Updates on the diagnosis and treatment of intracranial nerve malignant peripheral nerve sheath tumors
Bibliographic record
Abstract
BACKGROUND: Malignant peripheral nerve sheath tumors (MPNSTs) are rare entities and MPNSTs of intracranial nerves are even more sporadic. MPNSTs present diagnosis and treatment challenges since there are no defined diagnosis criteria and no established therapeutic strategies. METHODS: We reviewed literature for MPNST-related articles. We found 45 relevant studies in which 60 cases were described. RESULTS: We identified 60 cases of intracranial nerve MPNSTs. The age ranged from 3 to 75 years old. Male to female ratio was 1.5:1. The most involved cranial nerves (CNs) were CN VIII (60%), CN V (27%), and CN VII (10%). Most of the MPNSTs reported (47%) arose sporadically, 40% arose from a schwannoma, 8% arose from a neurofibroma, and 6% arose from an unspecified nerve tumor. Twenty patients had a history of radiation exposure, four patients had neurofibromatosis type 1 (NF1), four patients had neurofibromatosis type 2 (NF2), and NF2 was suspected in two other patients. Twenty-two patients were treated with radiotherapy and presented a higher survival rate. Seventy-two percent of patients died of their disease while 28% of patients survived. One-year survival rate was 33%. Forty-five percent of tumors recurred and 19% of patients had metastases. CONCLUSION: MPNSTs involving CNs are very rare. Diagnosis is made in regards to the histological and pathological findings. Imaging may help orient the diagnosis. A preexisting knowledge of the clinical situation is more likely to lead to a correct diagnosis. The mainstay of treatment is radical surgical resection with adjuvant radiotherapy. Since these tumors are associated with a poor prognosis, a close follow-up is mandatory.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".