Assessment of benign tumor burden by whole-body MRI in patients with neurofibromatosis 1
Bibliographic record
Abstract
People with neurofibromatosis 1 (NF1) have multiple benign neurofibromas and a 10% lifetime risk of developing malignant peripheral nerve sheath tumors (MPNSTs). Most MPNSTs develop from benign plexiform neurofibromas, so the burden of benign tumors may be a risk factor for developing MPNST. We studied 13 NF1 patients with MPNSTs and 26 age- and sex-matched controls (NF1 patients who did not have MPNSTs) with detailed clinical examinations and whole-body MRI to characterize their body burden of internal benign neurofibromas. Internal plexiform neurofibromas were identified in 22 (56%) of the 39 NF1 patients studied. All six of the NF1 patients with MPNSTs under 30 years of age had neurofibromas visualized on whole-body MRI, compared to only 3 of 11 matched NF1 controls under age 30 (p < 0.05). Both the median number of plexiform neurofibromas (p < 0.05) and the median neurofibroma volume (p < 0.01) on whole-body MRI were significantly greater among MPNST patients younger than 30 years of age than among controls. No significant differences in whole-body MRI findings were observed between NF1 patients with MPNSTs and controls who were 30 years of age or older. Whole-body MRI of NF1 patients allows assessment of the burden of internal neurofibromas, most of which are not apparent on physical examination. Whole-body imaging of young NF1 patients may allow those at highest risk for developing MPNST to be identified early in life. Close surveillance of these high-risk patients may permit earlier diagnosis and more effective treatment of MPNSTs that develop.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".