Computational Modeling of Ovarian Cancer Reveals Optimal Strategies for Therapy and Screening
Bibliographic record
Abstract
Abstract High-grade serous tubo-ovarian carcinoma (HGSC) is a major cause of cancer-related death. Whether treatment order—primary debulking surgery followed by adjuvant chemotherapy (PDS) or neo-adjuvant chemotherapy with interval surgery (NACT)—affects outcome is controversial. We developed a mathematical framework that holds for hierarchical or stochastic models of tumor initiation and reproduces HGSC clinical course. After estimating parameter values, we infer that most patients harbor chemo-resistant HGSC cells at diagnosis, and that if complete debulking (<1 mm residual tumor) can be achieved, PDS is superior to NACT due to better depletion of resistant cells. We further predict that earlier diagnosis of primary HGSC, followed by complete debulking, could improve survival, but its benefit in relapsed patients is likely to be limited. Our predictions are supported by primary clinical data from multiple cohorts. Our results have clear implications for these key issues in HGSC management. Significance Statement The optimal order and timing of surgery and chemotherapy, and the potential benefits of earlier diagnosis of HGSC, remain controversial. We developed a mathematical framework of tumor dynamics to address such issues, populated the model with primary clinical data and reliably recapitulated clinical observations. Our model prospectively predicts that: (1) PDS is superior to NACT when complete debulking is feasible; (2) timely adjuvant chemotherapy is critical for the outcome of PDS with <1mm, but not >1mm, residual tumors; (3) earlier detection of relapse is unlikely to be beneficial with current therapies; (4) earlier detection of primary HGSC, followed by complete debulking, could have substantial benefit. Our model provides insights into the evolutionary dynamics of HGSC, argues for new clinical trials to optimize HGSC therapy, and is potentially applicable to other tumor types.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".