The economic value of liquid biopsy for genomic profiling in advanced non-small cell lung cancer
Bibliographic record
Abstract
Background:Liquid biopsy (LB) can detect actionable genomic alterations in plasma circulating tumor circulating tumor DNA beyond tissue testing (TT) alone in advanced non-small cell lung cancer (NSCLC) patients. We estimated the cost-effectiveness of adding LB to TT in the Canadian healthcare system.Methods:A cost-effectiveness analysis was conducted using a decision analytic Markov model from the Canadian public payer (Ontario) perspective and a 2-year time horizon in patients with treatment-naïve stage IV non-squamous NSCLC and ⩽10 pack-year smoking history. LB was performed using the comprehensive genomic profiling Guardant360™ assay. Standard of care TT for each participating institution was performed. Costs and outcomes of molecular testing by LB + TT were compared to TT alone. Transition probabilities were calculated from the VALUE trial (NCT03576937). Sensitivity analyses were undertaken to assess uncertainty in the model.Results:Use of LB + TT identified actionable alterations in more patients, 68.5 versus 52.7% with TT alone. Use of the LB + TT strategy resulted in an incremental cost savings of $3065 CAD per patient (95% CI, 2195–3945) and a gain in quality-adjusted life-years of 0.02 (95% CI, 0.01–0.02) versus TT alone. More patients received chemo-immunotherapy based on TT with higher overall costs, whereas more patients received targeted therapy based on LB + TT with net cost savings. Major drivers of cost-effectiveness were drug acquisition costs and prevalence of actionable alterations.Conclusion:The addition of LB to TT as initial molecular testing of clinically selected patients with advanced NSCLC did not increase system costs and led to more patients receiving appropriate targeted therapy.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".