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Related Experiment Video

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Patient-derived Orthotopic Xenograft Models for Human Urothelial Cell Carcinoma and Colorectal Cancer Tumor Growth and Spontaneous Metastasis
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Machine learning prediction model for lateral lymph node metastasis in rectal cancer.

Longchun Dong1, Shiyong Du2, Hongjie Yang2,3,4,5

  • 1Department of Radiology, Tianjin Union Medical Center, The First Affiliated Hospital of Nankai University, Tianjin, China.

Frontiers in Gastroenterology (Lausanne, Switzerland)
|March 13, 2026
PubMed
Summary

Predicting lateral lymph node metastasis in rectal cancer is challenging. This study developed a logistic model using extramural vascular invasion, MRI cN stage, and enlarged lymph nodes to guide lateral lymph node dissection decisions.

Keywords:
lateral lymph node dissectionlymphatic metastasispathologyprognosisrectal neoplasms

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Area of Science:

  • Oncology
  • Surgical Oncology
  • Diagnostic Imaging

Background:

  • Preoperative diagnosis of lateral lymph node metastasis in rectal cancer remains a significant clinical challenge.
  • Accurate prediction is crucial for determining the necessity of lateral lymph node dissection (LLND).

Purpose of the Study:

  • To predict pathological characteristics of lateral lymph nodes in rectal cancer patients using preoperative clinical data.
  • To develop a logistic prediction model for lateral lymph node metastasis.

Main Methods:

  • Retrospective analysis of 143 rectal cancer patients who underwent total mesorectal excision (TME) and LLND.
  • Development of a logistic prediction model and nomogram using R software.
  • Segmentation of patients into training (80%) and validation (20%) sets.

Main Results:

  • The logistic model incorporated Extramural Vascular Invasion (EMVI), MRI clinical N stage (MRI cN stage), and Number of Enlarged Lateral Lymph Nodes (NoELLN).
  • The model demonstrated an accuracy of 0.62, sensitivity of 0.80, specificity of 0.43, and an Area Under the Curve (AUC) of 0.80 in the test dataset.
  • Identified 66 pathologically positive and 77 negative lateral lymph node cases.

Conclusions:

  • EMVI, MRI cN stage, and NoELLN are significant predictors of lateral lymph node pathology in rectal cancer.
  • The developed model provides valuable guidance for selecting patients eligible for LLND surgery.
  • Improved preoperative prediction can optimize treatment strategies and patient outcomes.