A Systematic Review and Meta-analysis of Retrospective Series of Regorafenib for Treatment of Metastatic Colorectal Cancer
Bibliographic record
Abstract
BACKGROUND: Metastatic colorectal cancer is a common disease encountered in oncology practice and treatment options beyond fluoropyrimidines, irinotecan, oxaliplatin and monoclonal antibodies against epidermal growth factor receptor and vascular endothelium growth factor (VEGF) are limited. Regorafenib, a new drug that targets tyrosine kinases such as VEGF receptor as well as others, has been added recently to the armamentarium for metastatic colorectal cancer. This report analyzes the published experience with this drug in clinical practice outside of clinical trials. MATERIALS AND METHODS: A literature search of major databases was performed for the identification of studies of regorafenib in metastatic colorectal cancer. Studies retained for further analysis were in English or French, describing 20 or more patients treated with regorafenib monotherapy and not part of a phase I, II or III trial. Results of the pooled analysis of retrospective studies were compared with results of the published phase III trials and a phase IIIb prospective study. RESULTS: Twelve publications including a total of 702 patients were included in the meta-analysis. Summary response rate was 2% [95% confidence interval (CI) =0.8-3.2%] and the disease control rate 38.14% (95% CI=32.35-43.93%). Summary survival rates were 3.34 months (95% CI=2.71-3.97 months) for progression-free and 7.27 months (95% CI=6.23-8.3 months) for overall survival. These were similar to the phase III and IIIb studies. Most common adverse effects were also consistent with those of the published phase III experience. CONCLUSION: This systematic review and meta-analysis confirmed a moderate efficacy of regorafenib in later-stage metastatic colorectal cancer in the everyday clinical practice setting outside of clinical trials. Future identification of biomarkers may aid in further tailoring of this treatment in order to obtain maximum clinical benefit.
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".