Clinicopathological Features Associated with Lymph Node Metastasis in Early Gastric Cancer: Analysis of a Single-Institution Experience in China
Bibliographic record
Abstract
BACKGROUND: An accurate assessment of potential lymph node metastasis is an important issue for the appropriate treatment of early gastric cancer. Minimizing the number of invasive procedures used in cancer therapy is critical for improving the patient's quality of life. OBJECTIVE: To evaluate the clinicopathological features associated with lymph node metastasis of early gastric cancer in patients from a single institution in China. METHODS: A retrospective review of data from 410 patients surgically treated for early gastric cancer at the First Affiliated Hospital (Nanjing, China) between 1998 and 2007, was conducted. The clinicopathological variables associated with lymph node metastasis were evaluated. RESULTS: Lymph node metastasis was observed in 12.20% of patients. The macroscopic type, tumour size, location in the stomach, depth of gastric carcinoma infiltration, and presence of vascular or lymphatic invasion showed a positive correlation with the incidence of lymph node metastasis by univariate analysis. Multivariate analyses revealed histological classification, macroscopic type, tumour size, depth of gastric carcinoma infiltration, and the presence of vascular or lymphatic invasion to be significantly and independently related to lymph node metastasis. The depth of gastric carcinoma infiltration was the strongest predictive factor for lymph node metastasis. For intramucosal cancer, tumour size was the unique risk factor for lymph node metastasis. For submucosal cancer, histological classification and tumour size were independent risk factors for lymph node metastasis. CONCLUSIONS: Histological classification, macroscopic type, tumour size, depth of gastric carcinoma infiltration, and the presence of vascular or lymphatic invasion are independent risk factors for lymph node metastasis in patients with early gastric cancer in China. Minimal invasive treatment, such as endoscopic mucosal resection, may be possible for highly selected cancers.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".