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Related Experiment Video

Updated: Jun 5, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

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A predictive model for lymph node yield in colon cancer resection specimens.

Garrett M Nash1, David Row, Alexander Weiss

  • 1Department of Surgery, Colorectal Service, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA. nashg@mskcc.org

Annals of Surgery
|December 21, 2010
PubMed
Summary

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Predicting lymph node yield in colon cancer resections is complex. Tumor size, location, resected pedicles, and tattoo use significantly influence lymph node counts, suggesting personalized minimums may be needed.

Area of Science:

  • Colorectal Surgery
  • Surgical Pathology
  • Oncology

Background:

  • Lymph node yield in colon resection specimens is crucial for accurate cancer staging and predicting patient outcomes.
  • Factors influencing lymph node yield are complex, involving patient, tumor, and surgical variables.

Purpose of the Study:

  • To develop a predictive model for lymph node yield in colon cancer resection specimens.
  • To identify key anatomic and surgical technique factors associated with lymph node quantity.

Main Methods:

  • Analysis of 152 elective colon neoplasm resections with detailed pathology and surgical data.
  • Lymph nodes were separated based on anatomic relationship to vascular pedicles and tumor.
  • Linear regression analysis was used to identify predictors of lymph node quantity.

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Last Updated: Jun 5, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

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Published on: April 18, 2025

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Published on: September 27, 2024

Main Results:

  • Tumor size, tumor location, number of resected vascular pedicles, and use of endoscopic tattoo significantly correlated with lymph node yield.
  • The model with four significant variables explained 19% of the variation in lymph node count.
  • Overall, 15 variables explained 23% of the variation in lymph node yield.

Conclusions:

  • Several tumor and surgical factors are significantly associated with lymph node yield in colon specimens.
  • A universal standard for the minimum number of lymph nodes may not be appropriate for all colon cancer resections.