Myoepithelial Carcinoma: The Role of Radiation Therapy. A Case Report and Analysis of Data From the Surveillance, Epidemiology, and End Results (SEER) Registry
Bibliographic record
Abstract
PURPOSE/OBJECTIVE: The role of radiation therapy in the treatment of myoepithelial carcinoma (MC) is unknown. We present a case of a high-grade soft-tissue MC in a pediatric patient and retrospectively examine the effect of postoperative radiation on survival in patients with MC. MATERIALS AND METHODS: Our patient was treated with 4 cycles of ifosfamide, cisplatin, and etoposide followed by 3 cycles of ifosfamide vincristine and etoposide. Radiation was delivered to a total dose of 5580 cGy in 180 cGy/fraction to the surgical bed with a 2 cm margin starting after the third cycle of chemotherapy. The Surveillance, Epidemiology, and End Results (SEER) registry database was queried for cases of surgically resected MC. Retrospective analysis was performed with the endpoint of overall survival (OS). RESULTS: Two hundred thirty-four cases of MC were identified; for 62 of these cases, the grade of the tumor wasidentified. Of these 62 patients, 27 received postoperative radiation. OS was improved with adjuvant radiation therapy in patients with grade III or IV MC (P<0.01) as determined by the log-rank test. CONCLUSIONS: This analysis of SEER data showed an OS benefit with adjuvant radiation therapy in the treatment of high-grade MC. Physicians should report all cases of MC to improve clinical decision making in the treatment of this rare disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".