Results of Stereotactic Body Radiotherapy With CyberKnife-M6 for Primary and Metastatic Lung Cancer
Bibliographic record
Abstract
Background: The aim of the study was to evaluate the efficacy of stereotactic body radiotherapy (SBRT) using the CyberKnife-M6 (CK-M6) with lung optimized treatment (LOT) module in patients with primary lung cancer and lung metastases. Methods: Forty-two lesions from 35 patients were treated between 2019 and 2022. Four-dimensional computed tomography images were obtained when the patients were in a free breathing modality. Tracking modality was selected prospectively according to the visibility of the target. The median prescribed dose was 48 Gy in four fractions (fx) (28 - 55 Gy/1- 7 fx). The median age was 68 years (47 - 82 years), and 43% of cases were adenocarcinoma. The median lesion size was 15 mm (6 - 36 mm). Results: Complete, partial and stable responses were obtained as 26%, 62%, and 9.5% at a median of 2 months (1 - 6 months), and 35.5%, 47.5% and 5% at the 12th month evaluation, respectively. Grade 3 and higher toxicity was not observed in any case. The mean and 2-year overall survival (OS) was 31.5 months and 54%, and the local recurrence-free survival (LRFS) was 29.6 months and 51%, respectively. In univariate analysis, target lesion type, complete response (CR), and higher esophagus maximum dose were favorable factors for OS and LRFS (P < 0.05). The CR at 12th month evaluation remained significant in multivariate analysis in terms of OS (hazard ratio = 8.602, 95% confidence interval: 1.05 - 70.01; P = 0.044). Conclusions: A mean LRFS of 29.6 months and OS of 31.5 months were obtained in patients with primary and metastatic lung cancer. With a median treatment time of 25 min, motion-managed strategy with CK-M6-LOT-based SBRT is an effective, safe, and comfortable treatment method for lung cancer.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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".