S1046 Predicting Histological Diagnosis After Endoscopic Submucosal Dissection With Demographic Characteristics and Endoscopic Lesion Characteristics: An Analysis of a Large Cohort in North America
Bibliographic record
Abstract
Introduction: Endoscopic submucosal dissection (ESD) continues to gain traction as an important treatment for gastric neoplasia. Some gastric low-grade dysplasia (LGD) and high-grade dysplasia (HGD) on endoscopic forceps biopsy (EFB) are diagnosed as gastric adenocarcinoma (GAC) after ESD. To date, there are no large studies from the Western population on demographic or endoscopic characteristics that can help clinicians differentiate GAC from dysplastic lesions during endoscopy. We aimed to determine which patient or lesion characteristics could predict the histological diagnosis of gastric lesions after ESD. Further, we evaluated the factors associated with the pathologic upstaging from EFB to ESD. Methods: This retrospective study analyzed data from 309 patients who had gastric ESD at 25 centers in North America between 2010 and 2019. We used logistic regression to identify patient demographics and endoscopic features that could predict HGD or GAC on the resected specimens and upstage diagnosis. Results: We analyzed 85 LGD, 85 HGD, and 139 GAC cases. 119 GAC cases were differentiated and 20 were poorly differentiated. 4.1% of LGD and 12.8% of HGD on EFB were upstaged to GAC after ESD. Higher dysplasia grades after ESD were more likely in older patients, tumors in the upper and lower thirds of the stomach, polypoid or depressed lesions (vs. flat non-depressed lesions), and ulcerated lesions. Tumor size was not a significant predictor of dysplasia grade. Logistic regression revealed age (odds ratio [OR] = 1.050, P = 0.00004), the presence of ulceration (OR = 2.763, P = 0.0016), and tumors in the upper third (OR = 2.348, P = 0.0123) or lower third (OR = 1.920, P = 0.0149) significantly predicted GAC. Depressed lesions were more common in HGD or GAC as compared to LGD (OR = 2.831, P = 0.0034). Larger tumors and depressed lesions were associated with differentiated GAC as compared to poorly differentiated GAC (P = 0.0079 and 0.0110, respectively). Conclusion: In this large North American cohort of gastric ESD, we found that tumor location in the upper and lower thirds, ulceration, and patient age may predict GAC. Endoscopists should be cognizant of these characteristics as up to 16.9% of lesion pathology from EFB may be upstaged to GAC after endoscopic resection. Our findings emphasize the importance of recognizing demographic and lesion-based predictors of gastric cancers to better guide clinicians during endoscopy.Table 1.: Logistic regression of demographic patient characteristics and endoscopic lesion characteristics for the presence of adenocarcinoma after resection by endoscopic submucosal dissection
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".