Analysis of Circulating Tumor Cells in Ovarian Cancer and Their Clinical Value as a Biomarker
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND/AIMS: Monitoring the appearance and progression of tumors are important for improving the survival rate of patients with ovarian cancer. This study aims to examine circulating tumor cells (CTCs) in epithelial ovarian cancer (EOC) patients to evaluate their clinical significance in comparison to the existing biomarker CA125. METHODS: Immuomagnetic bead screening, targeting epithelial antigens on ovarian cancer cells, combined with multiplex reverse transcriptase-polymerase chain reaction (Multiplex RT-PCR) was used to detect CTCs in 211 samples of peripheral blood (5 ml) from 109 EOC patients. CTCs and CA125 were measured in serial from 153 blood and 153 serum samples from 51 patients and correlations with treatment were analyzed. Immunohistochemistry was used to detect the expression of tumor-associated proteins in tumor tissues and compared with gene expression in CTCs from patients. RESULTS: CTCs were detected in 90% (98/109) of newly diagnosed patients. In newly diagnosed patients, the number of CTCs was correlated with stage (p=0.034). Patients with stage IA-IB disease had a CTC positive rate of 93% (13/14), much higher than the CA125 positive rate of only 64% (9/14) for the same patients. The numbers of CTCs changed with treatment, and the expression of EpCAM (p=0.003) and HER2 (p=0.035) in CTCs was correlated with resistance to chemotherapy. Expression of EpCAM in CTCs before treatment was also correlated with overall survival (OS) (p=0.041). CONCLUSION: Detection of CTCs allows early diagnose and expression of EpCAM in CTC positive patients predicts prognosis and should be helpful for monitoring treatment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it