Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Survival Tree01:19

Survival Tree

79
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.
 Building a Survival Tree
Constructing a...
79
Cancer Survival Analysis01:21

Cancer Survival Analysis

342
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
342
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Novel Decision Tree Models Predict The Overall Survival Of Patients With Submandibular Gland Cancer

Novel decision tree models predict the overall survival of patients with submandibular gland cancer

Shan-Shan Yang1, Xiong-Gang Yang2, Xiao-Hua Hu3

  • 1Hospital/School of Stomatology, Zunyi Medical University, No. 89, Wujiang East Road, Xinpu New District, Zunyi City, Guizhou Province, 563000, China.

Clinical Oral Investigations
|June 25, 2024

Related Experiment Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.1K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

249

View abstract on PubMed

Summary
This summary is machine-generated.

This study developed decision tree models to predict overall survival (OS) in submandibular gland cancer (SGC) patients. The models accurately identify key prognostic factors, aiding personalized treatment strategies for SGC survival.

Area of Science:

  • Oncology
  • Medical Informatics
  • Cancer Research

Background:

  • Accurate prediction of overall survival (OS) in submandibular gland cancer (SGC) is crucial for treatment planning.
  • The rarity of SGC cases presents challenges in developing reliable survival prediction models.

Purpose of the Study:

  • To identify key prognostic factors for OS in SGC patients using a large database.
  • To construct decision tree models for predicting OS probabilities at 12, 24, 60, and 120 months.

Main Methods:

  • Retrospective cohort study utilizing the Surveillance, Epidemiology and End Result (SEER) program.
  • Development of dichotomous decision tree models using the C5.0 algorithm, limited to 4 layers.
  • Evaluation of model performance using receiver operator characteristic (ROC) curves, accuracy rates, and area under the ROC curve (AUC).
Keywords:
Decision tree modelOverall survivalPredictionSEER database

Related Experiment Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.1K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

249

Main Results:

  • A total of 1,705 to 1,413 SGC patients were analyzed for survival at different time points.
  • Key prognostic factors influencing OS include age, sex, surgery, radiation, chemotherapy, tumor histology, summary stage, distant lymph node metastasis, and marital status.
  • Decision tree models demonstrated favorable consistency between predicted and actual survival statuses, with accuracy rates ranging from 0.737 to 0.866 and AUC values from 0.725 to 0.841.

Conclusions:

  • Decision tree models were successfully established to predict OS in SGC patients based on significant prognostic variables from the SEER database.
  • These models provide a comprehensive assessment of mortality risk.
  • The developed models have the potential to facilitate more personalized treatment strategies for SGC.
Submandibular gland cancer