Serum CA125 and ascites leptin level ratio predicts baseline clinical resistance to first-line platinum-based treatment and poor prognosis in patients with high grade serous ovarian cancer.
Bibliographic record
Abstract
About 20% of patients with high grade serous epithelial ovarian carcinoma (HGSOC) are intrinsically resistant to standard first-line platinum-based combination therapy. There is no marker yet available to identify these patients. In that context, all patients with HGSOC initially receive the same standard first-line platinum-based therapy, and those with intrinsically resistant diseases can only be identified retrospectively after they experienced early relapse to therapy. The aim of this study was to evaluate serum or ascites CA125 and ascites leptin in patients with intrinsic resistance and to compare them with those of sensitive patients. To this end, we enrolled 80 women with HGSOC who underwent cytoreductive surgery. Thirty seven were considered to have baseline clinical resistance to first-line therapy with progression-free survival < 6 months despite treatment. Serum were collected preoperatively and ascites samples were collected at the time of the surgery. The levels of CA125 and leptin were measured by ELISA. Patients with baseline clinical resistance to first-line therapy had a significantly poorer outcome compared to patients with sensitive HGSOC with an OS of 21 months versus 43 months. Median levels of serum CA125, ascites CA125 and ascites leptin were not significantly different between patients with sensitive and resistant HGSOC. Serum CA125/ascites leptin ratio was found to be significantly elevated in resistant patients compared to patients with drug-sensitive diseases. In ROC analysis, the AUC for serum CA125/ascites leptin ratio was higher than CA125 or leptin alone to differentiate patients with resistance from those with sensitive HGSOC. Elevated serum CA125/ascites leptin ratio was a predictor of poor OS in HGSOC patients. Thus, serum CA125/ascites leptin is a potential novel biomarker to predict baseline clinical resistance to first-line treatment and poor outcome in patients with HGSOC.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".