Effect of neoadjuvant immunotherapy and targeted therapies on surgical resection in patients with solid tumors: A systematic review and meta-analysis.
Bibliographic record
Abstract
511 Background: Neoadjuvant immunotherapy with anti-programmed cell death protein-1 (PD-1) or anti-programmed cell death ligand-1 (PD-L1) and tyrosine kinase inhibitor (TKI) therapy is currently being used to treat certain solid tumours prior to surgery. Neoadjuvant therapy may cause delays to resection potentially losing a window of opportunity. We explored the pooled proportion of patients with solid tumours receiving neoadjuvant therapy who completed planned surgical resection. Methods: Medline, CENTRAL and Embase databases were searched for single arm or randomized controlled trials studying neoadjuvant PD-1/PD-L1 immunotherapy or TKI therapy. Random-effects model was used to estimate the pooled proportion of patients undergoing planned resection, and weights were estimated using inverse variance method. Statistical heterogeneity was calculated using the I2 and chi-squared test. Results: From 368 relevant articles, eleven studies with a total of 382 patients receiving neoadjuvant PD-1 immunotherapy (n = 234) or neoadjuvant TKI therapy (n = 148) were analyzed. The types of tumours included hepatocellular carcinoma (1 study), renal cell carcinoma (8 studies), bladder carcinoma (1 study) or non-small cell lung cancer (1 study). The pooled proportion of patients who completed planned surgery after neoadjuvant therapy was 95% (95% CI 0.92 to 0.99). The overall partial response rate prior to surgery was 12% (95% CI 0.07 to 0.16) in the PD-1 therapy group and 46% (95% CI -0.12 to 1.03) in the TKI group. The pooled serious adverse events rate was 17% (95% CI 0.02 to 0.32) in the PD-1 therapy group and 29% (95% CI -0.10 to 0.68) in the TKI group. For all patients receiving neoadjuvant therapy, the pooled median overall survival was 23.41 months (95% CI 16.21 to 30.62) and median progression free survival was 7.46 months (95% CI 4.41 to 10.51). Conclusions: Neoadjuvant PD-1 or TKI therapy prior to surgery for solid tumours is safe, does not delay surgical resection and can result in a partial radiological response prior to surgery.
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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".