Successful Treatment of Primary Central Nervous System T-Cell Lymphoma With Induction Chemotherapy Followed by Consolidation With High-Dose Chemotherapy and Autologous Stem Cell Rescue
Bibliographic record
Abstract
Primary central nervous system T-cell lymphoma (PCNSTL) is an exceptionally rare subtype of non-Hodgkin lymphoma, comprising only 2% of primary CNS lymphoma cases. Due to its rarity, PCNSTL is often misdiagnosed, lacks standardized treatment guidelines, and carries a poor prognosis. We present a unique case of a 59-year-old-woman with a history of hypertension and hyperlipidemia who initially presented with sudden-onset aphasia and right-sided weakness. Suspected initially of having a cerebrovascular accident (CVA), she received tenecteplase and underwent investigation of CVA, which was largely unremarkable. Six months later, she returned with progressive nausea, vomiting, confusion, and word-finding difficulties. A magnetic resonance imaging (MRI) of the brain showed lesions in the left cerebellum and frontal lobe with vasogenic edema. A suboccipital craniotomy and biopsy confirmed anaplastic lymphoma kinase (ALK)-negative anaplastic large cell lymphoma. Positron emission tomography-computed tomography (PET-CT) showed no systemic disease and cerebrospinal fluid (CSF) analysis showed lymphomatous involvement. The patient was initiated on six cycles of methotrexate, cytarabine, and thiotepa (MATRix regimen without rituximab), followed by high-dose chemotherapy (HDC) with carmustine and thiotepa and autologous stem cell transplantation (ASCT). She tolerated treatment and transplant without complications and remains in complete remission 18 months post-transplant. To our knowledge, this is the first reported case of PCNSTL treated successfully with MATRix followed by HDC-ASCT. This case highlights the importance of considering rare CNS lymphomas in patients with atypical neurologic presentations and suggests the use of HDC-ASCT as a promising approach in a disease with no established standard of care.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".