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Improving the management of early colorectal cancers (eCRC) by using quantitative markers to predict lymph node involvement and thus the need for major resection of pT1 cancers


Background Since implementing the NHS bowel cancer screening programme, the rate of early colorectal cancer (eCRC; pT1) has increased threefold to 17%, but how these lesions should be managed is currently unclear.

Aim To improve risk stratification of eCRC by developing reproducible quantitative markers to build a multivariate model to predict lymph node metastasis (LNM).

Methods Our retrospective cohort of 207 symptomatic pT1 eCRC was assessed for quantitative markers. Associations between categorical data and LNM were performed using χ2 test and Fisher’s exact test. Multivariable modelling was performed using logistic regression. Youden’s rule gave the cut-point for LNM.

Results All significant parameters in the univariate analysis were included in a multivariate model; tumour stroma (95% CI 2.3 to 41.0; p=0.002), area of submucosal invasion (95% CI 2.1 to 284.6; p=0.011), poor tumour differentiation (95% CI 2.0 to 358.3; p=0.003) and lymphatic invasion (95% CI 1.3 to 192.6; p=0.028) were predictive of LNM. Youden’s rule gave a cut-off of p>5%, capturing 18/19 LNM (94.7%) cases and leading to a resection recommendation for 34% of cases. The model that only included quantitative factors were also significant, capturing 17/19 LNM cases (90%) and leading to resection rate of 35% of cases (72/206).

Conclusions In this study, we were able to reduce the potential resection rate of pT1 with the multivariate qualitative and/or quantitative model to 34% or 35% while detecting 95% or 90% of all LNM cases, respectively. While these findings need to be validated, this model could lead to a reduction of the major resection rate in eCRC.

  • colorectal neoplasms
  • diagnostic screening programs
  • morphological and microscopic findings

Data availability statement

All data relevant to the study are included in the article. These data were obtained through a local audit.

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