The role of the tumor primary chemosensitivity relative to the success of the medical-surgical management in patients with advanced ovarian carcinomas
Bibliographic record
Abstract
In patients with advanced ovarian carcinomas, the first-line treatment has historically relied on debulking surgery and platinum-based chemotherapy. If the major therapeutic/prognostic role of the surgery part is well understood, and integrated in disease-management algorithms, the impact of chemotherapy efficacy has been insufficiently addressed. This review describes the main indicators of the chemosensitivity reported in the literature (pathological response score & biomarkers; genomic alterations; DNA scars; imaging; and circulating tumor markers), and investigates the respective roles of the debulking surgery and tumor primary chemosensitivity relative to the success of the comprehensive medical-surgical treatment. The tumor primary chemosensitivity exhibits a major independent prognostic impact on the feasibility of complete interval debulking surgery after neoadjuvant chemotherapy, risk of subsequent platinum-resistant relapse, efficacy of subsequent maintenance therapies with bevacizumab or PARP inhibitors, progression-free survival, overall and long-term survival. While both the completeness of the surgery and the tumor primary chemosensitivity are undoubtedly major prognostic factors, the impact of the surgery may differ according to the primary chemosensitivity. This assumption raises a potential new concept: in patients with advanced ovarian carcinomas, the maximum tumor debulking should ideally be both biological (induced by systemic treatments) and physical (induced by surgery) for maximizing patient survival. Besides BRCA and HRD biomarkers, future trials and algorithms may integrate indicator(s) of the tumor primary chemosensitivity for guiding more subtly the surgical and medical management in first-line setting. Moreover, such a parameter would help in the development of novel approaches meant to reverse the resistance to chemotherapy and PARP inhibitors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".