive divergence （CD） algorithm, we develop the elastic contrastive divergence （eCD） algorithm which can train eRBMs efficiently. Results： We obtain several theoretical results on the rationality and properties of our model. We further evaluate the power of our model based on a challenging task -- predicting dichotomized survival time using the molecular profiling of tumors. The test results show that the prediction performance of eRBMs is much superior to that of the state-of-the-art methods. Conclusions： The proposed eRBMs are capable of dealing with the ＂p 〉〉 N＂ problems and have superior modeling performance over traditional methods. Our novel model is a promising method for future cancer data analysis.展开更多
基金Supported by the Chinese Nursing Association,No.ZHKY202111Scientific Research Program of School of Nursing,Chongqing Medical University,No.20230307Chongqing Science and Health Joint Medical Research Program,No.2024MSXM063.
文摘BACKGROUND Portal hypertension(PHT),primarily induced by cirrhosis,manifests severe symptoms impacting patient survival.Although transjugular intrahepatic portosystemic shunt(TIPS)is a critical intervention for managing PHT,it carries risks like hepatic encephalopathy,thus affecting patient survival prognosis.To our knowledge,existing prognostic models for post-TIPS survival in patients with PHT fail to account for the interplay among and collective impact of various prognostic factors on outcomes.Consequently,the development of an innovative modeling approach is essential to address this limitation.AIM To develop and validate a Bayesian network(BN)-based survival prediction model for patients with cirrhosis-induced PHT having undergone TIPS.METHODS The clinical data of 393 patients with cirrhosis-induced PHT who underwent TIPS surgery at the Second Affiliated Hospital of Chongqing Medical University between January 2015 and May 2022 were retrospectively analyzed.Variables were selected using Cox and least absolute shrinkage and selection operator regression methods,and a BN-based model was established and evaluated to predict survival in patients having undergone TIPS surgery for PHT.RESULTS Variable selection revealed the following as key factors impacting survival:age,ascites,hypertension,indications for TIPS,postoperative portal vein pressure(post-PVP),aspartate aminotransferase,alkaline phosphatase,total bilirubin,prealbumin,the Child-Pugh grade,and the model for end-stage liver disease(MELD)score.Based on the above-mentioned variables,a BN-based 2-year survival prognostic prediction model was constructed,which identified the following factors to be directly linked to the survival time:age,ascites,indications for TIPS,concurrent hypertension,post-PVP,the Child-Pugh grade,and the MELD score.The Bayesian information criterion was 3589.04,and 10-fold cross-validation indicated an average log-likelihood loss of 5.55 with a standard deviation of 0.16.The model’s accuracy,precision,recall,and F1 score were 0.90,0.92,0.97,and 0.95 respectively,with the area under the receiver operating characteristic curve being 0.72.CONCLUSION This study successfully developed a BN-based survival prediction model with good predictive capabilities.It offers valuable insights for treatment strategies and prognostic evaluations in patients having undergone TIPS surgery for PHT.
基金Supported by Xiao-Ping Chen Foundation for The Development of Science and Technology of Hubei Province,No.CXPJJH122002-061.
