Benefit in progression-free survival (PFS) to expect based on CA125 reduction at week 6 in recurrent ovarian cancer (ROC) patients: CALYPSO phase III trial data (a GINECO-GCIG study).
Bibliographic record
Abstract
5547 Background: Prediction of the expected survival benefit based on CA125 change in treated recurrent ovarian cancer (ROC) patients would be very useful. It may help for early selection of the best drug candidates during drug development, and for clinical trials. We used mathematical modeling to: 1) quantify the links between CA125 kinetics and progression-free survival (PFS) benefit, and 2) to estimate the CA125 decline required to observe a 50% PFS improvement. Methods: CALYPSO randomized phase III trial database, comparing 2 platinum-based regimens in ROC patients was used. The cohort was randomly split into a “learning dataset” (N=356) to estimate model parameters and a “validation dataset” (N=178) to validate model performances. A full parametric survival model was developed to quantify the links between tumor size changes; CA125 kinetics; prognostic factors and PFS. The predictive performance of the model was evaluated with simulations on the validation dataset. Results: PFS from 534 ROC patients was properly described by a parametric model with log-logistic distribution. The factors significantly linked to PFS were fractional changes in CA125 (ΔCA125) and in tumor size (ΔTS) from baseline at week 6; baseline CA125 (CA125BL); and patient therapy free interval. By reducing this model, ΔCA125 was a better predictor of PFS than ΔTS. Simulations verified the predictive performance of this model. Patients should achieve at least 49% ΔCA125 decline induced by treatment to observe 50% PFS improvement. This effect was independent on treatment arm. Conclusions: This is the first drug-independent parametric survival model quantifying links between PFS and CA125 kinetics in ROC. The CA125 modeled decline required to observe a 50% improvement in PFS in treated ROC patients was defined. It may be a surrogate marker of PFS gain, and may embody an early predictive tool for go/no go drug development decisions and for clinical trials. Validation in other datasets is warranted.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".