Gamma Knife radiosurgery for the management of cerebral metastases from non–small cell lung cancer
Bibliographic record
Abstract
OBJECT: Non-small cell lung cancer (NSCLC) is the most frequent cancer that metastasizes to brain. Stereotactic radiosurgery (SRS) has become the management of choice for most patients with such metastatic tumors. Therefore, the authors endeavored to elucidate the survival and SRS outcomes for patients with NSCLC metastasis at their center. METHODS: In this single-institution retrospective analysis, the authors reviewed their experience with NSCLC metastasis during a 10-year period from 2001 to 2010. Seven hundred twenty patients underwent Gamma Knife radiosurgery. A total of 1004 SRS procedures were performed, and 3143 tumors were treated. The NSCLC subtype was adenocarcinoma in 386 patients, squamous cell carcinoma in 111 patients, and large cell carcinoma in 34 patients. The median aggregate tumor volume was 4.5 cm(3) (range 0.1-88 cm(3)). RESULTS: The median survival time after diagnosis of brain metastasis from NSCLC was 12.6 months, and the median survival after SRS was 8.5 months. The 1-, 2-, and 5-year survival rates after SRS were 39%, 21%, and 10%, respectively. Postradiosurgery survival was decreased in patients treated with prior whole-brain radiation therapy compared with SRS alone (p = 0.003). Aggregate tumor volume was inversely related to survival after SRS (p < 0.001), and the histological subgroups demonstrated significant survival differences (p = 0.023). The overall local tumor control rate in the entire group was 92.8%. One hundred seventy-four patients (24%) underwent repeat SRS for new or resistant metastatic deposits. CONCLUSIONS: Stereotactic radiosurgery is an effective means of providing local control for NSCLC metastases. Neurological function and survival benefit from serial patient monitoring and repeat SRS for new tumors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.000 |
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".