文摘BACKGROUND Gallbladder cancer(GBC)is the most common malignant tumor of the biliary system,and is often undetected until advanced stages,making curative surgery unfeasible for many patients.Curative surgery remains the only option for long-term survival.Accurate postsurgical prognosis is crucial for effective treatment planning.tumor-node-metastasis staging,which focuses on tumor infiltration,lymph node metastasis,and distant metastasis,limits the accuracy of prognosis.Nomograms offer a more comprehensive and personalized approach by visually analyzing a broader range of prognostic factors,enhancing the precision of treatment planning for patients with GBC.AIM A retrospective study analyzed the clinical and pathological data of 93 patients who underwent radical surgery for GBC at Peking University People's Hospital from January 2015 to December 2020.Kaplan-Meier analysis was used to calculate the 1-,2-and 3-year survival rates.The log-rank test was used to evaluate factors impacting prognosis,with survival curves plotted for significant variables.Single-factor analysis revealed statistically significant differences,and multivariate Cox regression identified independent prognostic factors.A nomogram was developed and validated with receiver operating characteristic curves and calibration curves.Among 93 patients who underwent radical surgery for GBC,30 patients survived,accounting for 32.26%of the sample,with a median survival time of 38 months.The 1-year,2-year,and 3-year survival rates were 83.87%,68.82%,and 53.57%,respectively.Univariate analysis revealed that carbohydrate antigen 19-9 expre-ssion,T stage,lymph node metastasis,histological differentiation,surgical margins,and invasion of the liver,ex-trahepatic bile duct,nerves,and vessels(P≤0.001)significantly impacted patient prognosis after curative surgery.Multivariate Cox regression identified lymph node metastasis(P=0.03),histological differentiation(P<0.05),nerve invasion(P=0.036),and extrahepatic bile duct invasion(P=0.014)as independent risk factors.A nomogram model with a concordance index of 0.838 was developed.Internal validation confirmed the model's consistency in predicting the 1-year,2-year,and 3-year survival rates.CONCLUSION Lymph node metastasis,tumor differentiation,extrahepatic bile duct invasion,and perineural invasion are independent risk factors.A nomogram based on these factors can be used to personalize and improve treatment strategies.
基金Supported by the National Natural Science Foundation of China,No.81372163the Science and Technology Planning Project of Guilin,No.20190218-1+2 种基金the Openin Project of Key laboratory of High-Incidence-Tumor Prevention&Treatment(Guangxi Medical University),Ministry of Education,No.GKE-KF202101the Program of Guangxi Zhuang Autonomous Region health and Family Planning Commission,No.Z20210706the Innovation and Entrepreneurship Project of University Students in Guangxi,No.202110601002.
文摘BACKGROUND Hepatocellular carcinoma(HCC)is the most common primary liver malignancy with a rising incidence worldwide.The prognosis of HCC patients after radical resection remains poor.Radiomics is a novel machine learning method that extracts quantitative features from medical images and provides predictive information of cancer,which can assist with cancer diagnosis,therapeutic decision-making and prognosis improvement.AIM To develop and validate a contrast-enhanced computed tomography-based radiomics model for predicting the overall survival(OS)of HCC patients after radical hepatectomy.METHODS A total of 150 HCC patients were randomly divided into a training cohort(n=107)and a validation cohort(n=43).Radiomics features were extracted from the entire tumour lesion.The least absolute shrinkage and selection operator algorithm was applied for the selection of radiomics features and the construction of the radiomics signature.Univariate and multivariate Cox regression analyses were used to identify the independent prognostic factors and develop the predictive nomogram,incorporating clinicopathological characteristics and the radiomics signature.The accuracy of the nomogram was assessed with the concordance index,receiver operating characteristic(ROC)curve and calibration curve.The clinical utility was evaluated by decision curve analysis(DCA).Kaplan–Meier methodology was used to compare the survival between the low-and high-risk subgroups.RESULTS In total,seven radiomics features were selected to construct the radiomics signature.According to the results of univariate and multivariate Cox regression analyses,alpha-fetoprotein(AFP),neutrophil-to-lymphocyte ratio(NLR)and radiomics signature were included to build the nomogram.The C-indices of the nomogram in the training and validation cohorts were 0.736 and 0.774,respectively.ROC curve analysis for predicting 1-,3-,and 5-year OS confirmed satisfactory accuracy[training cohort,area under the curve(AUC)=0.850,0.791 and 0.823,respectively;validation cohort,AUC=0.905,0.884 and 0.911,respectively].The calibration curve analysis indicated a good agreement between the nomogram-prediction and actual survival.DCA curves suggested that the nomogram had more benefit than traditional staging system models.Kaplan-Meier survival analysis indicated that patients in the low-risk group had longer OS and disease-free survival(all P<0.0001).CONCLUSION The nomogram containing the radiomics signature,NLR and AFP is a reliable tool for predicting the OS of HCC patients.
基金National Natural Science Foundation of China,No.81860433the Natural Science Youth Foundation of Jiangxi Province,No.20192BAB215036+2 种基金the Foundation for Fostering Young Scholar of Nanchang University,No.PY201822National Natural Science Foundation of China,No.81960359the Key Technology Research and Development Program of Jiangxi Province,No.20202BBG73024.
