Classic and Novel Histopathologic Risk Factors for Lymph Node Metastasis in T1 Colorectal Cancer: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Treatment of endoscopically resected T1 colorectal cancers is based on the risk of lymph node metastasis. Risk is based on histopathologic features, although there is lack of consensus as to what constitutes high-risk features. OBJECTIVE: The purpose of this study was to conduct a systematic review and meta-analysis of histopathologic risk factors for lymph node metastasis. DATA SOURCES: A search of MEDLINE, Embase, Scopus, and Cochrane controlled register of trials for risk factors for lymph node metastasis was performed from inception until August 2018. STUDY SELECTION: Included patients must have had an oncologic resection to confirm lymph node status and reported at least 1 histopathologic risk factor. INTERVENTION: Rates of lymph node positivity were compared between patients with and without risk factors. MAIN OUTCOME MEASURES: We report the results of the meta-analysis as ORs. RESULTS: Of 8592 citations, 60 met inclusion criteria. Pooled analyses found that lymphovascular invasion, vascular invasion, neural invasion, and poorly differentiated histology were significantly associated with lymph node metastasis, as were depths of 1000 µm (OR = 2.76), 1500 µm (OR = 4.37), 2000 µm (OR = 2.37), submucosal level 3 depth (OR = 3.08), and submucosal level 2/3 (OR = 3.08) depth. Depth of 3000 µm, Haggitt level 4, and widths of 3000 µm and 4000 µm were not significantly associated with lymph node metastasis. Tumor budding (OR = 4.99) and poorly differentiated clusters (OR = 14.61) were also significantly associated with lymph node metastasis. LIMITATIONS: Included studies reported risk factors independently, making it impossible to examine the additive metastasis risk in patients with numerous risk factors. CONCLUSIONS: We identified 1500 μm as the depth most significantly associated with lymph node metastasis. Novel factors tumor budding and poorly differentiated clusters were also significantly associated with lymph node metastasis. These findings should help inform guidelines regarding risk stratification of T1 tumors and prompt additional investigation into the exact contribution of poorly differentiated clusters to lymph node metastasis.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".