Objective: To develop and validate a radiomics prediction model for individualized prediction of perineural invasion(PNI) in colorectal cancer(CRC).Methods: After computed tomography(CT) radiomics features extraction,...Objective: To develop and validate a radiomics prediction model for individualized prediction of perineural invasion(PNI) in colorectal cancer(CRC).Methods: After computed tomography(CT) radiomics features extraction, a radiomics signature was constructed in derivation cohort(346 CRC patients). A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen(CEA) level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation(separate from the cohort used to build the model) was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram.Results: The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell's concordance index(c-index): 0.817; 95% confidence interval(95% CI): 0.811–0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination(c-index: 0.803; 95% CI: 0.794–0.812).Conclusions: Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.展开更多
Background:Tuberculosis(TB)prevalence is closely associated with poverty in China,and poor patients face more barriers to treatment.Using an insurance-based approach,the China-Gates TB program Phase II was implemented...Background:Tuberculosis(TB)prevalence is closely associated with poverty in China,and poor patients face more barriers to treatment.Using an insurance-based approach,the China-Gates TB program Phase II was implemented between 2012 and 2014 in three cities in China to improve access to TB care and reduce the financial burden on patients,particularly among the poor.This study aims to assess the program effects on service use,and its equity impact across different income groups.Methods:Data from 788 and 775 patients at baseline and final evaluation were available for analysis respectively.Inpatient and outpatient service utilization,treatment adherence,and patient satisfaction were assessed before and after the program,across different income groups(extreme poverty,moderate poverty and non-poverty),and in various program cities,using descriptive statistics and multi-variate regression models.Key stakeholder interviews were conducted to qualitatively evaluate program implementation and impacts.Results:After program implementation,the hospital admission rate increased more for the extreme poverty group(48.5 to 70.7%)and moderate poverty group(45.0 to 68.1%),compared to the non-poverty group(52.9 to 643%).The largest increase in the number of outpatient visits was also for the extreme poverty group(4.6 to 5.7).The proportion of patients with good medication adherence increased by 15 percentage points in the extreme poverty group and by ten percentage points in the other groups.Satisfaction rates were high in all groups.Qualitative feedback from stakeholders also suggested that increased reimbursement rates,easier reimbursement procedures,and allowance improved patients'service utilization.Implementation of case-based payment made service provision more compliant to clinical pathways.Conclusion:Patients in extreme or moderate poverty benefited more from the program compared to a non-poverty group,indicating improved equity in TB service access.The pro-poor design of the program provides important丨essons to other TB programs in China and other countries to better address TB care for the poor.展开更多
Background:In response to the high financial burden of health services facing tuberculosis(TB)patients in China,the China-Gates TB project,PhaseⅡ,has implemented a new financing and payment model as an important comp...Background:In response to the high financial burden of health services facing tuberculosis(TB)patients in China,the China-Gates TB project,PhaseⅡ,has implemented a new financing and payment model as an important component of the overall project in three cities in eastern,central and western China.The model focuses on increasing the reimbursement rate for TB patients and reforming provider payment methods by replacing fee-for-service with a case-based payment approach.This study investigated changes in out-of-pocket(OOP)health expenditure and the financial burden on TB patients before and after the interventions,with a focus on potential differential impacts on patients from different income groups.Methods:Three sample counties in each of the three prefectures:Zhenjiang,Yichang and Hanzhong were chosen as study sites.TB patients who started and completed treatment before,and during the intervention period,were randomly sampled and surveyed at the baseline in 2013 and final evaluation in 2015 respectively.OOP health expenditure and percentage of patients incurring catastrophic health expenditure(CHE)were calculated for different income groups.OLS regression and Iogit regression were conducted to explore the intervention's impacts on patient OOP health expenditure and financial burden after adjusting for other covariates.Key-informant interviews and focus group discussions were conducted to understand the reasons for any observed changes.Results:Data from 738(baseline)and 735(evaluation)patients were available for analysis.Patient mean OOP health expenditure increased from RMB 3576 to RMB 5791,and the percentage of patients incurring CHE also increased after intervention.The percentage increase in OOP health expenditure and the likelihood of incurring CHE were significantly lower for patients from the highest income group as compared to the lowest.Qualitative findings indicated that increased use of health services not covered by the standard package of the model was likely to have caused the increase in financial burden.Conclusions:The implementation of the new financing and payment model did not protect patients,especially those from the lowest income group,from financial difficulty,due partly to their increased use of health service.More financial resources should be mobilized to increase financial protection,particularly for poor patients,while cost containment strategies need to be developed and effectively implemented to improve the effective coverage of essential healthcare in China.展开更多