文摘BACKGROUND Investigating molecular biomarkers that accurately predict prognosis is of considerable clinical significance.Accumulating evidence suggests that long noncoding ribonucleic acids(lncRNAs)are frequently aberrantly expressed in colorectal cancer(CRC).AIM To elucidate the prognostic function of multiple lncRNAs serving as biomarkers in CRC.METHODS We performed lncRNA expression profiling using the lncRNA mining approach in large CRC cohorts from The Cancer Genome Atlas(TCGA)database.Receiver operating characteristic analysis was performed to identify the optimal cutoff point at which patients could be classified into the high-risk or low-risk groups.Based on the Cox coefficient of the individual lncRNAs,we identified a ninelncRNA signature that was associated with the survival of CRC patients in the training set(n=175).The prognostic value of this nine-lncRNA signature was validated in the testing set(n=174)and TCGA set(n=349).The prognostic models,consisting of these nine CRC-specific lncRNAs,performed well for risk stratification in the testing set and TCGA set.Time-dependent receiver operating characteristic analysis indicated that this predictive model had good performance.RESULTS Multivariate Cox regression and stratification analysis demonstrated that this nine-lncRNA signature was independent of other clinical features in predicting overall survival.Functional enrichment analysis of Kyoto Encyclopedia of Genes and Genomes pathways and Gene Ontology terms further indicated that these nine prognostic lncRNAs were closely associated with carcinogenesis-associated pathways and biological functions in CRC.CONCLUSION A nine-lncRNA expression signature was identified and validated that could improve the prognosis prediction of CRC,thereby providing potential prognostic biomarkers and efficient therapeutic targets for patients with CRC.
基金the Deanship of Research and Graduate Studies at King Khalid University for funding this work through a Large Research Project under grant number RGP2/254/45.
文摘Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)scans.These factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma progression.This study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation accuracy.ResMHA-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention mechanisms.This powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range dependencies.By doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor boundaries.We rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 datasets.Notably,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse datasets.Furthermore,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset size.Radiomic features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival prediction.This model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing methods.This ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient survival.Importantly,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.
基金This study was supported by a grant from Tianjin Key Medical Discipline(Specialty)Construction Project.
文摘Background:Due to the high heterogeneity among hepatocellular carcinoma(HCC)patients receiving transarterial chemoembolization(TACE),the prognosis of patients varies significantly.The decisionmaking on the initiation and/or repetition of TACE under different liver functions is a matter of concern in clinical practice.Thus,we aimed to develop a prediction model for TACE candidates using risk stratification based on varied liver function.Methods:A total of 222 unresectable HCC patients who underwent TACE as their only treatment were included in this study.Cox proportional hazards regression was performed to select the independent risk factors and establish a predictive model for the overall survival(OS).The model was validated in patients with different Child-Pugh class and compared to previous TACE scoring systems.Results:The five independent risk factors,including alpha-fetoprotein(AFP)level,maximal tumor size,the increase of albumin-bilirubin(ALBI)grade score,tumor response,and the increase of aspartate aminotransferase(AST),were used to build a prognostic model(ASARA).In the training and validation cohorts,the OS of patients with ASARA score≤2 was significantly higher than that of patients with ASARA score>2(P<0.001,P=0.006,respectively).The ASARA model and its modified version“AS(ARA)”can effectively distinguish the OS(P<0.001,P=0.004)between patients with Child-Pugh class A and B,and the C-index was 0.687 and 0.706,respectively.For repeated TACE,the ASARA model was superior to Assessment for Retreatment with TACE(ART)and ALBI grade,maximal tumor size,AFP,and tumor response(ASAR)among Child-Pugh class A patients.For the first TACE,the performance of AS(ARA)was better than that of modified hepatoma arterial-embolization prognostic(mHAP),mHAP3,and ASA(R)models among Child-Pugh class B patients.Conclusions:The ASARA scoring system is valuable in the decision-making of TACE repetition for HCC patients,especially Child-Pugh class A patients.The modified AS(ARA)can be used to screen the ideal candidate for TACE initiation in Child-Pugh class B patients with poor liver function.
