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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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Artificial Intelligence-Based Risk Prediction Models for Complications After Tongue Cancer Surgery.

Dany Y Matar1, Anthony Y Matar2, Anahita Nimbalkar1

  • 1Department of Plastic and Reconstructive Surgery, Johns Hopkins School of Medicine, Baltimore, Maryland.

JAMA Otolaryngology-- Head & Neck Surgery
|June 18, 2026
PubMed
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This summary is machine-generated.

Machine learning (ML) models accurately predict 30-day postoperative complications for glossectomy patients with tongue cancer. These models led to the PRO-TONGUE risk calculator, improving individualized preoperative planning and patient care.

Area of Science:

  • Oncology
  • Surgical Oncology
  • Data Science in Medicine

Background:

  • Glossectomy for tongue tumors presents significant postoperative risks.
  • Current risk stratification tools lack individualized precision for surgical planning.

Purpose of the Study:

  • To develop and validate machine learning (ML) models for predicting major 30-day postoperative complications after glossectomy.
  • To compare the performance of ML models against the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) risk calculator.

Main Methods:

  • Retrospective cohort study using ACS-NSQIP data (2008-2024) from over 700 US hospitals.
  • Trained logistic regression and five ML models (neural network, support vector classifier, LightGBM, XGBoost, stacked generalization) on 85:15 train-validation split (2008-2023 data).

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  • Tested models on 2024 data, assessing prediction performance using risk stratification, discrimination (AUC-ROC, AUC-PR), and calibration (Brier score).

    • Main Results:

    • Developed PRO-TONGUE, an outcome-specific risk prediction tool for tongue cancer surgery, using data from 8266 adult patients.
    • ML models demonstrated performance comparable to the ACS-NSQIP calculator across outcomes.
    • XGBoost and LightGBM were optimal for specific complications, with superior prediction for bleeding requiring transfusion (AUC-ROC 0.88-0.90).

    Conclusions:

    • ML models show excellent performance in predicting major 30-day complications following glossectomy for tongue cancer.
    • The developed PRO-TONGUE tool offers individualized, interpretable risk estimates to enhance preoperative planning.
    • Further external validation of these ML-driven risk prediction models is warranted.