High-grade Transformation/Dedifferentiation in Salivary Gland Carcinomas: Occurrence Across Subtypes and Clinical Significance
Bibliographic record
Abstract
High-grade transformation (HGT) or dedifferentiation has been described in a variety of salivary gland carcinomas, including acinic cell carcinoma, secretory carcinoma, adenoid cystic carcinoma, epithelial-myoepithelial carcinoma, polymorphous adenocarcinoma, low-grade mucoepidermoid carcinoma, and hyalinizing clear cell carcinoma. High-grade (HG) transformed tumors are composed of a conventional low-grade component characterized by specific microscopic and immunohistochemical features for the given entity, intermingled with or juxtaposed to areas of HG morphology. This is usually either poorly differentiated adenocarcinoma, carcinoma not otherwise specified, or undifferentiated carcinoma, in which the original line of differentiation is lost. The HG component is composed of solid nests of anaplastic cells with large vesicular pleomorphic nuclei, prominent nucleoli, and abundant cytoplasm. Frequent mitoses and extensive necrosis may be present. The Ki-67 labeling index is consistently higher in the HG component. The molecular genetic mechanisms responsible for HGT of salivary gland carcinomas are largely unknown, though p53 inactivation and human epidermal growth factor receptor 2 overexpression and/or gene amplification have been demonstrated in the HG component in a few examples, the frequency varies for each histologic type. Salivary gland carcinomas with HGT are more aggressive than conventional carcinomas, with a higher local recurrence rate and a poorer prognosis. They have a high propensity for cervical lymph node metastasis suggesting a need for a wider resection and neck dissection. HGT of salivary gland carcinoma can occur either at initial presentation or less commonly at the time of recurrence, sometimes following postoperative radiotherapy. The potential for HGT in almost any type of salivary gland carcinoma warrants a thorough sampling of all salivary gland malignancies to prevent oversight of a HG component.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".