Longitudinal Assessment of Enhancing Foci of Abnormal Signal Intensity in Neurofibromatosis Type 1
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Patients with neurofibromatosis 1 are at increased risk of developing brain tumors, and differentiation from contrast-enhancing foci of abnormal signal intensity can be challenging. We aimed to longitudinally characterize rare, enhancing foci of abnormal signal intensity based on location and demographics. MATERIALS AND METHODS: A total of 109 MR imaging datasets from 19 consecutive patients (7 male; mean age, 8.6 years; range, 2.3–16.8 years) with neurofibromatosis 1 and a total of 23 contrast-enhancing parenchymal lesions initially classified as foci of abnormal signal intensity were included. The mean follow-up period was 6.5 years (range, 1–13.8 years). Enhancing foci of abnormal signal intensity were followed up with respect to presence, location, and volume. Linear regression analysis was performed. RESULTS: Location, mean peak volume, and decrease in enhancing volume over time of the 23 lesions were as follows: 10 splenium of the corpus callosum (295 mm3, 5 decreasing, 3 completely resolving, 2 surgical intervention for change in imaging appearance later confirmed to be gangliocytoma and astrocytoma WHO II), 1 body of the corpus callosum (44 mm3, decreasing), 2 frontal lobe white matter (32 mm3, 1 completely resolving), 3 globus pallidus (50 mm3, all completely resolving), 6 cerebellum (206 mm3, 3 decreasing, 1 completely resolving), and 1 midbrain (34 mm3). On average, splenium lesions began to decrease in size at 12.2 years, posterior fossa lesions at 17.1 years, and other locations at 9.4 years of age. CONCLUSIONS: Albeit very rare, contrast-enhancing lesions in patients with neurofibromatosis 1 may regress over time. Follow-up MR imaging aids in ascertaining regression. The development of atypical features should prompt further evaluation for underlying tumors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".