CyberKnife for inoperable renal tumors: Canadian pioneering experience.
Bibliographic record
Abstract
INTRODUCTION: Stereotactic ablative body radiotherapy (SABR) is currently under study regarding its clinical application in management of patients with kidney tumors. CyberKnife can accurately deliver ablative tumor radiation doses while preserving kidney function. We report Canada's first use of CyberKnife SABR system in treating primary kidney tumors. MATERIALS AND METHODS: Between January 2011 and February 2012, we treated three patients with renal tumors using CyberKnife SABR. Two patients had tumors in solitary kidney. The third patient had a recurrent tumor after two previous radiofrequency ablation treatments. Platinum seed fiducials were used for real time tumor tracking. Magnetic resonance imaging registration was used for tumor delineation in all cases. The patients were followed with regular renal scans and renal function tests. RESULTS: The mean age was 79 years. Mean tumor size was 21.3 cm3. A dose of 39 Gy in 3 fractions was delivered. The post treatment follow up times were 15 months, 13 months and 12 months. Local control was obtained in all three patients. No acute or chronic toxicity was reported. Kidney functions remained unaffected after treatment. CONCLUSION: CyberKnife is technically feasible for treatment of medically inoperable renal tumors or tumors in a solitary kidney.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".