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September 18, 2025JCO Precision Oncology

Interpretable Machine Learning Models for Predicting Lateral Pelvic Lymph Node Metastasis in Rectal Cancer: A Chinese Multicenter Retrospective Study

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Authors

TXTixian XiaoWZWei ZhaoZSZhen Sun

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Overview

Retrospective analysis shows machine learning predicts lymph node metastasis in rectal cancer, indicating its clinical relevance.

Key Points

  • The random forest model achieved AUC of 0.899 and accuracy of 0.827 in external validation.
  • Key predictors for lymph node metastasis included short-axis diameter of lymph nodes and tumor-to-anal verge distance.
  • SHAP analysis enhanced understanding of feature importance and patient-level predictions.
  • Data collected from four Chinese centers involved 411 cases with retrospective design.

Cite This Study

Xiao et al. (2025) studied this question.

synapsesocial.com/papers/68d463e931b076d99fa63353https://doi.org/10.1200/po-25-00192
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Predicting Lymph Node Metastasis in Rectal Cancer: Development and Validation of a Machine Learning Model Using Clinical Data2025 · 4 citations
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  3. 3Risk factors and development of machine learning diagnostic models for lateral lymph node metastasis in rectal cancer: multicentre study2024 · 8 citations
  4. 4Predicting sentinel lymph node metastasis in breast cancer using an interpretable machine learning approach based on multi-domain clinical features2026
  5. 5An interpretable machine learning model for predicting symptomatic pelvic lymphocele after pelvic lymphadenectomy in cervical cancer2026