Stereotactic radiotherapy for early lung cancer: Evidence-based approach and future directions
Bibliographic record
Abstract
AIM: To review key studies evaluating stereotactic radiotherapy in the setting of early-stage non-small cell lung cancer (NSCLC) for inoperable or high-risk patients, and discuss areas of ongoing research and clinical trials. BACKGROUND: The use of stereotactic radiotherapy for the treatment of early stage non-small cell lung cancer (NSCLC) has increased rapidly over the past decade. Numerous studies have reported outcomes for patients treated with SBRT who are unfit for surgical resection, or at high risk of surgical complications. MATERIALS AND METHODS: A narrative review. RESULTS: The preponderance of evidence suggests that SBRT is associated with excellent local control (∼90% at 3 years) and a favorable toxicity profile. In patients with higher operative risks, such as the elderly and patients with severe COPD, SBRT may provide a less-toxic treatment than surgery with similar oncologic outcomes. Ongoing studies are evaluating the use of SBRT for locally advanced or oligometastatic NSCLC. CONCLUSIONS: A large body of evidence now exists to support the use of SBRT for early-stage NSCLC. Decisions regarding the optimal choice of treatment should be individualized, and made in the context of a multidisciplinary team.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".