Implementation of Liquid Biopsy in Non-Small-Cell Lung Cancer: An Ontario Perspective
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer-related deaths in Canada, with non-small-cell lung cancer (NSCLC) accounting for the majority of cases. Timely access to comprehensive molecular profiling is critical for selecting biomarker-matched targeted therapies, which lead to improved outcomes in advanced NSCLC. Tissue biopsy samples are the gold standard for molecular profiling; however, several challenges can prevent timely and complete molecular profiling from being performed, causing delays in treatment or suboptimal therapy selection. Liquid biopsy offers a minimally invasive method for molecular profiling by analyzing circulating tumour DNA (ctDNA) and RNA (cfRNA) in plasma, potentially overcoming these barriers. This paper discusses the outcomes of a multidisciplinary working group in Ontario, which proposed three eligibility criteria for liquid biopsy reimbursement: (1) insufficient tissue for complete testing or failed tissue biomarker testing; (2) suspected advanced NSCLC where tissue biopsy is not feasible; and (3) high-risk patients who may deteriorate before tissue results are available. The group also addressed considerations for assay selection, implementation, and economic impact. These discussions aim to inform reimbursement and implementation strategies for liquid biopsy in Ontario's public healthcare system, recognizing the need for ongoing evaluation as technology and evidence evolve.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".