Gastric cancer(GC)is one of the most common malignant tumors with high mortality.Accurate diagnosis and treatment decisions for GC rely heavily on human experts’careful judgments on medical images.However,the improve...Gastric cancer(GC)is one of the most common malignant tumors with high mortality.Accurate diagnosis and treatment decisions for GC rely heavily on human experts’careful judgments on medical images.However,the improvement of the accuracy is hindered by imaging conditions,limited experience,objective criteria,and inter-observer discrepancies.Recently,the developments of machine learning,especially deep-learning algorithms,have been facilitating computers to extract more information from data automatically.Researchers are exploring the far-reaching applications of artificial intelligence(AI)in various clinical practices,including GC.Herein,we aim to provide a broad framework to summarize current research on AI in GC.In the screening of GC,AI can identify precancerous diseases and assist in early cancer detection with endoscopic examination and pathological confirmation.In the diagnosis of GC,AI can support tumor-node-metastasis(TNM)staging and subtype classification.For treatment decisions,AI can help with surgical margin determination and prognosis prediction.Meanwhile,current approaches are challenged by data scarcity and poor interpretability.To tackle these problems,more regulated data,unified processing procedures,and advanced algorithms are urgently needed to build more accurate and robust AI models for GC.展开更多
基金supported by the National Key Research and Development Program of China (No. 2017YFC1309100)the National Natural Scientific Foundation of China (No. 81771912, 81701782 and 81601469)
文摘Objective: To develop and validate a radiomics prediction model for individualized prediction of perineural invasion(PNI) in colorectal cancer(CRC).Methods: After computed tomography(CT) radiomics features extraction, a radiomics signature was constructed in derivation cohort(346 CRC patients). A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen(CEA) level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation(separate from the cohort used to build the model) was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram.Results: The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell's concordance index(c-index): 0.817; 95% confidence interval(95% CI): 0.811–0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination(c-index: 0.803; 95% CI: 0.794–0.812).Conclusions: Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.
文摘Background:Tuberculosis(TB)prevalence is closely associated with poverty in China,and poor patients face more barriers to treatment.Using an insurance-based approach,the China-Gates TB program Phase II was implemented between 2012 and 2014 in three cities in China to improve access to TB care and reduce the financial burden on patients,particularly among the poor.This study aims to assess the program effects on service use,and its equity impact across different income groups.Methods:Data from 788 and 775 patients at baseline and final evaluation were available for analysis respectively.Inpatient and outpatient service utilization,treatment adherence,and patient satisfaction were assessed before and after the program,across different income groups(extreme poverty,moderate poverty and non-poverty),and in various program cities,using descriptive statistics and multi-variate regression models.Key stakeholder interviews were conducted to qualitatively evaluate program implementation and impacts.Results:After program implementation,the hospital admission rate increased more for the extreme poverty group(48.5 to 70.7%)and moderate poverty group(45.0 to 68.1%),compared to the non-poverty group(52.9 to 643%).The largest increase in the number of outpatient visits was also for the extreme poverty group(4.6 to 5.7).The proportion of patients with good medication adherence increased by 15 percentage points in the extreme poverty group and by ten percentage points in the other groups.Satisfaction rates were high in all groups.Qualitative feedback from stakeholders also suggested that increased reimbursement rates,easier reimbursement procedures,and allowance improved patients'service utilization.Implementation of case-based payment made service provision more compliant to clinical pathways.Conclusion:Patients in extreme or moderate poverty benefited more from the program compared to a non-poverty group,indicating improved equity in TB service access.The pro-poor design of the program provides important丨essons to other TB programs in China and other countries to better address TB care for the poor.
