Circulating tumor DNA as a predictive biomarker for colorectal cancer postsurgical recurrence: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Purpose Colorectal carcinoma constitutes a predominant etiology of oncological mortality globally. This systematic review and meta-analysis elucidated the prognostic utility of circulating tumor DNA (ctDNA) as a predictive biomarker for postsurgical recurrence in colorectal cancer patients. Methods Two independent investigators conducted systematic literature search across PubMed, Web of Science, Embase, Scopus, and clinical trial registries. Studies investigating ctDNA prognostic significance for colorectal cancer recurrence were incorporated. Random-effects models were implemented utilizing restricted maximum likelihood methodology. The study quality was assessed using Newcastle–Ottawa Scale. Results Following screening of 2259 records, 11 studies were incorporated. ctDNA-positive patients exhibited significantly elevated recurrence risk as compared to ctDNA-negative counterparts (pooled HR: 2.34; 95% CI: 1.90–2.79; p < 0.001). Moderate heterogeneity was observed ( I 2 = 66.40%), attributable to patient stage distribution, sampling timing, detection platforms, and mutational panel variations. Stage I-III patients demonstrated exceptional consistency (HR: 2.04, I 2 = 0.00%). Detection platforms showed robust performance: droplet digital PCR (HR: 3.63), next-generation sequencing (HR: 2.67), and Safe-SeqS (HR: 2.16), with no significant differences ( p = 0.10). Adjuvant chemotherapy analysis revealed differential performance: treated patients (HR: 2.50; 95% CI: 2.08–2.93) versus untreated (HR: 1.70; 95% CI: 1.07–2.34; p = 0.04). Extended analysis confirmed prognostic utility for overall survival (HR: 2.24) and surveillance recurrence-free survival (HR: 3.54). Conclusions ctDNA represents a robust prognostic biomarker for postsurgical colorectal cancer recurrence with consistent cross-platform performance. Enhanced prognostic value in adjuvant chemotherapy patients supports personalized surveillance implementation, though methodological standardization remains warranted.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".