A non-small cell lung cancer fragile elderly patient treated with immunotherapy and non-ablative radiation therapy: a case report of a winning combination
Bibliographic record
Abstract
Background: Radiation therapy is used in the clinical scenario of oligo-metastatic lung cancer as a weapon to delay the subsequent line of systemic therapy, particularly in the case of oligo-progressive disease. In this setting, the integration of immunotherapy and radiotherapy plays an important role to achieve local control and improve progression-free survival (PFS). Case presentation: We reported the case of an elderly fragile patient affected by advanced non-small cell lung cancer treated with pembrolizumab as first systemic line and immuno-modulant radiation therapy at oligo-progression. More specifically, he underwent stereotactic body radiation therapy using non-ablative regimen (24 Gy in 3 fractions) achieving partial response with abscopal effect and without drug interruption. After one year, during immunotherapy mediastinal and parenchymal progression occurred and he received another radiation treatment using conventional non-ablative regimen (40 Gy in 20 fractions). Complete response was observed without severe side effects (his poor respiratory function did not change during both treatments). Conclusion: In this case report we showed that the association of immunotherapy and non-ablative radiation regimens may represent a safe and effective strategy to achieve complete response also in fragile patients, in whom the burden of side effects should be prioritized.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".