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Diagnostic Accuracy of Size-Based Preoperative CT Assessment for Predicting Lymph Node Metastasis in Colon Cancer: A Systematic Review and Meta-Analysis

Ann Gastroenterol Surg. 2026 Mar 24. doi: 10.1002/ags3.70218. Online ahead of print.

ABSTRACT

BACKGROUND: Accurate identification of preoperative lymph node metastasis is essential for planning colon cancer treatment. Computed tomography (CT) is widely used for staging, but its diagnostic performance based on size criteria alone remains unclear. This study aimed to evaluate the diagnostic accuracy of preoperative CT for detecting lymph node metastasis in colon cancer.

METHODS: A systematic search of MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials was conducted on June 12, 2025. Studies comparing preoperative CT with pathological evaluation and providing 2 × 2 contingency tables were included. Pooled sensitivity and specificity were calculated using a hierarchical summary receiver operating characteristic model and a bivariate random-effects model. Subgroup analyses were performed according to cancer location (colon only vs. colon plus rectal cancer) and diagnostic criteria (size alone vs. size plus morphology). Study quality was assessed using QUADAS-2.

RESULTS: Twenty-nine studies involving 5634 patients were included. The pooled sensitivity and specificity of CT for detecting lymph node metastasis were 0.693 (95% CI: 0.636-0.744) and 0.660 (95% CI: 0.581-0.731), respectively, with an area under the curve of 0.727. Meta-regression showed no statistically significant differences in diagnostic performance between colon-only studies and those including rectal cancer (p = 0.561 and 0.316), or between size-only and morphologic criteria (p = 0.822 and 0.536).

CONCLUSIONS: CT using size criteria alone for preoperative lymph node staging in colon cancer demonstrated only moderate diagnostic performance. These findings indicate that relying on lymph node size as the primary determinant may be insufficient for reliable clinical decision-making.

PMID:42495671 | PMC:PMC13393513 | DOI:10.1002/ags3.70218

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