Role of Stereotactic Body Radiation Therapy in Early Stage Small Cell Lung Cancer in the Era of Lung Cancer Screening
Bibliographic record
Abstract
With the obvious benefit from low dose computed tomography to reduce the lung cancer-specific mortality, lung cancer screening is on the rise. With the implementation of the screening programs, diagnosis of early stage lung cancer is expected to increase, and small cell lung cancer (SCLC) would account for 10% of screen-detected lung cancer. Apart from Concurrent chemoradiation (CRT), the present guidelines virtually do not support other options for radiation (RT). There is a paucity of data addressing the role of Stereotactic Body Radiation Therapy (SBRT) in SCLC and we conducted the current systematic review on this topic. We systematically searched literature using the electronic databases PubMed and Embase with no language, year or publication status restrictions. After removal of duplicate records, 3469 screened, 3446 excluded with reasons, 23 full-text articles were assessed for eligibility, and 7 studies (8 reports) were included. Unsuitability for surgery or refusal for surgery was the most common reason for the use of SBRT in early stage SCLC in the included studies. Variable patterns of SBRT-chemotherapy (CT) sequencing including concurrent, pre-CT and post-CT and radiation doses were noted. Within the reported studies overall survival (OS) at 1 year, 2 year and 3 year varied from 63% to 87%, 37% to 72%, and 35% to 72%, respectively. Distant metastasis was the most common pattern of failure ranging from 38% to 53%. There was no increase in the reported grade III toxicity. SBRT could be a potential option in stage I SCLC with comparable outcomes with no added toxicity. Acknowledging the limitations and absence of high-quality data, presently cautious interpretation is warranted and further studies are needed to establish the role of SBRT in SCLC.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".