Frontiers in oncology · 2026
Construction and validation of a prognostic model for seroma after breast cancer surgery: a dual-center retrospective cohort study.
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Chen Zongxin, Zhou Qian, Zhang Xinyue, Li Chaoming, Li Siyu, Yang Guanghua, Xue Xiaohong, Zhao Bin
Аннотация
OBJECTIVE: This study aimed to develop and validate a predictive model for postoperative seroma resolution in patients with breast cancer, thereby providing an evidence-based tool for individualized postoperative management.
METHODS: This was a dual-center retrospective cohort study. A total of 373 patients who underwent surgery for breast cancer were included and an independent validation cohort(n=84)according to study center. Candidate predictors were screened using univariable and multivariable Cox proportional hazards regression analyses, and a nomogram was subsequently constructed. Internal validation was performed using bootstrap resampling, and model performance was evaluated in terms of discrimination (C-index and time-dependent area under the receiver operating characteristic curve [AUC]), calibration, and clinical utility (decision curve analysis). Validation was conducted in an independent cohort.
RESULTS: Multivariable Cox regression identified four independent predictors: body mass index (BMI), concomitant diabetes mellitus, modified radical mastectomy, and the number of lymph nodes dissected. A nomogram incorporating these variables was established. Following 1, 000 bootstrap resamples in the training cohort, the model yielded a concordance index (C-index) of 0.76 (95% CI, 0.73-0.80). The model demonstrated good discriminatory ability in both the training and validation cohorts. In the training cohort, the AUC was 0.79 (95% CI, 0.75-0.83) on postoperative day 7 and 0.78 (95% CI, 0.74-0.82) on postoperative day 10. In the independent validation cohort, the corresponding AUCs were 0.90 (95% CI, 0.82-0.97) and 0.86 (95% CI, 0.75-0.96), respectively. Calibration plots showed good agreement between predicted and observed probabilities of complete seroma resolution on postoperative days 7 and 10 in both cohorts. Decision curve analysis further demonstrated that the model provided a meaningful net clinical benefit across a broad range of threshold probabilities at both time points, indicating favorable clinical applicability.
CONCLUSIONS: We developed a prognostic model for postoperative seroma resolution that may serve as an effective clinical tool to identify high-risk patients, guide early intervention, and reduce the risk of delayed or refractory seroma resolution.
METHODS: This was a dual-center retrospective cohort study. A total of 373 patients who underwent surgery for breast cancer were included and an independent validation cohort(n=84)according to study center. Candidate predictors were screened using univariable and multivariable Cox proportional hazards regression analyses, and a nomogram was subsequently constructed. Internal validation was performed using bootstrap resampling, and model performance was evaluated in terms of discrimination (C-index and time-dependent area under the receiver operating characteristic curve [AUC]), calibration, and clinical utility (decision curve analysis). Validation was conducted in an independent cohort.
RESULTS: Multivariable Cox regression identified four independent predictors: body mass index (BMI), concomitant diabetes mellitus, modified radical mastectomy, and the number of lymph nodes dissected. A nomogram incorporating these variables was established. Following 1, 000 bootstrap resamples in the training cohort, the model yielded a concordance index (C-index) of 0.76 (95% CI, 0.73-0.80). The model demonstrated good discriminatory ability in both the training and validation cohorts. In the training cohort, the AUC was 0.79 (95% CI, 0.75-0.83) on postoperative day 7 and 0.78 (95% CI, 0.74-0.82) on postoperative day 10. In the independent validation cohort, the corresponding AUCs were 0.90 (95% CI, 0.82-0.97) and 0.86 (95% CI, 0.75-0.96), respectively. Calibration plots showed good agreement between predicted and observed probabilities of complete seroma resolution on postoperative days 7 and 10 in both cohorts. Decision curve analysis further demonstrated that the model provided a meaningful net clinical benefit across a broad range of threshold probabilities at both time points, indicating favorable clinical applicability.
CONCLUSIONS: We developed a prognostic model for postoperative seroma resolution that may serve as an effective clinical tool to identify high-risk patients, guide early intervention, and reduce the risk of delayed or refractory seroma resolution.