Advances in the Treatment of Mucoepidermoid Carcinoma
Bibliographic record
Abstract
Mucoepidermoid carcinoma (MEC) represents 10-15% of salivary neoplasms. Due to their low incidence, it is challenging to conduct clinical trials and develop treatment guidelines. Although surgery is the most common approach for a resectable tumor, various treatment options such as chemotherapy, radiotherapy, and immunotherapy have been investigated. There is a need to implement a standardized treatment protocol to effectively manage MEC as it is a common histological subtype. Furthermore, it has become essential to assess chromosomal and genetic abnormalities recently identified with MEC, including alterations of CDKN2A , TP53 , CDKN2B , BAP1 , etc. These mutations are involved in the transformation of low-grade tumors to high-grade tumors, presenting a vital tool for evaluating the aggressive behavior of this carcinoma. Detailed immunohistochemical and translocation studies can help develop targeted therapies and monitor treatment response. Therefore, biomarker-driven research will immensely improve the outcome, especially in advanced cases. Based on thorough histology and chromosomal translocations, a more personalized treatment plan can improve the overall disease outcome. The purpose of this article is to elaborate on the current treatment advancements, particularly chemotherapy and targeted therapy, as an effective treatment modality for the management of MEC and highlight the comparison with traditional treatment approaches. World J Oncol. 2022;13(1):1-7 doi: https://doi.org/10.14740/wjon1412
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".