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Malignant colon polyps: predicting lymph node metastasis following endoscopic excision.

Samer A Naffouje1, Gregory Lauwers2, Jason Klapman3

  • 1GI Oncology Program, Surgical Oncology, H. Lee Moffitt Cancer Center, 12902 USF Magnolia Drive, CSB 8165, Tampa, FL, 33612, USA. samer.naffouje@moffitt.org.

International Journal of Colorectal Disease
|January 10, 2022
PubMed
Summary

Predicting lymph node metastasis (LNM) in malignant colon polyps (MCPs) is crucial. New scoring systems using clinical and histologic data accurately estimate LNM risk, aiding treatment decisions for pT1Nx MCPs.

Keywords:
Lymph node metastasisMalignant colon polypNCDB

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Area of Science:

  • Oncology
  • Gastroenterology
  • Surgical Pathology

Background:

  • Estimating lymph node metastasis (LNM) risk in malignant colon polyps (MCPs) is essential for guiding treatment.
  • Current histologic data, limited to tumor grade and lymphovascular invasion (LVI), inadequately predict LNM.
  • There is a need for improved risk stratification tools for MCPs.

Purpose of the Study:

  • To develop and validate scoring systems for predicting LNM in patients with pT1Nx adenocarcinoma arising in a colon polyp.
  • To identify key clinical and histologic predictors of LNM in MCPs.
  • To inform decision-making regarding oncologic resection for MCPs.

Main Methods:

  • Utilized the National Cancer Database (NCDB) for colon cancer (2004-2018).
  • Developed two scoring systems (Nomogram Scoring System - NSS, Simplified Scoring System - SSS) using multivariable regression on a training cohort (2004-2017).
  • Included predictors such as clinical characteristics, grade, and LVI; internally validated models on the 2018 cohort.

Main Results:

  • The training cohort included 6,069 patients; LNM rate was 11.2%.
  • Significant predictors of LNM included younger age, female sex, hindgut location, higher grade, and LVI.
  • Both NSS and SSS demonstrated high predictive accuracy, with a negative predictive value of 96% in the validation cohort.

Conclusions:

  • The developed NSS and SSS effectively estimate LNM risk in pT1Nx MCPs, comparable in predictability.
  • These tools can assist in clinical decision-making for oncologic resection.
  • External validation and incorporation of additional data are recommended for further model enhancement.