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Record W4378530850 · doi:10.1177/17588359231177008

Prognostic value of postoperative ctDNA detection in patients with early non-small cell lung cancer: a systematic review and meta-analysis

2023· review· en· W4378530850 on OpenAlexaboutno aff
Kaibo Guo, Jiamin Lu, Yidan Lou, Song Zheng

Bibliographic record

VenueTherapeutic Advances in Medical Oncology · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMeta-analysisCochrane LibraryInternal medicineOncologyLung cancerBiomarkerMEDLINESample size determinationSystematic reviewCancerAdjuvant therapyProspective cohort studyPathological

Abstract

fetched live from OpenAlex

Background: Circulating tumor DNA (ctDNA) has emerged as a potential biomarker for monitoring early non-small cell lung cancer (ENSCLC), particularly after radical surgery. However, the prognostic value of postoperative ctDNA is still being investigated due to the small sample size and heterogeneity of patients with ENSCLC in current trials. Moreover, the potential clinical utility of ctDNA assessment for administering adjuvant therapy (AT) in patients with ENSCLC is also an important area of active research. Objectives: We aimed to identify the prognostic value of postoperative ctDNA detection in ENSCLC patients with stages I-III. Design: This study type is a systematic review and meta-analysis. Data sources and methods: We conducted a search in the Cochrane Library, Embase, PubMed, and ScienceDirect for prospective or retrospective investigations involving patients with ENSCLC, gathering outcomes based on predefined end points. The literature review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and the Newcastle-Ottawa scale was employed to carry out a quality evaluation of the included studies. The primary end point of the study was to evaluate the association of ctDNA status in two time points (within 1 month after surgery and long-term postoperative monitoring with more than 3 months) with relapse-free survival (RFS) and overall survival (OS). In addition, the study investigated the role of ctDNA in predicting the response to AT. The secondary end points of the study were to determine the impact of ctDNA on RFS and OS in different subgroups of ENSCLC patients based on pathological subtypes and TNM staging. Results: In total, 2149 studies were screened, and 11 studies met the inclusion criteria for the analysis. The presence of ctDNA within 1 month after surgery as well as long-term postoperative ctDNA were both associated with poorer RFS [hazard ratio (HR) = 4.43; 95% CI: 3.23-6.07 and HR = 7.99; 95% CI: 3.28-19.44, respectively] and worse OS (HR = 5.07; 95% CI: 2.80-9.19 and HR = 7.49; 95% CI: 3.42-16.43, respectively). Most subgroup analyses yielded similar results. Moreover, ctDNA-positive patients could acquire survival benefits from AT (HR = 0.30; 95% CI: 0.16-0.54), while ctDNA-negative patients that received AT did not show significant improvement in RFS (HR = 1.18; 95% CI: 0.67-2.09). Conclusion: The postoperative ctDNA assessment is a promising approach to stratify the risk of relapse and death in ENSCLC patients. Our data suggest that patients with negative ctDNA in the postoperative setting may not benefit from AT, which warrants further investigation. This finding, if validated in prospective trials with a larger sample size, could aid in better-individualized treatment for patients and avoid potential side effects of AT. Registration: This study was designed in accordance with PRISMA and registered with PROSPERO (CRD42022311615).

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 imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.028
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0160.033
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.022
GPT teacher head0.351
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations7
Published2023
Admission routes1
Has abstractyes

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