ArticleAnnals of surgical oncology2026
Calibrated Absolute Risk of Lymph-Node-Metastasis After Non-curative Endoscopic Resection of pT1 Colorectal Cancer.
Article in Annals of surgical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundAfter endoscopic resection of T1 colorectal cancer (CRC), the decision to recommend completion radical surgery is primarily driven by pathologic "high-risk" features. However, these binary criteria identify broad heterogeneous groups and do not provide individualized estimates of lymph-node-metastasis (LNM) risk.
methodsThis study analyzed patients with pathologic T1 CRC who underwent radical resection after endoscopic resection from 2004 to 2024 at a single tertiary center. Five histologic features (lymphatic/venous/perineural invasion, tumor budding, poor differentiation, submucosal invasion ≥2000 μm, and positive resection margin) were assigned one point each to construct a composite pathologic score (0-5).
resultsAmong 1162 patients, LNM occurred for 148 patients (12.7 %). The composite score showed a stepwise gradient in LNM risk as follows: 6.6 % for score 0, 12.0 % for score 1, 29.2 % for score 2, and 66.7 % for scores 3 to 4. A threshold of ≥2 identified a high-risk group with substantially higher LNM prevalence than scores 0 to 1 (35.1 % vs 9.5 %; odds ratio [OR], 4.79), corresponding to an absolute risk difference of 24 % and number-needed-to-surgery (NNS) of 4.1. The score demonstrated acceptable discrimination (area under the curve, 0.673; 95 % confidence interval, 0.624-0.722) and good calibration, with close agreement between predicted and observed probabilities across all strata (29 % for score 2, 60 % for score 3, and 100 % for score 4).
conclusionsA simple composite pathologic score integrating five adverse features provides a calibrated, clinically interpretable estimate of LNM risk after endoscopic resection of T1 CRC. By translating routine pathology into absolute risk and NNS, this model offers a practical framework to support individualized recommendations for completion radical surgery.
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