The operation furnace profile for the high heat load zone was one of the important factors affecting the stable and high-quality production of the blast furnace,but it was difficult to monitor directly.To address this...The operation furnace profile for the high heat load zone was one of the important factors affecting the stable and high-quality production of the blast furnace,but it was difficult to monitor directly.To address this issue,an online calculation model for the operation furnace profile was proposed based on a dual-driven approach combining data and mechanisms,by integrating mechanism experiment,numerical simulation,and machine learning.The experimentally determined slag layer hanging temperature was 1130℃,and the thermal conductivity ranged from 1.32 to 1.96 m^(2)℃^(-1).Based on the 3D slag-hanging numerical simulation model,a database was constructed,containing 2294 sets of mechanism cases for the slag layer.The fusion of data modeling,heat transfer theory,and expert experience enabled the online calculation of key input variables for the operation furnace profile,particularly the quantification of the“black-box”variable of gas temperature.Simulated data were used as inputs,and light gradient boosting machine was applied to construct the online calculation model for the operation furnace profile.This model facilitated the online calculation of the slag layer thickness and other key indices.The coefficient of determination of the model exceeded 0.98,indicating high accuracy.A slag layer state judgment model was constructed,categorizing states as shedding,too thin,normal,and too thick.Real-time data were applied,and the average slag thickness in the high heat load area of the test data ranged from 40 to 80 mm,which was consistent with field experience.The absolute value of the Pearson correlation coefficient between slag layer thickness,thermocouple temperature,and heat load data was above 0.85,indicating that the calculated results closely aligned with the actual trends.A 3D visual online monitoring system for the operation furnace profile was created,and it has been successfully implemented at the blast furnace site.展开更多
BACKGROUND No single endoscopic feature can reliably predict the pathological nature of colorectal tumors(CRTs).AIM To establish and validate a simple online calculator to predict the pathological nature of CRTs based...BACKGROUND No single endoscopic feature can reliably predict the pathological nature of colorectal tumors(CRTs).AIM To establish and validate a simple online calculator to predict the pathological nature of CRTs based on white-light endoscopy.METHODS This was a single-center study.During the identification stage,530 consecutive patients with CRTs were enrolled from January 2015 to December 2021 as the derivation group.Logistic regression analysis was performed.A novel online calculator to predict the pathological nature of CRTs based on white-light images was established and verified internally.During the validation stage,two series of 110 images obtained using white-light endoscopy were distributed to 10 endoscopists[five highly experienced endoscopists and five less experienced endoscopists(LEEs)]for external validation before and after systematic training.RESULTS A total of 750 patients were included,with an average age of 63.6±10.4 years.Early colorectal cancer(ECRC)was detected in 351(46.8%)patients.Tumor size,left semicolon site,rectal site,acanthosis,depression and an uneven surface were independent risk factors for ECRC.The C-index of the ECRC calculator prediction model was 0.906(P=0.225,Hosmer-Lemeshow test).For the LEEs,significant improvement was made in the sensitivity,specificity and accuracy(57.6%vs 75.5%;72.3%vs 82.4%;64.2%vs 80.2%;P<0.05),respectively,after training with the ECRC online calculator prediction model.CONCLUSION A novel online calculator including tumor size,location,acanthosis,depression,and uneven surface can accurately predict the pathological nature of ECRC.展开更多
Background The role of adjuvant transarterial chemoembolisation(TACE)to reduce postoperative recurrence varies widely among patients undergoing hepatectomy with curative intent for hepatocellular carcinoma(HCC).Person...Background The role of adjuvant transarterial chemoembolisation(TACE)to reduce postoperative recurrence varies widely among patients undergoing hepatectomy with curative intent for hepatocellular carcinoma(HCC).Personalised predictive tool to select which patients may benefit from adjuvant TACE is lacking.This study aimed to develop and validate an online calculator for estimating the reduced risk of early recurrence from adjuvant TACE for patients with HCC.Methods From a multi-institutional database,2590 eligible patients undergoing curative-intent hepatectomy for HCC were enrolled,and randomly assigned to the training and validation cohorts.Independent predictors of early recurrence within 1 year of surgery were identified in the training cohort,and subsequently used to construct a model and corresponding prediction calculator.The predictive performance of the model was validated using