Efficacy of local therapy for oligoprogressive disease after programmed cell death 1 blockade in advanced non‐small cell lung cancer
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs) have dramatically changed the strategy used to treat patients with non-small-cell lung cancer (NSCLC); however, the vast majority of patients eventually develop progressive disease (PD) and acquire resistance to ICIs. Some patients experience oligoprogressive disease. Few retrospective studies have evaluated clinical efficacy in patients with oligometastatic progression who received local therapy after ICI treatment. We conducted a retrospective analysis of advanced NSCLC patients who received PD-1 inhibitor monotherapy with nivolumab or pembrolizumab to evaluate the effects of ICIs on the patterns of progression and the efficacy of local therapy for oligoprogressive disease. Of the 307 patients treated with ICIs, 148 were evaluated in our study; 42 were treated with pembrolizumab, and 106 were treated with nivolumab. Thirty-eight patients showed oligoprogression. Male sex, a lack of driver mutations, and smoking history were significantly correlated with the risk of oligoprogression. Primary lesions were most frequently detected at oligoprogression sites (15 patients), and 6 patients experienced abdominal lymph node (LN) oligoprogression. Four patients showed evidence of new abdominal LN oligometastases. There was no significant difference in overall survival (OS) between the local therapy group and the switch therapy group (reached vs. not reached, P = .456). We summarized clinical data on the response of oligoprogressive NSCLC to ICI therapy. The results may help to elucidate the causes of ICI resistance and indicate that the use of local therapy as the initial treatment in this setting is feasible treatment option.
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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".