Molecular Detection of Tumor Cells in Regional Lymph Nodes Is Associated With Disease Recurrence and Poor Survival in Node-Negative Colorectal Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: Up to 25% of patients with node-negative colorectal cancer (CRC) on conventional histopathologic analysis ultimately die of recurrent disease. We performed a systematic review with meta-analyses to clarify whether molecular detection of isolated tumor cells or micrometastases in regional lymph nodes indicates high risk of disease recurrence and poor survival in node-negative CRC. METHODS: The following databases were searched in August 2011 to identify studies on the prognostic significance of molecular tumor-cell detection in regional lymph nodes of node-negative CRC: MEDLINE, BIOSIS, Science Citation Index, EMBASE, CCMed, and publisher databases. We extracted hazard ratios (HRs) and associated 95% CIs from the identified studies and performed random-effects model meta-analyses on overall survival, disease-specific survival, and disease-free survival. RESULTS: A total of 39 studies with a cumulative sample size of 4,087 patients were included. Immunohistochemistry, reverse transcriptase polymerase chain reaction, and both techniques were applied in 30, seven, and two studies, respectively. Thirteen studies were graded with low risk of bias. Meta-analyses revealed that molecular tumor-cell detection in regional lymph nodes was associated with poor overall survival (HR, 2.20; 95% CI, 1.43 to 3.40), disease-specific survival (HR, 3.37; 95% CI, 2.31 to 4.93), and disease-free survival (HR, 2.24; 95% CI, 1.57-3.20). Subgroup analyses showed the prognostic significance of molecular tumor-cell detection of being independent of the applied detection method, molecular target, and number of retrieved lymph nodes. CONCLUSION: Molecular detection of occult disease in regional lymph nodes is associated with an increased risk of disease recurrence and poor survival in patients with node-negative CRC.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.033 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".