History and Development of Clinical Use of Functional Stereotaxy for Radiation Oncologists: From Its Origins to Its Current State
Bibliographic record
Abstract
BACKGROUND: Stereotactic radiosurgery (SRS) was originally conceived as a noninvasive alternative to functional neurosurgery by the Swedish neurosurgeon Lars Leksell. This review traces the historical development of stereotactic methods from early mechanical frames to advanced image-guided systems and examines the pivotal innovations that enable precise intracranial targeting for SRS. METHODS: Using PubMed and Google Scholar, we reviewed the literature on the general history of functional stereotactic neurosurgery and radiosurgery, its indications, and how their treatment methods evolved, focusing mainly on the early period from the end of the 18th century to the 1990s. RESULTS: The origins of stereotaxy as a principle and technique were traced back to the early 20th century with animal studies by Horsley and Clarke, later adapted for human use by Spiegel and Wycis, and then Talairach in the 1940s. This enabled the precise targeting of deep brain structures for lesion-based interventions in movement, pain, and psychiatric disorders. Deep Brain Stimulation (DBS) emerged in the 1980s as a reversible treatment for tremor. Stereotactic radiosurgery was conceived in 1951 as a noninvasive alternative functional approach and saw limited use until the 1990s due to imaging constraints. With MRI-guided planning, its application has expanded mostly to the management of benign and malignant tumors and vascular disorders, as well as for functional approaches, particularly for trigeminal neuralgia, tremor, epilepsy, and OCD. CONCLUSIONS: This review underscores how technological progress and shifting clinical priorities have transformed SRS from a niche neurosurgical technique into a cornerstone of modern clinical practice, with functional SRS representing its latest clinical field of expansion.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".