Non-invasive diagnosis of esophageal cancer by a simplified circulating cell-free DNA methylation assay targeting OTOP2 and KCNA3: a double-blinded, multicenter, prospective study
Bibliographic record
Abstract
BACKGROUND: Esophageal cancer (EC) is a highly lethal disease lacking early detection approaches. We previously identified that OTOP2 and KCNA3 were specifically hypermethylated in circulating cell-free DNA from patients with EC. We then developed a blood-based methylation assay targeting OTOP2 and KCNA3 (named "IEsohunter") for esophageal cancer noninvasive detection. This double-blinded, multicenter, prospective study aimed to comprehensively evaluate its clinical diagnostic performance. METHODS: Participants with EC, high-grade intraepithelial neoplasia (HGIN), other malignancies, benign gastrointestinal lesions, or no abnormalities were prospectively enrolled from 5 tertiary referral centers across China. Peripheral blood samples were collected, followed by plasma cell-free DNA methylation analysis using the IEsohunter test based on multiplex quantitative polymerase chain reaction adopting an algorithm-free interpretation strategy. The primary outcome was the diagnostic accuracy of IEsohunter test for EC. RESULTS: We prospectively enrolled 1116 participants, including 334 patients with EC, 71 with HGIN, and 711 controls. The areas under the receiver operating characteristic curves of the IEsohunter test for detecting EC and HGIN were 0.903 (95% CI 0.880-0.927) and 0.727 (95% CI 0.653-0.801), respectively. IEsohunter test showed sensitivities of 78.5% (95% CI 69.1-85.6), 87.3% (95% CI 79.4-92.4), 92.5% (95% CI 85.9-96.2), and 96.9% (95% CI 84.3-99.8) for stage I-IV EC, respectively, with an overall sensitivity of 87.4% (95% CI 83.4-90.6) and specificity of 93.3% (95% CI 91.2-94.9) for EC detection. The IEsohunter test status turned negative (100.0%, 47/47) after surgical resection of EC. CONCLUSIONS: The IEsohunter test showed high diagnostic accuracy for EC detection, indicating that it could potentially serve as a tool for noninvasive early detection and surveillance of EC.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".