Next-generation sequencing in oncology: challenges in economic evaluations
Bibliographic record
Abstract
INTRODUCTION: Next-generation sequencing (NGS) identifies genetic variants to inform personalized treatment plans. Insufficient evidence of cost-effectiveness impedes the integration of NGS into routine cancer care. The complexity of personalized treatment challenges conventional economic evaluation. Clearly delineating challenges informs future cost-effectiveness analyses to better value and contextualize health, preference-, and equity-based outcomes. AREAS COVERED: We conducted a scoping review to characterize the applied methods and outcomes of economic evaluations of NGS in oncology and identify existing challenges. We included 27 articles published since 2016 from a search of PubMed, Embase, and Web of Science. Identified challenges included defining the evaluative scope, managing evidentiary limitations including lack of causal evidence, incorporating preference-based utility, and assessing distributional and equity-based impacts. These challenges reflect the difficulty of generating high-quality clinical effectiveness and real-world evidence (RWE) for NGS-guided interventions. EXPERT OPINION: Adapting methodological approaches and developing life-cycle health technology assessment (HTA) guidance using RWE is crucial for implementing NGS in oncology. Healthcare systems, decision-makers, and HTA organizations are facing a pivotal opportunity to adapt to an evolving clinical paradigm and create innovative regulatory and reimbursement processes that will enable more sustainable, equitable, and patient-oriented healthcare.
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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 | 0.218 | 0.457 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".