Development and validation of a nomogram for predicting postoperative lymphedema risk in breast cancer patients

Breast cancer-related lymphedema (BCRL) is a common long-term complication after breast cancer treatment that impairs upper-limb function and quality of life. Early identification of high-risk patients enables timely risk-adapted surveillance and rehabilitation support. To develop and internally validate a nomogram incorporating clinical, surgical, and postoperative recovery-related factors to predict BCRL risk. This retrospective cohort study included 751 patients who underwent breast cancer surgery at a tertiary hospital between January 2022 and December 2023. Patients were randomly divided using outcome-stratified sampling into a training cohort (n=525) and a validation cohort (n=226). LASSO regression was used for predictor selection, followed by multivariable logistic regression to construct the prediction model. Model performance was evaluated using discrimination, calibration, decision-curve analysis, and bootstrap validation with 1,000 resamples. BCRL developed in 212 of 751 patients (28.2%). Independent predictors included age, BMI, clinical stage, hypertension, ALND, postoperative radiotherapy, drainage duration, wound complications, breast surgery type, and weekly exercise duration. The model showed high discrimination, with AUCs of 0.936 (95% CI, 0.915–0.958) in the training cohort and 0.939 (95% CI, 0.910–0.968) in the validation cohort. The optimism-corrected AUC after 1,000 bootstrap resamples was 0.925 (95% CI, 0.902–0.949). Calibration and decision-curve analyses indicated satisfactory performance and potential clinical utility. This study developed a postoperative nomogram for individualized BCRL risk prediction, with high apparent discrimination and satisfactory calibration in a single-center cohort. The model provides a practical tool for identifying patients at elevated risk who may benefit from intensified postoperative surveillance and rehabilitation support.