Early Recurrence of Ovarian Serous Borderline Tumor as High-grade Carcinoma: A Report of Two Cases
Bibliographic record
Abstract
Ovarian serous borderline tumors (SBTs) are characteristically associated with an indolent course. Recurrences are often delayed and usually show morphologic features of SBT or low-grade serous carcinoma. Transformation to high-grade carcinoma has rarely been documented. We report 2 cases of ovarian SBTs that recurred early as high-grade carcinomas. The first was a 50-year-old woman treated surgically and with chemotherapy for a FIGO stage 1C SBT with microinvasion, who experienced a recurrence in an axillary lymph node at 27 months. The recurrent tumor consisted of well-differentiated papillary serous tumor that resembled the primary tumor and poorly differentiated serous carcinoma. The patient died of progressive disease 43 months after her initial presentation. The second case was a 61-year-old woman treated surgically and with chemotherapy for a stage 3C micropapillary SBT with noninvasive implants. Eighteen months later, an incisional hernia was found that contained high-grade sarcomatoid-type carcinoma with microscopic foci of better differentiated tumor that resembled the primary SBT. This patient is alive with disease 24 months after her initial presentation. Whereas the malignant potential of SBTs remains controversial, the cases described herein demonstrate that SBTs can behave unpredictably and may rarely transform into high-grade carcinoma.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".