Malignant peripheral nerve sheath tumors in schwannomatosis: systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Malignant peripheral nerve sheath tumors (MPNSTs) are aggressive sarcomas commonly associated with neurofibromatosis type 1 (NF1), whose occurrence in schwannomatosis remains poorly understood. This study aimed to characterize MPNSTs in NF type 2 (NF2)- and SMARCB1-related schwannomatosis through a systematic review and meta-analysis of survival outcomes. METHODS: A comprehensive search of the PubMed, Embase, Scopus, SEER (Surveillance, Epidemiology, and End Results), and Web of Science databases was conducted from database inception through January 2024. Clinical, radiological, histopathological, and treatment data were extracted. Primary endpoints included overall survival (OS) and progression-free survival (PFS). Predictors of outcomes were analyzed using Kaplan-Meier survival curves and Cox proportional hazards models. RESULTS: A total of 39 cases were identified. NF2-related disease accounted for 67% of cases, while 28% were SMARCB1 related. The mean age at MPNST diagnosis was 33 years (range 9-79 years). Pain (41%) and weakness (26%) were the most common presenting symptoms. The mortality rate was 72.9%. No tumors were associated with LZTR1 mutations. Tumor locations varied widely, with involvement of the pelvis, thigh, skull base, forearm/hand, and cranial nerves. Trends suggested improved survival in the absence of S100 loss, gross-total resection, and a known schwannoma precursor. Conversely, having an intracranial or intraspinal lesion or having NF2 in the setting of prior radiation therapy were found to be associated with decreased survival and increased progression. A risk stratification tool predicted OS (HR 28.0, p < 0.0001) and PFS (HR 12.1, p < 0.0001). CONCLUSIONS: MPNSTs, although rare in schwannomatosis, can arise even in the absence of prior radiation exposure and may mimic benign schwannomas. A preliminary risk stratification tool may aid in identifying high-risk patients and optimizing treatment approaches, although validation is needed. Given the aggressive nature of MPNSTs and their potential for delayed diagnosis due to their rarity, vigilant monitoring and individualized treatment strategies are crucial. Future research should focus on refining risk prediction models and exploring targeted therapies for schwannomatosis-associated MPNSTs to improve patient outcomes.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.021 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".