Updated evaluation of endoscopic submucosal dissection versus surgery for early gastric cancer: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Endoscopic resection (ER) has been a standard treatment modality for early gastric cancer with ignorable risks of lymph node metastasis. As for EGCs within expanded indications, endoscopic submucosal dissection (ESD) has considerable advantages over endoscopic mucosal resection (EMR) regarding higher rate of en bloc resection, complete resection, but lower risk of local recurrence. Previous meta-analyses comparing ESD with surgery for EGC are scarce and not robust to reach definitive conclusions. METHODS: We searched PubMed, Web of Science, EMBASE, Cochrane Library Databases and Google Scholar through July 2019 to identify studies evaluating ESD vs surgery for EGC. Newcastle-Ottawa Quality Assessment Scale was used to assess the quality of enrolled studies. Patient baseline characteristics, procedure-related and prognosis outcomes, and adverse event data were extracted and pooled for analyses by the Review Manager 5.3 software. Grading of Recommendations Assessment, Development and Evaluation guidelines (GRADE) were used to assess the quality of evidence. Trial Sequential Analysis (TSA) was conducted to weaken random error and enhance the reliability of evidence. RESULTS: Totally 18 retrospective studies, involving 5993 patients, were included. ESD benefits were 128.38 min shorter operation duration [95%CI: (-204.68, -52.09), P = 0.001], 7.13 days shorter hospital stay [95%CI: (-7.98, -6.28), P < 0.00001], lower risk of procedure-related death [OR = 0.21, 95%CI: (0.07, 0.68), P = 0.009], lower risk of overall complication [OR = 0.47, 95%CI: (0.34, 0.63), P < 0.00001]. ESD was also associated with lower costs and better quality of life. However, ESD had lower rate of en bloc resection [OR = 0.07, 95%CI: (0.03, 0.21), P < 0.00001], histologically complete resection [OR = 0.07, 95%CI: (0.03, 0.14), P < 0.00001], curative resection [OR = 0.06, 95%CI: (0.01, 0.27), P = 0.002], and higher rate of local recurrence [OR = 5.42, 95%CI: (2.91, 10.11), P < 0.00001], metachronous cancer [OR = 10.84, 95%CI: (6.43, 18.26), P < 0.00001], synchronous cancer [OR = 6.59, 95%CI: (1.96, 22.1), P = 0.002]. ESD also led to lower disease-free survival [HR = 4.58, 95%CI: (2.79, 7.52), P < 0.00001] and recurrence-free survival [HR = 1.99, 95%CI: (1.38, 2.87), P = 0.0002]. No significant differences in overall survival (OS) and disease-specific survival (DSS) between ESD and surgery were observed. CONCLUSIONS: ESD offers a method of less expensive, less trauma, faster recovery and better quality of life compared to surgery for EGC. However, ESD is associated with higher risk of recurrence without compromising OS and DSS. Strict and careful surveillance after ESD is needed. Recurrent EGCs following ESD can usually be detected in early stage and successfully managed by repeated ESD. Accordingly, ESD technique provides an alternative to surgical resection for highly selected EGC patients.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".