The role of thoracic consolidative radiotherapy in the setting of immunotherapy in extensive stage small cell lung cancer
Bibliographic record
Abstract
The improvement in treatment strategies and outcomes in small cell lung cancer (SCLC) has lagged behind other cancers. The addition of immune checkpoint inhibitors (ICIs), durvalumab and atezolizumab, to the platinum-based chemotherapy in frontline setting has improved the survival in extensive stage SCLC, (ES-SCLC), albeit modestly, and is now the new standard of care. Prior to advent of immunotherapy into the therapeutic armamentarium in ES-SCLC, consolidative thoracic radiotherapy (TRT) was associated with improved thoracic control and survival outcomes. In the era of ICIs, the role of TRT is not well defined, chiefly because TRT was not incorporated in any immunotherapy trials, secondly due to concerns regarding the increased risks of pneumonitis, and finally uncertain magnitude of benefit with this combined approach. In principle, radiation can increase in the immunogenicity of tumor and hence the activity of immune checkpoint blockade, thereby increasing efficacy both locally and distantly. Such an approach has been promising in non-small cell lung cancer with ICIs improving outcomes after concurrent chemoradiation, but remains unanswered in ES-SCLC. It is, thus, possible that the modest improvement in survival by addition of ICIs to chemotherapy in ES-SCLC can be further improved by the incorporation of consolidative TRT in selected patients. Several early phase trials and retrospective studies have suggested that such an approach may be feasible and safe. Prospective trials are ongoing to answer whether adding radiation therapy to chemoimmunotherapy will improve outcomes in ES-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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".