Including age at diagnosis with effect modification in a breast cancer staging system improved survival prediction, with 3-year OS for stage IIIA varying from 83.1% at age 40 to 12.7% at age 90.
Does a novel prognostic staging system incorporating age at diagnosis improve overall survival prediction in adult females with invasive breast cancer compared to the current AJCC staging schema?
Incorporating age at diagnosis into breast cancer staging provides more refined risk stratification and accurate survival predictions compared to the current AJCC staging schema.
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Abstract Background: Accumulating evidence supports age at diagnosis as an independent prognostic variable for breast cancer-specific and overall survival (OS). Using SEER data, we previously developed a novel prognostic staging system that incorporates age at diagnosis and demonstrated more refined risk stratification compared with the current American Joint Committee on Cancer (AJCC) staging schema. We now aim to externally validate this novel breast cancer staging system. Methods: The National Cancer Database was used to identify adult females diagnosed with invasive breast cancer from 2010-2015. Women with prior history of malignancy, unknown vital status, or unknown AJCC clinical prognostic stage (CPS) variables were excluded. Serial multivariable Cox’s proportional hazards models were used to evaluate associations between OS and CPS +/- age +/- effect modification between CPS and age. Models were evaluated using the Akaike information criterion (AIC). Results: Among 663,659 patients, the median age was 60 years (IQR 51-70) and the median follow-up was 91.3 months (95% CI 91.2-91.4). At last follow-up, 133,273 patients (20.1%) were dead. The model that incorporated both age and effect modification had the lowest AIC (3320681, versus 3400173 for the model excluding age and 3326910 for the model including age without effect modification), indicative of the best fit. This model was used to predict OS at various timepoints, stratified by CPS. Within each stage group, differential survival was seen across the age spectrum, consistent with the initial model developed in SEER (Table). Within each stage group from IB-IIIC, women diagnosed at age 40 had the best survival, whereas women at the extremes of age had inferior survival. For example, 3-year OS for patients with stage IIIA disease was 0.648 across all ages, highest (0.831) for patients diagnosed at age 40, lower (0.714) for age 20, and lowest (0.127) for age 90. Conclusion: This analysis provides external validation of our novel prognostic staging system for breast cancer. Including age at diagnosis as a prognostic variable results in more refined risk stratification across all AJCC stage groups. Future editions of AJCC should consider utilization of not only anatomic and pathologic variables, but also age at diagnosis, to achieve more accurate survival predictions. While poorer OS is expected for older women due to age-related competing mortality risks, further research is necessary to understand why younger women with breast cancer have higher morality than those diagnosed closer to age 40. Citation Format: H. M. Johnson, W. Dong, Y. Shen, W. Irish, J. H. Wong, N. A. Vohra, N. Tamirisa. Validation of a Novel Prognostic Staging System for Breast Cancer Incorporating Age at Diagnosis abstract. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS2-02-27.
Johnson et al. (Tue,) reported a other. Including age at diagnosis with effect modification in a breast cancer staging system improved survival prediction, with 3-year OS for stage IIIA varying from 83.1% at age 40 to 12.7% at age 90.