Area of Submucosal Invasion and Width of Invasion Predicts Lymph Node Metastasis in pT1 Colorectal Cancers

BACKGROUND: The prediction of lymph node metastasis by current histopathological methods is imprecise. OBJECTIVE: The aim of this study was to evaluate currently used and possible new high-risk features associated with lymph node metastasis to identify the markers of lymph node metastasis. DESIGN/PATIENT/SETTING: Two hundred seven pT1 cancers were identified through the Northern and Yorkshire Cancer Registry and Information Services database and digitally scanned. Phenotypic and quantitative features of the pT1 cancers were evaluated. Lymph node metastasis and high-risk feature status were obtained through pathology reports of resections, and high-risk phenotypic features were identified. RESULTS: Lymph node metastasis was noted in 19 patients (9.2%). pT1 cancers with lymph node metastasis had a significantly wider area of invasion (p = 0.001) and greater area of submucosal invasion (p < 0.001) compared with pT1 cancers without lymph node metastasis. Qualitative features such as grade of differentiation and vascular and lymphatic invasion were significant predictors of lymph node metastasis (p < 0.0001, p = 0.039, and p = 0.018). Modified receiver-operating characteristics curves generated cutoff values of 11.5 mm for the width of invasion and 35 mm(2) for the area of submucosal invasion. When tested separately with other qualitative factors on multivariate analysis, both width greater than 11.5 mm (OR, 12.12; 95% CI, 2.19-67.23; p = 0.004) and area of submucosal invasion greater than 35 mm(2) (OR, 22.44; 95% CI, 2.7-186.63; p = 0.004) was predictive of lymph node metastasis. LIMITATIONS: This is a retrospective study and is limited by its small sample size. CONCLUSION: This study has shown that the width and area of submucosal invasion are potential predictors of lymph node metastasis and superior to the depth of invasion. Together with the other qualitative phenotypic features, these quantitative factors could be used to decide the most appropriate treatment for pT1 cancers