Atypical Teratoid Rhabdoid Tumor Diagnosis after Partial Resection of Dysembryoplastic Neuroepithelial Tumor: Case Report and Review of the Literature
Bibliographic record
Abstract
Dysembryoplastic neuroepithelial tumors (DNETs) are generally considered benign, slow-growing epilepsy-associated lesions. While rare cases of malignant transformation of DNET to high-grade glial tumors have been reported, to our knowledge there have been no reports of transformation/emergence of DNET to atypical teratoid rhabdoid tumor (AT/RT), a highly aggressive embryonal brain tumor. Here, we report the case of an 8-year-old boy who presented with an incidental finding of a small right insular lesion which grew slowly over 3 years. The patient first underwent surgery with subtotal tumor resection at age 11. Pathology was consistent with DNET. Following surgery, further tumor growth was evident, requiring fractionated radiotherapy and eventually chemotherapy, but continued tumor growth was witnessed. Three years after radiation, imaging showed dramatic further tumor growth, and the patient underwent a second debulking surgery. The pathology revealed a malignant tumor with BAF47-negative cells, suggestive of AT/RT. This report adds to our knowledge about the poorly understood behavior and natural history of DNETs and emphasizes the importance of lifelong clinical and neuroimaging follow-up of these lesions.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".