concordance indexes(C-indexes)and calibration curves,and compared with conventional HCC staging systems.The reduced risk of early recurrence when receiving adjuvant TACE was used to estimate the expected benefit from adjuvant TACE.Results The prediction model was developed by integrating eight factors that were independently associated with risk of early recurrence:alpha-fetoprotein level,maximum tumour size,tumour number,macrovascular and microvascular invasion,satellite nodules,resection margin and adjuvant TACE.The model demonstrated good calibration and discrimination in the training and validation cohorts(C-indexes:0.799 and 0.778,respectively),and performed better among the whole cohort than four conventional HCC staging systems(C-indexes:0.797 vs 0.562–0.673,all p<0.001).An online calculator was built to estimate the reduced risk of early recurrence from adjuvant TACE for patients with resected HCC.Conclusions The proposed calculator can be adopted to assist decision-making for clinicians and patients to determine which patients with resected HCC can significantly benefit from adjuvant TACE.WHAT IS ALREADY KNOWN ON THIS TOPIC⇒Previous studies have indicated that adjuvant transarterial chemoembolisation(TACE)may im-prove long-term survival in certain subgroups of patients with hepatocellular carcinoma(HCC)after hepatectomy.⇒However,these studies did not provide personalised risk assessment or net benefit estimation for indi-vidual patients,highlighting the need for a more refined prediction model.WHAT THIS STUDY ADDS⇒This study developed a risk prediction model in-corporating eight independent factors associat-ed with early recurrence after hepatectomy for HCC,demonstrating good predictive accuracy and discrimination.⇒The model outperformed four commonly used con-ventional HCC staging systems and facilitated the development of an online calculator to estimate in-dividual patient’s reduced risk of early recurrence using adjuvant TACE.HOW THIS STUDY MIGHT AFFECT RESEARCH,PRACTICE OR POLICY⇒The study’s findings may assist clinicians in decid-ing whether to use adjuvant TACE after hepatectomy for HCC,potentially improving patient outcomes.⇒Further research should validate the model with larger cohorts or those from other centres to assess its broader applicability.展开更多
Background:Although offering the best chance of potential cure for patients with localized perihilar cholangiocarcinoma(pCCA),resection has been associated with high morbidity and sometimes poor long-term outcomes due...Background:Although offering the best chance of potential cure for patients with localized perihilar cholangiocarcinoma(pCCA),resection has been associated with high morbidity and sometimes poor long-term outcomes due to recurrence.We sought to develop a predictive model to identify individuals at high risk for very early recurrence(VER)after curative-intent surgery for pCCA.Methods:Patients who underwent curative-intent surgery for pCCA between 2000-2023 were identified from a multi-institutional database.An eXtreme Gradient Boosting(XGBoost)model was developed to estimate the risk of VER,defined as recurrence within 6 months after resection.The relative importance of clinicopathologic factors was determined using SHapley Additive exPlanations(SHAP)values.Results:Among 434 patients undergoing curative-intent resection for pCCA,65(15.0%)patients developed VER.Median overall survival(OS)among patients with and without VER was 8.4[interquartile range(IQR)6.6-11.3]versus 38.5(IQR 31.9-45.7)months(P<0.001).An XGBoost model was able to stratify patients relative to the risk of VER[low-risk:6-month recurrence-free survival(RFS)94.6%vs.intermediate-risk:6-month RFS 88.3%vs.high-risk:6-month RFS 40.0%;P<0.001].Similarly,3-year OS incrementally worsened based on VER risk(low-risk:75.3%vs.intermediate-risk:19.5%vs.high-risk:4.6%;P<0.001).The SHAP algorithm identified age,preoperative carbohydrate antigen 19-9(CA19-9)levels,tumor size and differentiation/grade,as well as lymph node metastasis as the five most important predictors of VER.The predictive accuracy of the model was good in the training[c-index:0.74,95%confidence interval(CI):0.67-0.81]and internal validation(c-index:0.77,95%CI:0.71-0.83)cohorts.An easy-to-use risk calculator for VER was developed and made available online at:https://junkawashima.shinyapps.io/VER_hilar/.Conclusions:A novel,machine learning based model was able to predict accurately the chance of VER after curative-intent resection of pCCA.In turn,the tool may help surgeons in the selection of patients likely to benefit the most from resection,as well as counsel individuals about the anticipated risk of recurrence in the early post-operative period.展开更多
基金financially supported by the National Natural Science Foundation of China(Grant Nos.52404343 and 52274326)the Fundamental Research Funds for the Central Universities(Grant Nos.N2425031 and N25BJD007)+1 种基金the China Postdoctoral Science Foundation(Grant No.2024M760370)the Liaoning Province Science and Technology Plan Joint Program(Key Research and Development Program Project)(Grant No.2023JH2/101800058).
