Abstract P4-03-11: Population-based survival outcomes of pure vs mixed invasive lobular breast carcinoma in Ontario, Canada
Bibliographic record
Abstract
Abstract Purpose: We aim to determine incidence and survival rates of pure vs mixed invasive lobular breast carcinoma between 1990 and 2020 in the province of Ontario, Canada. We further evaluated patient and tumour factors that predict survival for invasive lobular carcinoma (ILC). Methods: Using population-based administrative healthcare datasets at Institute of Clinical Evaluative Sciences (ICES) Ontario, we calculated the crude 5-year incidence rates of pure ILC versus invasive ductal carcinoma (IDC) versus mixed ILC-IDC in the province of Ontario, Canada between 1990 and 2020. Kaplan-Meier survival curves were generated to determine the 5-, 10-, 15- and 20-year survival for ILC (and mixed ILC-IDC) as compared with IDC. Survival curves were compared using the log-rank test and stratified by stage. Using a multivariable Cox proportional hazards regression analysis, we identified patient (e.g. demographic, geographic, socioeconomic) and tumour (grade, stage, receptor subtype) factors that predicted survival for patients with ILC. Statistical analysis was performed using SAS® and P values < 0.05 were considered statistically significant. Results: We identified 18,551 (8%) pure ILC, 10,234 (4%) mixed ILC-IDC and 192,371 (81%) IDC cases. The crude incidence of pure ILC increased from 55.7 per 100,000 in 1990 to 80.2 per 100,000 in 2020. The crude incidence of mixed ILC-IDC peaked in the mid-2000s at 48.6 per 100,000 and subsequently declined to 32.1 per 100,000 in 2020. There was a significant difference in overall survival between the three breast cancer subtypes. Over a 30-year follow-up period (mean 9.3 +/- 7.3 years), overall survival of mixed ILC-IDC mirrors the survival of pure IDC, while women with pure ILC have inferior survival compared with IDC beginning after 10 years of follow-up (P < .001). The 20-year overall survival was 40% for ILC and 50% for IDC and mixed ILC-IDC. Older age > 55 years (vs. 50-54 years, P < .0001), lowest neighborhood income quintile (HR 1.1, P = .038), geographic location within Ontario (P < .01) and increasing Elixhauser Comorbidity Index score (P < .0001) predicted worse overall survival for ILC patients. Conversely, the increasing number of mammograms received in the five years prior to diagnosis predicted better overall survival (P < .0001). When stratified by cancer stage, the worse survival in ILC (compared with IDC and mixed ILC-IDC) was only observed for stage III patients (P = .01). Stage III and IV disease, grade 3 histology and ER/PR negativity predicted worse survival (P < .01). Conclusion: The crude incidence of ILC is increasing over time. Over a 30-year follow-up period (mean 9.3 +/- 7.3 years), ILC had worse overall survival compared with IDC and mixed ILC-IDC, particular stage III patients. Patient demographic and tumour factors predict overall survival in ILC. While treatment paradigms for ILC mirror that for IDC, our data demonstrates worse overall survival for ILC and a need for more research and treatments focused on improving long-term survival for ILC patients. Citation Format: David Lim, Vasily Giannakeas, Steven Narod, Kelly Metcalfe. Population-based survival outcomes of pure vs mixed invasive lobular breast carcinoma in Ontario, Canada [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 P4-03-11.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".