Cost-effectiveness of RAS testing in colorectal cancer. A systematic review of economic evaluations
Bibliographic record
Abstract
Background: Epidermal growth factor receptor (EGFR) inhibitors have shown benefit in the treatment of metastatic colorectal cancer (mCRC), particularly when combined with predictive biomarkers of response. The most important is RAS mutational status: international guidelines recommend to reserve anti-EGFR treatment only to RAS wild-type tumors, as they are likely to benefit from it. We aimed to review the cost-effectiveness of RAS testing in selecting mCRC patients for anti-EGFR treatment. Methods: We performed a systematic review of full economic evaluations comparing testing for RAS mutational status in mCRC patients prior to anti-EGFR treatment with no-testing. We searched Medline, the Health Technology Assessment Database, the National Health Service Economic Evaluation Database, Scopus, and ISI Web of Science for English papers published since 2000. The quality of the included studies was assessed using the Quality of Health Economic Studies scale. Results: We included 7 economic evaluations (2 cost-effectiveness analyses; 3 cost-utility analyses; 2 cost-effectiveness and cost utility analyses) conducted between 2000 and 2017 in various Countries (Switzerland; Germany; USA; Canada; Japan; China). All studies showed good quality and adopted the perspective of the healthcare system/payer, thus only direct medical costs were analyzed. All studies except one presented at least one strategy with a favorable ICER for RAS testing prior to anti-EGFR therapy. Conclusions: Even if testing for RAS mutational status prior to anti-EGFR therapy increases costs, still it is a cost-effective strategy compared to anti-EGFR therapy without testing. Nevertheless, existing economic evaluations showed some limitations that should be addressed with future research, such as the impact of anti-EGFR toxicity and the introduction of comprehensive molecular profiling. The treatment of mCRC is shifting to a more personalized approach which is essential for avoiding unnecessary toxicity and costs. Key messages: Testing mCRC patients for RAS status and administering EGFR inhibitors only to patients with RAS wild-type tumors is a more cost-effective strategy than treating all patients without testing. Systematic reviews of economic evaluations are fundamental to guide policy makers and clinicians in the selection of appropriate genomic applications.
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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 | 0.017 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.016 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".