Background:Low-grade endometrial stromal sarcoma(LG-ESS)is a rare tumor that lacks a prognostic prediction model.Our study aimed to develop a nomogram to predict overall survival of LG-ESS patients.Methods:A total of ...Background:Low-grade endometrial stromal sarcoma(LG-ESS)is a rare tumor that lacks a prognostic prediction model.Our study aimed to develop a nomogram to predict overall survival of LG-ESS patients.Methods:A total of 1172 patients confirmed to have LG-ESS between 1988 and 2015 were selected from the Surveillance,Epidemiology and End Results(SEER)database.They were further divided into a training cohort and a validation cohort.The Akaike information criterion was used to select variables for the nomogram.The discrimination and calibration of the nomogram were evaluated using concordance index(C-index),area under time-dependent receiver operating characteristic curve(time-dependent AUC),and calibration plots.The net benefits of the nomogram at different threshold probabilities were quantified and compared with those of the International Federation of Gynecology and Obstetrics(FIGO)criteria-based tumor staging using decision curve analysis(DCA).Net reclassification index(NRI)and integrated discrimination improvement(IDI)were also used to compare the nomogram’s clinical utilitywith that of the FIGO criteria-based tumor staging.The risk stratifications of the nomogram and the FIGO criteria-based tumor staging were compared.Results:Seven variables were selected to establish the nomogram for LG-ESS.The C-index(0.814 for the training cohort and 0.837 for the validation cohort)and the time-dependent AUC(>0.7)indicated satisfactory discriminative ability of the nomogram.The calibration plots showed favorable consistency between the prediction of the nomogram and actual observations in both the training and validation cohorts.The NRI values(training cohort:0.271 for 5-year and 0.433 for 10-year OS prediction;validation cohort:0.310 for 5-year and 0.383 for 10-year OS prediction)and IDI(training cohort:0.146 for 5-year and 0.185 for 10-year OS prediction;validation cohort:0.177 for 5-year and 0.191 for 10-year OS prediction)indicated that the established nomogram performed significantly better than the FIGO criteria-based 展开更多
目的:经外周中心静脉置管(peripherally inserted central catheters,PICC)在恶性肿瘤化疗中的应用越来越广泛,其导管相关血栓的发生率也呈现上升的趋势,显著影响患者的治疗及生存质量。方法:回顾性分析2014年12月至2015年12月就诊于西...目的:经外周中心静脉置管(peripherally inserted central catheters,PICC)在恶性肿瘤化疗中的应用越来越广泛,其导管相关血栓的发生率也呈现上升的趋势,显著影响患者的治疗及生存质量。方法:回顾性分析2014年12月至2015年12月就诊于西安交通大学第一附属医院的286例进行PICC置管的恶性肿瘤患者相关临床资料,并对潜在的危险因素进行最小绝对收缩选择算子(least absolute shrinkage and selection operator,LASSO)回归分析,最终构建列线图模型。结果:286例PICC置管患者中,72例出现导管相关血栓。将研究所纳入的27个潜在的血栓相关危险因素进行LASSO回归分析,结果显示进行外周血管穿刺时是否应用超声引导、患者既往是否接受过经外周静脉化疗、置管期间是否存在其他合并症以及置管时的血浆D-二聚体含量为影响PICC置管患者发生导管相关血栓的危险因素,最终应用上述风险因素构建列线图预测模型,其C-index指数为0.688,拟合曲线和校正后地拟合曲线均位于10%的误差范围内。结论:结合穿刺技术、既往治疗,合并症以及D二聚体等因素所构建的列线图可以较准确的预测PICC相关血栓形成的风险,为临床诊疗工作的开展提供一定的理论基础和数据支持。展开更多
AIM To integrate clinically significant variables related to prognosis after curative resection for gallbladder carcinoma(GBC) into a predictive nomogram.METHODS One hundred and forty-two GBC patients who underwent cu...AIM To integrate clinically significant variables related to prognosis after curative resection for gallbladder carcinoma(GBC) into a predictive nomogram.METHODS One hundred and forty-two GBC patients who underwent curative intent surgical resection at Peking Union Medical College Hospital(PUMCH) were included. This retrospective case study was conducted at PUMCH of the Chinese Academy of Medical Sciences and Peking Union Medical College(CAMS & PUMC) in China from January 1, 2003 to January 1, 2018. The continuous variable carbohydrate antigen 19-9(CA19-9) was converted into a categorical variable(cCA19-9) based on the normal reference range. Stages 0 to IIIA were merged into one category, while the remaining stages were grouped into another category. Pathological grade X(GX) was treated as a missing value. A multivariate Cox proportional hazards model was used to select variables to construct a nomogram. Discrimination and calibration of the nomogram were performed via the concordance index(C-index) and calibration plots. The performance of the nomogram was estimated using the calibration curve. Receiver operating characteristic(ROC) curve analysis and decision curve analysis(DCA) were performed to evaluate the predictive accuracy and net benefit of the nomogram, respectively.RESULTS Of these 142 GBC patients, 55(38.7%) were male, and the median and mean age were 64 and 63.9 years, respectively. Forty-eight(33.8%) patients in this cohort were censored in the survival analysis. The median survival time was 20 months. A series of methods, including the likelihood ratio test and Akaike information criterion(AIC) as well as stepwise, forward, and backward analyses, were used to select the model, and all yielded identical results. Jaundice [hazard ratio(HR) = 2.9; 95% confidence interval(CI): 1.60-5.27], cCA19-9(HR = 3.2; 95%CI: 1.91-5.39), stage(HR = 1.89; 95%CI: 1.16-3.09), and resection(R)(HR = 2.82; 95%CI: 1.54-5.16) were selected as significant predictors and combined into a survival time predictive nomogram(C-i展开更多
