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
| Autor: | Philip Quirke, Eu-Wing Toh, Katerina Kouvidi, Eva Morris, Scarlet Brockmoeller, S. J. Hepworth |
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| Rok vydání: | 2021 |
| Předmět: |
Oncology
Multivariate statistics medicine.medical_specialty Lymphovascular invasion Colorectal cancer Logistic regression Pathology and Forensic Medicine Risk Factors Stomach Neoplasms Internal medicine medicine Humans Neoplasm Invasiveness Lymph node Neoplasm Staging Retrospective Studies Univariate analysis business.industry Retrospective cohort study General Medicine medicine.disease Exact test medicine.anatomical_structure Lymphatic Metastasis Lymph Nodes Colorectal Neoplasms business |
| Zdroj: | Journal of Clinical Pathology. 75:545-550 |
| ISSN: | 1472-4146 0021-9746 |
| DOI: | 10.1136/jclinpath-2021-207482 |
| Popis: | BackgroundSince 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.AimTo improve risk stratification of eCRC by developing reproducible quantitative markers to build a multivariate model to predict lymph node metastasis (LNM).MethodsOur 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.ResultsAll 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).ConclusionsIn 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. |
| Databáze: | OpenAIRE |
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