TP53 as a Diagnostic Aid in the Distinction of Ovarian Mucinous Borderline Tumors From Mucinous Carcinoma
Bibliographic record
Abstract
Ovarian mucinous borderline tumors (MBTs) are clinically managed as benign neoplasms while the management of ovarian mucinous carcinomas (MC) is dependent on tumor stage. Despite the standardization of sampling of ovarian mucinous neoplasms, limited interobserver reproducibility between MBT and MC persists. Based on our recent finding that abnormal TP53 expression is associated with unfavorable outcome in MBT, we hypothesized that TP53 status might improve the reproducible distinction of MBT from MC. A virtual slide set of 85 consecutive ovarian mucinous neoplasms received at a single institution, with each case represented by 3 full sections, were reviewed by 3 pathologists in 2 iterations. The initial assessment was based solely on morphologic review, while the second iteration was performed with knowledge of TP53 status. The reproducibility of a trinary categorization (MBT, MBT with intraepithelial carcinoma [IEC], MC) significantly improved from a κ of 0.60 based on the initial morphologic assessment to a κ of 0.76 (t-test, P =0.0042) after consideration of TP53 immunohistochemistry (IHC) results. Six out of 85 patients died of disease, and in 2 of them, at least 1 pathologist assessed MBT with IEC and not MC even after integration of TP53 IHC. With the integration of TP53 IHC, substantial interobserver agreement for MBT and MC can be reached, particularly in cases with an uncertain degree of confluent growth. TP53 IHC can also be used to highlight and support the presence of IEC in MBT, however, discordances remained in 2 cases with adverse outcome.
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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 | 0.024 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".