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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Prediction of ameloblastoma recurrence using random forest-a machine learning algorithm.

R Wang1, K Y Li1, Y-X Su1

  • 1Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China.

International Journal of Oral and Maxillofacial Surgery
|December 18, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts ameloblastoma recurrence. The random forest model identified key factors like treatment time and tumor size, aiding in identifying high-risk patients for monitoring.

Keywords:
Jaw neoplasmsRisk factorsameloblastomamachine learningrecurrence

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Area of Science:

  • Oral and Maxillofacial Surgery
  • Machine Learning in Medicine
  • Oncology

Background:

  • Ameloblastoma is a common odontogenic tumor with a significant recurrence rate.
  • Predicting recurrence is crucial for effective patient management and follow-up.
  • Current prediction methods may lack precision for identifying high-risk cases.

Purpose of the Study:

  • To evaluate the efficacy of the random forest machine learning model in predicting ameloblastoma recurrence.
  • To identify key clinical parameters associated with ameloblastoma recurrence.
  • To develop a tool for better risk stratification of ameloblastoma patients.

Main Methods:

  • A retrospective cohort study of 150 ameloblastoma patients treated between 1999 and 2019.
  • Utilized a random forest algorithm with 14 clinical parameters to predict recurrence.
  • Performance was assessed using receiver operating characteristic (ROC) curves and area under the curve (AUC).

Main Results:

  • The random forest model demonstrated reliable accuracy in predicting ameloblastoma recurrence (AUCs of 0.777 and 0.825).
  • Recurrence was observed in 16.7% of patients over a mean follow-up of 103 months.
  • Key predictors of recurrence included time since treatment, initial surgery type, tumor size, and radiographic appearance.

Conclusions:

  • The random forest model is a valuable tool for predicting ameloblastoma recurrence.
  • Identifying high-risk patients can optimize monitoring strategies and improve outcomes.
  • This machine learning approach holds potential for application in predicting other diseases.