Prognostic impact and causality of age on oncological outcomes in women with endometrial cancer: a multimethod analysis of the randomised PORTEC-1, -2 and -3 trials
Bibliographic record
Abstract
Abstract Background Numerous studies have shown that elderly women with endometrial cancer (EC) have a higher risk of recurrence and cancer-related death. It is, however, unclear whether aging is a causal prognostic factor, or whether other risk factors become increasingly common with age. We address to this with a unique multi-method study design using state of the art statistical and causal inference techniques on datasets of three large randomised trials. Methods Data of 1801 women participating in the randomised PORTEC-1, -2 and -3 trials were used for statistical analyses and causal inference. The cohort included 714 patients with intermediate-risk EC, 427 high-intermediate risk EC patients and 660 high-risk EC patients. Associations of age with clinicopathological and molecular features were analysed using non-parametric tests. Multivariable competing risk analyses were performed to determine the independent prognostic value of age. To analyse age as a causal prognostic variable a deep learning Causal Inference model called AutoCI was used. Findings Median follow-up was 12·3 years for PORTEC-1, 10·5 years for PORTEC-2 and 6·1 years for PORTEC-3. Both overall recurrence and EC-specific deaths significantly increased with age. Moreover, elderly women had a higher incidence of deep myometrial invasion, serous tumour histology and p53abn tumours. Age was an independent risk factor for both overall recurrence (HR 1·02 per year, 95%CI 1·01-1·04; p=0·0012) and EC-specific death (HR 1·03 per year, 95%CI 1·01-1·05; p=0·0012), and was identified as a significant causal variable. Interpretation This study shows that advanced age is associated with more aggressive tumour features, and independently and causally related to worse oncological outcomes. Therefore, treatment for endometrial cancer in elderly women should not be de-escalated based on their age alone. Funding The PORTEC-1, -2 and -3 trials and the associated translational studies are supported by the Dutch Cancer Society.
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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 | 0.052 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".