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Enhancing Preoperative Outcome Prediction: A Comparative Retrospective Case-Control Study on Machine Learning versus

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Machine learning (ML) models show superior performance in predicting 90-day mortality after oncologic esophagectomy compared to the International Esodata Study Group (IESG) risk model. ML offers improved risk stratification for surgical decision-making.

Keywords:
artificial intelligenceesophagectomymachine learningrisk predictionupper gastrointestinal surgery

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Area of Science:

  • Oncology
  • Medical Informatics
  • Surgical Risk Prediction

Background:

  • Preoperative risk prediction is vital for informed decisions in oncologic esophagectomy.
  • The International Esodata Study Group (IESG) developed a risk model for 90-day mortality.
  • Machine learning (ML) offers a novel approach due to complex, non-linear risk factor interactions.

Purpose of the Study:

  • To evaluate the performance of the IESG risk model.
  • To compare the IESG model against ML models for predicting 90-day mortality after esophagectomy.
  • To assess the potential of ML in improving preoperative risk stratification.

Main Methods:

  • Trained and validated multiple ML classifiers on 552 patients from two independent centers.
  • Assessed model discrimination using Area Under the Receiver Operating Characteristics Curve (AUROC), Area Under the Precision-Recall Curve (AUPRC), and Matthews Correlation Coefficient (MCC).
  • Compared ML model performance against the established IESG risk categorization.

Main Results:

  • The overall 90-day mortality rate was 5.8%.
  • The IESG model provided adequate group-based risk prediction.
  • ML models demonstrated significantly superior discrimination: higher AUROCs (0.64 vs. 0.44), AUPRCs (0.25 vs. 0.11), and MCCs (0.27 vs. 0.15).

Conclusions:

  • ML models show promising potential for identifying high-risk patients before esophagectomy, outperforming conventional statistical models.
  • ML offers enhanced discrimination compared to the IESG model.
  • Larger datasets are required for future large-scale clinical implementation and higher predictive accuracy.