基金The whole study was funded by the Bill and Melinda Gates Foundation.
文摘Background:In response to the high financial burden of health services facing tuberculosis(TB)patients in China,the China-Gates TB project,PhaseⅡ,has implemented a new financing and payment model as an important component of the overall project in three cities in eastern,central and western China.The model focuses on increasing the reimbursement rate for TB patients and reforming provider payment methods by replacing fee-for-service with a case-based payment approach.This study investigated changes in out-of-pocket(OOP)health expenditure and the financial burden on TB patients before and after the interventions,with a focus on potential differential impacts on patients from different income groups.Methods:Three sample counties in each of the three prefectures:Zhenjiang,Yichang and Hanzhong were chosen as study sites.TB patients who started and completed treatment before,and during the intervention period,were randomly sampled and surveyed at the baseline in 2013 and final evaluation in 2015 respectively.OOP health expenditure and percentage of patients incurring catastrophic health expenditure(CHE)were calculated for different income groups.OLS regression and Iogit regression were conducted to explore the intervention's impacts on patient OOP health expenditure and financial burden after adjusting for other covariates.Key-informant interviews and focus group discussions were conducted to understand the reasons for any observed changes.Results:Data from 738(baseline)and 735(evaluation)patients were available for analysis.Patient mean OOP health expenditure increased from RMB 3576 to RMB 5791,and the percentage of patients incurring CHE also increased after intervention.The percentage increase in OOP health expenditure and the likelihood of incurring CHE were significantly lower for patients from the highest income group as compared to the lowest.Qualitative findings indicated that increased use of health services not covered by the standard package of the model was likely to have caused the increase in financial burden.Conclusions:The implementation of the new financing and payment model did not protect patients,especially those from the lowest income group,from financial difficulty,due partly to their increased use of health service.More financial resources should be mobilized to increase financial protection,particularly for poor patients,while cost containment strategies need to be developed and effectively implemented to improve the effective coverage of essential healthcare in China.
基金supported by the National Natural Science Foundation of China[grant numbers 82022036,91959130,81971776,62027901,81930053]National Key R&D Program of China[grant number 2017YFA0205200]+2 种基金the Beijing Natural Science Foundation[grant number Z20J00105]Strategic Priority Research Program of Chinese Academy of Sciences[grant number XDB38040200]the Youth Innovation Promotion Association CAS[grant number Y2021049].
文摘Gastric cancer(GC)is one of the most common malignant tumors with high mortality.Accurate diagnosis and treatment decisions for GC rely heavily on human experts’careful judgments on medical images.However,the improvement of the accuracy is hindered by imaging conditions,limited experience,objective criteria,and inter-observer discrepancies.Recently,the developments of machine learning,especially deep-learning algorithms,have been facilitating computers to extract more information from data automatically.Researchers are exploring the far-reaching applications of artificial intelligence(AI)in various clinical practices,including GC.Herein,we aim to provide a broad framework to summarize current research on AI in GC.In the screening of GC,AI can identify precancerous diseases and assist in early cancer detection with endoscopic examination and pathological confirmation.In the diagnosis of GC,AI can support tumor-node-metastasis(TNM)staging and subtype classification.For treatment decisions,AI can help with surgical margin determination and prognosis prediction.Meanwhile,current approaches are challenged by data scarcity and poor interpretability.To tackle these problems,more regulated data,unified processing procedures,and advanced algorithms are urgently needed to build more accurate and robust AI models for GC.
基金This study was supported by the National Natural Science Foundation of China （Grant Nos. 41201350 and 41371355）. We sincerely thank the University of North Carolina Bayesian Maximum Entropy （UNC-BME） laboratory at the UNC at Chapel Hill for supplying the BME codes.
