Safety Related to the Timing of Radiotherapy and Immune Checkpoint Inhibitors in Patients with Advanced Non-Small Cell Lung Cancer: A Single Institutional Experience
Bibliographic record
Abstract
BACKGROUND: The safety impact of radiotherapy (RT) timing relative to immune checkpoint inhibitors (ICIs) for advanced non-small-cell lung cancer (NSCLC) is unclear. We investigated if RT within 14 days (Interval 1) and 90 days (Interval 2) of ICI use is associated with toxicities compared to RT outside these intervals. METHODS: Advanced NSCLC patients treated with both RT and ICIs were reviewed. Toxicities were graded as per CTCAE v4.0 and attributed to either ICIs or RT by clinicians. Associations between RT timing and Grade ≥2 toxicities were analyzed using logistic regression models adjusted for patient, disease, and treatment factors (α = 0.05). RESULTS: Sixty-four patients were identified. Twenty received RT within Interval 1 and 40 within Interval 2. There were 20 Grade ≥2 toxicities in 18 (28%) patients; pneumonitis (6) and nausea (2) were most prevalent. One treatment-related death (immune encephalitis) was observed. Rates of patients with Grade ≥2 toxicities were 35%/25% in the group with/without RT within Interval 1 and 30%/25% in the group with/without RT within Interval 2. No significant association between RT timing relative to ICI use period and Grade ≥2 toxicities was observed. CONCLUSION: Albeit limited by the small sample size, the result suggested that pausing ICIs around RT use may not be necessary.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".