Abstract P4-03-15: Does age affect outcome? Data from a large cohort from British Columbia, 2005-2014
Bibliographic record
Abstract
Abstract Background Prior data has been conflicting about how age at diagnosis impacts patient outcomes and survival. Some studies suggest that younger age at diagnosis may negatively affect survival, independent of other disease characteristics. Accurate predictions of outcomes and patterns of relapse provide invaluable information to patients and help inform physician treatment recommendations, such as the role of extended adjuvant endocrine therapy. Purpose To determine the relapse free survival and overall survival data for all patients diagnosed with invasive breast cancer and treated at BC Cancer from 2005 to 2014. Methods Using the BC Cancer Breast Cancer Outcomes Unit (BCOU) database, we identified all patients referred with newly diagnosed invasive breast cancer at any stage between 2005 and 2014. For descriptive statistics, we analyzed clinical and pathological features at diagnosis and treatment specific variables compared across the following age cohorts: < 35, 35-39, 40-49, 50-59, 60-69, 70-79, and 80 years of age or more. To model the non-linear relationship of age at diagnosis as a continuous variable with the risk of relapse and death, we used an additive Cox proportional hazards model adjusting for subtype, LVI status, use of RT, chemotherapy, hormone therapy, and nodal status. We employed the fitted model to extract estimates for specific values of age while fixing other covariates at different values to create high and low risk cohorts. For subtypes Luminal B, HER2 positive and triple negative breast cancer, high-risk subgroups were defined as node-positive plus treatment with chemotherapy. Low risk was defined as Luminal A and node-negative. The extracted estimates were used to investigate the patterns of relapse among different ages via the means of adjusted cumulative incidence curves and to report the 10 year adjusted relapse free survival and overall survival estimates. Results We identified 24,469 patients who met the inclusion criteria with a median follow-up of 11.5 years. Patients < 35 and between 35-39 years of age were more likely to be diagnosed with breast cancer that was ductal histology, grade 3, LVI positive, HER2 positive, triple negative, and more advanced TNM stage at diagnosis. These younger patients were also more likely to undergo mastectomy, neoadjuvant and adjuvant chemotherapy compared to older age cohorts. Additive Cox proportional hazards revealed a statistically significant and clinically meaningful reduction in 10-year relapse free survival amongst patients with early-stage disease aged 30 and 35 as well as those aged 80, compared to patients aged 50 when adjusted for stage and treatment exposure. This was consistent across all high- and low-risk subgroups (Table 1). 10-year overall survival was significantly and meaningfully reduced in patients aged 30 and 80 compared to age 50 only amongst the high-risk patient populations. Conclusion Both younger and elderly age at breast cancer diagnosis were independent risk factors for poorer prognosis. To our knowledge, this is the largest patient cohort detailing such outcomes differences. Furthermore, this represents a more contemporary clinical context, compared to earlier publications. This work will help clinicians more accurately estimate disease trajectory, and may influence treatments recommendations. Other parameters for the entire cohort will be presented, including a more detailed identification and assessment of patient risk categories and its impact on outcomes. Table 1: 10 year estimates of relapse-free survival and overall survival age estimates by additive Cox proportional hazards model adjusting for subtype, LVI status, use of RT, chemotherapy, hormone therapy, and nodal status. Citation Format: Emily B. Jackson, Lovedeep Gondara, Caroline H. Speers, Karen Gelmon. Does age affect outcome? Data from a large cohort from British Columbia, 2005-2014 [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-03-15.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".