文摘The operation furnace profile for the high heat load zone was one of the important factors affecting the stable and high-quality production of the blast furnace,but it was difficult to monitor directly.To address this issue,an online calculation model for the operation furnace profile was proposed based on a dual-driven approach combining data and mechanisms,by integrating mechanism experiment,numerical simulation,and machine learning.The experimentally determined slag layer hanging temperature was 1130℃,and the thermal conductivity ranged from 1.32 to 1.96 m^(2)℃^(-1).Based on the 3D slag-hanging numerical simulation model,a database was constructed,containing 2294 sets of mechanism cases for the slag layer.The fusion of data modeling,heat transfer theory,and expert experience enabled the online calculation of key input variables for the operation furnace profile,particularly the quantification of the“black-box”variable of gas temperature.Simulated data were used as inputs,and light gradient boosting machine was applied to construct the online calculation model for the operation furnace profile.This model facilitated the online calculation of the slag layer thickness and other key indices.The coefficient of determination of the model exceeded 0.98,indicating high accuracy.A slag layer state judgment model was constructed,categorizing states as shedding,too thin,normal,and too thick.Real-time data were applied,and the average slag thickness in the high heat load area of the test data ranged from 40 to 80 mm,which was consistent with field experience.The absolute value of the Pearson correlation coefficient between slag layer thickness,thermocouple temperature,and heat load data was above 0.85,indicating that the calculated results closely aligned with the actual trends.A 3D visual online monitoring system for the operation furnace profile was created,and it has been successfully implemented at the blast furnace site.
基金Supported by Capital’s Funds for Health Improvement and Research,No.2020-4-2085Beijing Science and Technology Plan Project,No.Z211100002921028。
文摘BACKGROUND No single endoscopic feature can reliably predict the pathological nature of colorectal tumors(CRTs).AIM To establish and validate a simple online calculator to predict the pathological nature of CRTs based on white-light endoscopy.METHODS This was a single-center study.During the identification stage,530 consecutive patients with CRTs were enrolled from January 2015 to December 2021 as the derivation group.Logistic regression analysis was performed.A novel online calculator to predict the pathological nature of CRTs based on white-light images was established and verified internally.During the validation stage,two series of 110 images obtained using white-light endoscopy were distributed to 10 endoscopists[five highly experienced endoscopists and five less experienced endoscopists(LEEs)]for external validation before and after systematic training.RESULTS A total of 750 patients were included,with an average age of 63.6±10.4 years.Early colorectal cancer(ECRC)was detected in 351(46.8%)patients.Tumor size,left semicolon site,rectal site,acanthosis,depression and an uneven surface were independent risk factors for ECRC.The C-index of the ECRC calculator prediction model was 0.906(P=0.225,Hosmer-Lemeshow test).For the LEEs,significant improvement was made in the sensitivity,specificity and accuracy(57.6%vs 75.5%;72.3%vs 82.4%;64.2%vs 80.2%;P<0.05),respectively,after training with the ECRC online calculator prediction model.CONCLUSION A novel online calculator including tumor size,location,acanthosis,depression,and uneven surface can accurately predict the pathological nature of ECRC.
基金supported by the National Natural Science Foundation of China(no.82273074)Dawn Project Foundation of Shanghai(no.21SG36)+2 种基金Adjunct Talent Fund of Zhejiang Provincial People’s Hospital(no.2021-YT)the Natural Science Foundation of Shanghai(no.22ZR1477900)Shanghai Science and Technology Committee Rising-Star Programme(no.22QA1411600).
