Micropapillary Serous Borderline Ovarian Tumor in Early Pregnancy: Is Conservative Surgery Still a Viable Option?
Bibliographic record
Abstract
With the advent of improved sonographic techniques, it is quite common to find ovarian masses during pregnancy. Most of these are benign and are usually managed conservatively. It is rare to find a malignant ovarian tumor in pregnancy with a reported incidence of 1 in 12,000 to 1 in 47,000. Up to 8% of the adnexal masses could be borderline in nature. We present a case of a 29-year-old woman whose ultrasound in early first trimester (5 weeks) identified a solid-cystic ovarian mass with a raised CA125, suspicious of ovarian malignancy. She did not have any symptoms arising from this mass. She underwent a laparotomy and unilateral salpingo-oophorectomy at 6 weeks gestation. Frozen section was reported as borderline papillary serous ovarian tumor and hence omentectomy was done to complete the staging. Histology confirmed micropapillary variant of serous borderline tumor with a few foci of stromal micro-invasion. She was staged as FIGO stage IC. She was managed conservatively with regular follow-up at the antenatal clinic. She delivered a healthy baby at term by cesarean section. She is currently free of disease and is on regular follow-up at the gynae-oncology clinic. We wish to highlight that although borderline ovarian tumors (BOTs) in general have a very favorable prognosis, those diagnosed during pregnancy can be associated with aggressive features. The micropapillary variant of the serous BOT has the potential of malignant transformation and may represent an early stage in the continuum of development of low-grade serous tumors of the ovary. These patients should be closely followed up to detect any recurrence or progressive disease early. J Med Cases. 2016;7(9):406-410 doi: http://dx.doi.org/10.14740/jmc2617w
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| 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".