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 ext...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:Lung cancer is the most commonly diagnosed cancer worldwide.Its survival rate can be significantly improved by early screening.Biomarkers based on radiomics features have been found to provide important phy...Background:Lung cancer is the most commonly diagnosed cancer worldwide.Its survival rate can be significantly improved by early screening.Biomarkers based on radiomics features have been found to provide important physiological information on tumors and considered as having the potential to be used in the early screening of lung cancer.In this study,we aim to establish a radiomics model and develop a tool to improve the discrimination between benign and malignant pulmonary nodules.Methods:A retrospective study was conducted on 875 patients with benign or malignant pulmonary nodules who underwent computed tomography(CT)examinations between June 2013 and June 2018.We assigned 612 patients to a training cohort and 263 patients to a validation cohort.Radiomics features were extracted from the CT images of each patient.Least absolute shrinkage and selection operator(LASSO)was used for radiomics feature selection and radiomics score calculation.Multivariate logistic regression analysis was used to develop a classification model and radiomics nomogram.Radiomics score and clinical variables were used to distinguish benign and malignant pulmonary nodules in logistic model.The performance of the radiomics nomogram was evaluated by the area under the curve(AUC),calibration curve and Hosmer-Lemeshow test in both the training and validation cohorts.Results:A radiomics score was built and consisted of 20 features selected by LASSO from 1288 radiomics features in the training cohort.The multivariate logistic model and radiomics nomogram were constructed using the radiomics score and patients’age.Good discrimination of benign and malignant pulmonary nodules was obtained from the training cohort(AUC,0.836;95%confidence interval[CI]:0.793-0.879)and validation cohort(AUC,0.809;95%CI:0.745-0.872).The Hosmer-Lemeshow test also showed good performance for the logistic regression model in the training cohort(P=0.765)and validation cohort(P=0.064).Good alignment with the calibration curve indicated the good performance of the nomogram展开更多
Nomograms for predicting the risk of prostate cancer developed using other populations may introduce sizable bias when applied to a Chinese cohort. In the present study, we sought to develop a nomogram for predicting ...Nomograms for predicting the risk of prostate cancer developed using other populations may introduce sizable bias when applied to a Chinese cohort. In the present study, we sought to develop a nomogram for predicting the probability of a positive initial prostate biopsy in a Chinese population. A total of 535 Chinese men who underwent a prostatic biopsy for the detection of prostate cancer in the past decade with complete biopsy data were included. Stepwise logistic regression was used to determine the independent predictors of a positive initial biopsy. Age, prostate-specific antigen （PSA）, prostate volume （PV）, digital rectal examination （DRE） status, % free PSA and transrectal ultrasound （TRUS） findings were included in the analysis. A nomogram model was developed that was based on these independent predictors to calculate the probability of a positive initial prostate biopsy. A receiver-operating characteristic curve was used to assess the accuracy of using the nomogram and PSA levels alone for predicting positive prostate biopsy. The rate for positive initial prostate biopsy was 41.7% （223/535）. The independent variables used to predict a positive initial prostate biopsy were age, PSA, PV and DRE status. The areas under the receiver-operating characteristic curve for a positive initial prostate biopsy for PSA alone and the nomogram were 79.7% and 84.8%, respectively. Our results indicate that the risk of a positive initial prostate biopsy can be predicted to a satisfactory level in a Chinese population using our nomogram. The nomogram can be used to identify and counsel patients who should consider a prostate biopsy, ultimately enhancing accuracy in diagnosing prostate cancer.展开更多
基金supported by grants no.81670123 and no.81670144 from the National Natural Science Foundation of China(NSFC).
