MétaCan
Menu
← Back to cohort
Record W4405617673

Plasma-Based Comprehensive Genomic Profiling DNA Assays for Non-Small Cell Lung Cancer: A Health Technology Assessment.

2024· article· en· W4405617673 on OpenAlexaboutno aff

Bibliographic record

VenuePubMed · 2024
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsnot available
Fundersnot available
KeywordsLung cancerProfiling (computer programming)Computational biologyDNA profilingDNAMedicineBiologyOncologyComputer scienceGenetics
DOInot available

Abstract

fetched live from OpenAlex

Background: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, accounting for about 85% of all lung cancer cases. While some cases of NSCLC with actionable genomic alterations in the tumour cells may respond to standard therapies, they often show greater improvement with targeted therapies. The current standard of care in Ontario involves testing for actionable genomic alterations using both DNA and RNA panels via tissue testing alone. However, liquid biopsy testing may complement tissue testing by addressing some of its limitations. We conducted a health technology assessment of liquid biopsy testing using DNA panels for people with NSCLC, which included an evaluation of analytical validity, clinical validity, clinical utility, cost-effectiveness, the budget impact of publicly funding this technology, and patient preferences and values. Methods: We performed a systematic literature search of the clinical evidence. We assessed the risk of bias of each included study using the QUADAS-2, QUADAS-C, ROBINS-I, and ROBINS-E tools and the quality of the body of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group criteria. We performed a systematic economic literature search and conducted a cost-utility analysis of 4 potential liquid biopsy testing strategies in which liquid biopsy testing was added to tissue testing in various ways; our model used a 20-year time horizon and was conducted from a public payer perspective. We also analyzed the budget impact of publicly funding liquid biopsy testing for people with NSCLC in Ontario. To contextualize the potential value of liquid biopsy testing, we spoke with people with NSCLC and family members and care partners of people with NSCLC. Results: genes (GRADE: Moderate to High). However, for the other genes assessed, the sensitivity was either low or uncertain (GRADE: Very Low to High). Liquid biopsy testing also showed an overall high concordance with tissue testing (GRADE: High). Further, liquid biopsy testing was found to improve partial response rates, stable disease rates, and progressive disease rates for people with NSCLC with actionable genomic alterations who were receiving matched targeted therapies (GRADE: Moderate). However, we are uncertain about the clinical validity of liquid biopsy testing in predicting prognosis with standard therapies (GRADE: Very Low). Compared with tissue testing alone, we estimate that all 4 of the potential liquid biopsy testing strategies we evaluated would be more expensive and associated with an increase in quality-adjusted life-years (QALYs). The incremental cost-effectiveness ratio (ICER) of the strategy in which liquid biopsy testing is provided only for people with insufficient tissue for tissue testing ("insufficient tissue") was $96,738 per additional QALY; ICER estimates for the other 3 strategies ("tissue-first," "liquid-first," and "combined") were all higher at $147,636, $157,267, and $173,032, respectively. All 4 potential liquid biopsy testing strategies had a chance of being cost-effective of less than 1% at a willingness-to-pay (WTP) of $50,000 per QALY gained; only the insufficient tissue strategy had a probability of being cost-effective of more than 50% at a WTP of $100,000 per QALY gained. We estimate that the 5-year budget impact of publicly funding the insufficient tissue strategy would be $13.72 million. Publicly funding the other strategies would result in a 5-year budget impact ranging from $110.13 million to $134.24 million. All interview participants viewed liquid biopsy positively. Participants perceived liquid biopsy testing as less invasive than tissue testing, and those who had undergone both tissue and liquid biopsy testing perceived that the turnaround time for results was quicker for liquid biopsy testing. Barriers to accessing liquid biopsy testing include lack of awareness, cost, and geography. Conclusions: genes (GRADE: Low to High). The test has high concordance with tissue testing (87%-99%) but may miss some positive cases. We are uncertain about the clinical validity of liquid biopsy testing in predicting prognosis with standard therapies (GRADE: Very Low). However, we found that targeted therapies improve response rates (GRADE: Moderate) and survival (GRADE: Low) for people with NSCLC and actionable genomic alterations identified through liquid biopsy testing. Compared with tissue testing alone, all 4 potential liquid biopsy testing strategies that we evaluated are more costly but also associated with an increase in QALYs. We estimate that publicly funding liquid biopsy testing for people newly diagnosed with locally advanced or metastatic NSCLC (stage IIIB or IV) over 5 years would lead to an additional cost of $134.24 million for the combined strategy, $119.27 million for the liquid-first strategy, $110.13 million for the tissue-first strategy, and $13.72 million for the insufficient tissue strategy. People with NSCLC, family members, and care partners viewed liquid biopsy favourably. Those who had undergone both tissue and liquid biopsy testing perceived that the turnaround time for results was quicker for liquid biopsy testing. Current barriers to accessing liquid biopsy testing include lack of awareness, cost, and geography.

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 imitation

Not 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.

metaresearch head score (Codex)0.083
metaresearch head score (Gemma)0.198
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.083
Threshold uncertainty score0.438

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0830.198
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.017
Bibliometrics0.0100.010
Science and technology studies0.0000.001
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.026
GPT teacher head0.340
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venuePubMed→Same topicLung Cancer Treatments and Mutations→French-language works237,207→