Objective:To explore the feasibility of selecting appropriate respiratory motion management strategies for patients undergoing stereotactic radiotherapy for liver cancer using chi-square feature testing and machine learning techniques.Methods:A retrospective analysis was conducted on 95 liver cancer patients who underwent respiratory motion evaluation at Xiangya Hospital of Central South University between March 2022 and August 2024. Chi-square testing was used to screen features related to respiratory motion evaluation in liver cancer patients. Based on these features, predictive models were constructed using 4 machine learning classification algorithms: support vector machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), and adaptive boosting (AdaBoost). The predictive performance of these models was evaluated using accuracy, sensitivity, specificity, F1 value, and area under the curve (AUC).Results:The accuracy values for the SVM, RF, GBDT and AdaBoost models were 0.75, 0.75, 0.70, and 0.82, respectively. The sensitivity values were 0.82, 0.82, 0.64, and 0.82, respectively. The specificity values were 0.63, 0.63, 0.63, and 0.75, respectively. The F1 scores were 0.78, 0.78, 0.67, and 0.82, respectively. The AUC values were 0.85, 0.80, 0.76, and 0.85, respectively.Conclusions:The predictive models constructed by combining chi-square feature testing and machine learning methods can effectively predict the selection of respiratory motion management strategies. Among the models, the AdaBoost model demonstrated the best predictive performance for selecting respiratory motion management strategies.

Objective:To explore the feasibility of selecting appropriate respiratory motion management strategies for patients undergoing stereotactic radiotherapy for liver cancer using chi-square feature testing and machine learning techniques.Methods:A retrospective analysis was conducted on 95 liver cancer patients who underwent respiratory motion evaluation at Xiangya Hospital of Central South University between March 2022 and August 2024. Chi-square testing was used to screen features related to respiratory motion evaluation in liver cancer patients. Based on these features, predictive models were constructed using 4 machine learning classification algorithms: support vector machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), and adaptive boosting (AdaBoost). The predictive performance of these models was evaluated using accuracy, sensitivity, specificity, F1 value, and area under the curve (AUC).Results:The accuracy values for the SVM, RF, GBDT and AdaBoost models were 0.75, 0.75, 0.70, and 0.82, respectively. The sensitivity values were 0.82, 0.82, 0.64, and 0.82, respectively. The specificity values were 0.63, 0.63, 0.63, and 0.75, respectively. The F1 scores were 0.78, 0.78, 0.67, and 0.82, respectively. The AUC values were 0.85, 0.80, 0.76, and 0.85, respectively.Conclusions:The predictive models constructed by combining chi-square feature testing and machine learning methods can effectively predict the selection of respiratory motion management strategies. Among the models, the AdaBoost model demonstrated the best predictive performance for selecting respiratory motion management strategies.

Objective To investigate the effectiveness of a movable(with the distance between the temporal scalp and the detector being adjustable)array of optically pumped magnetometers for magnetoencephalography(OPM-MEG)in capturing auditory evoked response signals in healthy subjects living at low altitudes,and to provide a useful technical reference for subsequent exploration of the changes in brain functions in populations living at high altitudes on a long-term basis.Methods Forty healthy subjects living at a low altitude(470 m above sea level)were recruited.The distance between the scalp and the bilateral temporal lobe detector was adjusted,and the subjects'auditory responses in the temporal lobes were recorded at the distances of 0 mm,5 mm,10 mm,and 15 mm.For the different distances,the M100 peak signal strength,noise,signal-to-noise ratio(SNR),and latency were analyzed along with the corresponding auditory source localization maps.A single-factor analysis of variance was conducted to compare the differences in response signals at varying distances.Results As the distance between the scalp and the detector increased,the noise,the signal,and the SNR gradually weakened(P＜0.001).The noise and signal showed a tendency of linear decline.On the other hand,the SNR reached its maximum at 5 mm and did not show a tendency of linear decline.Latency was not affected by the distance(P=0.72).The results of the auditory stimulus source reconstruction were generally consistent.Conclusion When the distance between the detector and the scalp is 5 mm,the SNR value is the highest,resulting in high sensitivity and high signal strength.On the other hand,even when the distance between the detector and the scalp reaches 15 mm,the SNR of the OPM-MEG is still higher than 16 dB,which meets the clinical signal acquisition requirements.Furthermore,the auditory stimulus source reconstruction results were generally consistent.Changing the scalp-to-detector distance does not affect the applicability of the source localization results,validating the device's effectiveness in signal acquisition.

