Stereotactic Radiosurgery in the Management of Intracranial Gliomas
Bibliographic record
Abstract
Glial neoplasms are the most common primary intracranial malignancies. Treatment of high-grade gliomas has been frustrating, with less than 5% of patients surviving 5 years after a diagnosis of glioblastoma multiforme (GBM). Stereotactic radiosurgery (SRS) and fractionated strereotactic radiotherapy (F-SRT) provide means to either escalate the dose in primary treatment or to palliate recurrences. Because of their lower alpha/beta ratios and more focal nature, low-grade gliomas (LGG) are more attractive targets for stereotactically focused radiation. Results of available phase I-II data are reviewed for both low and high-grade gliomas. In the case of high-grade gliomas disappointing preliminary phase III data from RTOG 93-05 are discussed. Toxicity of SRS is discussed. Acute treatment toxicity of significance is unusual and generally self-limited. Occasionally an exacerbation of existing symptoms occurs. Late complications attributable to SRS are usually defined as necrosis within the treatment volume. The rate of necrosis can be hard to define in high-grade gliomas as tumor cells are often present in surgical specimens. New strategies in the application of stereotactic radiation are touched upon, these include: changes in planning and fractionation, concurrent use of chemotherapy, use of radiation modifiers and biologic agents. After reviewing the current data for high-grade gliomas, it appears that any apparent improvement in outcome seen in phase I-II trials is attributable to patient selection. The best evidence available does not support the use of SRS for primary high-grade gliomas. The somewhat limited experience in LGG also indicates a lack of benefit for patients treated with stereotactic radiosurgery or F-SRT. For a very select group of patients with small recurrent lesions, F-SRT may represent a safe, reasonable treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".