Growing Teratoma Syndrome with Synchronous Gliomatosis Peritonei during Chemotherapy in Ovarian Immature Teratoma: A Case Report and Literature Review
Bibliographic record
Abstract
Coexistent growing teratoma syndrome (GTS) and gliomatosis peritonei (GP) arising during chemotherapy of ovarian immature teratoma (IMT) is extremely rare and can be misdiagnosed as recurrent or progressive disease. We present a 33-year-old woman diagnosed with GTS with synchronous GP during chemotherapy of IMT. She underwent ovarian cystectomy due to ovarian immature teratoma and chemotherapy were administered. The α-fetoprotein (AFP) concentration decreased from 28.7 ng/mL to normal after the second cycle. Four days after the third cycle of chemotherapy, ultrasound and CT revealed an 8-cm mass with negative tumor markers in the pouch of Douglas. An exploratory laparotomy was conducted, and a smooth round cystic-solid 8-cm mass was noted in the pouch of Douglas. Extensive peritoneal seeding glial nodules were also observed on the surface of the uterus, peritoneum, and omentum. The patient underwent a partial omentectomy, intact resection of the tumor, and resection of most of the glial nodules. Postoperative pathology demonstrated a pure mature cystic teratoma component in the mass, as well as diffuse GP involving the uterine serosa, peritoneum, and omentum; this diagnosis of GTS with synchorous GP should be considered in IMT patients with mass newly identified during chemotherapy while tumor markers are normal after treatment.
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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 | 0.004 |
| 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) | 0.002 | 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".