Flow Cytometry in the Diagnosis of Peritoneal Carcinomatosis
Bibliographic record
Abstract
OBJECTIVES: Peritoneal carcinomatosis is the second major cause of ascites. Because of its frequency and poor prognosis, it is important to establish an accurate diagnosis. The aim of this study was to analyze the use of a DNA index, detemined by flow cytometry in the differential diagnosis of ascites, and to compare it to the cytopathological examination. METHODS: A prospective analysis was carried out on 67 patients (39 female, 28 male; mean age, 53+/-14 yr [range, 5-82]) with ascites of various etiologies. Peritoneal carcinomatosis was detected in 21 patients, whereas in 46 the ascites was of noncarcinomatosis origin. RESULTS: The sensitivity of the cytopathological examination for the diagnosis of peritoneal carcinomatosis was 42.9%, and the specificity was 100%. The mean DNA index determined by flow cytometry was similar for peritoneal carcinomatosis and noncarcinomatosis patients, being 1.28 versus 1.01, respectively, in the preparations without control lymphocytes and 1.28 versus 1.04, respectively, when control lymphocytes were added. The sensitivity of DNA index cytometry was 57.1% and specificity, 93.5%. The combined use of the DNA index and cytopathological examination did not show an advantage over the use of any of the tests individually, although the DNA index was able to detect half of the cases of peritoneal carcinomatosis in which cytopathological examination was negative. Although the sensitivity was higher when the parameters were associated, the DNA index did not offer a statistically significant advantage over the use of cytopathological examination alone, which in turn had higher specificity. CONCLUSION: The DNA index presented lower sensitivity for the diagnosis of peritoneal carcinomatosis when used alone, showing no advantage over conventional cytopathological examination. However, the DNA index was able to detect 50.0% of peritoneal carcinomatosis cases whose conventional cytopathological examinations were negative, and could be valuable in these situations.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".