Abstract A030: The role of liquid biopsies in optimizing early-onset non-small cell lung cancer (NSCLC) cancer screening and detection in underserved populations globally
Bibliographic record
Abstract
Abstract Non-small cell lung cancer remains a formidable global health challenge, compounded with high mortality rates that often occur due to prevalence of late-stage diagnoses. To improve patient outcomes, early detection is critical in the diagnostic pipeline. However, significant disparities in access to timely screening and diagnosis persist in early-onset cancer diagnoses, particularly in patients with Non-Small Cell Lung Carcinomas (NSCLC) from underserved populations worldwide. Liquid biopsies, a non-invasive diagnostic methodology analyzing tumor-derived components in bodily fluids from “cell-free DNA” (cfDNA), to detect cancer-tumour DNA (ctDNA), have been proposed as revolutionary in enhancing screening and early detection in other cancers. This paper reviews meta-analyses of the application of liquid biopsies in early-onset NSCLC diagnostics, as a diagnostic tool to improve clinical outcomes via timely diagnoses in underrepresented and underserved populations globally. It critically discusses the ethical concerns of the equitable implementation of these novel, prohibitively-expensive processes in diverse, sometimes remote communities. Leveraging recent meta-analyses (2021-2025), the paper presents quantitative data on diagnostic accuracy across various cancer types, underscoring the potential of this technology to bridge healthcare disparities and improve clinical outcomes across variable communities, especially for NSCLC. Citation Format: Nicole Ramlachan, Samuel M. West. The role of liquid biopsies in optimizing early-onset non-small cell lung cancer (NSCLC) cancer screening and detection in underserved populations globally [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr A030.
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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 | 0.036 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".