ArticleFrontiers in oncology2026
Construction and validation of a prognostic model for seroma after breast cancer surgery: a dual-center retrospective cohort study.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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.
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