Biomarker testing of lung cancer in North America versus globally.
Bibliographic record
Abstract
8541 Background: Biomarker testing is essential to optimize lung cancer (LC) care, yet uptake of testing is suboptimal due to lack of access, cost, and long turnaround times (TAT). Recent advances now require biomarker testing in early-stage LC. In 2024, the International Association for the Study of Lung Cancer (IASLC) launched a 2 nd global survey to measure improvements and barriers to implementation of testing. We compared results from North America (NA) with global results by high income (HIC) and low or middle income countries (LMIC). Methods: A multi-disciplinary committee of oncologists, pathologists, pulmonologists, epidemiologists, and advocacy partners created the survey. We used mixed methods, with focus groups and in-depth interviews informing the quantitative survey with IRB oversite. Chi-square tests were utilized to compare frequencies between NA v Other HIC (OHIC) and HIC v LMIC. Results: Of the 1677 responses globally, 1501 were from HIC and 176 from LMIC. HIC included 337 responses from NA (287 United States and 50 Canada). Nearly all NA respondents (99%) believe biomarker testing significantly impacts patient outcomes and 94% report a clear understanding of who should be tested (v 91% OHIC, p=0.09). In NA, 66% and 40% ranked biomarker testing as highly important in late- and early- stage LC, respectively (64% and 28% OHIC, p=0.68 and p<0.01). Only 45% of NA respondents were satisfied with biomarker testing conditions (v 52% OHIC, p=0.03), and 69% estimate at least half of LC patients receive biomarker testing (71% OHIC), an increase from 45% in the 2018 survey (p<0.01). We found 40% of respondents from NA sometimes or often began treatment prior to obtaining biomarker results (41% OHIC). Key barriers identified were cost (23%), time (22%), and sample quality (20%), consistent with global and OHIC trends. Mean TAT in NA was 17.1 days (SD 7.8) v 16.1 days (SD 9.0) in HIC. Insufficient tumor was the primary cause for re-biopsy in late and early-stage patients for NA (58%) and HIC (48%). Lastly, 14% of NA reported no additional training in next-generation sequencing beyond medical education (16% OHIC). Globally, conditions were worse in LMIC v HIC including those who sometimes or often begin treatment prior to obtaining biomarker results (73% v 41%, p<0.01) and those who are confident or extremely confident in the adequacy of testing at their institution (48% v 68%, p<0.01). Conclusions: Respondents from NA believe they understand the value of biomarker testing for LC and who should be tested. Testing practices have reportedly improved since 2018, yet less than half of NA respondents are satisfied with biomarker testing practices and many patients are still treated without biomarker information. Responses from NA were similar to OHIC, with some exceptions, but significant disparities were evident in LMIC. We identified key barriers that should be addressed to optimize testing practices and patient outcomes.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".