The global progress and quality assessment of research on the association between circulating tumor DNA and clinical prognosis: a systematic review
Bibliographic record
Abstract
Objective: Circulating tumor DNA (ctDNA) has shown potential as a prognostic biomarker in patients with solid tumors. This study aimed to systematically summarize the global application of ctDNA in the prognostic management of solid tumor patients and to evaluate the quality of the current studies. Methods: PubMed, Web of Science, Embase, Cochrane Library, Scopus, and clinical trials.gov databases were searched to collect cohort studies on ctDNA in the prognosis of solid tumor patients from January 2016 to May 2022. The language was limited to English. Information including general information, participants and cancer characteristics, ctDNA and outcome information were extracted. The quality of the studies was assessed using the Newcastle-Ottawa Scale checklist. Results: A total of 214 studies were included in the final analysis, encompassing 21,076 patients. The number of studies has increased annually from 2016 to 2022. The most common types of solid tumors studied were colorectal cancer (27.10 %), lung cancer (20.09 %), pancreatic cancer (16.82 %), and breast cancer (14.02 %). The top three journals by number of publications had an impact factor in 2023 greater than 10. Of the studies, the median sample size was 69 (interquartile range: 41-111), 69.81 % had a sample size <100, 68.92 % had a median/mean age ≥60 years, and 74.05 % were from developed countries. Multi-center studies accounted for 40.36 %. Additionally, 29.82 % of the studies had a bias risk score ≤6. Only 16.67 % of studies on liver cancer had a bias risk score >6. The primary criteria not met by the studies included "Adequacy of follow-up of cohorts" (33.33 %), "Assessment of outcome" (32.16 %) and "Representativeness of the exposed cohort" (27.49 %). Conclusions: The prognostic value of ctDNA in patients with solid tumors is gaining increasing attention, leading to a steady rise in the number of studies. However, many studies still suffer from small sample sizes and a lack of representativeness. Furthermore, details regarding ctDNA detection methods and results reporting are often insufficiently described. There is an urgent need to improve the quality of such research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".