Comparison of clinical staging of benign and malignant ovarian tissues with DNA flow cytometry
Bibliographic record
Abstract
The prevention, diagnosis and treatment of ovarian cancer are major issues. The outcome of patients with advanced ovarian cancer is poor despite aggressive therapy including surgery, combination drug chemotherapy and radiation treatments. From the literature, the direct correlation between DNA ploidy and survival is greatly enhanced using high resolution DNA measurements, which results in a coefficient of variation (CV) range between 1%-2% (1.42 ± 0.19, n=66) for trout red blood cells (TRBC) and 2%-3% (2.18 ± 0.46 SD, n=22) for tonsil nuclei derived from formalin-fixed, paraffin-embedded tissues (deparaffinated). DNA nuclear determinations from 50 ovarian cancer and 21 benign patients is presented. This was accomplished by measuring the DNA content of nuclei simultaneously isolated from deparaffinated tissues, stained with the fluorescent DNA specific dye, 4’, 6-diamidino-1-phenylindole (DAPI) and analyzed on a high resolution flow cytometer. A high percentage of aneuploidy (92.0%) was determined from the ovarian cancer patients, especially of the aneuploid DNA histogram types, such as hypodiploid, multiploid and hypertetraploid (64.0%), which have shown poor prognosis in a variety of cancers including ovarian. Furthermore, aneuploidy was detected in 23.8% of the benign patients. DNA flow cytometry may complement pathological assessment of ovarian cancer to better determine malignancy, thus justifying a closer follow-up with more specialized approaches to treatment in clinical trials that involve new therapies including the use of chemically defined, natural products, which may help offset the overall poor prognosis seen in ovarian cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".