文摘Background:Low-grade endometrial stromal sarcoma(LG-ESS)is a rare tumor that lacks a prognostic prediction model.Our study aimed to develop a nomogram to predict overall survival of LG-ESS patients.Methods:A total of 1172 patients confirmed to have LG-ESS between 1988 and 2015 were selected from the Surveillance,Epidemiology and End Results(SEER)database.They were further divided into a training cohort and a validation cohort.The Akaike information criterion was used to select variables for the nomogram.The discrimination and calibration of the nomogram were evaluated using concordance index(C-index),area under time-dependent receiver operating characteristic curve(time-dependent AUC),and calibration plots.The net benefits of the nomogram at different threshold probabilities were quantified and compared with those of the International Federation of Gynecology and Obstetrics(FIGO)criteria-based tumor staging using decision curve analysis(DCA).Net reclassification index(NRI)and integrated discrimination improvement(IDI)were also used to compare the nomogram’s clinical utilitywith that of the FIGO criteria-based tumor staging.The risk stratifications of the nomogram and the FIGO criteria-based tumor staging were compared.Results:Seven variables were selected to establish the nomogram for LG-ESS.The C-index(0.814 for the training cohort and 0.837 for the validation cohort)and the time-dependent AUC(>0.7)indicated satisfactory discriminative ability of the nomogram.The calibration plots showed favorable consistency between the prediction of the nomogram and actual observations in both the training and validation cohorts.The NRI values(training cohort:0.271 for 5-year and 0.433 for 10-year OS prediction;validation cohort:0.310 for 5-year and 0.383 for 10-year OS prediction)and IDI(training cohort:0.146 for 5-year and 0.185 for 10-year OS prediction;validation cohort:0.177 for 5-year and 0.191 for 10-year OS prediction)indicated that the established nomogram performed significantly better than the FIGO criteria-based
文摘目的:经外周中心静脉置管(peripherally inserted central catheters,PICC)在恶性肿瘤化疗中的应用越来越广泛,其导管相关血栓的发生率也呈现上升的趋势,显著影响患者的治疗及生存质量。方法:回顾性分析2014年12月至2015年12月就诊于西安交通大学第一附属医院的286例进行PICC置管的恶性肿瘤患者相关临床资料,并对潜在的危险因素进行最小绝对收缩选择算子(least absolute shrinkage and selection operator,LASSO)回归分析,最终构建列线图模型。结果:286例PICC置管患者中,72例出现导管相关血栓。将研究所纳入的27个潜在的血栓相关危险因素进行LASSO回归分析,结果显示进行外周血管穿刺时是否应用超声引导、患者既往是否接受过经外周静脉化疗、置管期间是否存在其他合并症以及置管时的血浆D-二聚体含量为影响PICC置管患者发生导管相关血栓的危险因素,最终应用上述风险因素构建列线图预测模型,其C-index指数为0.688,拟合曲线和校正后地拟合曲线均位于10%的误差范围内。结论:结合穿刺技术、既往治疗,合并症以及D二聚体等因素所构建的列线图可以较准确的预测PICC相关血栓形成的风险,为临床诊疗工作的开展提供一定的理论基础和数据支持。
基金Chinese Academy of Medical Sciences Innovation Fund for Medical Science,No.2017-I2M-4-003International Science and Technology Cooperation Projects,No.2015DFA30650 and No.2016YFE0107100+3 种基金Capital Special Research Project for Health Development,No.2014-2-4012Beijing Natural Science Foundation,No.L172055National Ten-thousand Talent ProgramBeijing Science and Technology Cooperation Special Award Subsidy Project
文摘AIM To integrate clinically significant variables related to prognosis after curative resection for gallbladder carcinoma(GBC) into a predictive nomogram.METHODS One hundred and forty-two GBC patients who underwent curative intent surgical resection at Peking Union Medical College Hospital(PUMCH) were included. This retrospective case study was conducted at PUMCH of the Chinese Academy of Medical Sciences and Peking Union Medical College(CAMS & PUMC) in China from January 1, 2003 to January 1, 2018. The continuous variable carbohydrate antigen 19-9(CA19-9) was converted into a categorical variable(cCA19-9) based on the normal reference range. Stages 0 to IIIA were merged into one category, while the remaining stages were grouped into another category. Pathological grade X(GX) was treated as a missing value. A multivariate Cox proportional hazards model was used to select variables to construct a nomogram. Discrimination and calibration of the nomogram were performed via the concordance index(C-index) and calibration plots. The performance of the nomogram was estimated using the calibration curve. Receiver operating characteristic(ROC) curve analysis and decision curve analysis(DCA) were performed to evaluate the predictive accuracy and net benefit of the nomogram, respectively.RESULTS Of these 142 GBC patients, 55(38.7%) were male, and the median and mean age were 64 and 63.9 years, respectively. Forty-eight(33.8%) patients in this cohort were censored in the survival analysis. The median survival time was 20 months. A series of methods, including the likelihood ratio test and Akaike information criterion(AIC) as well as stepwise, forward, and backward analyses, were used to select the model, and all yielded identical results. Jaundice [hazard ratio(HR) = 2.9; 95% confidence interval(CI): 1.60-5.27], cCA19-9(HR = 3.2; 95%CI: 1.91-5.39), stage(HR = 1.89; 95%CI: 1.16-3.09), and resection(R)(HR = 2.82; 95%CI: 1.54-5.16) were selected as significant predictors and combined into a survival time predictive nomogram(C-i
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.