文摘Background The role of adjuvant transarterial chemoembolisation(TACE)to reduce postoperative recurrence varies widely among patients undergoing hepatectomy with curative intent for hepatocellular carcinoma(HCC).Personalised predictive tool to select which patients may benefit from adjuvant TACE is lacking.This study aimed to develop and validate an online calculator for estimating the reduced risk of early recurrence from adjuvant TACE for patients with HCC.Methods From a multi-institutional database,2590 eligible patients undergoing curative-intent hepatectomy for HCC were enrolled,and randomly assigned to the training and validation cohorts.Independent predictors of early recurrence within 1 year of surgery were identified in the training cohort,and subsequently used to construct a model and corresponding prediction calculator.The predictive performance of the model was validated using concordance indexes(C-indexes)and calibration curves,and compared with conventional HCC staging systems.The reduced risk of early recurrence when receiving adjuvant TACE was used to estimate the expected benefit from adjuvant TACE.Results The prediction model was developed by integrating eight factors that were independently associated with risk of early recurrence:alpha-fetoprotein level,maximum tumour size,tumour number,macrovascular and microvascular invasion,satellite nodules,resection margin and adjuvant TACE.The model demonstrated good calibration and discrimination in the training and validation cohorts(C-indexes:0.799 and 0.778,respectively),and performed better among the whole cohort than four conventional HCC staging systems(C-indexes:0.797 vs 0.562–0.673,all p<0.001).An online calculator was built to estimate the reduced risk of early recurrence from adjuvant TACE for patients with resected HCC.Conclusions The proposed calculator can be adopted to assist decision-making for clinicians and patients to determine which patients with resected HCC can significantly benefit from adjuvant TACE.WHAT IS ALREADY KNOWN ON THIS TOPIC⇒Previous studies have indicated that adjuvant transarterial chemoembolisation(TACE)may im-prove long-term survival in certain subgroups of patients with hepatocellular carcinoma(HCC)after hepatectomy.⇒However,these studies did not provide personalised risk assessment or net benefit estimation for indi-vidual patients,highlighting the need for a more refined prediction model.WHAT THIS STUDY ADDS⇒This study developed a risk prediction model in-corporating eight independent factors associat-ed with early recurrence after hepatectomy for HCC,demonstrating good predictive accuracy and discrimination.⇒The model outperformed four commonly used con-ventional HCC staging systems and facilitated the development of an online calculator to estimate in-dividual patient’s reduced risk of early recurrence using adjuvant TACE.HOW THIS STUDY MIGHT AFFECT RESEARCH,PRACTICE OR POLICY⇒The study’s findings may assist clinicians in decid-ing whether to use adjuvant TACE after hepatectomy for HCC,potentially improving patient outcomes.⇒Further research should validate the model with larger cohorts or those from other centres to assess its broader applicability.
文摘Background:Although offering the best chance of potential cure for patients with localized perihilar cholangiocarcinoma(pCCA),resection has been associated with high morbidity and sometimes poor long-term outcomes due to recurrence.We sought to develop a predictive model to identify individuals at high risk for very early recurrence(VER)after curative-intent surgery for pCCA.Methods:Patients who underwent curative-intent surgery for pCCA between 2000-2023 were identified from a multi-institutional database.An eXtreme Gradient Boosting(XGBoost)model was developed to estimate the risk of VER,defined as recurrence within 6 months after resection.The relative importance of clinicopathologic factors was determined using SHapley Additive exPlanations(SHAP)values.Results:Among 434 patients undergoing curative-intent resection for pCCA,65(15.0%)patients developed VER.Median overall survival(OS)among patients with and without VER was 8.4[interquartile range(IQR)6.6-11.3]versus 38.5(IQR 31.9-45.7)months(P<0.001).An XGBoost model was able to stratify patients relative to the risk of VER[low-risk:6-month recurrence-free survival(RFS)94.6%vs.intermediate-risk:6-month RFS 88.3%vs.high-risk:6-month RFS 40.0%;P<0.001].Similarly,3-year OS incrementally worsened based on VER risk(low-risk:75.3%vs.intermediate-risk:19.5%vs.high-risk:4.6%;P<0.001).The SHAP algorithm identified age,preoperative carbohydrate antigen 19-9(CA19-9)levels,tumor size and differentiation/grade,as well as lymph node metastasis as the five most important predictors of VER.The predictive accuracy of the model was good in the training[c-index:0.74,95%confidence interval(CI):0.67-0.81]and internal validation(c-index:0.77,95%CI:0.71-0.83)cohorts.An easy-to-use risk calculator for VER was developed and made available online at:https://junkawashima.shinyapps.io/VER_hilar/.Conclusions:A novel,machine learning based model was able to predict accurately the chance of VER after curative-intent resection of pCCA.In turn,the tool may help surgeons in the selection of patients likely to benefit the most from resection,as well as counsel individuals about the anticipated risk of recurrence in the early post-operative period.
