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 ca展开更多
Background:Clinical outcome of adrenocortical carcinoma(ACC)varies because of its heterogeneous nature and reliable prognostic prediction model for adult ACC patients is limited.The objective of this study was to deve...Background:Clinical outcome of adrenocortical carcinoma(ACC)varies because of its heterogeneous nature and reliable prognostic prediction model for adult ACC patients is limited.The objective of this study was to develop and externally validate a nomogram for overall survival(OS)prediction in adult patients with ACC after surgery.Methods:Based on the data from the Surveillance Epidemiology,and End Results(SEER)database,adults patients diagnosed with ACC between January 1988 and December 2015 were identified and classified into a training set,comprised of 404 patients diagnosed between January 2007 and December 2015,and an internal validation set,com-prised of 318 patients diagnosed between January 1988 and December 2006.The endpoint of this study was OS.The nomogram was developed using a multivariate Cox proportional hazards regression algorithm in the training set and its performance was evaluated in terms of its discriminative ability,calibration,and clinical usefulness.The nomogram was then validated using the internal SEER validation,also externally validated using the Cancer Genome Atlas set(TCGA,82 patients diagnosed between 1998 and 2012)and a Chinese multicenter cohort dataset(82 patients diag-nosed between December 2002 and May 2018),respectively.Results:Age at diagnosis,T stage,N stage,and M stage were identified as independent predictors for OS.A nomo-gram incorporating these four predictors was constructed using the training set and demonstrated good calibration and discrimination(C-index 95%confidence interval[CI],0.715[0.679-0.751]),which was validated in the internal validation set(C-index[95%CI],0.672[0.637-0.707]),the TCGA set(C-index[95%CI],0.810[0.732-0.888])and the Chi-nese multicenter set(C-index[95%CI],0.726[0.633-0.819]),respectively.Encouragingly,the nomogram was able to successfully distinguished patients with a high-risk of mortality in all enrolled patients and in the subgroup analyses.Decision curve analysis indicated that the nomogram was clinically useful and applicable.Conclusions:T展开更多
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 ca
基金This work was supported by the Natural Science Foundation of China(81572514,U1301221,81402106,81272808,81825016)the Natural Science Foundation of Guangdong,China(2016A030313244)Grant[2013]163 from Key Laboratory of Malignant Tumor Molecular Mechanism and Translational Medicine of Guangzhou Bureau of Science and Information Technology,Grant KLB09001 from the Key Laboratory of Malignant Tumor Gene Regulation and Target Therapy of Guangdong Higher Education Institutes,and grants from the Guangdong Science and Technology Department(2015B050501004,2017B020227007).
文摘Background:Clinical outcome of adrenocortical carcinoma(ACC)varies because of its heterogeneous nature and reliable prognostic prediction model for adult ACC patients is limited.The objective of this study was to develop and externally validate a nomogram for overall survival(OS)prediction in adult patients with ACC after surgery.Methods:Based on the data from the Surveillance Epidemiology,and End Results(SEER)database,adults patients diagnosed with ACC between January 1988 and December 2015 were identified and classified into a training set,comprised of 404 patients diagnosed between January 2007 and December 2015,and an internal validation set,com-prised of 318 patients diagnosed between January 1988 and December 2006.The endpoint of this study was OS.The nomogram was developed using a multivariate Cox proportional hazards regression algorithm in the training set and its performance was evaluated in terms of its discriminative ability,calibration,and clinical usefulness.The nomogram was then validated using the internal SEER validation,also externally validated using the Cancer Genome Atlas set(TCGA,82 patients diagnosed between 1998 and 2012)and a Chinese multicenter cohort dataset(82 patients diag-nosed between December 2002 and May 2018),respectively.Results:Age at diagnosis,T stage,N stage,and M stage were identified as independent predictors for OS.A nomo-gram incorporating these four predictors was constructed using the training set and demonstrated good calibration and discrimination(C-index 95%confidence interval[CI],0.715[0.679-0.751]),which was validated in the internal validation set(C-index[95%CI],0.672[0.637-0.707]),the TCGA set(C-index[95%CI],0.810[0.732-0.888])and the Chi-nese multicenter set(C-index[95%CI],0.726[0.633-0.819]),respectively.Encouragingly,the nomogram was able to successfully distinguished patients with a high-risk of mortality in all enrolled patients and in the subgroup analyses.Decision curve analysis indicated that the nomogram was clinically useful and applicable.Conclusions:T