基金study conception and design:S.Kannan,S.Anusuyadata collection:S.Kannan+1 种基金analysis and interpretation of results:S.Kannan,S.Anusuyadraft manuscript preparation:S.Kannan.All authors reviewed the results and approved the final version of the manuscript.
文摘Gliomas,the most prevalent primary brain tumors,require accurate segmentation for diagnosis and risk assess-ment.In this paper,we develop a novel deep learning-based method,the Dynamic Hierarchical Attention for Improved Segmentation and Survival Prognosis(DHA-ISSP)model.The DHA-ISSP model combines a three-band 3D convolutional neural network(CNN)U-Net architecture with dynamic hierarchical attention mechanisms,enabling precise tumor segmentation and survival prediction.The DHA-ISSP model captures fine-grained details and contextual information by leveraging attention mechanisms at multiple levels,enhancing segmentation accuracy.By achieving remarkable results,our approach surpasses 369 competing teams in the 2020 Multimodal Brain Tumor Segmentation Challenge.With a Dice similarity coefficient of 0.89 and a Hausdorff distance of 4.8 mm,the DHA-ISSP model demonstrates its effectiveness in accurately segmenting brain tumors.We also extract radio mic characteristics from the segmented tumor areas using the DHA-ISSP model.By applying cross-validation of decision trees to the selected features,we identify crucial predictors for glioma survival,enabling personalized treatment strategies.Utilizing the DHA-ISSP model and the desired features,we assess patients’overall survival and categorize survivors into short,mid,in addition to long survivors.The proposed work achieved impressive performance metrics,including the highest accuracy of 0.91,precision of 0.84,recall of 0.92,F1 score of 0.88,specificity of 0.94,sensitivity of 0.92,area under the curve(AUC)value of 0.96,and the lowest mean absolute error value of 0.09 and mean squared error value of 0.18.These results clearly demonstrate the superiority of the proposed system in accurately segmenting brain tumors and predicting survival outcomes,highlighting its significant merit and potential for clinical applications.
基金supported by the National Key R&D Program of China (No. 2021YFF1201003)the Key R&D Program of Guangdong Province, China (No. 2021B0101420006)+2 种基金the National Science Fund for Distinguished Young Scholars (No. 81925023 and 82071892)the National Natural Science Foundation of China (No. 81771912 and 82071892)the National Natural Science Foundation for Young Scientists of China (No. 81701782 and 81901910).
文摘Objective: This study aimed to establish a method to predict the overall survival(OS) of patients with stage Ⅰ-Ⅲ colorectal cancer(CRC) through coupling radiomics analysis of CT images with the measurement of tumor ecosystem diversification.Methods: We retrospectively identified 161 consecutive patients with stage Ⅰ-Ⅲ CRC who had underwent radical resection as a training cohort. A total of 248 patients were recruited for temporary independent validation as external validation cohort 1, with 103 patients from an external institute as the external validation cohort 2. CT image features to describe tumor spatial heterogeneity leveraging the measurement of diversification of tumor ecosystem, were extracted to build a marker, termed the EcoRad signature. Multivariate Cox regression was used to assess the EcoRad signature, with a prediction model constructed to demonstrate its incremental value to the traditional staging system for OS prediction.Results: The EcoRad signature was significantly associated with OS in the training cohort [hazard ratio(HR)=6.670;95% confidence interval(95% CI): 3.433-12.956;P<0.001), external validation cohort 1(HR=2.866;95% CI: 1.646-4.990;P<0.001) and external validation cohort 2(HR=3.342;95% CI: 1.289-8.663;P=0.002).Incorporating the EcoRad signature into the prediction model presented a higher prediction ability(P<0.001) with respect to the C-index(0.813, 95% CI: 0.804-0.822 in the training cohort;0.758, 95% CI: 0.751-0.765 in the external validation cohort 1;and 0.746, 95% CI: 0.722-0.770 in external validation cohort 2), compared with the reference model that only incorporated tumor, node, metastasis(TNM) system, as well as a better calibration,improved reclassification and superior clinical usefulness.Conclusions: This study establishes a method to measure the spatial heterogeneity of CRC through coupling radiomics analysis with measurement of diversification of the tumor ecosystem, and suggests that this approach could effectively predict OS and could be used as a supplement for risk stratification among stage Ⅰ-Ⅲ CRC patients.
