Strategies for improving biomarker testing rates in non-small cell lung cancer in north America: a scoping review
Bibliographic record
Abstract
Background: Biomarker testing plays a pivotal role in the management of non-small cell lung cancer (NSCLC). However, despite advancements in diagnostic technologies and clinical guidelines recommending widespread use, the adoption of biomarker testing in routine practice remains inconsistent. To address these gaps, this study summarizes the common barriers to biomarker testing in NSCLC and highlights practical solutions to increase utilization. Methods: We conducted a scoping review, guided by the JBI Manual of Evidence Synthesis and PRISMA-ScR checklist, with a comprehensive search of six databases (Medline, Embase, Cochrane CENTRAL, CINAHL, Scopus, and Web of Science) focused on NSCLC biomarker testing in U.S. and Canadian adult populations. Eligible studies addressed barriers or proposed solutions to improve testing implementation and utilization. Titles and abstracts were independently screened by two reviewers using Covidence, and data were extracted and thematically synthesized to identify common challenges and actionable strategies. Results: A total of 7,192 records were identified through database searches, of which 27 studies underwent full-text screening, and 14 studies met the inclusion criteria. Three overarching themes were identified: operational barriers, communication and knowledge gaps, and access and financial challenges. Operational barriers were the most frequently reported, with time-related constraints in 85.7% of studies and insufficient tissue samples in 74%. Proposed solutions included streamlining reflex testing and standardizing policy protocols and workflows (both reported in 50% of studies), leveraging advanced testing technologies (43%), and adopting comprehensive next-generation sequencing (NGS) panels (14%). Communication and knowledge gaps were also prominent, with knowledge deficiencies reported in 57% of studies and challenges in care coordination in 64%. Solutions to these issues included supporting education and continuous learning (71%), promoting multidisciplinary collaboration and tumor boards (57%), and leveraging data and technology (43%). Access and financial barriers were primarily driven by inadequate funding, which was reported in 71% of studies, and limited access to NGS, reported in 29%. Proposed solutions included securing funding for biomarker testing infrastructure (29%) and improving reimbursement and coverage policies (14%). Conclusions: This study highlights critical gaps in biomarker testing for NSCLC, with operational challenges and inadequate funding emerging as the most significant barriers, while being accompanied by the fewest proposed solutions. In contrast, communication and knowledge deficits were well-addressed, presenting opportunities for immediate interventions, such as enhancing education and multidisciplinary collaboration.
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 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 | 0.096 | 0.259 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.039 | 0.034 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".