Liquid biopsy to identify Barrett’s oesophagus, dysplasia and oesophageal adenocarcinoma: the <i>EMERALD</i> multicentre study
Bibliographic record
Abstract
BACKGROUND: There is no clinically relevant serological marker for the early detection of oesophageal adenocarcinoma (EAC) and its precursor lesion, Barrett's oesophagus (BE). OBJECTIVE: To develop and test a blood-based assay for EAC and BE. DESIGN: ) was a large, international, multicentre biomarker cohort study involving 792 patient samples from 4 countries (NCT06381583) to develop and validate a circulating miRNA signature for the early detection of EAC and high-risk BE. Tissue-based miRNA sequencing and microarray datasets (n=134) were used to identify candidate miRNAs of diagnostic potential, followed by validation using 42 pairs of matched cancer and normal tissues. The usefulness of the candidate miRNAs was initially assessed using 108 sera (44 EAC, 34 EAC precursors and 30 non-disease controls). We finally trained a machine learning model (XGBoost+AdaBoost) on RT-qPCR results from circulating miRNAs from a training cohort (n=160) and independently tested it in an external cohort (n=295). RESULTS: After a strict process of biomarker discovery and selection, we identified six miRNAs that were overexpressed in all sera of patients compared with non-disease controls from three independent cohorts of different nationalities (miR-106b, miR-146a, miR-15a, miR-18a, miR-21 and miR-93). We established a six-miRNA diagnostic signature using the training cohort (area under the receiver operating characteristic curve (AUROC): 97.6%) and tested it in an independent cohort (AUROC: 91.9%). This assay could also identify patients with BE among patients with gastro-oesophageal reflux disease (AUROC: 94.8%, sensitivity: 92.8%, specificity: 85.1%). CONCLUSION: Using a comprehensive approach integrating unbiased genome-wide biomarker discovery and several independent experimental validations, we have developed and validated a novel blood test that might complement screening options for BE/EAC. TRIAL REGISTRATION NUMBER: NCT06381583.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".