Clinical Utility of Liquid Biopsy for the Early Diagnosis of EGFR-Mutant Advanced Lung Cancer Patients in a Real-Life Setting (CLEAR Study)
Bibliographic record
Abstract
Background: Lung cancer remains the leading cause of cancer mortality globally with EGFR mutations representing a significant driver in advanced non-small cell lung cancer (aNSCLC). The timely detection of these mutations is critical for initiating targeted therapy, yet tissue biopsy limitations often delay treatment. Methods: This multicenter prospective study evaluated the clinical utility of liquid biopsy (LBx) in real-life settings for the early diagnosis of EGFR mutations in patients with suspected aNSCLC. Circulating tumor DNA (ctDNA) was analyzed using the Cobas EGFR Mutation Test and compared to tissue-based next-generation sequencing (NGS). Results: Among 366 aNSCLC patients tested, LBx demonstrated a significantly shorter median turnaround time (TAT) of 3 days compared to 26 days for tissue NGS (p < 0.001) with 100% specificity and 65% sensitivity for EGFR mutation detection. LBx identified actionable EGFR mutations in cases where tissue biopsy was insufficient or unavailable, enabling 43.7% of patients to commence targeted therapy based on ctDNA results prior to biopsy confirmation. Conclusions: These findings highlight the potential of LBx to reduce diagnostic delays and improve access to personalized therapies in a real-world setting. Integrating LBx into routine diagnostic workflows may address current gaps in molecular testing, ensuring timely and precise treatment for aNSCLC patients.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".