Linac-Based Radiosurgery Treatment for a Pineal Parenchymal Tumor
Bibliographic record
Abstract
Pineal parenchymal tumors (PPTs) are uncommon in general and rare in the adult population. Currently, the optimal treatment for PPT of intermediate differentiation (PPTID) in older patients is unknown. Stereotactic radiosurgery (SRS) has been used as both primary and adjuvant therapy, with single or fractionated doses using Gamma Knife (Elekta, Stockholm, Sweden) or CyberKnife (Accuray Inc., Madison, WI). This article presents the case of a 77-year-old woman with a biopsy-confirmed PPTID. She was treated with single fraction VMAT (volumetric modulated arc therapy)-based SRS on a Varian TrueBeam Edge linac (Varian Medical Systems, Palo Alto, CA) using multiple non-coplanar arcs. Contouring and treatment planning were performed on contrast-enhanced MRI and CT images. Accurate patient set-up and immobilization were achieved with an open-faced thermoplastic mask, real-time motion management using an optical surface monitoring system, and kV CBCT prior to each arc. The patient has shown excellent response for tumor size with frequent follow-up up to 84 months, but 25 months later developed double-vision and headaches with MRI revealing decreasing tumor size but enhancement and FLAIR changes in the adjacent brain parenchyma suggestive of radiation necrosis. These changes stabilized as of 32 months of follow-up, and then started to decrease by 35 months.
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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) | 0.000 | 0.001 |
| 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 | 0.001 |
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".