Endoscopic Ultrasound Predicts Risk of Occult Intra-Abdominal Metastases in Localized Gastric Cancer: A Validation Study
Bibliographic record
Abstract
Background: In gastric cancer (GC) patients without imaging evidence of distant metastasis, diagnostic staging laparoscopy (DSL) is recommended to detect radiographically occult peritoneal metastasis (M1). DSL carries a risk for morbidity and its cost-effectiveness is unclear. Use of endoscopic ultrasound (EUS) to improve patient selection for DSL has been proposed but not validated. We aimed to validate an EUS-based risk classification system predicting risk for M1 disease. Methods: We retrospectively identified all GC patients without positron emission tomography (PET)/computed tomography (CT) evidence of distant metastasis who underwent staging EUS followed by DSL between 2010 and 2020. T1-2, N0 disease was EUS "low-risk"; T3-4 and/or N+ disease was "high-risk". Results: A total of 68 patients met inclusion criteria. DSL identified radiographically occult M1 disease in 17 patients (25%). Most patients had EUS T3 tumors (n = 59, 87%) and 48 (71%) patients were node-positive (N+). Five (7%) patients were classified EUS "low-risk" and 63 (93%) were classified "high-risk". Of 63 "high-risk" patients, 17 (27%) had M1 disease. The ability of "low-risk" EUS to predict M0 disease at laparoscopy was 100% and DSL would have been avoided in five patients (7%). This stratification algorithm showed a sensitivity of 100% (95% confidence interval (CI): 80.5-100%) and a specificity of 9.8% (95% CI: 3.3-21.4%). Conclusions: Use of an EUS-based risk classification system in GC patients without imaging evidence of metastasis helps identify a subset of patients at low-risk for laparoscopic M1 disease who may avoid DSL and proceed directly to neoadjuvant chemotherapy or resection with curative intent. Larger, prospective studies are needed to validate these findings.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".