Prediction of tumour response induced by chemotherapy using modelling of CA-125 kinetics in recurrent ovarian cancer patients
Bibliographic record
Abstract
BACKGROUND: The main objective of the present study was to establish the relationships between CA-125 kinetics and tumour size changes during treatment. METHODS: The data from the CALYPSO-randomised phase III trial, comparing two platinum-based regimens in recurrent ovarian cancer (ROC) patients, was randomly split into a 'learning data set' to estimate model parameters and a 'validation data set' to validate model performances. A kinetic-pharmacodynamic semi-mechanistic model was built to describe tumour size and CA-125 kinetics during chemotherapy. The ability of the model to predict tumour response induced by chemotherapy, based on CA-125 values, was assessed. RESULTS: Data from 535 ROC patients were used to model CA-125 kinetics and tumour size changes during the first 513 days after treatment initiation. Using the validated model, we could predict with accuracy the tumour size changes induced by chemotherapy based on the baseline imaging assessment and longitudinal CA-125 values (mean prediction error: 0.3%, mean absolute prediction error: 10.6%). CONCLUSIONS: Using a semi-mechanistic model, the dynamic relationships between tumour size changes and CA-125 kinetics induced by chemotherapy were established in ROC patients. A modelling approach allowed CA-125 to be assessed as a biomarker for tumour size dynamics, to predict treatment efficacy for research and clinical purposes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".