文摘Gastric cancer is the second leading cause of cancer deaths worldwide.Despite the great progress in the diagnosis and treatment of gastric cancer,the incidence and mortality rate of the disease in China are still relatively high.The high mortality rate of gastric cancer may be related to its low early diagnosis rate and poor prognosis.Much research has been focused on improving the sensitivity and specificity of diagnostic tools for gastric cancer,in order to more accurately predict the survival times of gastric cancer patients.Taking appropriate treatment measures is the key to reducing the mortality rate of gastric cancer.In the past decade,artificial intelligence technology has been applied to various fields of medicine as a branch of computer science.This article discusses the application and research status of artificial intelligence in gastric cancer diagnosis and survival prediction.
基金This work was supported by the National Social Science Foundation(NSSF)Research on intelligent recommendation of multi-modal resources for children’s graded reading in smart library(22BTQ033)the Science and Technology Research and Development Program Project of China railway group limited(Project No.2021-Special-08).
文摘With the rapid development of Open-Source(OS),more and more software projects are maintained and developed in the form of OS.These Open-Source projects depend on and influence each other,gradually forming a huge OS project network,namely an Open-Source Software ECOsystem(OSSECO).Unfortunately,not all OS projects in the open-source ecosystem can be healthy and stable in the long term,and more projects will go from active to inactive and gradually die.In a tightly connected ecosystem,the death of one project can potentially cause the collapse of the entire ecosystem network.How can we effectively prevent such situations from happening?In this paper,we first identify the basic project characteristics that affect the survival of OS projects at both project and ecosystem levels through the proportional hazards model.Then,we utilize graph convolutional networks based on the ecosystem network to extract the ecosystem environment characteristics of OS projects.Finally,we fuse basic project characteristics and environmental project characteristics and construct a Hybrid Structured Prediction Model(HSPM)to predict the OS project survival state.The experimental results show that HSPM significantly improved compared to the traditional prediction model.Our work can substantially assist OS project managers in maintaining their projects’health.It can also provide an essential reference for developers when choosing the right open-source project for their production activities.
基金Supported by The Internal Grant Agency of the Czech Ministry of Health(http://iga.mzcr.cz/public Web/),No.NT 12290/4the Charles University in Prague(http://www.cuni.cz/UKEN-1.html),No.SVV 260156/2015the Czech Ministry of Health(http://mzcr.cz),No.MZCR-RVO VFN64165
文摘AIM: To investigate the relationship between osteopontin plasma concentrations and the severity of portal hypertension and to assess osteopontin prognostic value.METHODS: A cohort of 154 patients with confirmed liver cirrhosis (112 ethylic, 108 men, age 34-72 years) were enrolled in the study. Hepatic venous pressure gradient (HVPG) measurement and laboratory and ultrasound examinations were carried out for all patients. HVPG was measured using a standard catheterization method with the balloon wedge technique. Osteopontin was measured using the enzyme-linked immunosorbent assay (ELISA) method in plasma. Patients were followed up with a specific focus on mortality. The control group consisted of 137 healthy age- and sex- matched individuals.RESULTS: The mean value of HVPG was 16.18 ± 5.6 mmHg. Compared to controls, the plasma levels of osteopontin in cirrhotic patients were significantly higher (P < 0.001). The plasma levels of osteopontin were positively related to HVPG (P = 0.0022, r = 0.25) and differed among the individual Child-Pugh groups of patients. The cut-off value of 80 ng/mL osteopontin distinguished patients with significant portal hypertension (HVPG above 10 mmHg) at 75% sensitivity and 63% specificity. The mean follow-up of patients was 3.7 ± 2.6 years. The probability of cumulative survival was 39% for patients with HVPG > 10 mmHg and 65% for those with HVPG ≤ 10 mmHg (P = 0.0086, odds ratio (OR), 2.92, 95% confidence interval (CI): 1.09-7.76). Osteopontin showed a similar prognostic value to HVPG. Patients with osteopontin values above 80 ng/mL had significantly lower cumulative survival compared to those with osteopontin ≤ 80 ng/mL (37% vs 56%, P = 0.00035; OR = 2.23, 95%CI: 1.06-4.68).CONCLUSION: Osteopontin is a non-invasive parameter of portal hypertension that distinguishes patients with clinically significant portal hypertension. It is a strong prognostic factor for survival.
