Thoracic Spinal Sclerosing Epithelioid Fibrosarcoma Mimicking Schwannoma: Case Report and Literature Review
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Sclerosing epithelioid fibrosarcoma (SEF) is a rare soft tissue sarcoma with high rates of local recurrence and distant metastasis. Primary spinal involvement is exceedingly uncommon and often misdiagnosed due to radiological and histopathological resemblance to more frequent spinal tumors. The objective of this study is to present a rare case of thoracic spinal SEF and to contextualize it within the available literature. METHODS: We describe the case of a 37-year-old woman presenting with progressive back pain and dysesthesia. MRI demonstrated a heterogeneously enhancing mass at the left T10-T11 neural foramen, initially interpreted as a common nerve sheath tumor. Gross total resection (GTR) was achieved, and histopathological analysis revealed a SEF. Clinical course, adjuvant therapies, and outcomes were evaluated, together with a review of previously reported spinal SEF cases. RESULTS: Despite GTR followed by adjuvant chemotherapy, local recurrence occurred 18 months later. The patient underwent subtotal resection (STR) with adjuvant proton therapy. At 18-month follow-up after the second procedure, she remained neurologically stable and disease-free. The literature review confirmed the rarity of spinal SEF, its frequent misdiagnosis, and the absence of standardized therapeutic protocols. CONCLUSIONS: Spinal SEF is a rare malignancy that can mimic benign spinal tumors, delaying diagnosis. Its management relies on multidisciplinary assessment, individualized therapy, and long-term follow-up. This report increases awareness of spinal SEF and provides additional evidence to support clinical decision-making in rare spinal tumors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".