Construction and Validation of a Nomogram to Predict Overall Survival in Very Young Female Patients with Curatively Resected Breast Cancer

Purpose: Young age is an independent negative predictor of breast cancer (BC) survival and correlates with the risk of local recurrence and contralateral BC. We aimed to design an effective and comprehensive nomogram to predict prognosis in very young patients with curatively resected BC. Methods: Female patients with a diagnosis of BC aged ≤35 years at presentation were identified from the SEER database as a training cohort. The validation cohort consisted of 1002 consecutive women with BC aged ≤35 years that had received curative resection for BC at the Sun Yat-sen University Cancer Center. A nomogram was built based on the identified variables in multivariate Cox proportional hazards model. The performance of the nomogram was quantified using Harrell’s concordance index (C-index) and calibration curves. Results: Overall, 10,872 young female patients who underwent surgery for BC were enrolled in the training cohort, while 1002 very young female BC patients were identified as independent validation cohort. Eight covariables (age, race, grade; ER, PR, and HER2 status; T, and N stages) were identified and incorporated to construct a nomogram. The C-index values of the nomogram were 0.727 (95% CI: 0.714–0.740) and 0.722 (95% CI: 0.666–0.778) for OS in the training and validation cohorts, respectively. The calibration curves showed a high degree of agreement between the predicted and actual observed survival rates in both training and validation cohorts. The nomogram displayed good calibration and acceptable discrimination. Based on the TPS of the nomogram model for OS with the X-tile program, patients were divided into 3 risk groups, which were easily discriminated on survival analyses for OS. Conclusion: We have successfully constructed an effective nomogram to predict survival outcomes for young female patients with curatively resected BC, which may provide individual survival prediction to benefit prognosis evaluation and individualized therapy.

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