基金supported by National Natural Science Foundation of China(No.82060520)Tianshan Cedar Talent Training Project of Science and Technology Department of Xinjiang Uygur Autonomous Region(No.2020XS14).
文摘Background:Breast cancer is one of the most common cancer in women and a proportion of patients experiences brain metastases with poor prognosis.The study aimed to construct a novel predictive clinical model to evaluate the overall survival(OS)of patients with postoperative brain metastasis of breast cancer(BCBM)and validate its effectiveness.Methods:From 2010 to 2020,a total of 310 female patients with BCBM were diagnosed in The Affiliated Cancer Hospital of Xinjiang Medical University,and they were randomly assigned to the training cohort and the validation cohort.Data of another 173 BCBM patients were collected from the Surveillance,Epidemiology,and End Results Program(SEER)database as an external validation cohort.In the training cohort,the least absolute shrinkage and selection operator(LASSO)Cox regression model was used to determine the fundamental clinical predictive indicators and the nomogram was constructed to predict OS.The model capability was assessed using receiver operating characteristic,C-index,and calibration curves.Kaplan-Meier survival analysis was performed to evaluate clinical effectiveness of the risk stratification system in the model.The accuracy and prediction capability of the model were verified using the validation and SEER cohorts.Results:LASSO Cox regression analysis revealed that lymph node metastasis,molecular subtype,tumor size,chemotherapy,radiotherapy,and lung metastasis were statistically significantly correlated with BCBM.The C-indexes of the survival nomogram in the training,validation,and SEER cohorts were 0.714,0.710,and 0.670,respectively,which showed good prediction capability.The calibration curves demonstrated that the nomogram had great forecast precision,and a dynamic diagram was drawn to increase the maneuverability of the results.The Risk Stratification System showed that the OS of lowrisk patients was considerably better than that of high-risk patients(P<0.001).Conclusion:The nomogram prediction model constructed in this study has a good predictive value,which can effectively evaluate the survival rate of patients with postoperative BCBM.
文摘Background： Restricted Boltzmann machines （RBMs） are endowed with the universal power of modeling （binary） joint distributions. Meanwhile, as a result of their confining network structure, training RBMs confronts less difficulties when dealing with approximation and inference issues. But little work has been developed to fully exploit the capacity of these models to analyze cancer data, e.g., cancer genomic, transcriptomic, proteomic and epigenomic data. On the other hand, in the cancer data analysis task, the number of features/predictors is usually much larger than the sample size, which is known as the ＇~ 〉〉 N＂ problem and is also ubiquitous in other bioinformatics and computational biology fields. The ＂p 〉〉 N＂ problem puts the bias-variance trade-off in a more crucial place when designing statistical learning methods. However, to date, few RBM models have been particularly designed to address this issue. Methods： We propose a novel RBMs model, called elastic restricted Boltzmann machines （eRBMs）, which incorporates the elastic regularization term into the likelihood function, to balance the model complexity and sensitivity. Facilitated by the classic contrastive divergence （CD） algorithm, we develop the elastic contrastive divergence （eCD） algorithm which can train eRBMs efficiently. Results： We obtain several theoretical results on the rationality and properties of our model. We further evaluate the power of our model based on a challenging task -- predicting dichotomized survival time using the molecular profiling of tumors. The test results show that the prediction performance of eRBMs is much superior to that of the state-of-the-art methods. Conclusions： The proposed eRBMs are capable of dealing with the ＂p 〉〉 N＂ problems and have superior modeling performance over traditional methods. Our novel model is a promising method for future cancer data analysis.
