A Rare Case of Pelvic Primitive Neuroectodermal Tumor in a 37-Year-Old Patient
Bibliographic record
Abstract
Primitive neuroectodermal tumors (PNETs) belong to the group of malignant bone tumors. Their frequency is about 1% of sarcoma. The principal distinctive feature is a translocation concerning the EWS gene or t(11;22)(q24; q12). They are even rarer when located in the extra-skeletal pelvic region. We report a case of a 37-year-old female patient with a PNET in the pelvic region that is found after a spontaneous abortion. The tumor is localized on the right latero-uterine ligament. The tumoral size is of 10 cm. After the coelioscopic diagnosis, the patient was treated with neo-adjuvant chemotherapy according to vincristine, ifosfamide, doxorubicin, and etoposide (VIDE) protocol, which ensured an important diminishing of the size of the tumor. She then had a total radical hysterectomy associated with a lymph node resection. The diagnosis was confirmed by histology, immunohistology examinations and molecular biology by hybridization in situ . The tumoral cells were classically small and round. They expressed CD99 and vimentin. Translocation t(11;12)(q24;q12) was positive. Two months later, the patient presented a tumoral relapse and received an adjuvant chemotherapy by vincristine, dactinomycin, and ifosfamide (VAI) twice with a limited chemical and hematological tolerance that brought along palliative treatment. Extra-skeletal PNETs found in the pelvic region are very rare. Our present case is also rare by the way in which it was found, by the age of the patient and its being in the pelvic region. Prognosis with these tumors is still very bad regardless of the optimization of a multidisciplinary answer. The research of new biomarkers and target therapy could be a hope for the treatment of these rare and pejorative tumors. J Med Cases. 2016;7(2):58-59 doi: http://dx.doi.org/10.14740/jmc2372w
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".