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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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Tree-based and sparse logistic models for predicting one-month postoperative performance status after surgery for

Satoshi Maki1,2, Yuki Shiratani3, Sumihisa Orita3,4

  • 1Chiba University, Chiba, Japan. satoshimaki@gmail.com.

European Spine Journal : Official Publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
|May 28, 2026
PubMed
Summary
This summary is machine-generated.

Random Forest models best predict one-month postoperative performance status (PS) after spinal metastasis surgery. These models identify patients likely to achieve good functional outcomes, aiding clinical decision-making for early postoperative status.

Keywords:
Machine learningMetastatic spinal tumorsPerformance statusPredictive model

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Area of Science:

  • Oncology
  • Neurosurgery
  • Data Science

Background:

  • Spinal metastases significantly impact patient quality of life and functional outcomes.
  • Accurate prediction of postoperative performance status (PS) is crucial for surgical planning and patient counseling.

Purpose of the Study:

  • To develop and validate prediction models for one-month postoperative performance status (PS) in patients undergoing surgery for spinal metastases.
  • To identify patients likely to achieve a favorable PS (0-2) one month after surgery.

Main Methods:

  • Retrospective analysis of a prospectively collected spine surgery registry.
  • Comparison of three tree-based models (Random Forest, XGBoost, CatBoost) and two regularized logistic regression models.
  • Nested cross-validation for model development and hyperparameter tuning, with specific strategies for handling missing data.

Main Results:

  • Random Forest demonstrated the highest discrimination (AUC-ROC 0.811 ± 0.079) and superior calibration.
  • Sparse elastic-net logistic regression offered good discrimination with a limited predictor set.
  • Model performance remained consistent in sensitivity analyses excluding predictors with high missingness.

Conclusions:

  • Tree-based models, especially Random Forest, show the most promising predictive performance for early postoperative functional status after spinal metastasis surgery.
  • Sparse elastic-net models provide interpretability with a reduced set of predictors.
  • Clinical implementation requires careful assessment of model calibration alongside discrimination.