Objective:To explore the health economic evaluation methods of disease screening,providing insights for scholars conducting related research.Methods:By integrating relevant content from modem epidemiology,health statistics,and health economics,it investigates the differences in conducting health economic evaluations of disease screening using authenticity indicators(sensitivity,specificity)and predictive value indicators(positive predictive value,negative predictive value),and combines empirical studies with cases from the literature.Results:Authenticity indicators(sensitivity,specificity)are intrinsic measures of diagnostic tests,unaffected by the disease prevalence in the sample population,making them more suitable for conducting health economic evaluations of disease screening.Moreover,authenticity indicators are easy to obtain,and the model calculations are straightforward and convenient.Case study results demonstrate that compared to risk score screening,the Incremental Cost-Effectiveness Ratio(ICER)of fasting plasma glucose(FPG)testing for type 2 diabetes is 626,indicating that for every additional diabetic patient identified through risk score screening,an extra 626 yuan is spent.Conclusion:It is recommended to use authenticity indicators for conducting health economic evaluations of disease screening,standardize the path methods for model selection,and continuously improve the evidence quality of health economic evaluations of disease screening in China.

Objective:To explore the health economic evaluation methods of disease screening,providing insights for scholars conducting related research.Methods:By integrating relevant content from modem epidemiology,health statistics,and health economics,it investigates the differences in conducting health economic evaluations of disease screening using authenticity indicators(sensitivity,specificity)and predictive value indicators(positive predictive value,negative predictive value),and combines empirical studies with cases from the literature.Results:Authenticity indicators(sensitivity,specificity)are intrinsic measures of diagnostic tests,unaffected by the disease prevalence in the sample population,making them more suitable for conducting health economic evaluations of disease screening.Moreover,authenticity indicators are easy to obtain,and the model calculations are straightforward and convenient.Case study results demonstrate that compared to risk score screening,the Incremental Cost-Effectiveness Ratio(ICER)of fasting plasma glucose(FPG)testing for type 2 diabetes is 626,indicating that for every additional diabetic patient identified through risk score screening,an extra 626 yuan is spent.Conclusion:It is recommended to use authenticity indicators for conducting health economic evaluations of disease screening,standardize the path methods for model selection,and continuously improve the evidence quality of health economic evaluations of disease screening in China.

Objective:To explore the health economic evaluation methods of disease screening,providing insights for scholars conducting related research.Methods:By integrating relevant content from modem epidemiology,health statistics,and health economics,it investigates the differences in conducting health economic evaluations of disease screening using authenticity indicators(sensitivity,specificity)and predictive value indicators(positive predictive value,negative predictive value),and combines empirical studies with cases from the literature.Results:Authenticity indicators(sensitivity,specificity)are intrinsic measures of diagnostic tests,unaffected by the disease prevalence in the sample population,making them more suitable for conducting health economic evaluations of disease screening.Moreover,authenticity indicators are easy to obtain,and the model calculations are straightforward and convenient.Case study results demonstrate that compared to risk score screening,the Incremental Cost-Effectiveness Ratio(ICER)of fasting plasma glucose(FPG)testing for type 2 diabetes is 626,indicating that for every additional diabetic patient identified through risk score screening,an extra 626 yuan is spent.Conclusion:It is recommended to use authenticity indicators for conducting health economic evaluations of disease screening,standardize the path methods for model selection,and continuously improve the evidence quality of health economic evaluations of disease screening in China.