基金Key R&D project of Shandong Province,Grant/Award Number:2018GSF118152
文摘Background:Lung cancer is the most commonly diagnosed cancer worldwide.Its survival rate can be significantly improved by early screening.Biomarkers based on radiomics features have been found to provide important physiological information on tumors and considered as having the potential to be used in the early screening of lung cancer.In this study,we aim to establish a radiomics model and develop a tool to improve the discrimination between benign and malignant pulmonary nodules.Methods:A retrospective study was conducted on 875 patients with benign or malignant pulmonary nodules who underwent computed tomography(CT)examinations between June 2013 and June 2018.We assigned 612 patients to a training cohort and 263 patients to a validation cohort.Radiomics features were extracted from the CT images of each patient.Least absolute shrinkage and selection operator(LASSO)was used for radiomics feature selection and radiomics score calculation.Multivariate logistic regression analysis was used to develop a classification model and radiomics nomogram.Radiomics score and clinical variables were used to distinguish benign and malignant pulmonary nodules in logistic model.The performance of the radiomics nomogram was evaluated by the area under the curve(AUC),calibration curve and Hosmer-Lemeshow test in both the training and validation cohorts.Results:A radiomics score was built and consisted of 20 features selected by LASSO from 1288 radiomics features in the training cohort.The multivariate logistic model and radiomics nomogram were constructed using the radiomics score and patients’age.Good discrimination of benign and malignant pulmonary nodules was obtained from the training cohort(AUC,0.836;95%confidence interval[CI]:0.793-0.879)and validation cohort(AUC,0.809;95%CI:0.745-0.872).The Hosmer-Lemeshow test also showed good performance for the logistic regression model in the training cohort(P=0.765)and validation cohort(P=0.064).Good alignment with the calibration curve indicated the good performance of the nomogram
文摘Nomograms for predicting the risk of prostate cancer developed using other populations may introduce sizable bias when applied to a Chinese cohort. In the present study, we sought to develop a nomogram for predicting the probability of a positive initial prostate biopsy in a Chinese population. A total of 535 Chinese men who underwent a prostatic biopsy for the detection of prostate cancer in the past decade with complete biopsy data were included. Stepwise logistic regression was used to determine the independent predictors of a positive initial biopsy. Age, prostate-specific antigen （PSA）, prostate volume （PV）, digital rectal examination （DRE） status, % free PSA and transrectal ultrasound （TRUS） findings were included in the analysis. A nomogram model was developed that was based on these independent predictors to calculate the probability of a positive initial prostate biopsy. A receiver-operating characteristic curve was used to assess the accuracy of using the nomogram and PSA levels alone for predicting positive prostate biopsy. The rate for positive initial prostate biopsy was 41.7% （223/535）. The independent variables used to predict a positive initial prostate biopsy were age, PSA, PV and DRE status. The areas under the receiver-operating characteristic curve for a positive initial prostate biopsy for PSA alone and the nomogram were 79.7% and 84.8%, respectively. Our results indicate that the risk of a positive initial prostate biopsy can be predicted to a satisfactory level in a Chinese population using our nomogram. The nomogram can be used to identify and counsel patients who should consider a prostate biopsy, ultimately enhancing accuracy in diagnosing prostate cancer.
