Improving the cytological diagnosis of high‐grade serous carcinoma in ascites with a panel of complementary biomarkers in cell blocks
Bibliographic record
Abstract
INTRODUCTION: Precise cytological diagnosis of pelvic high-grade serous carcinoma (HGSC) in ascites is important for tumour staging, therapeutic decision-making and prognostic evaluation. However, it can often be difficult to distinguish metastatic HGSC cells from reactive mesothelial cells based on morphology alone. Immunocytochemical analysis of ascites cell blocks has been used to obtain accurate diagnosis and provide a reliable basis for treatment decisions in the clinic. This study was performed to determine whether a panel of antibodies is necessary to achieve high specificity and sensitivity for the identification of HGSC cells. METHODS: Ascites samples from 70 cases (70/253, 27.7%) of histologically confirmed HGSC were postoperatively collected from 2012 to 2015 and were immunocytochemically analysed. RESULTS: The sensitivity and specificity of Ber-EP4 (a marker of HGSC) for detecting HGSC was 85.7% and 82.1%, respectively, whereas the sensitivity and specificity of HBME-1 for identifying mesothelial cells was 100% and 68.3%, respectively. To improve the rate of detection further of HGSC, 29 cases of ascites were also stained for E-cadherin (a marker of HGSC) and calretinin (a marker of mesothelial cells). The combination of Ber-EP4 and E-cadherin as markers of adenocarcinoma cells increased the sensitivity and specificity for HGSC detection to 100% and 88.9%, respectively. Meanwhile, the sensitivity and specificity for mesothelial cell identification increased to 100% and 90%, respectively, when HBME-1 and calretinin were combined. CONCLUSION: This panel of complementary biomarkers is valuable and ideal for the differential diagnosis of HGSC based on ascites cytology.
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How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".