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Record W4408161913 · doi:10.1002/path.6407

Cellular origins of mucinous ovarian carcinoma

2025· review· en· W4408161913 on OpenAlexaff
Nicola S. Meagher, Martin Köbel, Anthony N. Karnezis, Aline Talhouk, Michael S. Anglesio, Andrew Berchuck, Simon A. Gayther, Paul D.P. Pharoah, Penelope M. Webb, Susan J. Ramus, Kylie L. Gorringe

Bibliographic record

VenueThe Journal of Pathology · 2025
Typereview
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsUniversity of British ColumbiaUniversity of Calgary
FundersNational Health and Medical Research CouncilPeter MacCallum Foundation
KeywordsMucinous carcinomaOvarian carcinomaPathologyOvaryCarcinomaMedicineOncologyOvarian cancerAdenocarcinomaInternal medicineCancer

Abstract

fetched live from OpenAlex

Mucinous ovarian carcinoma (MOC) is a rare histotype of epithelial ovarian cancer. Its origins are obscure: while many mucinous tumours in the ovary are metastases from the gastrointestinal tract, MOC can occur as an ovarian primary; however, the cell of origin is not well established. In this review we summarise the pathological, epidemiological, and molecular evidence for the cellular origins of MOC. We propose a model for the origins of the various tumours of the ovary with mucinous differentiation. We distinguish Müllerian from gastrointestinal-type mucinous differentiation. A small proportion of the latter arise from teratoma and a distinct terminology has been proposed. Other gastrointestinal mucinous tumours are associated with Brenner tumours and arise from their associated benign lesions, Walthard nests. The remaining mucinous tumours develop either through mucinous metaplasia in established Müllerian tumours or with even greater plasticity through gastrointestinal metaplasia of epithelial or mesothelial ovarian inclusions. This model remains to be validated and mechanistically understood and we discuss future research directions. © 2025 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.337
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations13
Published2025
Admission routes1
Has abstractyes

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