Objective:To explore the health economic evaluation methods of disease screening,providing insights for scholars conducting related research.Methods:By integrating relevant content from modem epidemiology,health statistics,and health economics,it investigates the differences in conducting health economic evaluations of disease screening using authenticity indicators(sensitivity,specificity)and predictive value indicators(positive predictive value,negative predictive value),and combines empirical studies with cases from the literature.Results:Authenticity indicators(sensitivity,specificity)are intrinsic measures of diagnostic tests,unaffected by the disease prevalence in the sample population,making them more suitable for conducting health economic evaluations of disease screening.Moreover,authenticity indicators are easy to obtain,and the model calculations are straightforward and convenient.Case study results demonstrate that compared to risk score screening,the Incremental Cost-Effectiveness Ratio(ICER)of fasting plasma glucose(FPG)testing for type 2 diabetes is 626,indicating that for every additional diabetic patient identified through risk score screening,an extra 626 yuan is spent.Conclusion:It is recommended to use authenticity indicators for conducting health economic evaluations of disease screening,standardize the path methods for model selection,and continuously improve the evidence quality of health economic evaluations of disease screening in China.

Objective:To explore the health economic evaluation methods of disease screening,providing insights for scholars conducting related research.Methods:By integrating relevant content from modem epidemiology,health statistics,and health economics,it investigates the differences in conducting health economic evaluations of disease screening using authenticity indicators(sensitivity,specificity)and predictive value indicators(positive predictive value,negative predictive value),and combines empirical studies with cases from the literature.Results:Authenticity indicators(sensitivity,specificity)are intrinsic measures of diagnostic tests,unaffected by the disease prevalence in the sample population,making them more suitable for conducting health economic evaluations of disease screening.Moreover,authenticity indicators are easy to obtain,and the model calculations are straightforward and convenient.Case study results demonstrate that compared to risk score screening,the Incremental Cost-Effectiveness Ratio(ICER)of fasting plasma glucose(FPG)testing for type 2 diabetes is 626,indicating that for every additional diabetic patient identified through risk score screening,an extra 626 yuan is spent.Conclusion:It is recommended to use authenticity indicators for conducting health economic evaluations of disease screening,standardize the path methods for model selection,and continuously improve the evidence quality of health economic evaluations of disease screening in China.

Objective:To explore the health economic evaluation methods of disease screening,providing insights for scholars conducting related research.Methods:By integrating relevant content from modem epidemiology,health statistics,and health economics,it investigates the differences in conducting health economic evaluations of disease screening using authenticity indicators(sensitivity,specificity)and predictive value indicators(positive predictive value,negative predictive value),and combines empirical studies with cases from the literature.Results:Authenticity indicators(sensitivity,specificity)are intrinsic measures of diagnostic tests,unaffected by the disease prevalence in the sample population,making them more suitable for conducting health economic evaluations of disease screening.Moreover,authenticity indicators are easy to obtain,and the model calculations are straightforward and convenient.Case study results demonstrate that compared to risk score screening,the Incremental Cost-Effectiveness Ratio(ICER)of fasting plasma glucose(FPG)testing for type 2 diabetes is 626,indicating that for every additional diabetic patient identified through risk score screening,an extra 626 yuan is spent.Conclusion:It is recommended to use authenticity indicators for conducting health economic evaluations of disease screening,standardize the path methods for model selection,and continuously improve the evidence quality of health economic evaluations of disease screening in China.

Objective:To explore the health economic evaluation methods of disease screening,providing insights for scholars conducting related research.Methods:By integrating relevant content from modem epidemiology,health statistics,and health economics,it investigates the differences in conducting health economic evaluations of disease screening using authenticity indicators(sensitivity,specificity)and predictive value indicators(positive predictive value,negative predictive value),and combines empirical studies with cases from the literature.Results:Authenticity indicators(sensitivity,specificity)are intrinsic measures of diagnostic tests,unaffected by the disease prevalence in the sample population,making them more suitable for conducting health economic evaluations of disease screening.Moreover,authenticity indicators are easy to obtain,and the model calculations are straightforward and convenient.Case study results demonstrate that compared to risk score screening,the Incremental Cost-Effectiveness Ratio(ICER)of fasting plasma glucose(FPG)testing for type 2 diabetes is 626,indicating that for every additional diabetic patient identified through risk score screening,an extra 626 yuan is spent.Conclusion:It is recommended to use authenticity indicators for conducting health economic evaluations of disease screening,standardize the path methods for model selection,and continuously improve the evidence quality of health economic evaluations of disease screening in China.