Objective:Serum tumor markers(STMs)are important indicators associated with metastasis in patients with breast cancer(BC).This study focuses on predicting the risk of axillary lymph node metastasis(ALNM)in patients with invasive BC in Xinjiang by combining STMs and clinicopathological factors.Methods:Data from 3,360 patients diagnosed with invasive BC and treated at the Affiliated Cancer Hospital of Xinjiang Medical University between 2015 and 2019 were analyzed,focusing on 11 relevant demographic and clinical factors.Five ma-chine learning(ML)algorithms were used to develop predictive models for ALNM.Their performance was compared using metrics such as area under the curve(AUC),accuracy,Kappa value,and Brier score.The best-performing model was then compared with a nomogram based on Logistic regression(LR)to determine the final model.Shapley additive explanations(SHAP)values were used to rank the importance of factors contributing to ALNM.Results:Of the 3,266 patients studied,1,368(41.89%)developed ALNM.Among the five constructed ML models,eXtreme gradient boosting(XGBoost)demonstrated the best predictive performance with an AUC of 0.768,an accuracy of 0.735,and a Kappa value of 0.450.In both the training and validation sets,the XGBoost model outperformed the LR-based nomogram(training set AUC and Brier score:0.822(0.810～0.820)vs.0.742(0.721～0.763),0.170(0.163～0.177)vs.0.197(0.189～0.204);validation set AUC and Brier score:0.769(0.740～0.770)vs.0.747(0.716～0.779),0.190(0.178～0.202)vs.0.195(0.189～0.204)).Therefore,XGBoost was selec-ted as the final predictive model.SHAP analysis identified T stage,age,molecular subtype,and CEA level as the four most influential factors for ALNM prediction.Conclusions:The XGBoost model effectively predicts the risk of ALNM in patients with invasive BC based on STMs and clinicopathological features,outperforming traditional nomograms.SHAP analysis highlighted T stage as the most critical factor influencing ALNM.

Objective:Serum tumor markers(STMs)are important indicators associated with metastasis in patients with breast cancer(BC).This study focuses on predicting the risk of axillary lymph node metastasis(ALNM)in patients with invasive BC in Xinjiang by combining STMs and clinicopathological factors.Methods:Data from 3,360 patients diagnosed with invasive BC and treated at the Affiliated Cancer Hospital of Xinjiang Medical University between 2015 and 2019 were analyzed,focusing on 11 relevant demographic and clinical factors.Five ma-chine learning(ML)algorithms were used to develop predictive models for ALNM.Their performance was compared using metrics such as area under the curve(AUC),accuracy,Kappa value,and Brier score.The best-performing model was then compared with a nomogram based on Logistic regression(LR)to determine the final model.Shapley additive explanations(SHAP)values were used to rank the importance of factors contributing to ALNM.Results:Of the 3,266 patients studied,1,368(41.89%)developed ALNM.Among the five constructed ML models,eXtreme gradient boosting(XGBoost)demonstrated the best predictive performance with an AUC of 0.768,an accuracy of 0.735,and a Kappa value of 0.450.In both the training and validation sets,the XGBoost model outperformed the LR-based nomogram(training set AUC and Brier score:0.822(0.810～0.820)vs.0.742(0.721～0.763),0.170(0.163～0.177)vs.0.197(0.189～0.204);validation set AUC and Brier score:0.769(0.740～0.770)vs.0.747(0.716～0.779),0.190(0.178～0.202)vs.0.195(0.189～0.204)).Therefore,XGBoost was selec-ted as the final predictive model.SHAP analysis identified T stage,age,molecular subtype,and CEA level as the four most influential factors for ALNM prediction.Conclusions:The XGBoost model effectively predicts the risk of ALNM in patients with invasive BC based on STMs and clinicopathological features,outperforming traditional nomograms.SHAP analysis highlighted T stage as the most critical factor influencing ALNM.

Objective:To analyze the clinicopathological characteristics and incidence trend of primary breast cancer patients in Xinjiang Uygur Autonomous Region from 2015 to 2021,and provide references for clinical prevention and treatment of breast cancer in Xinjiang.Methods:Retrospective analysis was made on the general demographic data,molecular typing,pathological typing and surgical types of 10 867 patients with primary breast cancer who were diagnosed,treated and hospitalized in The Affiliated Cancer Hospital of Xinjiang Medical University from 2015 to 2021,to explore the change trend with different periods.Results:A total of 10 867 patients were included,with an average age of diagnosis of 51.34±10.96 years and a median age of diagnosis of 50 years.The average and median ages of diagnosis showed an upward trend with each year.The marital status is mainly married[9 854 patients(90.6％)].The majority of patients are from northern Xinjiang(69.35％).The molecular typing is mainly Luminal type[5 665 patients(72％)],and the proportion of Luminal type and HER-2 overexpression type continues to increase over time,while the proportion of triple negative type significantly decreases.Clinical stage Ⅱ was the most common[4 405 patients(41.7％)],followed by stage Ⅰ[3 057 patients(29.0％)].The pathological type is mainly invasive ductal carcinoma[9 307 patients(85.6％)].The main surgical method is modified radical surgery[5 822 patients(74.6％)],while other surgical methods such as breast conserving surgery have shown an increasing trend over the years.Conclusion:From 2015 to 2021,the number of breast cancer patients in Xinjiang will increase year by year,and the average age of diagnosis will be older.The molecular subtype is mainly Luminal B type,and the pathological type is mainly invasive ductal carcinoma;The proportion of early breast cancer patients increased with age.The main surgical method is modified radical surgery,which shows a decreasing trend with increasing years,while breast conserving surgery and other surgical procedures show an increasing trend.

Objective:To analyze the clinicopathological characteristics and incidence trend of primary breast cancer patients in Xinjiang Uygur Autonomous Region from 2015 to 2021,and provide references for clinical prevention and treatment of breast cancer in Xinjiang.Methods:Retrospective analysis was made on the general demographic data,molecular typing,pathological typing and surgical types of 10 867 patients with primary breast cancer who were diagnosed,treated and hospitalized in The Affiliated Cancer Hospital of Xinjiang Medical University from 2015 to 2021,to explore the change trend with different periods.Results:A total of 10 867 patients were included,with an average age of diagnosis of 51.34±10.96 years and a median age of diagnosis of 50 years.The average and median ages of diagnosis showed an upward trend with each year.The marital status is mainly married[9 854 patients(90.6％)].The majority of patients are from northern Xinjiang(69.35％).The molecular typing is mainly Luminal type[5 665 patients(72％)],and the proportion of Luminal type and HER-2 overexpression type continues to increase over time,while the proportion of triple negative type significantly decreases.Clinical stage Ⅱ was the most common[4 405 patients(41.7％)],followed by stage Ⅰ[3 057 patients(29.0％)].The pathological type is mainly invasive ductal carcinoma[9 307 patients(85.6％)].The main surgical method is modified radical surgery[5 822 patients(74.6％)],while other surgical methods such as breast conserving surgery have shown an increasing trend over the years.Conclusion:From 2015 to 2021,the number of breast cancer patients in Xinjiang will increase year by year,and the average age of diagnosis will be older.The molecular subtype is mainly Luminal B type,and the pathological type is mainly invasive ductal carcinoma;The proportion of early breast cancer patients increased with age.The main surgical method is modified radical surgery,which shows a decreasing trend with increasing years,while breast conserving surgery and other surgical procedures show an increasing trend.