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  9. Predictive And Prognostic Markers
  11. Development And Validation Of Machine Learning Models For Predicting Tumor Progression In Oscc

Development and Validation of Machine Learning Models for Predicting Tumor Progression in OSCC

Xueying Mei1,2,3, Wenhao Luo1,2,3, Wan Duan1,2,3

  • 1Hospital of Stomatology, Sun Yat-sen University, Guangzhou, Guangdong, China.

Oral Diseases
|October 27, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning models can predict oral squamous cell carcinoma (OSCC) progression using patient data. Neutrophil count was the most significant predictor, aiding in personalized risk assessment for OSCC patients.

Area of Science:

  • Oncology
  • Medical Informatics
  • Biostatistics

Background:

  • Oral squamous cell carcinoma (OSCC) is a significant global health concern.
  • Accurate prediction of OSCC progression is crucial for timely intervention and improved patient outcomes.

Purpose of the Study:

  • To develop and validate machine learning (ML) models for predicting tumor progression in oral squamous cell carcinoma (OSCC) patients.
  • To identify key clinical, pathological, and hematological features that predict OSCC progression.
  • To provide individual risk estimations for OSCC patient outcomes.

Main Methods:

  • A retrospective study involving 1163 OSCC patients from March 2009 to October 2021.
  • Collection of clinical, pathological, and hematological data.
  • Exploration of six ML algorithms, with performance evaluated using accuracy, sensitivity, specificity, F1 score, and AUC. SHAP values were used for feature importance analysis.
Keywords:
hematological indicatormachine learningoral squamous cell carcinomaprogression

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Main Results:

  • The Logistic Regression model demonstrated superior performance.
  • The best performing model achieved a sensitivity of 94.7% and specificity of 55.3% in the development cohort (AUC: 0.76 ± 0.09).
  • Neutrophil count was identified as the most predictive feature for OSCC progression.

Conclusions:

  • ML models can significantly enhance the clinical prediction of OSCC progression.
  • These predictive tools, utilizing basic patient information, can offer individualized risk assessments.
  • The findings may assist clinicians in directing targeted interventions for OSCC management.
validation