Unraveling the Mysteries of Perineural Invasion in Benign and Malignant Conditions
Bibliographic record
Abstract
Perineural invasion (PNI) is defined as the dissemination of neoplastic cells within the perineural space. PNI can be a strong indicator of malignancy and is linked to poor prognosis and adverse outcomes in various malignant neoplasms; nevertheless, it can also be seen in benign pathologic conditions. In this review article, we discuss various signaling pathways and neurotrophic factors implicated in the development and progression of PNI. We also describe the methodology, benefits, and limitations of different in vitro, ex vivo, and in vivo models of PNI. The spectrum of presentation for PNI can range from diffuse spread within large nerves ("named" nerves) all the way through localized spread into unnamed microscopic nerves. Therefore, the clinical significance of PNI is related to its extent rather than its mere presence or absence. In this article, we discuss the guidelines for the identification and quantification of PNI in different malignant neoplasms based on the College of American Pathologists (CAP) and World Health Organization (WHO) recommendations. We also describe benign pathologic conditions and neoplasms demonstrating PNI and potential mimics of PNI. Finally, we explore avenues for the future development of targeted therapy options via modulation of signaling pathways involved in PNI.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".