Diagnostic accuracy of haptoglobin within ovarian cyst fluid as a potential point‐of‐care test for epithelial ovarian cancer: an observational study
Bibliographic record
Abstract
OBJECTIVE: To investigate haptoglobin within ovarian cyst fluid (OCF) as a diagnostic biomarker for epithelial ovarian cancer (EOC) and develop an in vitro diagnostic point-of-care device test (IVDPCT) for use in the operating theatre. DESIGN: Retrospective and prospective cohort study. SETTING: South-East Asia. POPULATION: Women with suspicious ovarian cysts. METHODS: Proteomic, immunohistochemical and ELISA methods measured haptoglobin in OCF to differentiate benign and EOCs. Diagnostic performance of haptoglobin was compared with CA125, risk malignancy indices (RMI) and frozen section. Blinded validation of the IVDPCT was performed. MAIN OUTCOME MEASURES: Prediction of malignancy. RESULTS: Haptoglobin concentration measured by ELISA was 0.70 ± 0.09 mg/ml in patients with benign cysts (n = 87), 6.22 ± 0.53 mg/ml in early stage-EOC (n = 17), and 6.57 ± 0.65 mg/ml in late stage-EOC (n = 20). Haptoglobin in EOCs was significantly higher than in benign cysts (P < 0.0001). Haptoglobin using rapid colorimetric assay (RCA) on a training set had a sensitivity of 97.3% and a specificity 92.0%, comparable to ELISA and frozen sections. The haptoglobin AUROC curve was 0.999 (95% CI 0.997-1.000) compared with 0.895 (95% CI 0.814-0.977, P < 0.05) for CA125. Haptoglobin performed significantly better than all the RMIs (P < 0.01). Blinded validation studies showed a minor drop in average diagnostic performance (sensitivity 85.2% and specificity 90.5%) compared with the training set. However, when compared with frozen section, haptoglobin was no worse in diagnostic accuracy for malignancy. CONCLUSION: Haptoglobin was identified as a biomarker for the detection of EOC with potential as a point-of-care diagnostic tool. TWEETABLE ABSTRACT: Haptoglobin within ovarian cyst fluid: a biomarker for epithelial ovarian cancer and point-of-care diagnostics.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".