What is the optimal strategy to confirm the diagnosis of epithelial ovarian carcinoma (EOC) prior to neoadjuvant chemotherapy (NAC)?
Bibliographic record
Abstract
5511 Background: NAC has been increasingly utilized in clinical practice yet no standard diagnostic strategy has been defined for EOC prior to the administration of NAC. We reviewed the diagnostic process for patients receiving NAC at our centre to determine if an optimal diagnostic strategy could be determined. Methods: A retrospective chart review of all patients known to receive NAC followed by cytoreductive surgery for presumed EOC between 1994 and 2007 was performed. Diagnostic strategies were defined as histology, cytology, and clinical. Performance of these strategies in predicting final pathology, based on expert pathology review of surgery specimens, was compared using Fisher's exact test. Results: 152 patients were included. Initial diagnosis was made on the basis of: cytology (paracentesis/thoracentesis)- 89 (59%); percutaneous biopsy- 40 (26%), radiology and CA-125–18 (12%), surgical biopsy -5 (3%). The final diagnosis was consistent with invasive EOC in 145 patients (95%). The remaining 7 were ovarian LMP (4), ovarian carcinosarcoma (1), endometrial serous cancer (1), and GI tumor (1). The diagnostic accuracies of the 3 strategies differed: histology (43/45), cytology (87/89), and clinical (15/18), p = 0.039. 17% of patients had an alternate final diagnosis when clinical parameters were the only basis for the diagnosis of EOC prior to NAC. A specific EOC subtype was identified pre-op in 82 patients (histology-31 cases, cytology-51 cases). Subtype differed between pre- and post-treatment samples in 13% of histology and 8% of cytology cases. Conclusions: Diagnosis of EOC based on cytology or histology-based strategies are superior to clinical factors alone. Even in a centre with trained gynecologic cytopathologists, cytology and biopsy strategies preclude accurate subtype diagnosis in a significant number of patients. These data are important for clinical practice and the design of future clinical trials. No significant financial relationships to disclose.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".