Objective:We aimed to develop a nomogram in corporating multidetector computed tomography(MDCT)imaging features and clinicopathological indicators for the preoperative prediction of axillary lymph node metastasis(ALNM)in patients with triple-negative breast cancer(TNBC).Methods:We retrospectively analyzed data from 265 female patients with pathologically confirmed TNBC treated at Harbin Medical University Cancer Hospital between November 2020 and October 2024.Patients were randomly assigned into a training cohort(n=161)and a validation cohort(n=104)in a 6:4 ratio.Feature selection was performed using least absolute shrinkage and selection operator(LASSO)regression with 10-fold cross-validation.Independent predictors of ALNM were identified by multivariate Logistic regression analysis,and a nomogram was constructed accordingly.Model performance was assessed using receiver operating characteristic(ROC)curves,calib-ration plots,and decision curve analysis(DCA).Results:Three independent predictors of ALNM were identified:clinical N-stage(odds ratio[OR]=6.789;95%confidence interval[CI]:2.203-22.20;P=0.001),short-axis diameter of lymph nodes on CT(OR=1.686;95%CI:1.349-2.257;P<0.001),and cortical thickness(OR=6.296;95%CI:2.170-19.310;P=0.001).The nomogram showed strong discrimination,with areas under the ROC curve(AUC)of 0.918(95%CI:0.860-0.977)in the training cohort and 0.885(95%CI:0.809-0.962)in the validation cohort.Calibration was confirmed by Hosmer-Lemeshow tests(P=0.609 and P=0.694 for training and validation cohorts,respectively).DCA demon-strated clinical utility across probability thresholds of 0.02-0.96 and 0.03-0.87 in the training and validation cohorts,respectively.Conclu-sions:This nomogram,integrating MDCT imaging features and clinical indicators,provides a practical tool for individualized preoperative risk assessment and may aid clinical decision-making in patients with TNBC.

Objective:We aimed to develop a nomogram in corporating multidetector computed tomography(MDCT)imaging features and clinicopathological indicators for the preoperative prediction of axillary lymph node metastasis(ALNM)in patients with triple-negative breast cancer(TNBC).Methods:We retrospectively analyzed data from 265 female patients with pathologically confirmed TNBC treated at Harbin Medical University Cancer Hospital between November 2020 and October 2024.Patients were randomly assigned into a training cohort(n=161)and a validation cohort(n=104)in a 6:4 ratio.Feature selection was performed using least absolute shrinkage and selection operator(LASSO)regression with 10-fold cross-validation.Independent predictors of ALNM were identified by multivariate Logistic regression analysis,and a nomogram was constructed accordingly.Model performance was assessed using receiver operating characteristic(ROC)curves,calib-ration plots,and decision curve analysis(DCA).Results:Three independent predictors of ALNM were identified:clinical N-stage(odds ratio[OR]=6.789;95%confidence interval[CI]:2.203-22.20;P=0.001),short-axis diameter of lymph nodes on CT(OR=1.686;95%CI:1.349-2.257;P<0.001),and cortical thickness(OR=6.296;95%CI:2.170-19.310;P=0.001).The nomogram showed strong discrimination,with areas under the ROC curve(AUC)of 0.918(95%CI:0.860-0.977)in the training cohort and 0.885(95%CI:0.809-0.962)in the validation cohort.Calibration was confirmed by Hosmer-Lemeshow tests(P=0.609 and P=0.694 for training and validation cohorts,respectively).DCA demon-strated clinical utility across probability thresholds of 0.02-0.96 and 0.03-0.87 in the training and validation cohorts,respectively.Conclu-sions:This nomogram,integrating MDCT imaging features and clinical indicators,provides a practical tool for individualized preoperative risk assessment and may aid clinical decision-making in patients with TNBC.