Brain and spine melanotic schwannoma: A rare occurrence and diagnostic dilemma
Bibliographic record
Abstract
PURPOSE: Melanotic schwannoma (MS) was formerly known as a rare variant of schwannoma. The behavior of MS is unpredictable, with a tendency for recurrence and metastasis. The purpose of this study was to illustrate the imaging characteristics of these rare and misdiagnosed tumors. The prognosis of MS is discussed, along with the importance of follow-up exams to assess for recurrence and metastasis. Furthermore, we compare our results with those previously published on MS in order to have a better understanding of this rare entity METHODS: Three MS cases were encountered between 2008 and 2015 at our institute. All available data were reviewed, including the clinical history, imaging findings, operative notes, and the histopathology results. A follow-up magnetic resonance imaging (MRI) scan was also reviewed up to 23 months post surgery to assess for recurrence. RESULTS: Three cases of MS are included: one in the brain and two in the spine. The brain lesion was in the occipital region. The spine lesions were thoracic intramedullary and sacral intradural. All cases were hypointense on T2-weighted imaging. Gross total resection was achieved for all lesions without adjuvant therapy. To date, the brain lesion recurred 15 months after surgery. CONCLUSIONS: MS is a rare and distinct entity rather than a variant of schwannoma, and it poses both diagnostic and management dilemmas. Although MS has characteristic MRI features, including T1 and T2 shortening, the preoperative diagnosis is always challenging. Accurate diagnosis is crucial for management planning, including long-term follow-up exams to assess for recurrence and metastasis.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.003 |
| 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".