Assessing circulating tumour DNA (ctDNA) as a prognostic biomarker in locally advanced rectal cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Circulating tumour DNA (ctDNA) has emerged as a promising biomarker in various cancer types, including locally advanced rectal cancer (LARC), offering potential insights into disease progression, treatment response and recurrence. This review aims to comprehensively evaluate the utility of ctDNA as a prognostic biomarker in LARC. METHODS: PubMed, EMBASE and Web of Science were searched as part of our review. Studies investigating the utility of ctDNA in locally advanced rectal cancer (LARC) were assessed for eligibility. Quality assessment of included studies was performed using the Newcastle Ottawa Scale (NOS) risk of bias tool. Outcomes extracted included basic participant characteristics, ctDNA details and survival data. A meta-analysis was performed on eligible studies to determine pooled recurrence-free survival (RFS). RESULTS: Twenty-two studies involving 1676 participants were included in our analysis. Methodological quality categorised by the Newcastle Ottawa Scale was generally satisfactory across included studies. ctDNA detected at various time intervals was generally associated with poor outcomes across included studies. Meta-analysis demonstrated a pooled hazard ratio of 8.87 (95% CI 4.91-16.03) and 15.15 (95% CI 8.21-27.95), indicating an increased risk of recurrence with ctDNA positivity in the post-neoadjuvant and post-operative periods respectively. CONCLUSION: Our systematic review provides evidence supporting the prognostic utility of ctDNA in patients with LARC, particularly in identifying patients at higher risk of disease recurrence in the post-neoadjuvant and post-operative periods.
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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 | 0.015 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".