Circulating tumor DNA as prognostic markers of non-small cell lung cancer (NSCLC): a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Circulating tumor DNA (ctDNA) has recently garnered attention as a promising prognostic biomarker in cancer patients. This review aimed to evaluate the prognostic significance of ctDNA in patients with non-small cell lung cancer (NSCLC) at different treatment timepoints. Methods: A comprehensive search of PubMed, Web of Science, Embase, Cochrane Library, Scopus, ClinicalTrials.gov and World Health Organization International Clinical Trials Registry Platform (WHO-ICTRP) was performed covering the period from January 2016 to May 2022, with updates monitored until June 2024. Studies reporting ctDNA positivity versus negativity and associated survival outcomes were included. Hazard ratios (HRs) or risk ratios (RRs) were pooled using random-effects models for relapse-free survival (RFS), overall survival (OS), and recurrence risk. The risk of bias of observational studies was assessed by the Newcastle-Ottawa Scale (NOS). Results: Fifty-two studies were included. Total sample sizes of these studies ranged from 12 to 330 participants. Baseline ctDNA positivity was associated with worse RFS [HR =2.23, 95% confidence interval (CI): 1.82-2.75] in all NSCLCs. Among resectable NSCLC, postoperative ctDNA was strongly associated with inferior RFS (HR =5.64, 95% CI: 3.88-8.19) and OS (HR =4.17, 95% CI: 2.22-7.84). Similar trends were noted after full-course treatment for both resectable and unresectable patients. CtDNA detection often preceded radiographic or clinical recurrence, supporting its potential as an early indicator of relapse. Conclusions: CtDNA positivity serves as a robust prognostic marker of worse survival and higher recurrence in NSCLC patients throughout the treatment cycle. Early ctDNA detection may facilitate timely therapeutic interventions and improve patient outcomes.
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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".