Growing Teratoma Syndrome of the Ovary: Review of Literature and First Report of a Carcinoid Tumor Arising in a Growing Teratoma of the Ovary
Bibliographic record
Abstract
We report the first case of a secondary tumor arising from a peritoneal nodule of mature teratoma in a patient with growing teratoma syndrome (GTS) of the ovary. The patient originally presented 19 years ago with an immature teratoma of the ovary and positive retroperitoneal lymph nodes. After surgery and chemotherapy, mature teratomas recurred as abdominal and pelvic masses after 1, 6, and 19 years. Upon the last recurrence, a trabecular carcinoid tumor developed in a mature teratoma associated with the liver. This case illustrates the importance of long-term follow-up for patients with GTS of the ovary, where the recurrent masses can appear many years after the primary tumor, compress the abdominal and pelvic structures and give rise to secondary neoplasms. In addition, we present a literature review of GTS of the ovary and some novel observations about this entity. On the basis of our review of ovarian GTS cases in the literature, we have found that ovarian GTS nodules tend to appear for the first time within 2 years of the initial primary. They remain confined almost exclusively to the pelvis, abdomen, and the retroperitoneum and do not venture to distant systemic sites. This new information may help identify and screen women with germ cell tumors of the ovary at risk for GTS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".