Emerging Lab-on-a-Chip Approaches for Liquid Biopsy in Lung Cancer: Status in CTCs and ctDNA Research and Clinical Validation
Bibliographic record
Abstract
Despite the intensive efforts dedicated to cancer diagnosis and treatment, lung cancer (LCa) remains the leading cause of cancer-related mortality, worldwide. The poor survival rate among lung cancer patients commonly results from diagnosis at late-stage, limitations in characterizing tumor heterogeneity and the lack of non-invasive tools for detection of residual disease and early recurrence. Henceforth, research on liquid biopsies has been increasingly devoted to overcoming these major limitations and improving management of LCa patients. Liquid biopsy is an emerging field that has evolved significantly in recent years due its minimally invasive nature and potential to assess various disease biomarkers. Several strategies for characterization of circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) have been developed. With the aim of standardizing diagnostic and follow-up practices, microfluidic devices have been introduced to improve biomarkers isolation efficiency and specificity. Nonetheless, implementation of lab-on-a-chip platforms in clinical practice may face some challenges, considering its recent application to liquid biopsies. In this review, recent advances and strategies for the use of liquid biopsies in LCa management are discussed, focusing on high-throughput microfluidic devices applied for CTCs and ctDNA isolation and detection, current clinical validation studies and potential clinical utility.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".