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Enregistrement W4405617673

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

2024· article· en· W4405617673 sur OpenAlexaboutno aff

Notice bibliographique

RevuePubMed · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueLung Cancer Treatments and Mutations
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLung cancerProfiling (computer programming)Computational biologyDNA profilingDNAMedicineBiologyOncologyComputer scienceGenetics
DOInon disponible

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,083
score de la tête « metaresearch » (Gemma)0,198
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,083
Score d'incertitude au seuil0,438

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0830,198
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0050,017
Bibliométrie0,0100,010
Études des sciences et des technologies0,0000,001
Communication savante0,0040,003
Science ouverte0,0020,003
Intégrité de la recherche0,0030,002
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,026
Tête enseignante GPT0,340
Écart entre enseignants0,313 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2024
Routes d'admission1
Résumé présentoui

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